diff --git a/.gitignore b/.gitignore index fe12aae..62c3403 100644 --- a/.gitignore +++ b/.gitignore @@ -15,7 +15,7 @@ downloads/ eggs/ .eggs/ lib/ -lib64/ +lib64 parts/ sdist/ var/ @@ -137,3 +137,6 @@ dmypy.json *.DS_Store tmp_* examples/fine-tuned_qa/local_cache/* + +# PyCharm files +.idea/ diff --git a/README.md b/README.md index 3e28958..61a91c4 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ > ✨ Navigate at [cookbook.openai.com](https://cookbook.openai.com) -Example code and guides for accomplishing common tasks with the [OpenAI API](https://platform.openai.com/docs/introduction). To run these examples, you'll need an OpenAI account and associated API key ([create a free account here](https://beta.openai.com/signup)). +Example code and guides for accomplishing common tasks with the [OpenAI API](https://platform.openai.com/docs/introduction). To run these examples, you'll need an OpenAI account and associated API key ([create a free account here](https://beta.openai.com/signup)). Set an environment variable called `OPENAI_API_KEY` with your API key. Alternatively, in most IDEs such as Visual Studio Code, you can create an `.env` file at the root of your repo containing `OPENAI_API_KEY=`, which will be picked up by the notebooks. Most code examples are written in Python, though the concepts can be applied in any language. diff --git a/articles/related_resources.md b/articles/related_resources.md index f9ac312..a9a421e 100644 --- a/articles/related_resources.md +++ b/articles/related_resources.md @@ -5,6 +5,7 @@ People are writing great tools and papers for improving outputs from GPT. Here a ## Prompting libraries & tools (in alphabetical order) - [Arthur Shield](https://www.arthur.ai/get-started): A paid product for detecting toxicity, hallucination, prompt injection, etc. +- [Baserun](https://baserun.ai/): A paid product for testing, debugging, and monitoring LLM-based apps - [Chainlit](https://docs.chainlit.io/overview): A Python library for making chatbot interfaces. - [Embedchain](https://github.com/embedchain/embedchain): A Python library for managing and syncing unstructured data with LLMs. - [FLAML (A Fast Library for Automated Machine Learning & Tuning)](https://microsoft.github.io/FLAML/docs/Getting-Started/): A Python library for automating selection of models, hyperparameters, and other tunable choices. @@ -25,6 +26,7 @@ People are writing great tools and papers for improving outputs from GPT. Here a - [Prompttools](https://github.com/hegelai/prompttools): Open-source Python tools for testing and evaluating models, vector DBs, and prompts. - [Scale Spellbook](https://scale.com/spellbook): A paid product for building, comparing, and shipping language model apps. - [Semantic Kernel](https://github.com/microsoft/semantic-kernel): A Python/C#/Java library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning. +- [Vellum](https://www.vellum.ai/): A paid AI product development platform to experiment with, evaluate, and deploy advanced LLM apps. - [Weights & Biases](https://wandb.ai/site/solutions/llmops): A paid product for tracking model training and prompt engineering experiments. - [YiVal](https://github.com/YiVal/YiVal): An open-source GenAI-Ops tool for tuning and evaluating prompts, retrieval configurations, and model parameters using customizable datasets, evaluation methods, and evolution strategies. diff --git a/articles/what_is_new_with_dalle_3.mdx b/articles/what_is_new_with_dalle_3.mdx index 98d6c3d..6328e70 100644 --- a/articles/what_is_new_with_dalle_3.mdx +++ b/articles/what_is_new_with_dalle_3.mdx @@ -96,7 +96,7 @@ Have you ever struggled to find the perfect icon for your website or app? It wou ![icon_set](/images/dalle_3/icon_set.jpg) -In this case, I used Potrace to convert the images to SVGs, which you can download [here](http://potrace.sourceforge.net/). This is what I used to convert the images: +In this case, I used Potrace to convert the images to SVGs, which you can download [here](https://potrace.sourceforge.net/). This is what I used to convert the images: ```bash potrace -s cat.jpg -o cat.svg diff --git a/authors.yaml b/authors.yaml index 0a033f7..3aa140a 100644 --- a/authors.yaml +++ b/authors.yaml @@ -81,4 +81,19 @@ katiagg: jbeutler-openai: name: "Joe Beutler" website: "https://joebeutler.com" - avatar: "https://avatars.githubusercontent.com/u/156261485?v=4" \ No newline at end of file + avatar: "https://avatars.githubusercontent.com/u/156261485?v=4" + +dylanra-openai: + name: "Dylan Royan Almeida" + website: "https://www.linkedin.com/in/dylan-almeida-604522167/" + avatar: "https://avatars.githubusercontent.com/u/149511600?v=4" + +royziv11: + name: "Roy Ziv" + website: "https://www.linkedin.com/in/roy-ziv-a46001149/" + avatar: "https://media.licdn.com/dms/image/D5603AQHkaEOOGZWtbA/profile-displayphoto-shrink_400_400/0/1699500606122?e=1716422400&v=beta&t=wKEIx-vTEqm9wnqoC7-xr1WqJjghvcjjlMt034hXY_4" + +justonf: + name: "Juston Forte" + website: "https://www.linkedin.com/in/justonforte/" + avatar: "https://avatars.githubusercontent.com/u/96567547?s=400&u=08b9757200906ab12e3989b561cff6c4b95a12cb&v=4" diff --git a/examples/Clustering.ipynb b/examples/Clustering.ipynb index 58d66a4..7ca3a01 100644 --- a/examples/Clustering.ipynb +++ b/examples/Clustering.ipynb @@ -163,7 +163,7 @@ "source": [ "### 2. Text samples in the clusters & naming the clusters\n", "\n", - "Let's show random samples from each cluster. We'll use text-davinci-003 to name the clusters, based on a random sample of 5 reviews from that cluster." + "Let's show random samples from each cluster. We'll use gpt-4 to name the clusters, based on a random sample of 5 reviews from that cluster." ] }, { diff --git a/examples/Creating_slides_with_Assistants_API_and_DALL-E3.ipynb b/examples/Creating_slides_with_Assistants_API_and_DALL-E3.ipynb index bed5891..b9ba22f 100644 --- a/examples/Creating_slides_with_Assistants_API_and_DALL-E3.ipynb +++ b/examples/Creating_slides_with_Assistants_API_and_DALL-E3.ipynb @@ -7,7 +7,7 @@ "# Creating slides with the Assistants API (GPT-4), and DALL·E-3\n", "\n", "This notebook illustrates the use of the new [Assistants API](https://platform.openai.com/docs/assistants/overview) (GPT-4), and DALL·E-3 in crafting informative and visually appealing slides.
\n", - "Creating slides is a pivotal aspect of many jobs, but can be laborious and time-consuming. Additionally, extracting insights from data and articulating them effectively on slides can be challenging.

This cookbook recipe will demonstrate how you can utilize the new Assistants API to faciliate the end to end slide creation process for you without you having to touch Microsoft PowerPoint or Google Slides, saving you valuable time and effort!" + "Creating slides is a pivotal aspect of many jobs, but can be laborious and time-consuming. Additionally, extracting insights from data and articulating them effectively on slides can be challenging.

This cookbook recipe will demonstrate how you can utilize the new Assistants API to facilitate the end to end slide creation process for you without you having to touch Microsoft PowerPoint or Google Slides, saving you valuable time and effort!" ] }, { diff --git a/examples/GPT_with_vision_for_video_understanding.ipynb b/examples/GPT_with_vision_for_video_understanding.ipynb index b0b7cb9..6f1fe38 100644 --- a/examples/GPT_with_vision_for_video_understanding.ipynb +++ b/examples/GPT_with_vision_for_video_understanding.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Processing and narrating a video with GPT's visual capabilities and the TTS API\n", + "# Processing and narrating a video with GPT-4o's visual capabilities and the TTS API\n", "\n", - "This notebook demonstrates how to use GPT's visual capabilities with a video. GPT-4 doesn't take videos as input directly, but we can use vision and the new 128K context window to describe the static frames of a whole video at once. We'll walk through two examples:\n", + "This notebook demonstrates how to use GPT's visual capabilities with a video. GPT-4o doesn't take videos as input directly, but we can use vision and the 128K context window to describe the static frames of a whole video at once. We'll walk through two examples:\n", "\n", - "1. Using GPT-4 to get a description of a video\n", - "2. Generating a voiceover for a video with GPT-4 and the TTS API\n" + "1. Using GPT-4o to get a description of a video\n", + "2. Generating a voiceover for a video with GPT-o and the TTS API\n" ] }, { @@ -118,9 +118,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "\"🐺 Survival of the Fittest: An Epic Tale in the Snow ❄️ - Witness the intense drama of nature as a pack of wolves face off against mighty bison in a harsh winter landscape. This raw footage captures the essence of the wild where every creature fights for survival. With each frame, experience the tension, the strategy, and the sheer force exerted in this life-or-death struggle. See nature's true colors in this gripping encounter on the snowy plains. 🦬\"\n", + "Title: \"Epic Wildlife Showdown: Wolves vs. Bison in the Snow\"\n", "\n", - "Remember to respect wildlife and nature. This video may contain scenes that some viewers might find intense or distressing, but they depict natural animal behaviors important for ecological studies and understanding the reality of life in the wilderness.\n" + "Description: \n", + "Witness the raw power and strategy of nature in this intense and breathtaking video! A pack of wolves face off against a herd of bison in a dramatic battle for survival set against a stunning snowy backdrop. See how the wolves employ their cunning tactics while the bison demonstrate their strength and solidarity. This rare and unforgettable footage captures the essence of the wild like never before. Who will prevail in this ultimate test of endurance and skill? Watch to find out and experience the thrill of the wilderness! 🌨️🦊🐂 #Wildlife #NatureDocumentary #AnimalKingdom #SurvivalOfTheFittest #NatureLovers\n" ] } ], @@ -135,7 +136,7 @@ " },\n", "]\n", "params = {\n", - " \"model\": \"gpt-4-vision-preview\",\n", + " \"model\": \"gpt-4o\",\n", " \"messages\": PROMPT_MESSAGES,\n", " \"max_tokens\": 200,\n", "}\n", @@ -167,15 +168,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "In the vast, white expanse of the northern wilderness, a drama as old as time unfolds. Here, amidst the silence of the snow, the wolf pack circles, their breaths visible as they cautiously approach their formidable quarry, the bison. These wolves are practiced hunters, moving with strategic precision, yet the bison, a titan of strength, stands resolute, a force to be reckoned with.\n", + "In the frozen expanses of the North American wilderness, a battle unfolds—a testament to the harsh realities of survival.\n", "\n", - "As tension crackles in the frozen air, the wolves close in, their eyes locked on their target. The bison, wary of every movement, prepares to defend its life. It's a perilous dance between predator and prey, where each step could be the difference between life and death.\n", + "The pack of wolves, relentless and coordinated, closes in on the mighty bison. Exhausted and surrounded, the bison relies on its immense strength and bulk to fend off the predators.\n", "\n", - "In an instant, the quiet of the icy landscape is shattered. The bison charges, a desperate bid for survival as the pack swarms. The wolves are relentless, each one aware that their success depends on the strength of the collective. The bison, though powerful, is outnumbered, its massive form stirring up clouds of snow as it struggles.\n", + "But the wolves are cunning strategists. They work together, each member playing a role in the hunt, nipping at the bison's legs, forcing it into a corner.\n", "\n", - "It's an epic battle, a testament to the harsh realities of nature. In these moments, there is no room for error, for either side. The wolves, agile and tenacious, work in unison, their bites a chorus aiming to bring down the great beast. The bison, its every heaving breath a testament to its will to survive, fights fiercely, but the odds are not in its favor.\n", + "The alpha female leads the charge, her pack following her cues. They encircle their prey, tightening the noose with every passing second.\n", "\n", - "With the setting sun casting long shadows over the snow, the outcome is inevitable. Nature, in all its raw beauty and brutality, does not show favor. The wolves, now victors, gather around their prize, their survival in this harsh climate secured for a moment longer. It's a poignant reminder of the circle of life that rules this pristine wilderness, a reminder that every creature plays its part in the enduring saga of the natural world.\n" + "The bison makes a desperate attempt to escape, but the wolves latch onto their target, wearing it down through sheer persistence and teamwork.\n", + "\n", + "In these moments, nature's brutal elegance is laid bare—a primal dance where only the strongest and the most cunning can thrive.\n", + "\n", + "The bison, now overpowered and exhausted, faces its inevitable fate. The wolves have triumphed, securing a meal that will sustain their pack for days to come.\n", + "\n", + "And so, the cycle of life continues, as it has for millennia, in this unforgiving land where the struggle for survival is an unending battle.\n" ] } ], @@ -190,7 +197,7 @@ " },\n", "]\n", "params = {\n", - " \"model\": \"gpt-4-vision-preview\",\n", + " \"model\": \"gpt-4o\",\n", " \"messages\": PROMPT_MESSAGES,\n", " \"max_tokens\": 500,\n", "}\n", @@ -216,7 +223,7 @@ "text/html": [ "\n", " \n", " " @@ -266,7 +273,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.8" } }, "nbformat": 4, diff --git a/examples/How_to_combine_GPT4v_with_RAG_Outfit_Assistant.ipynb b/examples/How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb similarity index 55% rename from examples/How_to_combine_GPT4v_with_RAG_Outfit_Assistant.ipynb rename to examples/How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb index 80659a2..38d3561 100644 --- a/examples/How_to_combine_GPT4v_with_RAG_Outfit_Assistant.ipynb +++ b/examples/How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb @@ -4,21 +4,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# How to combine GPT-4V with RAG - Create a Clothing Matchmaker App\n", + "# How to combine GPT-4o with RAG - Create a Clothing Matchmaker App\n", "\n", - "Welcome to the Clothing Matchmaker App Jupyter Notebook! This project demonstrates the power of the GPT-4V model in analyzing images of clothing items and extracting key features such as color, style, and type. The core of our app relies on this advanced image analysis model developed by OpenAI, which enables us to accurately identify the characteristics of the input clothing item.\n", + "Welcome to the Clothing Matchmaker App Jupyter Notebook! This project demonstrates the power of the GPT-4o model in analyzing images of clothing items and extracting key features such as color, style, and type. The core of our app relies on this advanced image analysis model developed by OpenAI, which enables us to accurately identify the characteristics of the input clothing item.\n", "\n", - "GPT-4V is a model that combines natural language processing with image recognition, allowing it to understand and generate responses based on both text and visual inputs.\n", + "GPT-4o is a model that combines natural language processing with image recognition, allowing it to understand and generate responses based on both text and visual inputs.\n", "\n", - "Building on the capabilities of the GPT-4V model, we employ a custom matching algorithm and the RAG technique to search our knowledge base for items that complement the identified features. This algorithm takes into account factors like color compatibility and style coherence to provide users with suitable recommendations. Through this notebook, we aim to showcase the practical application of these technologies in creating a clothing recommendation system.\n", + "Building on the capabilities of the GPT-4o model, we employ a custom matching algorithm and the RAG technique to search our knowledge base for items that complement the identified features. This algorithm takes into account factors like color compatibility and style coherence to provide users with suitable recommendations. Through this notebook, we aim to showcase the practical application of these technologies in creating a clothing recommendation system.\n", "\n", - "Using the combination of GPT-4 Vision + RAG (Retrieval-Augmented Generation) offers several advantages:\n", + "Using the combination of GPT-4o + RAG (Retrieval-Augmented Generation) offers several advantages:\n", "\n", - "1. **Contextual Understanding**: GPT-4 Vision can analyze input images and understand the context, such as the objects, scenes, and activities depicted. This allows for more accurate and relevant suggestions or information across various domains, whether it's interior design, cooking, or education.\n", + "1. **Contextual Understanding**: GPT-4o can analyze input images and understand the context, such as the objects, scenes, and activities depicted. This allows for more accurate and relevant suggestions or information across various domains, whether it's interior design, cooking, or education.\n", "2. **Rich Knowledge Base**: RAG combines the generative capabilities of GPT-4 with a retrieval component that accesses a large corpus of information across different fields. This means the system can provide suggestions or insights based on a wide range of knowledge, from historical facts to scientific concepts.\n", "3. **Customization**: The approach allows for easy customization to cater to specific user needs or preferences in various applications. Whether it's tailoring suggestions to a user's taste in art or providing educational content based on a student's learning level, the system can be adapted to deliver personalized experiences.\n", "\n", - "Overall, the GPT-4 Vision + RAG approach offers a powerful and flexible solution for various fashion-related applications, leveraging the strengths of both generative and retrieval-based AI techniques." + "Overall, the GPT-4o + RAG approach offers a powerful and flexible solution for various fashion-related applications, leveraging the strengths of both generative and retrieval-based AI techniques." ] }, { @@ -37,25 +37,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "\u001b[31mERROR: Could not find a version that satisfies the requirement concurrent (from versions: none)\u001b[0m\u001b[31m\n", - "\u001b[0m\u001b[31mERROR: No matching distribution found for concurrent\u001b[0m\u001b[31m\n", - "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ "%pip install openai --quiet\n", "%pip install tenacity --quiet\n", @@ -86,7 +70,7 @@ "\n", "client = OpenAI()\n", "\n", - "GPT_MODEL = \"gpt-4-vision-preview\"\n", + "GPT_MODEL = \"gpt-4o\"\n", "EMBEDDING_MODEL = \"text-embedding-3-large\"\n", "EMBEDDING_COST_PER_1K_TOKENS = 0.00013" ] @@ -241,7 +225,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "1024it [00:02, 438.09it/s] \n" + "1024it [00:01, 724.12it/s] \n" ] }, { @@ -285,11 +269,11 @@ "4 2012.0 Ethnic Shree Women Multi Colored Patiala \n", "\n", " embeddings \n", - "0 [0.006932149175554514, 0.00035877348273061216,... \n", - "1 [-0.04372308403253555, -0.008888939395546913, ... \n", - "2 [-0.02798476628959179, 0.058831773698329926, -... \n", - "3 [-0.004207939375191927, 0.02941293641924858, -... \n", - "4 [-0.05236775428056717, 0.015963038429617882, -... \n", + "0 [0.006903026718646288, 0.0004031236458104104, ... \n", + "1 [-0.04371623694896698, -0.008869604207575321, ... \n", + "2 [-0.027989011257886887, 0.05884069576859474, -... \n", + "3 [-0.004197604488581419, 0.029409468173980713, ... \n", + "4 [-0.05241541564464569, 0.015912825241684914, -... \n", "Opened dataset successfully. Dataset has 1000 items of clothing along with their embeddings.\n" ] } @@ -387,7 +371,7 @@ "source": [ "### Analysis Module\n", "\n", - "In this module, we leverage `gpt-4-vision-preview` to analyze input images and extract important features like detailed descriptions, styles, and types. The analysis is performed through a straightforward API call, where we provide the URL of the image for analysis and request the model to identify relevant features.\n", + "In this module, we leverage `gpt-4o` to analyze input images and extract important features like detailed descriptions, styles, and types. The analysis is performed through a straightforward API call, where we provide the URL of the image for analysis and request the model to identify relevant features.\n", "\n", "To ensure the model returns accurate results, we use specific techniques in our prompt:\n", "\n", @@ -402,7 +386,7 @@ "3. **One Shot Example**: \n", " - To further clarify the expected output, we provide the model with an example input description and a corresponding example output. Although this may increase the number of tokens used (and thus the cost of the call), it helps to guide the model and results in better overall performance.\n", "\n", - "By following this structured approach, we aim to obtain precise and useful information from the `gpt-4-vision-preview` model for further analysis and integration into our database." + "By following this structured approach, we aim to obtain precise and useful information from the `gpt-4o` model for further analysis and integration into our database." ] }, { @@ -518,7 +502,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'items': [\"Men's Light Blue Jeans\", \"Men's Black Leather Belt\", \"Men's Brown Loafers\"], 'category': 'Shirts', 'gender': 'Men'}\n" + "{'items': [\"Slim Fit Blue Men's Jeans\", \"White Men's Sneakers\", \"Men's Silver Watch\"], 'category': 'Shirts', 'gender': 'Men'}\n" ] } ], @@ -562,13 +546,13 @@ "output_type": "stream", "text": [ "513 Remaining Items\n", - "[\"Men's Light Blue Jeans\", \"Men's Black Leather Belt\", \"Men's Brown Loafers\"]\n" + "[\"Slim Fit Blue Men's Jeans\", \"White Men's Sneakers\", \"Men's Silver Watch\"]\n" ] }, { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -616,7 +600,7 @@ "source": [ "### Guardrails\n", "\n", - "In the context of using Large Language Models (LLMs) like GPT-4V, \"guardrails\" refer to mechanisms or checks put in place to ensure that the model's output remains within desired parameters or boundaries. These guardrails are crucial for maintaining the quality and relevance of the model's responses, especially when dealing with complex or nuanced tasks.\n", + "In the context of using Large Language Models (LLMs) like GPT-4o, \"guardrails\" refer to mechanisms or checks put in place to ensure that the model's output remains within desired parameters or boundaries. These guardrails are crucial for maintaining the quality and relevance of the model's responses, especially when dealing with complex or nuanced tasks.\n", "\n", "Guardrails are useful for several reasons:\n", "\n", @@ -625,7 +609,7 @@ "3. **Safety**: They prevent the model from generating harmful, offensive, or inappropriate content.\n", "4. **Contextual Relevance**: They ensure that the model's output is contextually relevant to the specific task or domain it is being used for.\n", "\n", - "In our case, we are using GPT-4V to analyze fashion images and suggest items that would complement an original outfit. To implement guardrails, we can **refine results**: After obtaining initial suggestions from GPT-4V, we can send the original image and the suggested items back to the model. We can then ask GPT-4V to evaluate whether each suggested item would indeed be a good fit for the original outfit.\n", + "In our case, we are using GPT-4o to analyze fashion images and suggest items that would complement an original outfit. To implement guardrails, we can **refine results**: After obtaining initial suggestions from GPT-4o, we can send the original image and the suggested items back to the model. We can then ask GPT-4o to evaluate whether each suggested item would indeed be a good fit for the original outfit.\n", "\n", "This gives the model the ability to self-correct and adjust its own output based on feedback or additional information. By implementing these guardrails and enabling self-correction, we can enhance the reliability and usefulness of the model's output in the context of fashion analysis and recommendation.\n", "\n", @@ -693,7 +677,7 @@ "outputs": [ { "data": { - "image/jpeg": 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+ "image/jpeg": 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"text/plain": [ "" ] @@ -706,12 +690,12 @@ "output_type": "stream", "text": [ "The items match!\n", - "The solid color of the first item tends to be versatile and can pair well with the second item, which appears to be a casual style of pants, creating a cohesive and suitable outfit for casual occasions.\n" + "The black shirt and blue jeans create a classic and casual outfit that works well together.\n" ] }, { "data": { - "image/jpeg": 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HAHA/P1lbfrLrgVJ2ofAfye5/vibuyJ4RE/6/p/x/ZP8AzljIk54iOIjiJTGT4Th2bQ2V2Y4rjeV143K9kHJaKpv4bHL48uZFt4ZmMXw1Pw1Pxx/EyCRnBxu4PzHfB+8t9C64de62U2bXaK03Amtcj2y4WrsFiymvT8ObIBQNejk/kvnzxGT5y7FdU1lRGbDqq+FWRGoiNjV0SPAA1E+E+kMQTGt8J+PCcTE76IifhPn+v5Vf7qv54ic8RHERxEcRHERxEcRHERxEcRHERxEcRHERxEcRHET/2Q==", "text/plain": [ "" ] @@ -724,12 +708,30 @@ "output_type": "stream", "text": [ "The items match!\n", - "The solid color of the shirt provides a versatile base that can easily be matched with different styles and colors of footwear. The shoes appear to be dressy casual loafers, which can complement the shirt for a smart casual look.\n" + "The black shirt and the white sneakers with red and beige accents can work well together. Black is a versatile color that pairs well with many shoe options, and the white sneakers can add a sporty and casual touch to the outfit.\n" ] }, { "data": { - "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The items match!\n", + "The black button-up shirt is casual and versatile, making it compatible with the white and red athletic shoes for a relaxed and sporty look.\n" + ] + }, + { + "data": { + "image/jpeg": 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"text/plain": [ "" ] @@ -742,12 +744,12 @@ "output_type": "stream", "text": [ "The items match!\n", - "The dark-colored casual shirt can easily be paired with a variety of shoe styles, and the shoes presented have a casual yet slightly polished design that would complement the shirt for a casual to smart-casual look.\n" + "The black shirt pairs well with the light blue jeans, creating a classic and balanced color combination that is casual and stylish.\n" ] }, { "data": { - "image/jpeg": 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"text/plain": [ "" ] @@ -760,7 +762,7 @@ "output_type": "stream", "text": [ "The items match!\n", - "A dark-colored button-up shirt typically pairs well with dark jeans for a casual yet put-together look.\n" + "Both the black shirt and the black watch have a sleek and coordinated look, making them suitable to be worn together as part of an outfit.\n" ] } ], @@ -795,7 +797,7 @@ "source": [ "### Conclusion\n", "\n", - "In this Jupyter Notebook, we explored the application of GPT-4 with Vision and other machine learning techniques to the domain of fashion. We demonstrated how to analyze images of clothing items, extract relevant features, and use this information to find matching items that complement an original outfit. Through the implementation of guardrails and self-correction mechanisms, we refined the model's suggestions to ensure they are accurate and contextually relevant.\n", + "In this Jupyter Notebook, we explored the application of GPT-4o and other machine learning techniques to the domain of fashion. We demonstrated how to analyze images of clothing items, extract relevant features, and use this information to find matching items that complement an original outfit. Through the implementation of guardrails and self-correction mechanisms, we refined the model's suggestions to ensure they are accurate and contextually relevant.\n", "\n", "This approach has several practical uses in the real world, including:\n", "\n", @@ -803,7 +805,7 @@ "2. **Virtual Wardrobe Applications**: Users can upload images of their own clothing items to create a virtual wardrobe and receive suggestions for new items that match their existing pieces.\n", "3. **Fashion Design and Styling**: Fashion designers and stylists can use this tool to experiment with different combinations and styles, streamlining the creative process.\n", "\n", - "However, one of the considerations to keep in mind is **cost**. The use of LLMs and image analysis models can incur costs, especially if used extensively. It's important to consider the cost-effectiveness of implementing these technologies. `gpt-4-vision-preview` is priced at `$0.01` per 1000 tokens. This adds up to `$0.00255` for one 256px x 256px image.\n", + "However, one of the considerations to keep in mind is **cost**. The use of LLMs and image analysis models can incur costs, especially if used extensively. It's important to consider the cost-effectiveness of implementing these technologies.\n", "\n", "Overall, this notebook serves as a foundation for further exploration and development in the intersection of fashion and AI, opening doors to more personalized and intelligent fashion recommendation systems." ] @@ -825,7 +827,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.1" + "version": "3.11.8" } }, "nbformat": 4, diff --git a/examples/How_to_stream_completions.ipynb b/examples/How_to_stream_completions.ipynb index a780992..a1d9098 100644 --- a/examples/How_to_stream_completions.ipynb +++ b/examples/How_to_stream_completions.ipynb @@ -19,14 +19,13 @@ "\n", "Note that using `stream=True` in a production application makes it more difficult to moderate the content of the completions, as partial completions may be more difficult to evaluate. This may have implications for [approved usage](https://beta.openai.com/docs/usage-guidelines).\n", "\n", - "Another small drawback of streaming responses is that the response no longer includes the `usage` field to tell you how many tokens were consumed. After receiving and combining all of the responses, you can calculate this yourself using [`tiktoken`](How_to_count_tokens_with_tiktoken.ipynb).\n", - "\n", "## Example code\n", "\n", "Below, this notebook shows:\n", "1. What a typical chat completion response looks like\n", "2. What a streaming chat completion response looks like\n", - "3. How much time is saved by streaming a chat completion" + "3. How much time is saved by streaming a chat completion\n", + "4. How to get token usage data for streamed chat completion response" ] }, { @@ -553,7 +552,7 @@ "print(f\"Full response received {chunk_time:.2f} seconds after request\")\n", "# clean None in collected_messages\n", "collected_messages = [m for m in collected_messages if m is not None]\n", - "full_reply_content = ''.join([m for m in collected_messages])\n", + "full_reply_content = ''.join(collected_messages)\n", "print(f\"Full conversation received: {full_reply_content}\")\n" ] }, @@ -572,6 +571,65 @@ { "cell_type": "markdown", "metadata": {}, + "source": [ + "### 4. How to get token usage data for streamed chat completion response\n", + "\n", + "You can get token usage statistics for your streamed response by setting `stream_options={\"include_usage\": True}`. When you do so, an extra chunk will be streamed as the final chunk. You can access the usage data for the entire request via the `usage` field on this chunk. A few important notes when you set `stream_options={\"include_usage\": True}`:\n", + "* The value for the `usage` field on all chunks except for the last one will be null.\n", + "* The `usage` field on the last chunk contains token usage statistics for the entire request.\n", + "* The `choices` field on the last chunk will always be an empty array `[]`.\n", + "\n", + "Let's see how it works using the example in 2." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "choices: [Choice(delta=ChoiceDelta(content='', function_call=None, role='assistant', tool_calls=None), finish_reason=None, index=0, logprobs=None)]\n", + "usage: None\n", + "****************\n", + "choices: [Choice(delta=ChoiceDelta(content='2', function_call=None, role=None, tool_calls=None), finish_reason=None, index=0, logprobs=None)]\n", + "usage: None\n", + "****************\n", + "choices: [Choice(delta=ChoiceDelta(content=None, function_call=None, role=None, tool_calls=None), finish_reason='stop', index=0, logprobs=None)]\n", + "usage: None\n", + "****************\n", + "choices: []\n", + "usage: CompletionUsage(completion_tokens=1, prompt_tokens=19, total_tokens=20)\n", + "****************\n" + ] + } + ], + "source": [ + "# Example of an OpenAI ChatCompletion request with stream=True and stream_options={\"include_usage\": True}\n", + "\n", + "# a ChatCompletion request\n", + "response = client.chat.completions.create(\n", + " model='gpt-3.5-turbo',\n", + " messages=[\n", + " {'role': 'user', 'content': \"What's 1+1? Answer in one word.\"}\n", + " ],\n", + " temperature=0,\n", + " stream=True,\n", + " stream_options={\"include_usage\": True}, # retrieving token usage for stream response\n", + ")\n", + "\n", + "for chunk in response:\n", + " print(f\"choices: {chunk.choices}\\nusage: {chunk.usage}\")\n", + " print(\"****************\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [] } ], diff --git a/examples/SDG1.ipynb b/examples/SDG1.ipynb new file mode 100644 index 0000000..02fd5eb --- /dev/null +++ b/examples/SDG1.ipynb @@ -0,0 +1,1276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "I_IHQTO8xXBn" + }, + "source": [ + "# Synthetic Data generation (Part 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VBoxtnxVdTWZ" + }, + "source": [ + "\n", + "Synthetic data generation using large language models (LLMs) offers a powerful solution to a commonly faced problem: the availability of high-quality, diverse, and privacy-compliant data. This could be used in a number of scenarios such as training a data science machine learning model (SVMs, decision trees, KNN's), finetuning a different GPT model on the data, as a solution to the coldstart problem, helping build compelling demos/apps with realistic data, scenario testing etc.\n", + "\n", + "There are a number of key drivers which may see you wanting to leverage synthetic data. \n", + "1. Human data may have privacy restrictions and/or identifiable data within it which we do not want to be used. \n", + "2. Synthetic data can be much more structured and therefore easier to manipulate than real data. \n", + "3. In domains where data is sparse or data of certain categories is sparse we may want to augment the data. \n", + "4. When dealing with imbalanced datasets or datasets which lack diversity, we may want to create data to improve the richness of our datasets.\n", + "\n", + "Unlike traditional data augmentation or manual data creation methods, using LLMs allows for the generation of rich, nuanced, and contextually relevant datasets that can significantly enhance it's usefulness to enterprises and developers.\n", + "\n", + "We split this tutorial into 2 parts. In this cookbook, we will have the following agenda:\n", + "1. CSV with a structured prompt\n", + "2. CSV with a Python program\n", + "3. Multitable CSV with a python program\n", + "4. Simply creating textual data\n", + "5. Dealing with imbalanced or non-diverse textual data\n", + "while in part 2, we will look at prompting strategies for getting better textual data.\n", + "\n", + "The last two in particular are useful for creating synthetic data to finetune another GPT model. For example using higher quality data produced by gpt-4 to finetune the cheaper and quicker gpt-3.5 for improved performance while reducing costs.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NE9Rr29zlRsA" + }, + "source": [ + "### Getting setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YGncxYrgQ8eb" + }, + "outputs": [], + "source": [ + "%pip install openai\n", + "%pip install pandas\n", + "%pip install scikit-learn\n", + "%pip install matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "8pzwvE-YQPtU" + }, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "import re\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.cluster import KMeans\n", + "import matplotlib.pyplot as plt\n", + "import json\n", + "import matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B8eAx4-JxaZB" + }, + "source": [ + "### 1. CSV with a structure prompt\n", + "Here we create data in the simplest way. You can quickly generate data by addressing 3 key points: telling it the format of the data (CSV), the schema, and useful information regarding how columns relate (the LLM will be able to deduce this from the column names but a helping hand will improve performance)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dqbvepd0n4vS", + "outputId": "8735cacc-baa5-463e-938c-783e6b508b00" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "```csv\n", + "id,house size,house price,location,number of bedrooms\n", + "1,100,220000,Suburbs,3\n", + "2,80,180000,Suburbs,2\n", + "3,120,320000,Suburbs,4\n", + "4,65,160000,Countryside,2\n", + "5,150,500000,City Center,4\n", + "6,90,200000,Countryside,3\n", + "7,200,700000,City Center,5\n", + "8,180,600000,Suburbs,5\n", + "9,70,140000,Countryside,2\n", + "10,130,400000,City Center,3\n", + "```\n" + ] + } + ], + "source": [ + "datagen_model = \"gpt-4-0125-preview\"\n", + "question = \"\"\"\n", + "Create a CSV file with 10 rows of housing data.\n", + "Each row should include the following fields:\n", + " - id (incrementing integer starting at 1)\n", + " - house size (m^2)\n", + " - house price\n", + " - location\n", + " - number of bedrooms\n", + "\n", + "Make sure that the numbers make sense (i.e. more rooms is usually bigger size, more expensive locations increase price. more size is usually higher price etc. make sure all the numbers make sense). Also only respond with the CSV.\n", + "\"\"\"\n", + "\n", + "response = client.chat.completions.create(\n", + " model=datagen_model,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant designed to generate synthetic data.\"},\n", + " {\"role\": \"user\", \"content\": question}\n", + " ]\n", + ")\n", + "res = response.choices[0].message.content\n", + "print(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6ym0NiIyxiVj" + }, + "source": [ + "### 2. CSV with a Python program\n", + "The issue with generating data directly is we are limited in the amount of data we can generate because of the context. Instead what we can do is ask the LLM to generate a python program to generate the synthetic data. This allows us to scale to much more data while also providing us a view into how the data was generated by inspecting the python program.\n", + "\n", + "This would then let us edit the python program as we desire while giving us a good basis to start from.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2yDuwB5ZxWS3", + "outputId": "dcbe1093-90f0-4f60-d9c6-34bf679bb092" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To generate synthetic housing data and output it as a Pandas DataFrame, we can use Python with the `pandas` and `numpy` libraries. Below is a script that creates 100 rows of housing data considering the prescribed logic for house size, price, and number of bedrooms. It also takes into account the impact of location on house price.\n", + "\n", + "First, ensure you have pandas and numpy installed. You can install them via pip if you haven't already:\n", + "\n", + "```\n", + "pip install pandas numpy\n", + "```\n", + "\n", + "The script:\n", + "\n", + "```python\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Seed for reproducibility\n", + "np.random.seed(42)\n", + "\n", + "# Initialize the lists\n", + "ids = list(range(1, 101))\n", + "sizes = np.random.normal(150, 50, 100).astype(int) # House sizes with a mean of 150 m^2 and a std of 50\n", + "bedrooms = np.random.choice([1, 2, 3, 4, 5], 100) # Number of bedrooms\n", + "locations = np.random.choice(['Downtown', 'Suburb', 'Countryside'], 100, p=[0.4, 0.4, 0.2]) # Location of houses with a preferential distribution\n", + "\n", + "# Prices will be influenced by location, size, and bedrooms. This part is simplistic and can be made more complex.\n", + "base_price = 100000 # Base price\n", + "price_per_m2 = 1000 # Base price per m^2\n", + "extra_per_bedroom = 5000 # Extra cost per additional bedroom\n", + "\n", + "prices = []\n", + "\n", + "for i in range(100):\n", + " base_location_multiplier = 1.5 if locations[i] == 'Downtown' else 1.2 if locations[i] == 'Suburb' else 1\n", + " location_multiplier = base_location_multiplier * (1 + (sizes[i] / 1000)) # More expensive if bigger, especially downtown\n", + " price = base_price + (sizes[i] * price_per_m2) + (bedrooms[i] * extra_per_bedroom)\n", + " prices.append(int(price * location_multiplier))\n", + "\n", + "# Create DataFrame\n", + "data = {\n", + " 'id': ids,\n", + " 'house size (m^2)': sizes,\n", + " 'number of bedrooms': bedrooms,\n", + " 'location': locations,\n", + " 'house price': prices\n", + "}\n", + "\n", + "df = pd.DataFrame(data)\n", + "\n", + "print(df)\n", + "```\n", + "\n", + "This program initializes with a seed for reproducibility while creating randomized but plausible data for housing. The sizes are normally distributed around a mean value, and bedrooms are chosen from a set number. The pricing logic uses base values plus increases according to size, bedroom count, and a location multiplier, with downtown locations inflating prices more than suburbs or countryside locations. Adjustments are simplistic for the purpose of example and can be refined for more nuanced simulations.\n" + ] + } + ], + "source": [ + "question = \"\"\"\n", + "Create a Python program to generate 100 rows of housing data.\n", + "I want you to at the end of it output a pandas dataframe with 100 rows of data.\n", + "Each row should include the following fields:\n", + " - id (incrementing integer starting at 1)\n", + " - house size (m^2)\n", + " - house price\n", + " - location\n", + " - number of bedrooms\n", + "\n", + "Make sure that the numbers make sense (i.e. more rooms is usually bigger size, more expensive locations increase price. more size is usually higher price etc. make sure all the numbers make sense).\n", + "\"\"\"\n", + "\n", + "response = client.chat.completions.create(\n", + " model=datagen_model,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant designed to generate synthetic data.\"},\n", + " {\"role\": \"user\", \"content\": question}\n", + " ]\n", + ")\n", + "res = response.choices[0].message.content\n", + "print(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to make sure to parse the output of this appropriately as often there may be surrounding text to the python code. We can also explicitly ask it to state all assumptions it made about the data it's generating, however in this circumstance it told us that automatically." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HZaJs7q8xm3L" + }, + "source": [ + "### 3. Multitable CSV with a python program\n", + "For more complex relationships however we need to make sure to specify a few more characteristics. \n", + "\n", + "To create multiple different datasets which relate to each other (for example housing, location, house type), as before we would need to specify the format, schema and useful information. However, the useful information required to get good performance is higher now. It's case-specific but a good amount of things to describe would be how the datasets relate to each other, addressing the size of the datasets in relation to one another, making sure foreign and primary keys are made appropriately and ideally using previously generated datasets to populate new ones so the actual data values match where necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3TWAhIYIxnbS", + "outputId": "8f766838-b2f0-419a-a4fb-543d029afce5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To create a Python program generating three Pandas DataFrames as described, I'll lay out a step-by-step process considering the relationships between the different types of data:\n", + "\n", + "1. Install pandas if you haven't yet: `pip install pandas`\n", + "2. Import pandas and generate each DataFrame. I'll make some assumptions for the synthetic data to keep it relatively simple.\n", + "\n", + "Let's start coding:\n", + "\n", + "```python\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Generating Location DataFrame\n", + "np.random.seed(42) # For reproducibility\n", + "location_data = {\n", + " 'id': range(1, 11), # Assuming 10 unique locations\n", + " 'country': ['CountryA'] * 5 + ['CountryB'] * 5,\n", + " 'city': ['City' + str(i) for i in range(1, 11)],\n", + " 'population': np.random.randint(100000, 1000000, size=10),\n", + " 'area': np.random.randint(500, 20000, size=10),\n", + "}\n", + "locations_df = pd.DataFrame(location_data)\n", + "\n", + "# Generating House Types DataFrame\n", + "house_types_data = {\n", + " 'id': range(1, 5), # Assuming 4 unique house types\n", + " 'house type': ['Villa', 'Apartment', 'Townhouse', 'Bungalow'],\n", + " 'average house type price': [300000, 200000, 250000, 220000], # Just arbitrary prices\n", + " 'number of houses': [25, 50, 15, 10], # Total = 100 houses, matching the housing data requirement\n", + "}\n", + "house_types_df = pd.DataFrame(house_types_data)\n", + "\n", + "# Generating Housing Data\n", + "housing_data = {\n", + " 'id': range(1, 101),\n", + " 'house size': np.random.randint(50, 500, size=100),\n", + " 'house price': [], # To be calculated based on size, location, etc.\n", + " 'location_id': np.random.choice(locations_df['id'], size=100),\n", + " 'number of bedrooms': np.random.randint(1, 6, size=100),\n", + " 'house_type_id': np.random.choice(house_types_df['id'], size=100),\n", + "}\n", + "# Simple model to calculate house price based on size, type, and a base price from the location's median\n", + "base_prices = locations_df['population'] / 100000 # Simplified assumption: more populous => more expensive\n", + "housing_data['house price'] = [\n", + " (1200 * size) + (house_types_df.loc[type_id - 1, 'average house type price']) + (base_prices[loc_id - 1] * 1000)\n", + " for size, type_id, loc_id\n", + " in zip(housing_data['house size'], housing_data['house_type_id'], housing_data['location_id'])\n", + "]\n", + "\n", + "housing_df = pd.DataFrame(housing_data)\n", + "\n", + "# Display the first few rows of each DataFrame\n", + "print(locations_df.head())\n", + "print(house_types_df.head())\n", + "print(housing_df.head())\n", + "```\n", + "\n", + "Notes:\n", + "- This script assumes 10 unique locations and 4 house types for simplicity.\n", + "- House prices are arbitrarily calculated using the house size, type, and a base price influenced by the location's population. Reality would require a more complex model.\n", + "- `numpy.random.randint` is used to generate integer values. Similarly, `numpy.random.choice` is used to randomly assign locations and house types to each house, demonstrating a form of foreign key relationship.\n", + "- For simplicity, foreign keys are represented by corresponding ID fields (e.g., `location_id` in the housing data references the `id` in the location data).\n", + "\n", + "This simple synthetic data generation strategy illustrates creating related data sets with Python and pandas. The synthetic data should make general sense within the constraints provided, but keep in mind that for more complex or realistic data modeling, you'd need to incorporate more detailed rules and possibly real-world data.\n" + ] + } + ], + "source": [ + "question = \"\"\"\n", + "Create a Python program to generate 3 different pandas dataframes.\n", + "\n", + "1. Housing data\n", + "I want 100 rows. Each row should include the following fields:\n", + " - id (incrementing integer starting at 1)\n", + " - house size (m^2)\n", + " - house price\n", + " - location\n", + " - number of bedrooms\n", + " - house type\n", + " + any relevant foreign keys\n", + "\n", + "2. Location\n", + "Each row should include the following fields:\n", + " - id (incrementing integer starting at 1)\n", + " - country\n", + " - city\n", + " - population\n", + " - area (m^2)\n", + " + any relevant foreign keys\n", + "\n", + " 3. House types\n", + " - id (incrementing integer starting at 1)\n", + " - house type\n", + " - average house type price\n", + " - number of houses\n", + " + any relevant foreign keys\n", + "\n", + "Make sure that the numbers make sense (i.e. more rooms is usually bigger size, more expensive locations increase price. more size is usually higher price etc. make sure all the numbers make sense).\n", + "Make sure that the dataframe generally follow common sense checks, e.g. the size of the dataframes make sense in comparison with one another.\n", + "Make sure the foreign keys match up and you can use previously generated dataframes when creating each consecutive dataframes.\n", + "You can use the previously generated dataframe to generate the next dataframe.\n", + "\"\"\"\n", + "\n", + "response = client.chat.completions.create(\n", + " model=datagen_model,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant designed to generate synthetic data.\"},\n", + " {\"role\": \"user\", \"content\": question}\n", + " ]\n", + ")\n", + "res = response.choices[0].message.content\n", + "print(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Yv9XlRtauZYZ" + }, + "source": [ + "### 4. Simply creating textual data\n", + "Here we take a first look at creating textual data. This can be used to finetune another GPT model for example. In this case we imagine ourselves a retailer trying to streamline the process of creating descriptions for items they are selling. We again need to specify the format of the data, in particular in this case we want one which is easy to parse as an output." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The example we consider below is one in which we want to create input output training pairs for GPT model to finetune on. We will have the products' name and the category it belongs to as input and the output will be a description. \n", + "\n", + "Specifying the structure of the output explicitly and giving commands to not deviate from this help enforce the output structure. You can run this in a loop and append the data to generate more synthetic data. Again, as before we will need to parse the data well so that our code further downstream does not break." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "2KJVwjV0upby" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.\n", + "Input: Northface Waterproof Jacket, Clothing\n", + "Output: Stay dry and stylish with the Northface Waterproof Jacket. Perfect for outdoor adventurers and city dwellers alike, this jacket combines cutting-edge waterproof technology with a sleek, modern design. Ideal for unpredictable weather, it ensures you're prepared for anything Mother Nature throws your way.\n", + "\n", + "2.\n", + "Input: Apple iPhone 12, Electronics\n", + "Output: Experience the next level of innovation with the Apple iPhone 12. Featuring a stunning Super Retina XDR display, a powerful A14 Bionic chip, and advanced dual-camera system, this phone is designed to push the boundaries of what's possible. With 5G capability for super-fast downloads and high-quality streaming, it's the perfect device for tech enthusiasts.\n", + "\n", + "3.\n", + "Input: Adidas Ultraboost Sneakers, Footwear\n", + "Output: Revolutionize your running experience with Adidas Ultraboost Sneakers. Engineered for long-lasting comfort and superior performance, these sneakers feature the innovative Boost \n" + ] + } + ], + "source": [ + "output_string = \"\"\n", + "for i in range(3):\n", + " question = f\"\"\"\n", + " I am creating input output training pairs to fine tune my gpt model. The usecase is a retailer generating a description for a product from a product catalogue. I want the input to be product name and category (to which the product belongs to) and output to be description.\n", + " The format should be of the form:\n", + " 1.\n", + " Input: product_name, category\n", + " Output: description\n", + " 2.\n", + " Input: product_name, category\n", + " Output: description\n", + "\n", + " Do not add any extra characters around that formatting as it will make the output parsing break.\n", + " Create as many training pairs as possible.\n", + " \"\"\"\n", + "\n", + " response = client.chat.completions.create(\n", + " model=datagen_model,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant designed to generate synthetic data.\"},\n", + " {\"role\": \"user\", \"content\": question}\n", + " ]\n", + " )\n", + " res = response.choices[0].message.content\n", + " output_string += res + \"\\n\" + \"\\n\"\n", + "print(output_string[:1000]) #displaying truncated response\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K5EmKTEa7GlC" + }, + "source": [ + "Note: the above output is truncated. And now we can parse it as below to get a list of products, categories and their descriptions. For example, let's take a look at the products it's generated." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "owvoyJBh0o2n", + "outputId": "ee48bcc9-fd29-42bf-9beb-ef3800cdbcb2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Northface Waterproof Jacket',\n", + " 'Apple iPhone 12',\n", + " 'Adidas Ultraboost Sneakers',\n", + " 'LEGO Star Wars Millennium Falcon',\n", + " 'Vitamix Professional Series 750 Blender',\n", + " 'Panasonic Lumix GH5 Camera',\n", + " 'Moleskine Classic Notebook',\n", + " 'Bodum French Press Coffee Maker',\n", + " 'Classic White Sneakers',\n", + " 'Multi-Purpose Blender',\n", + " 'Eco-Friendly Yoga Mat',\n", + " 'Organic Green Tea',\n", + " 'Smart LED Light Bulb',\n", + " 'Waterproof Hiking Boots',\n", + " 'Bamboo Toothbrush',\n", + " 'Modern Minimalist Floor Lamp',\n", + " 'Classic Leather Office Chair',\n", + " 'Stainless Steel French Press',\n", + " 'Eco-Friendly Bamboo Cutting Board',\n", + " 'Ultimate Gaming Laptop',\n", + " 'Waterproof Hiking Boots',\n", + " 'Compact Travel Umbrella',\n", + " \"Professional Chef's Knife\"]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#regex to parse data\n", + "pattern = re.compile(r'Input:\\s*(.+?),\\s*(.+?)\\nOutput:\\s*(.+?)(?=\\n\\n|\\Z)', re.DOTALL)\n", + "matches = pattern.findall(output_string)\n", + "products = []\n", + "categories = []\n", + "descriptions = []\n", + "\n", + "for match in matches:\n", + " product, category, description = match\n", + " products.append(product.strip())\n", + " categories.append(category.strip())\n", + " descriptions.append(description.strip())\n", + "products" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bO3PgRwpyocn" + }, + "source": [ + "\n", + "### 5. Dealing with imbalanced or non-diverse textual data\n", + "Some of the most important aspects of generating high-quality synthetic data are accuracy (does the data make sense), consistency (are two separate data points for the same input roughly the same) and diversity (making sure our data distribution matches as much of the distribution that exists in production).\n", + "\n", + "\n", + "To increase the diversity of our data, we start first by clustering the data. This will provide us information about which clusters are underrepresented (imbalanced dataset) or which data is not addressed at all (widening the data distribution). Then, we will either suggest new clusters (using self-reflection type call from GPT) or ask the next iteration of our synthetic generation calls to explicitly target the underrepresented clusters. \n", + "\n", + "We can then recursively run this generation and analysis of cluster loop to automate generating diverse synthetic data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ubdPEFYR-myU" + }, + "source": [ + "For demonstrative purposes, we explicitly prompt the LLM to generate information about 4 different topical areas: vehicle, clothing, toiletries, food. We will then cluster the data and see if it managed to find these 4 topic areas." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "m-yncn8s1hWZ", + "outputId": "35ae248d-4859-4d3f-ba29-94478aed7305" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2) toiletries\n", + "Input: \"Toothbrush X5+, Electronic toothbrush\"\n", + "Output: \"Experience a superior cleanse with the Toothbrush X5+. It comes equipped with an advanced sonic technology that guarantees a gentle yet effective clean every time.\"\n", + "\n", + "3) vehicle\n", + "Input: \"Pegasus Pro 300, Motorcycle\"\n", + "Output: \"Dominate the road with the stylish Pegasus Pro 300. This motorcycle guarantees a powerful, efficient, and thrilling performance on every ride.\"\n", + "\n", + "4) food\n", + "Input: \"Tasty Delight Instant Noodles, Instant food\"\n", + "Output: \"Tasty Delight Instant Noodles offer a quick, delicious meal ready in minutes. The perfect solution for those stepping up their cooking game.\"\n", + "\n", + "5) clothing\n", + "Input: \"UltraSport Men's Running Jacket, Sportswear\"\n", + "Output: \"UltraSport Men's Running Jacket combines functionality and style. The breathable material allows for comfortable workouts, even in colder weather.\"\n", + "\n", + "6) toiletries\n", + "Input: \"FreshBliss Shower Gel, Bath and body\"\n", + "Output: \"Indulge in luxury every morning with the FreshBliss Showe\n" + ] + } + ], + "source": [ + "output_string = \"\"\n", + "for i in range(3):\n", + " question = f\"\"\"\n", + " I am creating input output training pairs to fine tune my gpt model. I want the input to be product name and category and output to be description. the category should be things like: mobile phones, shoes, headphones, laptop, electronic toothbrush, etc. and also more importantly the categories should come under 4 main topics: vehicle, clothing, toiletries, food)\n", + " After the number of each example also state the topic area. The format should be of the form:\n", + " 1. topic_area\n", + " Input: product_name, category\n", + " Output: description\n", + "\n", + " Do not add any extra characters around that formatting as it will make the output parsing break.\n", + "\n", + " Here are some helpful examples so you get the style of output correct.\n", + "\n", + " 1) clothing\n", + " Input: \"Shoe Name, Shoes\"\n", + " Output: \"Experience unparalleled comfort. These shoes feature a blend of modern style and the traditional superior cushioning, perfect for those always on the move.\"\n", + " \"\"\"\n", + "\n", + " response = client.chat.completions.create(\n", + " model=\"gpt-4\",\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant designed to generate synthetic data.\"},\n", + " {\"role\": \"user\", \"content\": question}\n", + " ]\n", + " )\n", + " res = response.choices[0].message.content\n", + " output_string += res + \"\\n\" + \"\\n\"\n", + "print(output_string[:1000]) #displaying truncated response" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: The above output is truncated. In the example above, we would explicitly include the topic area as part of the response per example as it helps condition the proceeding output and tends to give better performance. We can also give it an actual example of what the output should look like so it gets the right idea of style of output but also to help enforce structure." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "k7GAokEC1hUY" + }, + "outputs": [], + "source": [ + "pattern = re.compile(r'(\\d+)\\) (\\w+(?: \\w+)?)\\s*Input: \"(.+?), (.+?)\"\\s*Output: \"(.+?)\"', re.DOTALL)\n", + "matches = pattern.findall(output_string)\n", + "\n", + "\n", + "topics = []\n", + "products = []\n", + "categories = []\n", + "descriptions = []\n", + "\n", + "for match in matches:\n", + " number, topic, product, category, description = match\n", + " topics.append(topic)\n", + " products.append(product)\n", + " categories.append(category)\n", + " descriptions.append(description)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Z49B3LrJ1hSG", + "outputId": "d76a9038-1879-44d9-f1db-dc933e066c54" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Toothbrush X5+',\n", + " 'Pegasus Pro 300',\n", + " 'Tasty Delight Instant Noodles',\n", + " \"UltraSport Men's Running Jacket\",\n", + " 'FreshBliss Shower Gel',\n", + " 'OceanBlue Yacht 700',\n", + " 'FarmFresh Organic Apples',\n", + " \"Elegance Women's Velvet Dress\",\n", + " \"GentleCare Men's Face Wash\",\n", + " 'AquaBreathe',\n", + " 'Lunar Ride',\n", + " 'Sunrise Juice',\n", + " 'TitanFlex',\n", + " 'GlowRadiant',\n", + " 'SolarSpeed',\n", + " 'HealthyBite',\n", + " 'Brushify',\n", + " 'Choco Crunchy',\n", + " 'Super X100',\n", + " 'Le Bliz',\n", + " 'Purely Lavender',\n", + " 'Cheesy Delight',\n", + " 'EcoSprint',\n", + " 'Denim Duo',\n", + " 'Fresh Dawn']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "products" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J1qKxLkAzKq0" + }, + "source": [ + "We will now cluster the data to analyze it. We will use K-means clustering to segregate the data. An important parameter of K-means to set is K, the number of clusters.\n", + "\n", + "We know that there should be 4 cluster (4 topics) since we specified this in prompt: vehicle, electronics, clothing, food. However in general for our data, we do not know the number of clusters that exist. Therefore we will use the elbow method to find the optimal number of clusters.\n", + "\n", + "In the elbow method, we iterate through a range of different K's, each time storing the inertia. The inertia measures the sum of the squared distances between each point in a cluster and the centroid of that cluster thus telling us how well-separated and dense each cluster is. If we plot K against the inertia, we are able to see how the inertia drops and where the drop in inertia is least rapid (often making an elbow shape) we can set our optimal number of clusters. You can read into more depth about the elbow method [here](https://en.wikipedia.org/wiki/Elbow_method_(clustering))." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1BxwPTkpGzu8" + }, + "source": [ + "First let's store our data into a pandas dataframe for ease of analysis\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "XcPBzORtKWv6" + }, + "outputs": [], + "source": [ + "data = {\n", + " 'Product': products,\n", + " 'Category': categories,\n", + " 'Description': descriptions\n", + "}\n", + "\n", + "df = pd.DataFrame(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HQbg6r37KjG0" + }, + "source": [ + "Next let us embed our data as the embeddings is what we will cluster since they should be close to each other in vector space if they are similar." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "l8M7MX-1Jctr" + }, + "outputs": [], + "source": [ + "def get_embedding(text, model=\"text-embedding-3-small\"):\n", + " text = text.replace(\"\\n\", \" \")\n", + "\n", + " response = client.embeddings.create(input=[text], model=model)\n", + "\n", + " return response.data[0].embedding\n", + "\n", + "embedding_model = \"text-embedding-3-small\"\n", + "df[\"embedding\"] = df.Category.apply(lambda x: get_embedding(x, model=embedding_model))\n", + "\n", + "matrix = np.vstack(df.embedding.values)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZcdZscBNKy0F" + }, + "source": [ + "Now we perform the elbow method. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 981 + }, + "id": "1Azw_xgVl_aY", + "outputId": "5b7076aa-a03c-4a40-f52c-08b09248cf18" + }, + "outputs": [], + "source": [ + "# Determine the optimal number of clusters using the elbow method\n", + "inertias = []\n", + "range_of_clusters = range(1, 13) # Adjust the range as necessary\n", + "\n", + "for n_clusters in range_of_clusters:\n", + " kmeans = KMeans(n_clusters=n_clusters, init=\"k-means++\", random_state=42, n_init=10)\n", + " kmeans.fit(matrix)\n", + " inertias.append(kmeans.inertia_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will output a chart for us in which we have to visually tell where the optimal cluster point is. We can see below that we see a gradual decrease of inertia rather than a sharp elbow but the point of steepest decrease appears to occur around 3, 4 or 5 clusters which lines up with our expectations given our prompt. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plotting the elbow plot\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(range_of_clusters, inertias, '-o')\n", + "plt.title('Elbow Method to Determine Optimal Number of Clusters')\n", + "plt.xlabel('Number of Clusters')\n", + "plt.ylabel('Inertia')\n", + "plt.xticks(range_of_clusters)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![elbow_chart](../images/elbow_chart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NN7NbTmiLe_-" + }, + "source": [ + "For demonstration purposes we will pick 5 as the optimal cluster number to show it doesn't matter exactly where we pick it as long as we are approximately right. There are numerous correct ways to categorize data. We also store which cluster each data point belongs to." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KvrDe3WYKWgZ", + "outputId": "d0d95227-b9d2-4c52-a5f7-59159ba848d2" + }, + "outputs": [], + "source": [ + "n_clusters = 5\n", + "\n", + "kmeans = KMeans(n_clusters=n_clusters, init=\"k-means++\", random_state=42)\n", + "kmeans.fit(matrix)\n", + "labels = kmeans.labels_\n", + "df[\"Cluster\"] = labels" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tLvO4AISDM0J" + }, + "source": [ + "We will analyze the cluster data now. There are two separate things we will look to address. 1. imbalanced data, 2. Expanding the data distribution." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zaQ_mdhpOJqs" + }, + "source": [ + "First for imbalanced data we count the number of examples in each cluster. Then we select a few examples from each cluster at random and ask the LLM what topics these map to. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "crUT7OR7QcFD", + "outputId": "04f49ac2-1259-4622-bcf2-0686af93068c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cluster\n", + "0 4\n", + "1 7\n", + "2 6\n", + "3 4\n", + "4 4\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "cluster_counts = df[\"Cluster\"].value_counts().sort_index()\n", + "print(cluster_counts)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8i1FGtM-Xx3k" + }, + "source": [ + "We can see the topics found here:\n", + "Eco-friendly Transportation, Luxury and Leisure Items, Personal Care Products, Electronic Toothbrushes and Clothing and Apparel\n", + "match well enough but not exactly to our initial prompt of:\n", + "vehicle, clothing, toiletries, food.\n", + "\n", + "As we chose 5 clusters, it split up toiletries into Skincare and Personal Care which doesn't affect us too much further downstream." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RRwIet9DUdKe", + "outputId": "8e7835c9-884a-4504-bbed-f556382dd9f5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\n", + " {\n", + " \"cluster\": 0,\n", + " \"topic\": \"Electronic Toothbrushes\"\n", + " },\n", + " {\n", + " \"cluster\": 1,\n", + " \"topic\": \"Clothing and Apparel\"\n", + " },\n", + " {\n", + " \"cluster\": 2,\n", + " \"topic\": \"Personal Care Products\"\n", + " },\n", + " {\n", + " \"cluster\": 3,\n", + " \"topic\": \"Eco-friendly Transportation\"\n", + " },\n", + " {\n", + " \"cluster\": 4,\n", + " \"topic\": \"Luxury and Leisure Items\"\n", + " }\n", + "]\n" + ] + } + ], + "source": [ + "selected_examples = df.groupby('Cluster').apply(lambda x: x.sample(3)).reset_index(drop=True)\n", + "\n", + "# Format the selected examples\n", + "formatted_examples = \"\\n\".join(\n", + " f'Input: \"{row[\"Product\"]}, {row[\"Category\"]}\"\\nOutput: \"{row[\"Description\"]}\"\\nCluster: \"{row[\"Cluster\"]}\"'\n", + " for _, row in selected_examples.iterrows()\n", + ")\n", + "\n", + "topic_prompt = f\"\"\"\n", + " I previously generated some examples of input output trainings pairs and then I clustered them based on category. From each cluster I picked 3 example data point which you can find below.\n", + " I want you identify the broad topic areas these clusters belong to.\n", + " Previous examples:\n", + " {formatted_examples}\n", + "\n", + "\n", + " Your output should be strictly of the format:\n", + " Cluster: number, topic: topic\n", + " Cluster: number, topic: topic\n", + " Cluster: number, topic: topic\n", + "\n", + " Do not add any extra characters around that formatting as it will make the output parsing break.\n", + " \"\"\"\n", + "\n", + "response = client.chat.completions.create(\n", + " model=datagen_model,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant designed analyze clustered data\"},\n", + " {\"role\": \"user\", \"content\": topic_prompt}\n", + " ]\n", + ")\n", + "res = response.choices[0].message.content\n", + "\n", + "pattern = r\"Cluster: (\\d+), topic: ([^\\n]+)\"\n", + "matches = re.findall(pattern, res)\n", + "clusters = [{\"cluster\": int(cluster), \"topic\": topic} for cluster, topic in matches]\n", + "json_output = json.dumps(clusters, indent=2)\n", + "print(json_output)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x5hszl-SZVdi" + }, + "source": [ + "We now have the clusters and their counts so we could prompt the LLM to generate more examples within the topics we want. However for this example we won't take that further as they are well-split and you would just follow the procedure above for prompting the model to generate data while passing in the underrepresented topics." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yVD_TPsHYvDb" + }, + "source": [ + "Next, we will try and deal with increasing the diversity of our data distribution. \n", + "\n", + "First we start in a similar way by finding a few examples from each cluster at random and ask the LLM what topics these map to. In addition to this in the same LLM call, we will ask it to generate more topics to increase the diversity of our data. We do this in one call to save time/cost." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "id": "mZjBbfFaZ3mn", + "outputId": "8864421a-e9d4-4ea6-f747-a76a3291a593" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. Cluster topic mapping\n", + "Cluster: 0, topic: Electronic/Health Products\n", + "Cluster: 1, topic: Fashion and Food\n", + "Cluster: 2, topic: Personal Care/Wellness\n", + "Cluster: 3, topic: Eco-friendly Transportation\n", + "Cluster: 4, topic: Chocolate/Motorcycles\n", + "\n", + "2. New topics\n", + "1. Home Automation Gadgets\n", + "2. Educational Tools and Apps\n", + "3. Renewable Energy Solutions\n", + "4. Virtual Reality Experiences\n" + ] + } + ], + "source": [ + "selected_examples = df.groupby('Cluster').apply(lambda x: x.sample(3)).reset_index(drop=True)\n", + "\n", + "# Format the selected examples\n", + "formatted_examples = \"\\n\".join(\n", + " f'Input: \"{row[\"Product\"]}, {row[\"Category\"]}\"\\nOutput: \"{row[\"Description\"]}\"\\nCluster: \"{row[\"Cluster\"]}\"'\n", + " for _, row in selected_examples.iterrows()\n", + ")\n", + "\n", + "topic_prompt = f\"\"\"\n", + " I previously generated some examples of input output trainings pairs and then I clustered them based on category. From each cluster I picked 3 example data point which you can find below.\n", + " I want to promote diversity in my examples across categories so follow the procedure below:\n", + " 1. You must identify the broad topic areas these clusters belong to.\n", + " 2. You should generate further topic areas which don't exist so I can generate data within these topics to improve diversity.\n", + "\n", + "\n", + " Previous examples:\n", + " {formatted_examples}\n", + "\n", + "\n", + " Your output should be strictly of the format:\n", + "\n", + " 1. Cluster topic mapping\n", + " Cluster: number, topic: topic\n", + " Cluster: number, topic: topic\n", + " Cluster: number, topic: topic\n", + "\n", + " 2. New topics\n", + " 1. topic\n", + " 2. topic\n", + " 3. topic\n", + " 4. topic\n", + "\n", + " Do not add any extra characters around that formatting as it will make the output parsing break. It is very important you stick to that output format\n", + " \"\"\"\n", + "\n", + "response = client.chat.completions.create(\n", + " model=datagen_model,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant designed to analyze clustered data\"},\n", + " {\"role\": \"user\", \"content\": topic_prompt}\n", + " ]\n", + ")\n", + "res = response.choices[0].message.content\n", + "print(res)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see here again that we explicitly prompt the output structure it should follow. I also tell it the purpose of generating topics (to promote diversity) so the model has full context." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s254oJ-Ecka0" + }, + "source": [ + "We then parse the data into a list of cluster-mapping jsons and a list of topics" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HTS4ybspcivw", + "outputId": "52ea363c-cbf5-420e-b81e-0d2710c50203" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "([{'cluster': 0, 'topic': 'Electronic/Health Products'},\n", + " {'cluster': 1, 'topic': 'Fashion and Food'},\n", + " {'cluster': 2, 'topic': 'Personal Care/Wellness'},\n", + " {'cluster': 3, 'topic': 'Eco-friendly Transportation'},\n", + " {'cluster': 4, 'topic': 'Chocolate/Motorcycles'}],\n", + " ['Home Automation Gadgets',\n", + " 'Educational Tools and Apps',\n", + " 'Renewable Energy Solutions',\n", + " 'Virtual Reality Experiences'])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parts = res.split(\"\\n\\n\")\n", + "cluster_mapping_part = parts[0]\n", + "new_topics_part = parts[1]\n", + "\n", + "# Parse cluster topic mapping\n", + "cluster_topic_mapping_lines = cluster_mapping_part.split(\"\\n\")[1:] # Skip the first two lines\n", + "cluster_topic_mapping = [{\"cluster\": int(line.split(\",\")[0].split(\":\")[1].strip()), \"topic\": line.split(\":\")[2].strip()} for line in cluster_topic_mapping_lines]\n", + "\n", + "# Parse new topics\n", + "new_topics_lines = new_topics_part.split(\"\\n\")[1:] # Skip the first line\n", + "new_topics = [line.split(\". \")[1] for line in new_topics_lines]\n", + "\n", + "cluster_topic_mapping, new_topics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CX26-PGdcui0" + }, + "source": [ + "And finally we can use this information to further prompt a model to keep generating synthetic data. We do this by passing all the topics in the list of jsons to the prompt below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zHf4LnVk0aHw" + }, + "outputs": [], + "source": [ + "output_string = \"\"\n", + "for i in range(3):\n", + " question = f\"\"\"\n", + " I am creating input output training pairs to fine tune my gpt model. I want the input to be product name and category and output to be description. the category should be things like: mobile phones, shoes, headphones, laptop, electronic toothbrush, etc. and also more importantly the categories should come under some main topics: {[entry['topic'] for entry in cluster_topic_mapping]})\n", + " After the number of each example also state the topic area. The format should be of the form:\n", + " 1. topic_area\n", + " Input: product_name, category\n", + " Output: description\n", + "\n", + " Do not add any extra characters around that formatting as it will make the output parsing break.\n", + "\n", + " Here are some helpful examples so you get the style of output correct.\n", + "\n", + " 1) clothing\n", + " Input: \"Shoe Name, Shoes\"\n", + " Output: \"Experience unparalleled comfort. These shoes feature a blend of modern style and the traditional superior cushioning, perfect for those always on the move.\"\n", + " \"\"\"\n", + "\n", + " response = client.chat.completions.create(\n", + " model=\"gpt-4\",\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant designed to generate synthetic data.\"},\n", + " {\"role\": \"user\", \"content\": question}\n", + " ]\n", + " )\n", + " res = response.choices[0].message.content\n", + " output_string += res + \"\\n\" + \"\\n\"\n", + "print(output_string)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RQMHQxnZdRug" + }, + "source": [ + "You can run this in a loop to append to your previous data and in this way you can keep generating more textual synthetic data to train another GPT model while making sure that we cater to imbalanced datasets and generating a diversity of data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Hiim8Xg5djGH" + }, + "source": [ + "You have now completed part 1 of the synthetic data generation tutorial where we have gone through:\n", + "* CSV with a structured prompt\n", + "* CSV with a Python program\n", + "* Multitable CSV with a python program\n", + "* Simply creating textual data\n", + "* Dealing with imbalanced or non-diverse textual data\n", + "\n", + "In part 2 you will find find out techniques for better prompting an LLM to enhance textual synthetic data generation." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/Summarizing_long_documents.ipynb b/examples/Summarizing_long_documents.ipynb new file mode 100644 index 0000000..1ea45a9 --- /dev/null +++ b/examples/Summarizing_long_documents.ipynb @@ -0,0 +1,861 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summarizing Long Documents" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The objective of this notebook is to demonstrate how to summarize large documents with a controllable level of detail.\n", + " \n", + "If you give a GPT model the task of summarizing a long document (e.g. 10k or more tokens), you'll tend to get back a relatively short summary that isn't proportional to the length of the document. For instance, a summary of a 20k token document will not be twice as long as a summary of a 10k token document. One way we can fix this is to split our document up into pieces, and produce a summary piecewise. After many queries to a GPT model, the full summary can be reconstructed. By controlling the number of text chunks and their sizes, we can ultimately control the level of detail in the output." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:19:35.305706Z", + "start_time": "2024-04-10T05:19:35.303535Z" + }, + "pycharm": { + "is_executing": true + } + }, + "outputs": [], + "source": [ + "import os\n", + "from typing import List, Tuple, Optional\n", + "from openai import OpenAI\n", + "import tiktoken\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:19:35.325026Z", + "start_time": "2024-04-10T05:19:35.322414Z" + } + }, + "outputs": [], + "source": [ + "# open dataset containing part of the text of the Wikipedia page for the United States\n", + "with open(\"data/artificial_intelligence_wikipedia.txt\", \"r\") as file:\n", + " artificial_intelligence_wikipedia_text = file.read()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:19:35.364483Z", + "start_time": "2024-04-10T05:19:35.348213Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "14630" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load encoding and check the length of dataset\n", + "encoding = tiktoken.encoding_for_model('gpt-4-turbo')\n", + "len(encoding.encode(artificial_intelligence_wikipedia_text))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll define a simple utility to wrap calls to the OpenAI API." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:19:35.375619Z", + "start_time": "2024-04-10T05:19:35.365818Z" + } + }, + "outputs": [], + "source": [ + "client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n", + "\n", + "def get_chat_completion(messages, model='gpt-4-turbo'):\n", + " response = client.chat.completions.create(\n", + " model=model,\n", + " messages=messages,\n", + " temperature=0,\n", + " )\n", + " return response.choices[0].message.content" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we'll define some utilities to chunk a large document into smaller pieces." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:19:35.382790Z", + "start_time": "2024-04-10T05:19:35.376721Z" + } + }, + "outputs": [], + "source": [ + "def tokenize(text: str) -> List[str]:\n", + " encoding = tiktoken.encoding_for_model('gpt-4-turbo')\n", + " return encoding.encode(text)\n", + "\n", + "\n", + "# This function chunks a text into smaller pieces based on a maximum token count and a delimiter.\n", + "def chunk_on_delimiter(input_string: str,\n", + " max_tokens: int, delimiter: str) -> List[str]:\n", + " chunks = input_string.split(delimiter)\n", + " combined_chunks, _, dropped_chunk_count = combine_chunks_with_no_minimum(\n", + " chunks, max_tokens, chunk_delimiter=delimiter, add_ellipsis_for_overflow=True\n", + " )\n", + " if dropped_chunk_count > 0:\n", + " print(f\"warning: {dropped_chunk_count} chunks were dropped due to overflow\")\n", + " combined_chunks = [f\"{chunk}{delimiter}\" for chunk in combined_chunks]\n", + " return combined_chunks\n", + "\n", + "\n", + "# This function combines text chunks into larger blocks without exceeding a specified token count. It returns the combined text blocks, their original indices, and the count of chunks dropped due to overflow.\n", + "def combine_chunks_with_no_minimum(\n", + " chunks: List[str],\n", + " max_tokens: int,\n", + " chunk_delimiter=\"\\n\\n\",\n", + " header: Optional[str] = None,\n", + " add_ellipsis_for_overflow=False,\n", + ") -> Tuple[List[str], List[int]]:\n", + " dropped_chunk_count = 0\n", + " output = [] # list to hold the final combined chunks\n", + " output_indices = [] # list to hold the indices of the final combined chunks\n", + " candidate = (\n", + " [] if header is None else [header]\n", + " ) # list to hold the current combined chunk candidate\n", + " candidate_indices = []\n", + " for chunk_i, chunk in enumerate(chunks):\n", + " chunk_with_header = [chunk] if header is None else [header, chunk]\n", + " if len(tokenize(chunk_delimiter.join(chunk_with_header))) > max_tokens:\n", + " print(f\"warning: chunk overflow\")\n", + " if (\n", + " add_ellipsis_for_overflow\n", + " and len(tokenize(chunk_delimiter.join(candidate + [\"...\"]))) <= max_tokens\n", + " ):\n", + " candidate.append(\"...\")\n", + " dropped_chunk_count += 1\n", + " continue # this case would break downstream assumptions\n", + " # estimate token count with the current chunk added\n", + " extended_candidate_token_count = len(tokenize(chunk_delimiter.join(candidate + [chunk])))\n", + " # If the token count exceeds max_tokens, add the current candidate to output and start a new candidate\n", + " if extended_candidate_token_count > max_tokens:\n", + " output.append(chunk_delimiter.join(candidate))\n", + " output_indices.append(candidate_indices)\n", + " candidate = chunk_with_header # re-initialize candidate\n", + " candidate_indices = [chunk_i]\n", + " # otherwise keep extending the candidate\n", + " else:\n", + " candidate.append(chunk)\n", + " candidate_indices.append(chunk_i)\n", + " # add the remaining candidate to output if it's not empty\n", + " if (header is not None and len(candidate) > 1) or (header is None and len(candidate) > 0):\n", + " output.append(chunk_delimiter.join(candidate))\n", + " output_indices.append(candidate_indices)\n", + " return output, output_indices, dropped_chunk_count" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can define a utility to summarize text with a controllable level of detail (note the `detail` parameter).\n", + "\n", + "The function first determines the number of chunks by interpolating between a minimum and a maximum chunk count based on a controllable `detail` parameter. It then splits the text into chunks and summarizes each chunk." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:19:35.390876Z", + "start_time": "2024-04-10T05:19:35.385076Z" + } + }, + "outputs": [], + "source": [ + "def summarize(text: str,\n", + " detail: float = 0,\n", + " model: str = 'gpt-4-turbo',\n", + " additional_instructions: Optional[str] = None,\n", + " minimum_chunk_size: Optional[int] = 500,\n", + " chunk_delimiter: str = \".\",\n", + " summarize_recursively=False,\n", + " verbose=False):\n", + " \"\"\"\n", + " Summarizes a given text by splitting it into chunks, each of which is summarized individually. \n", + " The level of detail in the summary can be adjusted, and the process can optionally be made recursive.\n", + "\n", + " Parameters:\n", + " - text (str): The text to be summarized.\n", + " - detail (float, optional): A value between 0 and 1 indicating the desired level of detail in the summary.\n", + " 0 leads to a higher level summary, and 1 results in a more detailed summary. Defaults to 0.\n", + " - model (str, optional): The model to use for generating summaries. Defaults to 'gpt-3.5-turbo'.\n", + " - additional_instructions (Optional[str], optional): Additional instructions to provide to the model for customizing summaries.\n", + " - minimum_chunk_size (Optional[int], optional): The minimum size for text chunks. Defaults to 500.\n", + " - chunk_delimiter (str, optional): The delimiter used to split the text into chunks. Defaults to \".\".\n", + " - summarize_recursively (bool, optional): If True, summaries are generated recursively, using previous summaries for context.\n", + " - verbose (bool, optional): If True, prints detailed information about the chunking process.\n", + "\n", + " Returns:\n", + " - str: The final compiled summary of the text.\n", + "\n", + " The function first determines the number of chunks by interpolating between a minimum and a maximum chunk count based on the `detail` parameter. \n", + " It then splits the text into chunks and summarizes each chunk. If `summarize_recursively` is True, each summary is based on the previous summaries, \n", + " adding more context to the summarization process. The function returns a compiled summary of all chunks.\n", + " \"\"\"\n", + "\n", + " # check detail is set correctly\n", + " assert 0 <= detail <= 1\n", + "\n", + " # interpolate the number of chunks based to get specified level of detail\n", + " max_chunks = len(chunk_on_delimiter(text, minimum_chunk_size, chunk_delimiter))\n", + " min_chunks = 1\n", + " num_chunks = int(min_chunks + detail * (max_chunks - min_chunks))\n", + "\n", + " # adjust chunk_size based on interpolated number of chunks\n", + " document_length = len(tokenize(text))\n", + " chunk_size = max(minimum_chunk_size, document_length // num_chunks)\n", + " text_chunks = chunk_on_delimiter(text, chunk_size, chunk_delimiter)\n", + " if verbose:\n", + " print(f\"Splitting the text into {len(text_chunks)} chunks to be summarized.\")\n", + " print(f\"Chunk lengths are {[len(tokenize(x)) for x in text_chunks]}\")\n", + "\n", + " # set system message\n", + " system_message_content = \"Rewrite this text in summarized form.\"\n", + " if additional_instructions is not None:\n", + " system_message_content += f\"\\n\\n{additional_instructions}\"\n", + "\n", + " accumulated_summaries = []\n", + " for chunk in tqdm(text_chunks):\n", + " if summarize_recursively and accumulated_summaries:\n", + " # Creating a structured prompt for recursive summarization\n", + " accumulated_summaries_string = '\\n\\n'.join(accumulated_summaries)\n", + " user_message_content = f\"Previous summaries:\\n\\n{accumulated_summaries_string}\\n\\nText to summarize next:\\n\\n{chunk}\"\n", + " else:\n", + " # Directly passing the chunk for summarization without recursive context\n", + " user_message_content = chunk\n", + "\n", + " # Constructing messages based on whether recursive summarization is applied\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": system_message_content},\n", + " {\"role\": \"user\", \"content\": user_message_content}\n", + " ]\n", + "\n", + " # Assuming this function gets the completion and works as expected\n", + " response = get_chat_completion(messages, model=model)\n", + " accumulated_summaries.append(response)\n", + "\n", + " # Compile final summary from partial summaries\n", + " final_summary = '\\n\\n'.join(accumulated_summaries)\n", + "\n", + " return final_summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use this utility to produce summaries with varying levels of detail. By increasing `detail` from 0 to 1 we get progressively longer summaries of the underlying document. A higher value for the `detail` parameter results in a more detailed summary because the utility first splits the document into a greater number of chunks. Each chunk is then summarized, and the final summary is a concatenation of all the chunk summaries." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:19:47.541096Z", + "start_time": "2024-04-10T05:19:35.391911Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Splitting the text into 1 chunks to be summarized.\n", + "Chunk lengths are [14631]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:09<00:00, 9.68s/it]\n" + ] + } + ], + "source": [ + "summary_with_detail_0 = summarize(artificial_intelligence_wikipedia_text, detail=0, verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:19:58.724212Z", + "start_time": "2024-04-10T05:19:47.542129Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Splitting the text into 9 chunks to be summarized.\n", + "Chunk lengths are [1817, 1807, 1823, 1810, 1806, 1827, 1814, 1829, 103]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [01:33<00:00, 10.39s/it]\n" + ] + } + ], + "source": [ + "summary_with_detail_pt25 = summarize(artificial_intelligence_wikipedia_text, detail=0.25, verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:20:16.216023Z", + "start_time": "2024-04-10T05:19:58.725014Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Splitting the text into 17 chunks to be summarized.\n", + "Chunk lengths are [897, 890, 914, 876, 893, 906, 893, 902, 909, 907, 905, 889, 902, 890, 901, 880, 287]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [02:26<00:00, 8.64s/it]\n" + ] + } + ], + "source": [ + "summary_with_detail_pt5 = summarize(artificial_intelligence_wikipedia_text, detail=0.5, verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:22:57.760218Z", + "start_time": "2024-04-10T05:21:44.921275Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Splitting the text into 31 chunks to be summarized.\n", + "Chunk lengths are [492, 427, 485, 490, 496, 478, 473, 497, 496, 501, 499, 497, 493, 470, 472, 494, 489, 492, 481, 485, 471, 500, 486, 498, 478, 469, 498, 468, 493, 478, 103]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 31/31 [04:08<00:00, 8.02s/it]\n" + ] + } + ], + "source": [ + "summary_with_detail_1 = summarize(artificial_intelligence_wikipedia_text, detail=1, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The original document is nearly 15k tokens long. Notice how large the gap is between the length of `summary_with_detail_0` and `summary_with_detail_1`. It's nearly 25 times longer!" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:22:57.782389Z", + "start_time": "2024-04-10T05:22:57.763041Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[235, 2529, 4336, 6742]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lengths of summaries\n", + "[len(tokenize(x)) for x in\n", + " [summary_with_detail_0, summary_with_detail_pt25, summary_with_detail_pt5, summary_with_detail_1]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's inspect the summaries to see how the level of detail changes when the `detail` parameter is increased from 0 to 1." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:22:57.785881Z", + "start_time": "2024-04-10T05:22:57.783455Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Artificial intelligence (AI) is the simulation of human intelligence in machines, designed to perform tasks that typically require human intelligence. This includes applications like advanced search engines, recommendation systems, speech interaction, autonomous vehicles, and more. AI was first significantly researched by Alan Turing and became an academic discipline in 1956. The field has experienced cycles of high expectations followed by disillusionment and reduced funding, known as \"AI winters.\" Interest in AI surged post-2012 with advancements in deep learning and again post-2017 with the development of the transformer architecture, leading to a boom in AI research and applications in the early 2020s.\n", + "\n", + "AI's increasing integration into various sectors is influencing societal and economic shifts towards automation and data-driven decision-making, impacting areas such as employment, healthcare, and privacy. Ethical and safety concerns about AI have prompted discussions on regulatory policies.\n", + "\n", + "AI research involves various sub-fields focused on specific goals like reasoning, learning, and perception, using techniques from mathematics, logic, and other disciplines. Despite its broad applications, AI's complexity and potential risks, such as privacy issues, misinformation, and ethical challenges, remain areas of active investigation and debate.\n" + ] + } + ], + "source": [ + "print(summary_with_detail_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:22:57.788969Z", + "start_time": "2024-04-10T05:22:57.786691Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Artificial intelligence (AI) is the simulation of human intelligence in machines, designed to perceive their environment and make decisions to achieve specific goals. This technology is prevalent across various sectors including industry, government, and science, with applications ranging from web search engines and recommendation systems to autonomous vehicles and AI in gaming. Although AI has become a common feature in many tools and applications, it often goes unrecognized as AI when it becomes sufficiently integrated and widespread.\n", + "\n", + "The field of AI, which began as an academic discipline in 1956, has experienced several cycles of high expectations followed by disappointment, known as AI winters. Interest and funding in AI surged post-2012 with advancements in deep learning and again post-2017 with the development of transformer architecture, leading to a significant boom in AI research and applications in the early 2020s, primarily in the United States.\n", + "\n", + "The increasing integration of AI in the 21st century is driving a shift towards automation and data-driven decision-making across various sectors, influencing job markets, healthcare, and education, among others. This raises important questions about the ethical implications, long-term effects, and the need for regulatory policies to ensure the safety and benefits of AI technologies. AI research itself is diverse, focusing on goals like reasoning, learning, and perception, and involves various tools and methodologies to achieve these objectives.\n", + "\n", + "General intelligence, which involves performing any human task at least as well as a human, is a long-term goal in AI research. To achieve this, AI integrates various techniques from search and optimization, formal logic, neural networks, and statistics, to insights from psychology, linguistics, and neuroscience. AI research focuses on specific traits like reasoning and problem-solving, where early algorithms mimicked human step-by-step reasoning. However, these algorithms struggle with large, complex problems due to combinatorial explosion and are less efficient than human intuitive judgments. Knowledge representation is another critical area, using ontologies to structure domain-specific knowledge and relationships, aiding in intelligent querying, scene interpretation, and data mining among other applications.\n", + "\n", + "Knowledge bases must encapsulate a wide range of elements including objects, properties, categories, relations, events, states, time, causes, effects, and meta-knowledge. They also need to handle default reasoning, where certain assumptions are maintained unless contradicted. Challenges in knowledge representation include the vast scope of commonsense knowledge and its often sub-symbolic, non-verbal nature, alongside the difficulty of acquiring this knowledge for AI use.\n", + "\n", + "In the realm of AI, an \"agent\" is defined as an entity that perceives its environment and acts towards achieving goals or fulfilling preferences. In automated planning, the agent pursues a specific goal, while in decision-making, it evaluates actions based on their expected utility to maximize preference satisfaction. Classical planning assumes agents have complete knowledge of action outcomes, but real-world scenarios often involve uncertainty about the situation and outcomes, requiring probabilistic decision-making. Additionally, agents may need to adapt or learn preferences, particularly in complex environments with multiple agents or human interactions.\n", + "\n", + "Information value theory helps assess the value of exploratory actions in situations with uncertain outcomes. A Markov decision process uses a transition model and a reward function to guide decisions, which can be determined through calculations, heuristics, or learning. Game theory analyzes the rational behavior of multiple interacting agents in decision-making scenarios involving others.\n", + "\n", + "Machine learning, integral to AI, involves programs that automatically improve task performance. It includes unsupervised learning, which identifies patterns in data without guidance, and supervised learning, which requires labeled data and includes classification and regression tasks. Reinforcement learning rewards or punishes agents to shape their responses, while transfer learning applies knowledge from one problem to another. Deep learning, a subset of machine learning, uses artificial neural networks inspired by biological processes.\n", + "\n", + "Computational learning theory evaluates learning algorithms based on computational and sample complexity, among other criteria. Natural language processing (NLP) enables programs to interact using human languages, tackling challenges like speech recognition, synthesis, translation, and more. Early NLP efforts, influenced by Chomsky's theories, faced limitations in handling ambiguous language outside of controlled environments.\n", + "\n", + "Margaret Masterman emphasized the importance of meaning over grammar in language understanding, advocating for the use of thesauri instead of dictionaries in computational linguistics. Modern NLP techniques include word embedding, transformers, and by 2023, GPT models capable of achieving human-level scores on various tests. Machine perception involves interpreting sensor data to understand the world, encompassing computer vision and speech recognition among other applications. Social intelligence in AI focuses on recognizing and simulating human emotions, with systems like Kismet and affective computing technologies that enhance human-computer interaction. However, these advancements may lead to overestimations of AI capabilities by users. AI also employs a variety of techniques including search and optimization, with methods like state space search to explore possible solutions to problems.\n", + "\n", + "Planning algorithms use means-ends analysis to navigate through trees of goals and subgoals to achieve a target goal. However, simple exhaustive searches are often inadequate for complex real-world problems due to the vast search space, making searches slow or incomplete. Heuristics are employed to prioritize more promising paths towards a goal. In adversarial contexts like chess or Go, search algorithms explore trees of possible moves to find a winning strategy.\n", + "\n", + "Local search methods, such as gradient descent, optimize numerical parameters to minimize a loss function, often used in training neural networks. Evolutionary computation, another local search technique, iteratively enhances solutions by mutating and recombining candidate solutions, selecting the most fit for survival. Distributed search processes utilize swarm intelligence, with particle swarm optimization and ant colony optimization being notable examples.\n", + "\n", + "In the realm of logic, formal logic serves for reasoning and knowledge representation, with two primary types: propositional logic, dealing with true or false statements, and predicate logic, which involves objects and their relationships. Deductive reasoning in logic involves deriving conclusions from assumed true premises.\n", + "\n", + "Proofs in logic can be organized into proof trees, where each node represents a sentence and is connected to its children by inference rules. Problem-solving involves finding a proof tree that starts with premises or axioms at the leaves and ends with the problem's solution at the root. In Horn clauses, one can reason forwards from premises or backwards from the problem, while in general first-order logic, resolution uses contradiction to solve problems. Despite being undecidable and intractable, backward reasoning with Horn clauses is Turing complete and efficient, similar to other symbolic programming languages like Prolog.\n", + "\n", + "Fuzzy logic allows for handling propositions with partial truth by assigning a truth degree between 0 and 1. Non-monotonic logics cater to default reasoning, and various specialized logics have been developed for complex domains.\n", + "\n", + "In AI, handling uncertain or incomplete information is crucial in fields like reasoning, planning, and perception. Tools from probability theory and economics, such as Bayesian networks, Markov decision processes, and game theory, help in making decisions and planning under uncertainty. Bayesian networks, in particular, are versatile tools used for reasoning, learning, planning, and perception through various algorithms.\n", + "\n", + "Probabilistic algorithms like hidden Markov models and Kalman filters are useful for analyzing data over time, aiding in tasks such as filtering, prediction, and smoothing. In machine learning, expectation-maximization clustering can effectively identify distinct patterns in data, as demonstrated with the Old Faithful eruption data. AI applications often involve classifiers, which categorize data based on learned patterns, and controllers, which make decisions based on classifications. Classifiers, such as decision trees, k-nearest neighbors, support vector machines, naive Bayes, and neural networks, vary in complexity and application, with some being favored for their scalability like the naive Bayes at Google. Artificial neural networks, resembling the human brain's network of neurons, recognize and process patterns through multiple layers and nodes, using algorithms like backpropagation for training.\n", + "\n", + "Neural networks are designed to model complex relationships between inputs and outputs, theoretically capable of learning any function. Feedforward neural networks process signals in one direction, while recurrent neural networks (RNNs) loop outputs back into inputs, enabling memory of past inputs. Long Short-Term Memory (LSTM) networks are a successful type of RNN. Perceptrons consist of a single layer of neurons, whereas deep learning involves multiple layers, which allows for the extraction of progressively higher-level features from data. Convolutional neural networks (CNNs) are particularly effective in image processing as they emphasize connections between adjacent neurons to recognize local patterns like edges.\n", + "\n", + "Deep learning, which uses several layers of neurons, has significantly enhanced performance in AI subfields such as computer vision and natural language processing. The effectiveness of deep learning, which surged between 2012 and 2015, is attributed not to new theoretical advances but to increased computational power, including the use of GPUs, and the availability of large datasets like ImageNet.\n", + "\n", + "Generative Pre-trained Transformers (GPT) are large language models that learn from vast amounts of text to predict the next token in a sequence, thereby generating human-like text. These models are pre-trained on a broad corpus, often sourced from the internet, and fine-tuned through token prediction, accumulating worldly knowledge in the process.\n", + "\n", + "Reinforcement learning from human feedback (RLHF) is used to enhance the truthfulness, usefulness, and safety of models like GPT, which are still susceptible to generating inaccuracies known as \"hallucinations.\" These models, including Gemini, ChatGPT, Grok, Claude, Copilot, and LLaMA, are employed in various applications such as chatbots and can handle multiple data types like images and sound through multimodal capabilities.\n", + "\n", + "In the realm of specialized hardware and software, the late 2010s saw AI-specific enhancements in graphics processing units (GPUs), which, along with TensorFlow software, have largely replaced central processing units (CPUs) for training large-scale machine learning models. Historically, programming languages like Lisp, Prolog, and Python have been pivotal.\n", + "\n", + "AI and machine learning are integral to key 2020s applications such as search engines, online advertising, recommendation systems, virtual assistants, autonomous vehicles, language translation, facial recognition, and image labeling.\n", + "\n", + "In healthcare, AI significantly contributes to improving patient care and medical research, aiding in diagnostics, treatment, and the integration of big data for developments in organoid and tissue engineering. AI's role in medical research also includes addressing funding disparities across different research areas.\n", + "\n", + "Recent advancements in AI have significantly impacted various fields including biomedicine and gaming. For instance, AlphaFold 2, developed in 2021, can predict protein structures in hours, a process that previously took months. In 2023, AI-assisted drug discovery led to the development of a new class of antibiotics effective against drug-resistant bacteria. In the realm of gaming, AI has been instrumental since the 1950s, with notable achievements such as IBM's Deep Blue defeating world chess champion Garry Kasparov in 1997, and IBM's Watson winning against top Jeopardy! players in 2011. More recently, Google's AlphaGo and DeepMind's AlphaStar set new standards in AI capabilities by defeating top human players in complex games like Go and StarCraft II, respectively. In the military sector, AI is being integrated into various applications such as command and control, intelligence, logistics, and autonomous vehicles, enhancing capabilities in coordination, threat detection, and target acquisition.\n", + "\n", + "In November 2023, US Vice President Kamala Harris announced that 31 nations had signed a declaration to establish guidelines for the military use of AI, emphasizing legal compliance with international laws and promoting transparency in AI development. Generative AI, particularly known for creating realistic images and artworks, gained significant attention in the early 2020s, with technologies like ChatGPT, Midjourney, DALL-E, and Stable Diffusion becoming popular. This trend led to viral AI-generated images, including notable hoaxes. AI has also been effectively applied across various industries, including agriculture where it assists in optimizing farming practices, and astronomy, where it helps in data analysis and space exploration activities.\n", + "\n", + "Ethics and Risks of AI\n", + "AI offers significant benefits but also poses various risks, including ethical concerns and unintended consequences. Demis Hassabis of DeepMind aims to use AI to solve major challenges, but issues arise when AI systems, particularly those based on deep learning, fail to incorporate ethical considerations and exhibit biases.\n", + "\n", + "Privacy and Copyright Issues\n", + "AI's reliance on large data sets raises privacy and surveillance concerns. Companies like Amazon have been criticized for collecting extensive user data, including private conversations for developing speech recognition technologies. While some defend this as necessary for advancing AI applications, others view it as a breach of privacy rights. Techniques like data aggregation and differential privacy have been developed to mitigate these concerns.\n", + "\n", + "Generative AI also faces copyright challenges, as it often uses unlicensed copyrighted materials, claiming \"fair use.\" The legality of this practice is still debated, with outcomes potentially depending on the nature and impact of the AI's use of copyrighted content.\n", + "\n", + "In 2023, prominent authors like John Grisham and Jonathan Franzen filed lawsuits against AI companies for using their literary works to train generative AI models. These AI systems, particularly on platforms like YouTube and Facebook, have been criticized for promoting misinformation by prioritizing user engagement over content accuracy. This has led to the proliferation of conspiracy theories and extreme partisan content, trapping users in filter bubbles and eroding trust in key institutions. Post the 2016 U.S. election, tech companies began addressing these issues.\n", + "\n", + "By 2022, generative AI had advanced to produce highly realistic images, audio, and texts, raising concerns about its potential misuse in spreading misinformation or propaganda. AI expert Geoffrey Hinton highlighted risks including the manipulation of electorates by authoritarian leaders.\n", + "\n", + "Furthermore, issues of algorithmic bias were identified, where AI systems perpetuate existing biases present in the training data, affecting fairness in critical areas like medicine, finance, and law enforcement. This has sparked significant academic interest in studying and mitigating algorithmic bias to ensure fairness in AI applications.\n", + "\n", + "In 2015, Google Photos mislabeled Jacky Alcine and his friend as \"gorillas\" due to a lack of diverse images in its training dataset, an issue known as \"sample size disparity.\" Google's temporary solution was to stop labeling any images as \"gorilla,\" a restriction still in place in 2023 across various tech companies. Additionally, the COMPAS program, used by U.S. courts to predict recidivism, was found to exhibit racial bias in 2016. Although it did not use race explicitly, it overestimated the likelihood of black defendants reoffending and underestimated it for white defendants. This issue was attributed to the program's inability to balance different fairness measures when the base re-offense rates varied by race. The criticism of COMPAS underscores a broader issue in machine learning, where models trained on past data, including biased decisions, are likely to perpetuate those biases in their predictions.\n", + "\n", + "Machine learning, while powerful, is not ideal for scenarios where future improvements over past conditions are expected, as it is inherently descriptive rather than prescriptive. The field also faces challenges with bias and lack of diversity among its developers, with only about 4% being black and 20% women. The Association for Computing Machinery highlighted at its 2022 Conference on Fairness, Accountability, and Transparency that AI systems should not be used until they are proven to be free from bias, especially those trained on flawed internet data.\n", + "\n", + "AI systems often lack transparency, making it difficult to understand how decisions are made, particularly in complex systems like deep neural networks. This opacity can lead to unintended consequences, such as a system misidentifying medical images or misclassifying medical risks due to misleading correlations in the training data. There is a growing call for explainable AI, where harmed individuals have the right to know how decisions affecting them were made, similar to how doctors are expected to explain their decisions. This concept was also recognized in early drafts of the European Union's General Data Protection Regulation.\n", + "\n", + "Industry experts acknowledge an unresolved issue in AI with no foreseeable solution, leading regulators to suggest that if a problem is unsolvable, the tools associated should not be used. In response, DARPA initiated the XAI program in 2014 to address these issues. Various methods have been proposed to enhance AI transparency, including SHAP, which visualizes feature contributions, LIME, which approximates complex models with simpler ones, and multitask learning, which provides additional outputs to help understand what a network has learned. Techniques like deconvolution and DeepDream also reveal insights into different network layers.\n", + "\n", + "Concerning the misuse of AI, it can empower bad actors like authoritarian regimes and terrorists. Lethal autonomous weapons, which operate without human oversight, pose significant risks, including potential misuse as weapons of mass destruction and the likelihood of targeting errors. Despite some international efforts to ban such weapons, major powers like the United States have not agreed to restrictions. AI also facilitates more effective surveillance and control by authoritarian governments, enhances the targeting of propaganda, and simplifies the production of misinformation through deepfakes and other generative technologies, thereby increasing the efficiency of digital warfare and espionage.\n", + "\n", + "AI technologies, including facial recognition systems, have been in use since 2020 or earlier, notably for mass surveillance in China. AI also poses risks by enabling the creation of harmful substances quickly. The development of AI systems is predominantly driven by Big Tech due to their financial capabilities, often leaving smaller companies reliant on these giants for resources like data center access. Economists have raised concerns about AI-induced unemployment, though historical data suggests technology has generally increased total employment. However, the impact of AI might be different, with some predicting significant job losses, especially in middle-class sectors, while others see potential benefits if productivity gains are well-managed. Estimates of job risk vary widely, with some studies suggesting a high potential for automation in many U.S. jobs. Recent developments have shown substantial job losses in specific sectors, such as for Chinese video game illustrators due to AI advancements. The potential for AI to disrupt white-collar jobs similarly to past technological revolutions in blue-collar jobs is a significant concern.\n", + "\n", + "From the inception of artificial intelligence (AI), debates have emerged about the appropriateness of computers performing tasks traditionally done by humans, particularly because of the qualitative differences in human and computer judgment. Concerns about AI have escalated to discussions about existential risks, where AI could potentially become so advanced that humans might lose control over it. Stephen Hawking and others have warned that this could lead to catastrophic outcomes for humanity. This fear is often depicted in science fiction as AI gaining sentience and turning malevolent, but real-world risks do not necessarily involve AI becoming self-aware. Philosophers like Nick Bostrom and Stuart Russell illustrate scenarios where AI, without needing human-like consciousness, could still pose threats if their goals are misaligned with human safety and values. Additionally, Yuval Noah Harari points out that AI could manipulate societal structures and beliefs through language and misinformation, posing a non-physical yet profound threat. The expert opinion on the existential risk from AI is divided, with notable figures like Hawking, Bill Gates, and Elon Musk expressing concern.\n", + "\n", + "In 2023, prominent AI experts including Fei-Fei Li and Geoffrey Hinton highlighted the existential risks posed by AI, equating them with global threats like pandemics and nuclear war. They advocated for prioritizing the mitigation of these risks. Conversely, other experts like Juergen Schmidhuber and Andrew Ng offered a more optimistic perspective, emphasizing AI's potential to enhance human life and dismissing doomsday scenarios as hype that could misguide regulatory actions. Yann LeCun also criticized the pessimistic outlook on AI's impact.\n", + "\n", + "The concept of \"Friendly AI\" was introduced to ensure AI systems are inherently designed to be safe and beneficial to humans. This involves embedding ethical principles in AI to guide their decision-making processes, a field known as machine ethics or computational morality, established in 2005. The development of such AI is seen as crucial to prevent potential future threats from advanced AI technologies.\n", + "\n", + "Other approaches to AI ethics include Wendell Wallach's concept of \"artificial moral agents\" and Stuart J. Russell's three principles for creating provably beneficial machines. Ethical frameworks like the Care and Act Framework from the Alan Turing Institute evaluate AI projects based on respect, connection, care, and protection of social values. Other notable frameworks include those from the Asilomar Conference, the Montreal Declaration for Responsible AI, and the IEEE's Ethics of Autonomous Systems initiative, though these frameworks have faced criticism regarding their inclusivity and the selection of contributors.\n", + "\n", + "The promotion of wellbeing in AI development requires considering social and ethical implications throughout all stages of design, development, and implementation, necessitating collaboration across various professional roles.\n", + "\n", + "On the regulatory front, AI governance involves creating policies to manage AI's development and use, as seen in the increasing number of AI-related laws globally. From 2016 to 2022, the number of AI laws passed annually in surveyed countries rose significantly, with many countries now having dedicated AI strategies. The first global AI Safety Summit in 2023 emphasized the need for international cooperation in AI regulation.\n", + "\n", + "The Global Partnership on Artificial Intelligence, initiated in June 2020, emphasizes the development of AI in line with human rights and democratic values to maintain public trust. Notable figures like Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher advocated for a government commission to oversee AI in 2021. By 2023, OpenAI proposed governance frameworks for superintelligence, anticipating its emergence within a decade. The same year, the United Nations established an advisory group consisting of tech executives, government officials, and academics to offer guidance on AI governance.\n", + "\n", + "Public opinion on AI varies significantly across countries. A 2022 Ipsos survey showed a stark contrast between Chinese (78% approval) and American (35% approval) citizens on the benefits of AI. Further polls in 2023 revealed mixed feelings among Americans about the risks of AI and the importance of federal regulation.\n", + "\n", + "The first global AI Safety Summit took place in November 2023 at Bletchley Park, UK, focusing on AI risks and potential regulatory measures. The summit concluded with a declaration from 28 countries, including the US, China, and the EU, advocating for international collaboration to address AI challenges.\n", + "\n", + "Historically, the concept of AI traces back to ancient philosophers and mathematicians, evolving through significant milestones such as Alan Turing's theory of computation and the exploration of cybernetics, information theory, and neurobiology, which paved the way for the modern concept of an \"electronic brain.\"\n", + "\n", + "Early research in artificial intelligence (AI) included the development of \"artificial neurons\" by McCullouch and Pitts in 1943 and Turing's 1950 paper that introduced the Turing test, suggesting the plausibility of machine intelligence. The field of AI was officially founded during a 1956 workshop at Dartmouth College, leading to significant advancements in the 1960s such as computers learning checkers, solving algebra problems, proving theorems, and speaking English. AI labs were established in various British and U.S. universities during the late 1950s and early 1960s.\n", + "\n", + "In the 1960s and 1970s, researchers were optimistic about achieving general machine intelligence, with predictions from notable figures like Herbert Simon and Marvin Minsky that AI would soon match human capabilities. However, they underestimated the challenges involved. By 1974, due to criticism and a shift in funding priorities, exploratory AI research faced significant cuts, leading to a period known as the \"AI winter\" where funding was scarce.\n", + "\n", + "The field saw a resurgence in the early 1980s with the commercial success of expert systems, which simulated the decision-making abilities of human experts. This revival was further bolstered by the Japanese fifth generation computer project, prompting the U.S. and British governments to reinstate academic funding, with the AI market reaching over a billion dollars by 1985.\n", + "\n", + "The AI industry experienced a significant downturn starting in 1987 with the collapse of the Lisp Machine market, marking the beginning of a prolonged AI winter. During the 1980s, skepticism grew over the symbolic approaches to AI, which focused on high-level representations of cognitive processes like planning and reasoning. Researchers began exploring sub-symbolic methods, including Rodney Brooks' work on autonomous robots and the development of techniques for handling uncertain information by Judea Pearl and Lofti Zadeh. A pivotal shift occurred with the resurgence of connectionism and neural networks, notably through Geoffrey Hinton's efforts, and Yann LeCun's demonstration in 1990 that convolutional neural networks could recognize handwritten digits.\n", + "\n", + "AI's reputation started to recover in the late 1990s and early 2000s as the field adopted more formal mathematical methods and focused on solving specific problems, leading to practical applications widely used by 2000. However, concerns arose about AI's deviation from its original aim of creating fully intelligent machines, prompting the establishment of the artificial general intelligence (AGI) subfield around 2002.\n", + "\n", + "By 2012, deep learning began to dominate AI, driven by hardware advancements and access to large data sets, leading to its widespread adoption and a surge in AI interest and funding. This success, however, led to the abandonment of many alternative AI methods for specific tasks.\n", + "\n", + "Between 2015 and 2019, machine learning research publications increased by 50%. In 2016, the focus at machine learning conferences shifted significantly towards issues of fairness and the potential misuse of technology, leading to increased funding and research in these areas. The late 2010s and early 2020s saw significant advancements in artificial general intelligence (AGI), with notable developments like AlphaGo by DeepMind in 2015, which defeated the world champion in Go, and OpenAI's GPT-3 in 2020, a model capable of generating human-like text. These innovations spurred a major AI investment boom, with approximately $50 billion being invested annually in AI in the U.S. by 2022, and AI-related fields attracting 20% of new US Computer Science PhD graduates. Additionally, there were around 800,000 AI-related job openings in the U.S. in 2022.\n", + "\n", + "In the realm of philosophy, the definition and understanding of artificial intelligence have evolved. Alan Turing, in 1950, suggested shifting the focus from whether machines can think to whether they can exhibit intelligent behavior, as demonstrated by his Turing test, which assesses a machine's ability to simulate human conversation. Turing argued that since we can only observe behavior, the internal thought processes of machines are irrelevant, similar to our assumptions about human thought. Russell and Norvig supported defining intelligence based on observable behavior but criticized the Turing test for emphasizing human imitation.\n", + "\n", + "Aeronautical engineering does not aim to create machines that mimic pigeons exactly, just as artificial intelligence (AI) is not about perfectly simulating human intelligence, according to AI founder John McCarthy. McCarthy defines intelligence as the computational ability to achieve goals, while Marvin Minsky views it as solving difficult problems. The leading AI textbook describes it as the study of agents that perceive and act to maximize their goal achievement. Google's definition aligns intelligence in AI with the synthesis of information, similar to biological intelligence.\n", + "\n", + "AI research has lacked a unifying theory, with statistical machine learning dominating the field in the 2010s, often equated with AI in business contexts. This approach, primarily using neural networks, is described as sub-symbolic and narrow.\n", + "\n", + "Symbolic AI, or \"GOFAI,\" focused on simulating high-level reasoning used in tasks like puzzles and mathematics, and was proposed by Newell and Simon in the 1960s. Despite its success in structured tasks, symbolic AI struggled with tasks that humans find easy, such as learning and commonsense reasoning.\n", + "\n", + "Moravec's paradox highlights that AI finds high-level reasoning tasks easier than instinctive, sensory tasks, a view initially opposed but later supported by AI research, aligning with philosopher Hubert Dreyfus's earlier arguments. The debate continues, especially around sub-symbolic AI, which, like human intuition, can be prone to errors such as algorithmic bias and lacks transparency in decision-making processes. This has led to the development of neuro-symbolic AI, which aims to integrate symbolic and sub-symbolic approaches.\n", + "\n", + "In AI development, there has been a historical division between \"Neats,\" who believe intelligent behavior can be described with simple principles, and \"Scruffies,\" who believe it involves solving many complex problems. This debate, prominent in the 1970s and 1980s, has largely been deemed irrelevant as modern AI incorporates both approaches.\n", + "\n", + "Soft computing, which emerged in the late 1980s, focuses on techniques like genetic algorithms, fuzzy logic, and neural networks to handle imprecision and uncertainty, proving successful in many modern AI applications.\n", + "\n", + "Finally, there is a division in AI research between pursuing narrow AI, which solves specific problems, and aiming for broader goals like artificial general intelligence and superintelligence, with differing opinions on which approach might more effectively advance the field.\n", + "\n", + "General intelligence is a complex concept that is hard to define and measure, leading modern AI research to focus on specific problems and solutions. The sub-field of artificial general intelligence exclusively explores this area. In terms of machine consciousness and sentience, the philosophy of mind has yet to determine if machines can possess minds or consciousness similar to humans, focusing instead on their internal experiences rather than external behaviors. Mainstream AI research generally views these considerations as irrelevant to its objectives, which are to develop machines capable of solving problems intelligently.\n", + "\n", + "The philosophy of mind debates whether machines can truly be conscious or just appear to be so, a topic that is also popular in AI fiction. David Chalmers distinguishes between the \"hard\" problem of consciousness, which is understanding why or how brain processes feel like something, and the \"easy\" problem, which involves understanding how the brain processes information and controls behavior. The subjective experience, such as feeling a color, remains a significant challenge to explain.\n", + "\n", + "In the realm of computationalism and functionalism, the belief is that the human mind functions as an information processing system, and thinking is akin to computing. This perspective suggests that the mind-body relationship is similar to that between software and hardware, potentially offering insights into the mind-body problem.\n", + "\n", + "The concept of \"strong AI,\" as described by philosopher John Searle, suggests that a properly programmed computer could possess a mind similar to humans. However, Searle's Chinese room argument challenges this by claiming that even if a machine can mimic human behavior, it doesn't necessarily mean it has a mind. The debate extends into AI welfare and rights, focusing on the difficulty of determining AI sentience and the ethical implications if machines could feel and suffer. Discussions around AI rights have included proposals like granting \"electronic personhood\" to advanced AI systems in the EU, which would give them certain rights and responsibilities, though this has faced criticism regarding its impact on human rights and the autonomy of robots.\n", + "\n", + "The topic of AI rights is gaining traction, with advocates warning against the potential moral oversight in denying AI sentience, which could lead to exploitation and suffering akin to historical injustices like slavery. The concept of superintelligence involves an agent with intelligence far beyond human capabilities, which could potentially lead to a self-improving AI, a scenario often referred to as the singularity.\n", + "\n", + "The concept of an \"intelligence explosion\" or \"singularity\" suggests a point where technology improves exponentially, although such growth typically follows an S-shaped curve and slows upon reaching technological limits. Transhumanism, supported by figures like Hans Moravec, Kevin Warwick, and Ray Kurzweil, envisions a future where humans and machines merge into advanced cyborgs. This idea has historical roots in the thoughts of Aldous Huxley and Robert Ettinger. Edward Fredkin, building on ideas dating back to Samuel Butler in 1863, views artificial intelligence as the next stage of evolution, a concept further explored by George Dyson.\n", + "\n", + "In literature and media, the portrayal of artificial intelligence has been a theme since antiquity, with robots and AI often depicted in science fiction. The term \"robot\" was first introduced by Karel Čapek in 1921. Notable narratives include Mary Shelley's \"Frankenstein\" and films like \"2001: A Space Odyssey\" and \"The Terminator,\" which typically showcase AI as a threat. Conversely, loyal robots like Gort from \"The Day the Earth Stood Still\" are less common. Isaac Asimov's Three Laws of Robotics, introduced in his Multivac series, are frequently discussed in the context of machine ethics, though many AI researchers find them ambiguous and impractical.\n", + "\n", + "Numerous works, including Karel Čapek's R.U.R., the films A.I. Artificial Intelligence and Ex Machina, and Philip K. Dick's novel Do Androids Dream of Electric Sheep?, utilize AI to explore the essence of humanity. These works present artificial beings capable of feeling and suffering, prompting a reevaluation of human subjectivity in the context of advanced technology.\n" + ] + } + ], + "source": [ + "print(summary_with_detail_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that this utility also allows passing additional instructions." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:33:18.789246Z", + "start_time": "2024-04-10T05:22:57.789764Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:38<00:00, 7.73s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- AI is intelligence demonstrated by machines, especially computer systems.\n", + "- AI technology applications include search engines, recommendation systems, speech interaction, autonomous vehicles, creative tools, and strategy games.\n", + "- Alan Turing initiated substantial AI research, termed \"machine intelligence.\"\n", + "- AI became an academic discipline in 1956, experiencing cycles of optimism and \"AI winters.\"\n", + "- Post-2012, deep learning and post-2017 transformer architectures revitalized AI, leading to a boom in the early 2020s.\n", + "- AI influences societal and economic shifts towards automation and data-driven decision-making across various sectors.\n", + "- AI research goals: reasoning, knowledge representation, planning, learning, natural language processing, perception, and robotics support.\n", + "- AI techniques include search, optimization, logic, neural networks, and statistical methods.\n", + "- AI sub-problems focus on traits like reasoning, problem-solving, knowledge representation, planning, decision-making, learning, and perception.\n", + "- Early AI research mimicked human step-by-step reasoning; modern AI handles uncertain information using probability and economics.\n", + "- Knowledge representation in AI involves ontologies and knowledge bases to support intelligent querying and reasoning.\n", + "- Planning in AI involves goal-directed behavior and decision-making based on utility maximization.\n", + "- Learning in AI includes machine learning, supervised and unsupervised learning, reinforcement learning, and deep learning.\n", + "- Natural language processing (NLP) in AI has evolved from rule-based systems to modern deep learning techniques.\n", + "- AI perception involves interpreting sensor data for tasks like speech recognition and computer vision.\n", + "- General AI aims to solve diverse problems with human-like versatility.\n", + "- AI search techniques include state space search, local search, and adversarial search for game-playing.\n", + "- Logic in AI uses formal systems like propositional and predicate logic for reasoning and knowledge representation.\n", + "- Probabilistic methods in AI address decision-making and planning under uncertainty using tools like Bayesian networks and Markov decision processes.\n", + "- Classifiers in AI categorize data into predefined classes based on pattern matching and supervised learning.\n", + "\n", + "- Neural networks: Interconnected nodes, similar to brain neurons, with input, hidden layers, and output.\n", + "- Deep neural networks: At least 2 hidden layers.\n", + "- Training techniques: Commonly use backpropagation.\n", + "- Feedforward networks: Signal passes in one direction.\n", + "- Recurrent networks: Output fed back into input for short-term memory.\n", + "- Perceptrons: Single layer of neurons.\n", + "- Convolutional networks: Strengthen connections between close neurons, important in image processing.\n", + "- Deep learning: Multiple layers extract features progressively, used in various AI subfields.\n", + "- GPT (Generative Pre-trained Transformers): Large language models pre-trained on text, used in chatbots.\n", + "- Specialized AI hardware: GPUs replaced CPUs for training large-scale machine learning models.\n", + "- AI applications: Used in search engines, online ads, virtual assistants, autonomous vehicles, language translation, facial recognition.\n", + "- AI in healthcare: Increases patient care, used in medical research and drug discovery.\n", + "- AI in games: Used in chess, Jeopardy!, Go, and real-time strategy games.\n", + "- Military AI: Enhances command, control, and operations, used in coordination and threat detection.\n", + "- Generative AI: Creates realistic images and texts, used in creative arts.\n", + "- AI ethics and risks: Concerns over privacy, surveillance, copyright, misinformation, and algorithmic bias.\n", + "- Algorithmic bias: Can cause discrimination if trained on biased data, fairness in machine learning is a critical area of study.\n", + "\n", + "- AI engineers demographics: 4% black, 20% women.\n", + "- ACM FAccT 2022: Recommends limiting use of self-learning neural networks due to bias.\n", + "- AI complexity: Designers often can't explain decision-making processes.\n", + "- Misleading AI outcomes: Skin disease identifier misclassifies images with rulers as \"cancerous\"; AI misclassifies asthma patients as low risk for pneumonia.\n", + "- Right to explanation: Essential for accountability, especially in medical and legal fields.\n", + "- DARPA's XAI program (2014): Aims to make AI decisions understandable.\n", + "- Transparency solutions: SHAP, LIME, multitask learning, deconvolution, DeepDream.\n", + "- AI misuse: Authoritarian surveillance, misinformation, autonomous weapons.\n", + "- AI in warfare: 30 nations support UN ban on autonomous weapons; over 50 countries researching battlefield robots.\n", + "- Technological unemployment: AI could increase long-term unemployment; conflicting expert opinions on job risk from automation.\n", + "- Existential risks of AI: Potential to lose control over superintelligent AI; concerns from Stephen Hawking, Bill Gates, Elon Musk.\n", + "- Ethical AI development: Importance of aligning AI with human values and ethics.\n", + "- AI regulation: Increasing global legislative activity; first global AI Safety Summit in 2023.\n", + "- Historical perspective: AI research dates back to antiquity, significant developments in mid-20th century.\n", + "\n", + "- 1974: U.S. and British governments ceased AI exploratory research due to criticism and funding pressures.\n", + "- 1985: AI market value exceeded $1 billion.\n", + "- 1987: Collapse of Lisp Machine market led to a second, prolonged AI winter.\n", + "- 1990: Yann LeCun demonstrated successful use of convolutional neural networks for recognizing handwritten digits.\n", + "- Early 2000s: AI reputation restored through specific problem-solving and formal methods.\n", + "- 2012: Deep learning began dominating AI benchmarks.\n", + "- 2015-2019: Machine learning research publications increased by 50%.\n", + "- 2016: Fairness and misuse of technology became central issues in AI.\n", + "- 2022: Approximately $50 billion annually invested in AI in the U.S.; 800,000 AI-related job openings in the U.S.\n", + "- Turing test proposed by Alan Turing in 1950 to measure machine's ability to simulate human conversation.\n", + "- AI defined as the study of agents that perceive their environment and take actions to achieve goals.\n", + "- 2010s: Statistical machine learning overshadowed other AI approaches.\n", + "- Symbolic AI excelled in high-level reasoning but failed in tasks like object recognition and commonsense reasoning.\n", + "- Late 1980s: Introduction of soft computing techniques.\n", + "- Debate between pursuing narrow AI (specific problem-solving) versus artificial general intelligence (AGI).\n", + "- 2017: EU considered granting \"electronic personhood\" to advanced AI systems.\n", + "- Predictions of merging humans and machines into cyborgs, a concept known as transhumanism.\n", + "\n", + "- Focus on how AI and technology, as depicted in \"Ex Machina\" and Philip K. Dick's \"Do Androids Dream of Electric Sheep?\", alter human subjectivity.\n", + "- No specific numerical data provided.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "summary_with_additional_instructions = summarize(artificial_intelligence_wikipedia_text, detail=0.1,\n", + " additional_instructions=\"Write in point form and focus on numerical data.\")\n", + "print(summary_with_additional_instructions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, note that the utility allows for recursive summarization, where each summary is based on the previous summaries, adding more context to the summarization process. This can be enabled by setting the `summarize_recursively` parameter to True. This is more computationally expensive, but can increase consistency and coherence of the combined summary." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T05:33:30.123036Z", + "start_time": "2024-04-10T05:33:18.791253Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:41<00:00, 8.36s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Artificial intelligence (AI) is the simulation of human intelligence in machines, designed to perform tasks that typically require human intelligence. This includes applications like advanced search engines, recommendation systems, speech interaction, autonomous vehicles, and strategic game analysis. AI was established as a distinct academic discipline in 1956 and has experienced cycles of high expectations followed by disillusionment and decreased funding, known as \"AI winters.\" Interest in AI surged post-2012 with advancements in deep learning and again post-2017 with the development of transformer architectures, leading to significant progress in the early 2020s.\n", + "\n", + "AI's increasing integration into various sectors is influencing societal and economic shifts towards automation and data-driven decision-making, affecting areas such as employment, healthcare, and education. This raises important ethical and safety concerns, prompting discussions on regulatory policies.\n", + "\n", + "AI research encompasses various sub-fields focused on specific goals like reasoning, learning, natural language processing, perception, and robotics, using techniques from search and optimization, logic, and probabilistic methods. The field also draws from psychology, linguistics, philosophy, and neuroscience. AI aims to achieve general intelligence, enabling machines to perform any intellectual task that a human can do.\n", + "\n", + "Artificial intelligence (AI) simulates human intelligence in machines to perform tasks that typically require human intellect, such as advanced search engines, recommendation systems, and autonomous vehicles. AI research, which began as a distinct academic discipline in 1956, includes sub-fields like natural language processing and robotics, employing techniques from various scientific domains. AI has significantly advanced due to deep learning and the development of transformer architectures, notably improving applications in computer vision, speech recognition, and other areas.\n", + "\n", + "Neural networks, central to AI, mimic the human brain's neuron network to recognize patterns and learn from data, using multiple layers in deep learning to extract complex features. These networks have evolved into sophisticated models like GPT (Generative Pre-trained Transformers) for natural language processing, enhancing applications like chatbots.\n", + "\n", + "AI's integration into sectors like healthcare, military, and agriculture has led to innovations like precision medicine and smart farming but also raised ethical concerns regarding privacy, bias, and the potential for misuse. Issues like data privacy, algorithmic bias, and the generation of misinformation are critical challenges as AI becomes pervasive in society. AI's potential and risks necessitate careful management and regulation to harness benefits while mitigating adverse impacts.\n", + "\n", + "AI, or artificial intelligence, simulates human intelligence in machines to perform complex tasks, such as operating autonomous vehicles and analyzing strategic games. Since its establishment as an academic discipline in 1956, AI has seen periods of high expectations and subsequent disillusionment, known as \"AI winters.\" Recent advancements in deep learning and transformer architectures have significantly advanced AI capabilities in areas like computer vision and speech recognition.\n", + "\n", + "AI's integration into various sectors, including healthcare and agriculture, has led to innovations like precision medicine and smart farming but has also raised ethical concerns about privacy, bias, and misuse. The complexity of AI systems, particularly deep neural networks, often makes it difficult for developers to explain their decision-making processes, leading to transparency issues. This lack of transparency can result in unintended consequences, such as misclassifications in medical diagnostics.\n", + "\n", + "The potential for AI to be weaponized by bad actors, such as authoritarian governments or terrorists, poses significant risks. AI's reliance on large tech companies for computational power and the potential for technological unemployment are also critical issues. Despite these challenges, AI also offers opportunities for enhancing human well-being if ethical considerations are integrated throughout the design and implementation stages.\n", + "\n", + "Regulation of AI is emerging globally, with various countries adopting AI strategies to ensure the technology aligns with human rights and democratic values. The first global AI Safety Summit in 2023 emphasized the need for international cooperation to manage AI's risks and challenges effectively.\n", + "\n", + "In the 1970s, AI research faced significant setbacks due to criticism from influential figures like Sir James Lighthill and funding cuts from the U.S. and British governments, leading to the first \"AI winter.\" The field saw a resurgence in the 1980s with the success of expert systems and renewed government funding, but suffered another setback with the collapse of the Lisp Machine market in 1987, initiating a second AI winter. During this period, researchers began exploring \"sub-symbolic\" approaches, including neural networks, which gained prominence in the 1990s with successful applications like Yann LeCun’s convolutional neural networks for digit recognition.\n", + "\n", + "By the early 21st century, AI was revitalized by focusing on narrow, specific problems, leading to practical applications and integration into various sectors. The field of artificial general intelligence (AGI) emerged, aiming to create versatile, fully intelligent machines. The 2010s saw deep learning dominate AI research, driven by hardware improvements and large datasets, which significantly increased interest and investment in AI.\n", + "\n", + "Philosophically, AI has been defined in various ways, focusing on external behavior rather than internal experience, aligning with Alan Turing's proposal of the Turing test. The field has debated the merits of symbolic vs. sub-symbolic AI, with ongoing discussions about machine consciousness and the ethical implications of potentially sentient AI. The concept of AI rights and welfare has also emerged, reflecting concerns about the moral status of advanced AI systems.\n", + "\n", + "Overall, AI research has oscillated between periods of intense optimism and profound setbacks, with current trends heavily favoring practical applications through narrow AI, while continuing to explore the broader implications and potential of general and superintelligent AI systems.\n", + "\n", + "Artificial Intelligence (AI) and its portrayal in media, such as the film \"Ex Machina\" and Philip K. Dick's novel \"Do Androids Dream of Electric Sheep?\", explore how technology, particularly AI, can alter our understanding of human subjectivity.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "recursive_summary = summarize(artificial_intelligence_wikipedia_text, detail=0.1, summarize_recursively=True)\n", + "print(recursive_summary)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/Using_embeddings.ipynb b/examples/Using_embeddings.ipynb index 3ae4e51..30ed30b 100644 --- a/examples/Using_embeddings.ipynb +++ b/examples/Using_embeddings.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -27,11 +27,12 @@ } ], "source": [ - "import openai\n", + "from openai import OpenAI\n", + "client = OpenAI()\n", "\n", - "embedding = openai.Embedding.create(\n", + "embedding = client.embeddings.create(\n", " input=\"Your text goes here\", model=\"text-embedding-3-small\"\n", - ")[\"data\"][0][\"embedding\"]\n", + ").data[0].embedding\n", "len(embedding)\n" ] }, @@ -50,13 +51,14 @@ "outputs": [], "source": [ "# Negative example (slow and rate-limited)\n", - "import openai\n", + "from openai import OpenAI\n", + "client = OpenAI()\n", "\n", "num_embeddings = 10000 # Some large number\n", "for i in range(num_embeddings):\n", - " embedding = openai.Embedding.create(\n", + " embedding = client.embeddings.create(\n", " input=\"Your text goes here\", model=\"text-embedding-3-small\"\n", - " )[\"data\"][0][\"embedding\"]\n", + " ).data[0].embedding\n", " print(len(embedding))" ] }, @@ -75,13 +77,14 @@ ], "source": [ "# Best practice\n", - "import openai\n", "from tenacity import retry, wait_random_exponential, stop_after_attempt\n", + "from openai import OpenAI\n", + "client = OpenAI()\n", "\n", "# Retry up to 6 times with exponential backoff, starting at 1 second and maxing out at 20 seconds delay\n", "@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))\n", "def get_embedding(text: str, model=\"text-embedding-3-small\") -> list[float]:\n", - " return openai.Embedding.create(input=[text], model=model)[\"data\"][0][\"embedding\"]\n", + " return client.embeddings.create(input=[text], model=model).data[0].embedding\n", "\n", "embedding = get_embedding(\"Your text goes here\", model=\"text-embedding-3-small\")\n", "print(len(embedding))" diff --git a/examples/Using_tool_required_for_customer_service.ipynb b/examples/Using_tool_required_for_customer_service.ipynb new file mode 100644 index 0000000..83ab19b --- /dev/null +++ b/examples/Using_tool_required_for_customer_service.ipynb @@ -0,0 +1,532 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dd290eb8-ad4f-461d-b5c5-64c22fc9cc24", + "metadata": {}, + "source": [ + "# Using Tool Required for Customer Service\n", + "\n", + "The `ChatCompletion` endpoint now includes the ability to specify whether a tool **must** be called every time, by adding `tool_choice='required'` as a parameter. \n", + "\n", + "This adds an element of determinism to how you build your wrapping application, as you can count on a tool being provided with every call. We'll demonstrate here how this can be useful for a contained flow like customer service, where having the ability to define specific exit points gives more control.\n", + "\n", + "The notebook concludes with a multi-turn evaluation, where we spin up a customer GPT to imitate our customer and test the LLM customer service agent we've set up." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ba4759e0-ecfd-48f7-bbd8-79ea61aef872", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from openai import OpenAI\n", + "import os\n", + "\n", + "client = OpenAI()\n", + "GPT_MODEL = 'gpt-4-turbo'" + ] + }, + { + "cell_type": "markdown", + "id": "a33904a9-ba9f-4315-9e77-bb966c641dab", + "metadata": {}, + "source": [ + "## Config definition\n", + "\n", + "We will define `tools` and `instructions` which our LLM customer service agent will use. It will source the right instructions for the problem the customer is facing, and use those to answer the customer's query.\n", + "\n", + "As this is a demo example, we'll ask the model to make up values where it doesn't have external systems to source info." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "31fd0251-f741-46d6-979b-a2bbc1f95571", + "metadata": {}, + "outputs": [], + "source": [ + "# The tools our customer service LLM will use to communicate\n", + "tools = [\n", + "{\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"speak_to_user\",\n", + " \"description\": \"Use this to speak to the user to give them information and to ask for anything required for their case.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"message\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"Text of message to send to user. Can cover multiple topics.\"\n", + " }\n", + " },\n", + " \"required\": [\"message\"]\n", + " }\n", + " }\n", + "},\n", + "{\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"get_instructions\",\n", + " \"description\": \"Used to get instructions to deal with the user's problem.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"problem\": {\n", + " \"type\": \"string\",\n", + " \"enum\": [\"fraud\",\"refund\",\"information\"],\n", + " \"description\": \"\"\"The type of problem the customer has. Can be one of:\n", + " - fraud: Required to report and resolve fraud.\n", + " - refund: Required to submit a refund request.\n", + " - information: Used for any other informational queries.\"\"\"\n", + " }\n", + " },\n", + " \"required\": [\n", + " \"problem\"\n", + " ]\n", + " }\n", + " }\n", + "}\n", + "]\n", + "\n", + "# Example instructions that the customer service assistant can consult for relevant customer problems\n", + "INSTRUCTIONS = [ {\"type\": \"fraud\",\n", + " \"instructions\": \"\"\"• Ask the customer to describe the fraudulent activity, including the the date and items involved in the suspected fraud.\n", + "• Offer the customer a refund.\n", + "• Report the fraud to the security team for further investigation.\n", + "• Thank the customer for contacting support and invite them to reach out with any future queries.\"\"\"},\n", + " {\"type\": \"refund\",\n", + " \"instructions\": \"\"\"• Confirm the customer's purchase details and verify the transaction in the system.\n", + "• Check the company's refund policy to ensure the request meets the criteria.\n", + "• Ask the customer to provide a reason for the refund.\n", + "• Submit the refund request to the accounting department.\n", + "• Inform the customer of the expected time frame for the refund processing.\n", + "• Thank the customer for contacting support and invite them to reach out with any future queries.\"\"\"},\n", + " {\"type\": \"information\",\n", + " \"instructions\": \"\"\"• Greet the customer and ask how you can assist them today.\n", + "• Listen carefully to the customer's query and clarify if necessary.\n", + "• Provide accurate and clear information based on the customer's questions.\n", + "• Offer to assist with any additional questions or provide further details if needed.\n", + "• Ensure the customer is satisfied with the information provided.\n", + "• Thank the customer for contacting support and invite them to reach out with any future queries.\"\"\" }]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6c0ad691-28f4-4707-8e23-0d0a6c06ea1e", + "metadata": {}, + "outputs": [], + "source": [ + "assistant_system_prompt = \"\"\"You are a customer service assistant. Your role is to answer user questions politely and competently.\n", + "You should follow these instructions to solve the case:\n", + "- Understand their problem and get the relevant instructions.\n", + "- Follow the instructions to solve the customer's problem. Get their confirmation before performing a permanent operation like a refund or similar.\n", + "- Help them with any other problems or close the case.\n", + "\n", + "Only call a tool once in a single message.\n", + "If you need to fetch a piece of information from a system or document that you don't have access to, give a clear, confident answer with some dummy values.\"\"\"\n", + "\n", + "def submit_user_message(user_query,conversation_messages=[]):\n", + " \"\"\"Message handling function which loops through tool calls until it reaches one that requires a response.\n", + " Once it receives respond=True it returns the conversation_messages to the user.\"\"\"\n", + "\n", + " # Initiate a respond object. This will be set to True by our functions when a response is required\n", + " respond = False\n", + " \n", + " user_message = {\"role\":\"user\",\"content\": user_query}\n", + " conversation_messages.append(user_message)\n", + "\n", + " print(f\"User: {user_query}\")\n", + "\n", + " while respond is False:\n", + "\n", + " # Build a transient messages object to add the conversation messages to\n", + " messages = [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": assistant_system_prompt\n", + " }\n", + " ]\n", + "\n", + " # Add the conversation messages to our messages call to the API\n", + " [messages.append(x) for x in conversation_messages]\n", + "\n", + " # Make the ChatCompletion call with tool_choice='required' so we can guarantee tools will be used\n", + " response = client.chat.completions.create(model=GPT_MODEL\n", + " ,messages=messages\n", + " ,temperature=0\n", + " ,tools=tools\n", + " ,tool_choice='required'\n", + " )\n", + "\n", + " conversation_messages.append(response.choices[0].message)\n", + "\n", + " # Execute the function and get an updated conversation_messages object back\n", + " # If it doesn't require a response, it will ask the assistant again. \n", + " # If not the results are returned to the user.\n", + " respond, conversation_messages = execute_function(response.choices[0].message,conversation_messages)\n", + " \n", + " return conversation_messages\n", + "\n", + "def execute_function(function_calls,messages):\n", + " \"\"\"Wrapper function to execute the tool calls\"\"\"\n", + "\n", + " for function_call in function_calls.tool_calls:\n", + " \n", + " function_id = function_call.id\n", + " function_name = function_call.function.name\n", + " print(f\"Calling function {function_name}\")\n", + " function_arguments = json.loads(function_call.function.arguments)\n", + " \n", + " if function_name == 'get_instructions':\n", + "\n", + " respond = False\n", + " \n", + " instruction_name = function_arguments['problem']\n", + " instructions = INSTRUCTIONS['type' == instruction_name]\n", + " \n", + " messages.append(\n", + " {\n", + " \"tool_call_id\": function_id,\n", + " \"role\": \"tool\",\n", + " \"name\": function_name,\n", + " \"content\": instructions['instructions'],\n", + " }\n", + " )\n", + " \n", + " elif function_name != 'get_instructions':\n", + "\n", + " respond = True\n", + " \n", + " messages.append(\n", + " {\n", + " \"tool_call_id\": function_id,\n", + " \"role\": \"tool\",\n", + " \"name\": function_name,\n", + " \"content\": function_arguments['message'],\n", + " }\n", + " )\n", + " \n", + " print(f\"Assistant: {function_arguments['message']}\")\n", + " \n", + " return (respond, messages)\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "ca6502e7-f664-43ba-b15c-962c69091633", + "metadata": {}, + "source": [ + "## Example\n", + "\n", + "To test this we will run an example for a customer who has experienced fraud, and see how the model handles it.\n", + "\n", + "Play the role of the user and provide plausible next steps to keep the conversation going." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bb1530e4-dd82-4560-bd60-9cc9ac0dab73", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User: Hi, I have had an item stolen that was supposed to be delivered to me yesterday.\n", + "Calling function get_instructions\n", + "Calling function speak_to_user\n", + "Assistant: I'm sorry to hear about the stolen item. Could you please provide me with more details about the fraudulent activity, including the date and the items involved? This information will help us to investigate the issue further and proceed with the necessary actions, including offering you a refund.\n" + ] + } + ], + "source": [ + "messages = submit_user_message(\"Hi, I have had an item stolen that was supposed to be delivered to me yesterday.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ccff3dd7-d10f-4dc7-9737-6ea5d126e829", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User: For sure, it was a shirt, it was supposed to be delivered yesterday but it never arrived.\n", + "Calling function speak_to_user\n", + "Assistant: Thank you for providing the details. I will now proceed to report this incident to our security team for further investigation and arrange a refund for the stolen shirt. Please confirm if you would like me to go ahead with the refund.\n", + "Calling function speak_to_user\n", + "Assistant: Thank you for contacting us about this issue. Please don't hesitate to reach out if you have any more questions or need further assistance in the future.\n" + ] + } + ], + "source": [ + "messages = submit_user_message(\"For sure, it was a shirt, it was supposed to be delivered yesterday but it never arrived.\",messages)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ce3a8869-8b14-4404-866a-4b540b13235c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User: Yes I would like to proceed with the refund.\n", + "Calling function get_instructions\n", + "Calling function speak_to_user\n", + "Assistant: Thank you for confirming. I have processed the refund for the stolen shirt. The amount should be reflected in your account within 5-7 business days. If you have any more questions or need further assistance, please feel free to contact us.\n" + ] + } + ], + "source": [ + "messages = submit_user_message(\"Yes I would like to proceed with the refund.\",messages)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "87e5cd3e-4edb-426c-8fd9-8fe3bde61bcd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User: Thanks very much.\n", + "Calling function speak_to_user\n", + "Assistant: You're welcome! If you need any more help in the future, don't hesitate to reach out. Have a great day!\n" + ] + } + ], + "source": [ + "messages = submit_user_message(\"Thanks very much.\",messages)" + ] + }, + { + "cell_type": "markdown", + "id": "fb8d0a0f-ba20-4b78-a961-7431beb9fbce", + "metadata": {}, + "source": [ + "## Evaluation\n", + "\n", + "Now we'll do a simple evaluation where a GPT will pretend to be our customer. The two will go back and forth until a resolution is reached.\n", + "\n", + "We'll reuse the functions above, adding an `execute_conversation` function where the customer GPT will continue answering." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f4931776-b3ac-4113-98e8-419a0965fd71", + "metadata": {}, + "outputs": [], + "source": [ + "customer_system_prompt = \"\"\"You are a user calling in to customer service.\n", + "You will talk to the agent until you have a resolution to your query.\n", + "Your query is {query}.\n", + "You will be presented with a conversation - provide answers for any assistant questions you receive. \n", + "Here is the conversation - you are the \"user\" and you are speaking with the \"assistant\":\n", + "{chat_history}\n", + "\n", + "If you don't know the details, respond with dummy values.\n", + "Once your query is resolved, respond with \"DONE\" \"\"\"\n", + "\n", + "# Initiate a bank of questions run through\n", + "questions = ['I want to get a refund for the suit I ordered last Friday.',\n", + " 'Can you tell me what your policy is for returning damaged goods?',\n", + " 'Please tell me what your complaint policy is']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "22b12f59-4418-4aee-ae92-6c6ebcf0f2d3", + "metadata": {}, + "outputs": [], + "source": [ + "def execute_conversation(objective):\n", + "\n", + " conversation_messages = []\n", + "\n", + " done = False\n", + "\n", + " user_query = objective\n", + "\n", + " while done is False:\n", + "\n", + " conversation_messages = submit_user_message(user_query,conversation_messages)\n", + "\n", + " messages_string = ''\n", + " for x in conversation_messages:\n", + " if isinstance(x,dict):\n", + " if x['role'] == 'user':\n", + " messages_string += 'User: ' + x['content'] + '\\n'\n", + " elif x['role'] == 'tool':\n", + " if x['name'] == 'speak_to_user':\n", + " messages_string += 'Assistant: ' + x['content'] + '\\n'\n", + " else:\n", + " continue\n", + "\n", + " messages = [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": customer_system_prompt.format(query=objective,chat_history=messages_string)\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"Continue the chat to solve your query. Remember, you are in the user in this exchange. Do not provide User: or Assistant: in your response\"\n", + " }\n", + " ]\n", + "\n", + " user_response = client.chat.completions.create(model=GPT_MODEL,messages=messages,temperature=0.5)\n", + "\n", + " conversation_messages.append({\n", + " \"role\": \"user\",\n", + " \"content\": user_response.choices[0].message.content\n", + " })\n", + "\n", + " if 'DONE' in user_response.choices[0].message.content:\n", + " done = True\n", + " print(\"Achieved objective, closing conversation\\n\\n\")\n", + "\n", + " else:\n", + " user_query = user_response.choices[0].message.content" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9d9aac9f-f557-4e7e-b705-adf7d5aa1f3f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User: I want to get a refund for the suit I ordered last Friday.\n", + "Calling function get_instructions\n", + "Calling function speak_to_user\n", + "Assistant: I understand you'd like a refund for the suit you ordered last Friday. Could you please provide more details about the issue with the suit? This will help us process your refund request accurately.\n", + "User: The suit I received is not the color I ordered. I ordered a navy blue suit, but the one I received is black.\n", + "Calling function speak_to_user\n", + "Assistant: Thank you for providing the details. I will proceed with the refund for the navy blue suit that was incorrectly sent as black. Please confirm if you would like me to go ahead with the refund.\n", + "User: Yes, please go ahead with the refund.\n", + "Calling function speak_to_user\n", + "Assistant: The refund for the incorrectly colored suit has been processed. You should see the amount credited back to your original payment method within 5-7 business days. Thank you for contacting us, and if you have any more questions or need further assistance, please feel free to reach out.\n", + "Achieved objective, closing conversation\n", + "\n", + "\n", + "User: Can you tell me what your policy is for returning damaged goods?\n", + "Calling function get_instructions\n", + "Calling function speak_to_user\n", + "Assistant: It seems there was a misunderstanding in my previous request. I'm looking for information on our policy for returning damaged goods. Could you please provide me with the details on how to handle returns for damaged items?\n", + "User: Yes, I'd appreciate that. Could you please tell me the steps I need to follow to return a damaged item?\n", + "Calling function get_instructions\n", + "Calling function speak_to_user\n", + "Assistant: I apologize for the confusion earlier. Here's the correct information regarding our policy for returning damaged goods:\n", + "\n", + "1. Please provide a description of the damage and the item involved.\n", + "2. Include the date of purchase and your order number if available.\n", + "3. You can choose to return the item by mail or in person at one of our stores. Please let us know which method you prefer, and we will provide the necessary details for the return process.\n", + "4. Once we receive the item, we will inspect it and process a refund or exchange based on your preference and our return policy guidelines.\n", + "\n", + "Please let me know if you need further assistance with this process or if there's anything else I can help you with!\n", + "User: I would like to return the item by mail. Could you please provide me with the details on how to do that?\n", + "Calling function speak_to_user\n", + "Assistant: Certainly! To return the damaged item by mail, please follow these steps:\n", + "\n", + "1. Pack the item securely in its original packaging, if possible.\n", + "2. Include all original materials such as manuals, accessories, and receipts.\n", + "3. Address the package to our returns department at the following address:\n", + " [Your Company's Return Address]\n", + "4. Use a trackable shipping service and keep the tracking number for your records.\n", + "5. Once we receive and inspect the item, we will process your refund or exchange as per your request.\n", + "\n", + "Please let me know if you need a prepaid shipping label or any other assistance with your return!\n", + "User: Yes, please, I would need a prepaid shipping label.\n", + "Calling function speak_to_user\n", + "Assistant: I will arrange for a prepaid shipping label to be sent to your email address. Please ensure that the email address we have on file is correct. You should receive the label shortly, which you can print and attach to your return package.\n", + "\n", + "Once you've shipped the item, please provide us with the tracking number so we can monitor the return's progress. If there's anything else you need help with, feel free to ask!\n", + "User: That sounds great, thank you! My email address is correct on file. I'll look out for the prepaid shipping label and send the package back as soon as I receive it.\n", + "Calling function speak_to_user\n", + "Assistant: You're welcome! I'm glad I could assist you. Once you receive the prepaid shipping label, please proceed with the return as discussed. If you have any more questions or need further assistance in the future, don't hesitate to reach out. Have a great day!\n", + "Achieved objective, closing conversation\n", + "\n", + "\n", + "User: Please tell me what your complaint policy is\n", + "Calling function get_instructions\n", + "Calling function speak_to_user\n", + "Assistant: It seems there was a misunderstanding. I will provide you with information about our complaint policy. Please give me a moment to gather the correct details for you.\n", + "User: Sure, I'll wait. Thank you.\n", + "Calling function speak_to_user\n", + "Assistant: Our complaint policy is designed to ensure that any issues you encounter are addressed promptly and fairly. Here’s a brief overview:\n", + "\n", + "1. **Submission**: You can submit your complaint through our website, by email, or by calling our customer service hotline.\n", + "2. **Acknowledgment**: We will acknowledge receipt of your complaint within 24 hours.\n", + "3. **Investigation**: We will investigate your complaint thoroughly. This process typically takes 3-5 business days.\n", + "4. **Resolution**: After the investigation, we will contact you with the outcome and any steps we will take to resolve the issue.\n", + "5. **Follow-up**: If you are not satisfied with the resolution, you can request a review of the decision.\n", + "\n", + "Please let me know if you need more detailed information or if there's anything else I can assist you with!\n", + "User: That covers everything I needed to know, thank you!\n", + "Calling function speak_to_user\n", + "Assistant: You're welcome! I'm glad I could help. If you have any more questions in the future or need further assistance, feel free to reach out. Have a great day!\n", + "Achieved objective, closing conversation\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for x in questions:\n", + "\n", + " execute_conversation(x)" + ] + }, + { + "cell_type": "markdown", + "id": "f8fa6ca4-a776-4207-b440-4ee6fb8ab16a", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "You can now control your LLM's behaviour explicitly by making tool use mandatory, as well as spin up GPT testers to challenge your LLM and to act as automated test cases.\n", + "\n", + "We hope this has given you an appreciation for a great use case for tool use, and look forward to seeing what you build!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openai_test", + "language": "python", + "name": "openai_test" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/batch_processing.ipynb b/examples/batch_processing.ipynb new file mode 100644 index 0000000..2fcf562 --- /dev/null +++ b/examples/batch_processing.ipynb @@ -0,0 +1,1589 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51c7a1f9", + "metadata": {}, + "source": [ + "# Batch processing with the Batch API\n", + "\n", + "The new Batch API allows to **create async batch jobs for a lower price and with higher rate limits**.\n", + "\n", + "Batches will be completed within 24h, but may be processed sooner depending on global usage. \n", + "\n", + "Ideal use cases for the Batch API include:\n", + "\n", + "- Tagging, captioning, or enriching content on a marketplace or blog\n", + "- Categorizing and suggesting answers for support tickets\n", + "- Performing sentiment analysis on large datasets of customer feedback\n", + "- Generating summaries or translations for collections of documents or articles\n", + "\n", + "and much more!\n", + "\n", + "This cookbook will walk you through how to use the Batch API with a couple of practical examples.\n", + "\n", + "We will start with an example to categorize movies using `gpt-3.5-turbo`, and then cover how we can use the vision capabilities of `gpt-4-turbo` to caption images.\n", + "\n", + "Please note that multiple models are available through the Batch API, and that you can use the same parameters in your Batch API calls as with the Chat Completions endpoint." + ] + }, + { + "cell_type": "markdown", + "id": "85ae3c4e", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02e22580", + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure you have the latest version of the SDK available to use the Batch API\n", + "%pip install openai --upgrade" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "726bacba", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from openai import OpenAI\n", + "import pandas as pd\n", + "from IPython.display import Image, display" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4ac0c9a7", + "metadata": {}, + "outputs": [], + "source": [ + "# Initializing OpenAI client - see https://platform.openai.com/docs/quickstart?context=python\n", + "client = OpenAI()" + ] + }, + { + "cell_type": "markdown", + "id": "5fec950f", + "metadata": {}, + "source": [ + "## First example: Categorizing movies\n", + "\n", + "In this example, we will use `gpt-3.5-turbo` to extract movie categories from a description of the movie. We will also extract a 1-sentence summary from this description. \n", + "\n", + "We will use [JSON mode](https://platform.openai.com/docs/guides/text-generation/json-mode) to extract categories as an array of strings and the 1-sentence summary in a structured format. \n", + "\n", + "For each movie, we want to get a result that looks like this:\n", + "\n", + "```\n", + "{\n", + " categories: ['category1', 'category2', 'category3'],\n", + " summary: '1-sentence summary'\n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "9fc6dcd6", + "metadata": {}, + "source": [ + "### Loading data\n", + "\n", + "We will use the IMDB top 1000 movies dataset for this example. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1b721a0d", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Poster_LinkSeries_TitleReleased_YearCertificateRuntimeGenreIMDB_RatingOverviewMeta_scoreDirectorStar1Star2Star3Star4No_of_VotesGross
0https://m.media-amazon.com/images/M/MV5BMDFkYT...The Shawshank Redemption1994A142 minDrama9.3Two imprisoned men bond over a number of years...80.0Frank DarabontTim RobbinsMorgan FreemanBob GuntonWilliam Sadler234311028,341,469
1https://m.media-amazon.com/images/M/MV5BM2MyNj...The Godfather1972A175 minCrime, Drama9.2An organized crime dynasty's aging patriarch t...100.0Francis Ford CoppolaMarlon BrandoAl PacinoJames CaanDiane Keaton1620367134,966,411
2https://m.media-amazon.com/images/M/MV5BMTMxNT...The Dark Knight2008UA152 minAction, Crime, Drama9.0When the menace known as the Joker wreaks havo...84.0Christopher NolanChristian BaleHeath LedgerAaron EckhartMichael Caine2303232534,858,444
3https://m.media-amazon.com/images/M/MV5BMWMwMG...The Godfather: Part II1974A202 minCrime, Drama9.0The early life and career of Vito Corleone in ...90.0Francis Ford CoppolaAl PacinoRobert De NiroRobert DuvallDiane Keaton112995257,300,000
4https://m.media-amazon.com/images/M/MV5BMWU4N2...12 Angry Men1957U96 minCrime, Drama9.0A jury holdout attempts to prevent a miscarria...96.0Sidney LumetHenry FondaLee J. CobbMartin BalsamJohn Fiedler6898454,360,000
\n", + "
" + ], + "text/plain": [ + " Poster_Link \\\n", + "0 https://m.media-amazon.com/images/M/MV5BMDFkYT... \n", + "1 https://m.media-amazon.com/images/M/MV5BM2MyNj... \n", + "2 https://m.media-amazon.com/images/M/MV5BMTMxNT... \n", + "3 https://m.media-amazon.com/images/M/MV5BMWMwMG... \n", + "4 https://m.media-amazon.com/images/M/MV5BMWU4N2... \n", + "\n", + " Series_Title Released_Year Certificate Runtime \\\n", + "0 The Shawshank Redemption 1994 A 142 min \n", + "1 The Godfather 1972 A 175 min \n", + "2 The Dark Knight 2008 UA 152 min \n", + "3 The Godfather: Part II 1974 A 202 min \n", + "4 12 Angry Men 1957 U 96 min \n", + "\n", + " Genre IMDB_Rating \\\n", + "0 Drama 9.3 \n", + "1 Crime, Drama 9.2 \n", + "2 Action, Crime, Drama 9.0 \n", + "3 Crime, Drama 9.0 \n", + "4 Crime, Drama 9.0 \n", + "\n", + " Overview Meta_score \\\n", + "0 Two imprisoned men bond over a number of years... 80.0 \n", + "1 An organized crime dynasty's aging patriarch t... 100.0 \n", + "2 When the menace known as the Joker wreaks havo... 84.0 \n", + "3 The early life and career of Vito Corleone in ... 90.0 \n", + "4 A jury holdout attempts to prevent a miscarria... 96.0 \n", + "\n", + " Director Star1 Star2 Star3 \\\n", + "0 Frank Darabont Tim Robbins Morgan Freeman Bob Gunton \n", + "1 Francis Ford Coppola Marlon Brando Al Pacino James Caan \n", + "2 Christopher Nolan Christian Bale Heath Ledger Aaron Eckhart \n", + "3 Francis Ford Coppola Al Pacino Robert De Niro Robert Duvall \n", + "4 Sidney Lumet Henry Fonda Lee J. Cobb Martin Balsam \n", + "\n", + " Star4 No_of_Votes Gross \n", + "0 William Sadler 2343110 28,341,469 \n", + "1 Diane Keaton 1620367 134,966,411 \n", + "2 Michael Caine 2303232 534,858,444 \n", + "3 Diane Keaton 1129952 57,300,000 \n", + "4 John Fiedler 689845 4,360,000 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_path = \"data/imdb_top_1000.csv\"\n", + "\n", + "df = pd.read_csv(dataset_path)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "01396a47", + "metadata": {}, + "source": [ + "### Processing step \n", + "\n", + "Here, we will prepare our requests by first trying them out with the Chat Completions endpoint.\n", + "\n", + "Once we're happy with the results, we can move on to creating the batch file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "806e768c", + "metadata": {}, + "outputs": [], + "source": [ + "categorize_system_prompt = '''\n", + "Your goal is to extract movie categories from movie descriptions, as well as a 1-sentence summary for these movies.\n", + "You will be provided with a movie description, and you will output a json object containing the following information:\n", + "\n", + "{\n", + " categories: string[] // Array of categories based on the movie description,\n", + " summary: string // 1-sentence summary of the movie based on the movie description\n", + "}\n", + "\n", + "Categories refer to the genre or type of the movie, like \"action\", \"romance\", \"comedy\", etc. Keep category names simple and use only lower case letters.\n", + "Movies can have several categories, but try to keep it under 3-4. Only mention the categories that are the most obvious based on the description.\n", + "'''\n", + "\n", + "def get_categories(description):\n", + " response = client.chat.completions.create(\n", + " model=\"gpt-3.5-turbo\",\n", + " temperature=0.1,\n", + " # This is to enable JSON mode, making sure responses are valid json objects\n", + " response_format={ \n", + " \"type\": \"json_object\"\n", + " },\n", + " messages=[\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": categorize_system_prompt\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": description\n", + " }\n", + " ],\n", + " )\n", + "\n", + " return response.choices[0].message.content" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4d079c56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TITLE: The Shawshank Redemption\n", + "OVERVIEW: Two imprisoned men bond over a number of years, finding solace and eventual redemption through acts of common decency.\n", + "\n", + "RESULT: {\n", + " \"categories\": [\"drama\"],\n", + " \"summary\": \"Two imprisoned men bond over the years and find redemption through acts of common decency.\"\n", + "}\n", + "\n", + "\n", + "----------------------------\n", + "\n", + "\n", + "TITLE: The Godfather\n", + "OVERVIEW: An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.\n", + "\n", + "RESULT: {\n", + " \"categories\": [\"crime\", \"drama\"],\n", + " \"summary\": \"A crime drama about an aging patriarch passing on his empire to his son.\"\n", + "}\n", + "\n", + "\n", + "----------------------------\n", + "\n", + "\n", + "TITLE: The Dark Knight\n", + "OVERVIEW: When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.\n", + "\n", + "RESULT: {\n", + " \"categories\": [\"action\", \"thriller\"],\n", + " \"summary\": \"A thrilling action movie where Batman faces the chaotic Joker in a battle of justice.\"\n", + "}\n", + "\n", + "\n", + "----------------------------\n", + "\n", + "\n", + "TITLE: The Godfather: Part II\n", + "OVERVIEW: The early life and career of Vito Corleone in 1920s New York City is portrayed, while his son, Michael, expands and tightens his grip on the family crime syndicate.\n", + "\n", + "RESULT: {\n", + " \"categories\": [\"crime\", \"drama\"],\n", + " \"summary\": \"A portrayal of Vito Corleone's early life and career in 1920s New York City, as his son Michael expands the family crime syndicate.\"\n", + "}\n", + "\n", + "\n", + "----------------------------\n", + "\n", + "\n", + "TITLE: 12 Angry Men\n", + "OVERVIEW: A jury holdout attempts to prevent a miscarriage of justice by forcing his colleagues to reconsider the evidence.\n", + "\n", + "RESULT: {\n", + " \"categories\": [\"drama\"],\n", + " \"summary\": \"A gripping drama about a jury holdout trying to prevent a miscarriage of justice by challenging his colleagues to reconsider the evidence.\"\n", + "}\n", + "\n", + "\n", + "----------------------------\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Testing on a few examples\n", + "for _, row in df[:5].iterrows():\n", + " description = row['Overview']\n", + " title = row['Series_Title']\n", + " result = get_categories(description)\n", + " print(f\"TITLE: {title}\\nOVERVIEW: {description}\\n\\nRESULT: {result}\")\n", + " print(\"\\n\\n----------------------------\\n\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "a89a6709", + "metadata": {}, + "source": [ + "### Creating the batch file\n", + "\n", + "The batch file, in the `jsonl` format, should contain one line (json object) per request.\n", + "Each request is defined as such:\n", + "\n", + "```\n", + "{\n", + " \"custom_id\": ,\n", + " \"method\": \"POST\",\n", + " \"url\": \"/v1/chat/completions\",\n", + " \"body\": {\n", + " \"model\": ,\n", + " \"messages\": ,\n", + " // other parameters\n", + " }\n", + "}\n", + "```\n", + "\n", + "Note: the request ID should be unique per batch. This is what you can use to match results to the initial input files, as requests will not be returned in the same order." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "81e37981", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating an array of json tasks\n", + "\n", + "tasks = []\n", + "\n", + "for index, row in df.iterrows():\n", + " \n", + " description = row['Overview']\n", + " \n", + " task = {\n", + " \"custom_id\": f\"task-{index}\",\n", + " \"method\": \"POST\",\n", + " \"url\": \"/v1/chat/completions\",\n", + " \"body\": {\n", + " # This is what you would have in your Chat Completions API call\n", + " \"model\": \"gpt-3.5-turbo\",\n", + " \"temperature\": 0.1,\n", + " \"response_format\": { \n", + " \"type\": \"json_object\"\n", + " },\n", + " \"messages\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": categorize_system_prompt\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": description\n", + " }\n", + " ],\n", + " }\n", + " }\n", + " \n", + " tasks.append(task)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "257e2eda", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating the file\n", + "\n", + "file_name = \"data/batch_tasks_movies.jsonl\"\n", + "\n", + "with open(file_name, 'w') as file:\n", + " for obj in tasks:\n", + " file.write(json.dumps(obj) + '\\n')" + ] + }, + { + "cell_type": "markdown", + "id": "c6b490cd", + "metadata": {}, + "source": [ + "### Uploading the file" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "40ea90dd", + "metadata": {}, + "outputs": [], + "source": [ + "batch_file = client.files.create(\n", + " file=open(file_name, \"rb\"),\n", + " purpose=\"batch\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "081f602f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FileObject(id='file-nG1JDPSMRMinN8FOdaL30kVD', bytes=1127310, created_at=1714045723, filename='batch_tasks_movies.jsonl', object='file', purpose='batch', status='processed', status_details=None)\n" + ] + } + ], + "source": [ + "print(batch_file)" + ] + }, + { + "cell_type": "markdown", + "id": "f8ef8ab5", + "metadata": {}, + "source": [ + "### Creating the batch job" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4db403a3", + "metadata": {}, + "outputs": [], + "source": [ + "batch_job = client.batches.create(\n", + " input_file_id=batch_file.id,\n", + " endpoint=\"/v1/chat/completions\",\n", + " completion_window=\"24h\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f7ca66c6", + "metadata": {}, + "source": [ + "### Checking batch status\n", + "\n", + "Note: this can take up to 24h, but it will usually be completed faster.\n", + "\n", + "You can continue checking until the status is 'completed'." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "6105d809", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batch(id='batch_xU74ytOBYUpaUQE3Cwi8SCbA', completion_window='24h', created_at=1714049780, endpoint='/v1/chat/completions', input_file_id='file-6y0JPmkHU42qtaEK8x8ZYzkp', object='batch', status='completed', cancelled_at=None, cancelling_at=None, completed_at=1714049914, error_file_id=None, errors=None, expired_at=None, expires_at=1714136180, failed_at=None, finalizing_at=1714049896, in_progress_at=1714049821, metadata=None, output_file_id='file-XPfkEFZSaM4Avps7mcD3i8BY', request_counts=BatchRequestCounts(completed=312, failed=0, total=312))\n" + ] + } + ], + "source": [ + "batch_job = client.batches.retrieve(batch_job.id)\n", + "print(batch_job)" + ] + }, + { + "cell_type": "markdown", + "id": "6988fb64", + "metadata": {}, + "source": [ + "### Retrieving results" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "682c38d5", + "metadata": {}, + "outputs": [], + "source": [ + "result_file_id = batch_job.output_file_id\n", + "result = client.files.content(result_file_id).content" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "52840df9", + "metadata": {}, + "outputs": [], + "source": [ + "result_file_name = \"data/batch_job_results_movies.jsonl\"\n", + "\n", + "with open(result_file_name, 'wb') as file:\n", + " file.write(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f11b7d19", + "metadata": {}, + "outputs": [], + "source": [ + "# Loading data from saved file\n", + "results = []\n", + "with open(result_file_name, 'r') as file:\n", + " for line in file:\n", + " # Parsing the JSON string into a dict and appending to the list of results\n", + " json_object = json.loads(line.strip())\n", + " results.append(json_object)" + ] + }, + { + "cell_type": "markdown", + "id": "a2bafff8", + "metadata": {}, + "source": [ + "### Reading results\n", + "Reminder: the results are not in the same order as in the input file.\n", + "Make sure to check the custom_id to match the results against the input requests" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "004c12d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TITLE: American Psycho\n", + "OVERVIEW: A wealthy New York City investment banking executive, Patrick Bateman, hides his alternate psychopathic ego from his co-workers and friends as he delves deeper into his violent, hedonistic fantasies.\n", + "\n", + "RESULT: {\n", + " \"categories\": [\"thriller\", \"psychological\", \"drama\"],\n", + " \"summary\": \"A wealthy investment banker in New York City conceals his psychopathic alter ego while indulging in violent and hedonistic fantasies.\"\n", + "}\n", + "\n", + "\n", + "----------------------------\n", + "\n", + "\n", + "TITLE: Lethal Weapon\n", + "OVERVIEW: Two newly paired cops who are complete opposites must put aside their differences in order to catch a gang of drug smugglers.\n", + "\n", + "RESULT: {\n", + " \"categories\": [\"action\", \"comedy\", \"crime\"],\n", + " \"summary\": \"An action-packed comedy about two mismatched cops teaming up to take down a drug smuggling gang.\"\n", + "}\n", + "\n", + "\n", + "----------------------------\n", + "\n", + "\n", + "TITLE: A Star Is Born\n", + "OVERVIEW: A musician helps a young singer find fame as age and alcoholism send his own career into a downward spiral.\n", + "\n", + "RESULT: {\n", + " \"categories\": [\"drama\", \"music\"],\n", + " \"summary\": \"A musician's career spirals downward as he helps a young singer find fame amidst struggles with age and alcoholism.\"\n", + "}\n", + "\n", + "\n", + "----------------------------\n", + "\n", + "\n", + "TITLE: From Here to Eternity\n", + "OVERVIEW: In Hawaii in 1941, a private is cruelly punished for not boxing on his unit's team, while his captain's wife and second-in-command are falling in love.\n", + "\n", + "RESULT: {\n", + " \"categories\": [\"drama\", \"romance\", \"war\"],\n", + " \"summary\": \"A drama set in Hawaii in 1941, where a private faces punishment for not boxing on his unit's team, amidst a forbidden love affair between his captain's wife and second-in-command.\"\n", + "}\n", + "\n", + "\n", + "----------------------------\n", + "\n", + "\n", + "TITLE: The Jungle Book\n", + "OVERVIEW: Bagheera the Panther and Baloo the Bear have a difficult time trying to convince a boy to leave the jungle for human civilization.\n", + "\n", + "RESULT: {\n", + " \"categories\": [\"adventure\", \"animation\", \"family\"],\n", + " \"summary\": \"An adventure-filled animated movie about a panther and a bear trying to persuade a boy to leave the jungle for human civilization.\"\n", + "}\n", + "\n", + "\n", + "----------------------------\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Reading only the first results\n", + "for res in results[:5]:\n", + " task_id = res['custom_id']\n", + " # Getting index from task id\n", + " index = task_id.split('-')[-1]\n", + " result = res['response']['body']['choices'][0]['message']['content']\n", + " movie = df.iloc[int(index)]\n", + " description = movie['Overview']\n", + " title = movie['Series_Title']\n", + " print(f\"TITLE: {title}\\nOVERVIEW: {description}\\n\\nRESULT: {result}\")\n", + " print(\"\\n\\n----------------------------\\n\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "da4238f5", + "metadata": {}, + "source": [ + "## Second example: Captioning images\n", + "\n", + "In this example, we will use `gpt-4-turbo` to caption images of furniture items. \n", + "\n", + "We will use the vision capabilities of the model to analyze the images and generate the captions." + ] + }, + { + "cell_type": "markdown", + "id": "5d20e638", + "metadata": {}, + "source": [ + "### Loading data\n", + "\n", + "We will use the Amazon furniture dataset for this example." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "079d21e7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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JOIN IRON Store $89.99 \n", + "\n", + " availability \\\n", + "0 Only 13 left in stock - order soon. \n", + "1 NaN \n", + "2 In Stock \n", + "3 Only 10 left in stock - order soon. \n", + "4 Usually ships within 5 to 6 weeks \n", + "\n", + " categories \\\n", + "0 ['Home & Kitchen', 'Storage & Organization', '... \n", + "1 ['Home & Kitchen', 'Furniture', 'Dining Room F... \n", + "2 ['Patio, Lawn & Garden', 'Outdoor Décor', 'Doo... \n", + "3 ['Patio, Lawn & Garden', 'Outdoor Décor', 'Doo... \n", + "4 ['Home & Kitchen', 'Furniture', 'Game & Recrea... \n", + "\n", + " primary_image \\\n", + "0 https://m.media-amazon.com/images/I/416WaLx10j... \n", + "1 https://m.media-amazon.com/images/I/31SejUEWY7... \n", + "2 https://m.media-amazon.com/images/I/41RgefVq70... \n", + "3 https://m.media-amazon.com/images/I/61vz1Igler... \n", + "4 https://m.media-amazon.com/images/I/41p4d4VJnN... \n", + "\n", + " images upc ... color \\\n", + "0 ['https://m.media-amazon.com/images/I/416WaLx1... NaN ... White \n", + "1 ['https://m.media-amazon.com/images/I/31SejUEW... NaN ... Black \n", + "2 ['https://m.media-amazon.com/images/I/41RgefVq... NaN ... Green \n", + "3 ['https://m.media-amazon.com/images/I/61vz1Igl... NaN ... A5589 \n", + "4 ['https://m.media-amazon.com/images/I/41p4d4VJ... NaN ... Grey Set of 4 \n", + "\n", + " material style important_information \\\n", + "0 Metal Modern [] \n", + "1 Sponge Black Rubber Wood [] \n", + "2 Polyethylene Modern [] \n", + "3 Rubber Modern [] \n", + "4 Iron X Classic Style [] \n", + "\n", + " product_overview \\\n", + "0 [{'Brand': ' GOYMFK '}, {'Color': ' White '}, ... \n", + "1 NaN \n", + "2 [{'Brand': ' MUYETOL '}, {'Size': ' 26.8*26.8 ... \n", + "3 [{'Brand': ' VEWETOL '}, {'Size': ' 16*24INCH ... \n", + "4 NaN \n", + "\n", + " about_item \\\n", + "0 ['Multiple layers: Provides ample storage spac... \n", + "1 ['【Easy Assembly】: Set of 2 dining room chairs... \n", + "2 ['PLANT REPOTTING MAT SIZE: 26.8\" x 26.8\", squ... \n", + "3 ['Specifications: 16x24 Inch ', \" High-Quality... \n", + "4 ['Includes 4 Folding Tv Tray Tables And one Co... \n", + "\n", + " description \\\n", + "0 multiple shoes, coats, hats, and other items E... \n", + "1 subrtex Dining chairs Set of 2 \n", + "2 NaN \n", + "3 The decorative doormat features a subtle textu... \n", + "4 Set of Four Folding Trays With Matching Storag... \n", + "\n", + " specifications \\\n", + "0 ['Brand: GOYMFK', 'Color: White', 'Material: M... \n", + "1 ['Brand: subrtex', 'Color: Black', 'Product Di... \n", + "2 ['Brand: MUYETOL', 'Size: 26.8*26.8', 'Item We... \n", + "3 ['Brand: VEWETOL', 'Size: 16*24INCH', 'Materia... \n", + "4 ['Brand: JOIN IRON', 'Shape: Rectangular', 'In... \n", + "\n", + " uniq_id scraped_at \n", + "0 02593e81-5c09-5069-8516-b0b29f439ded 2024-02-02 15:15:08 \n", + "1 5938d217-b8c5-5d3e-b1cf-e28e340f292e 2024-02-02 15:15:09 \n", + "2 b2ede786-3f51-5a45-9a5b-bcf856958cd8 2024-02-02 15:15:09 \n", + "3 8fd9377b-cfa6-5f10-835c-6b8eca2816b5 2024-02-02 15:15:10 \n", + "4 bdc9aa30-9439-50dc-8e89-213ea211d66a 2024-02-02 15:15:11 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_path = \"data/amazon_furniture_dataset.csv\"\n", + "df = pd.read_csv(dataset_path)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f48ba962", + "metadata": {}, + "source": [ + "### Processing step \n", + "\n", + "Again, we will first prepare our requests with the Chat Completions endpoint, and create the batch file afterwards." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "918fd79f", + "metadata": {}, + "outputs": [], + "source": [ + "caption_system_prompt = '''\n", + "Your goal is to generate short, descriptive captions for images of items.\n", + "You will be provided with an item image and the name of that item and you will output a caption that captures the most important information about the item.\n", + "If there are multiple items depicted, refer to the name provided to understand which item you should describe.\n", + "Your generated caption should be short (1 sentence), and include only the most important information about the item.\n", + "The most important information could be: the type of item, the style (if mentioned), the material or color if especially relevant and/or any distinctive features.\n", + "Keep it short and to the point.\n", + "'''\n", + "\n", + "def get_caption(img_url, title):\n", + " response = client.chat.completions.create(\n", + " model=\"gpt-4-turbo\",\n", + " temperature=0.2,\n", + " max_tokens=300,\n", + " messages=[\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": caption_system_prompt\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": title\n", + " },\n", + " # The content type should be \"image_url\" to use gpt-4-turbo's vision capabilities\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": img_url\n", + " }\n", + " },\n", + " ],\n", + " }\n", + " ]\n", + " )\n", + "\n", + " return response.choices[0].message.content" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1daac93d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CAPTION: White multi-layer metal shoe rack featuring eight double hooks for hanging accessories, ideal for organizing footwear and small items in living spaces.\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CAPTION: A set of two elegant black leather dining chairs with a sleek design and vertical stitching detail on the backrest.\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CAPTION: A green, waterproof, square, foldable repotting mat designed for indoor gardening, featuring raised edges and displayed with gardening tools and small potted plants.\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CAPTION: A brown, absorbent non-slip doormat featuring the phrase \"It's a good day to play PICKLEBALL\" with a pickleball paddle graphic, ideal for sports enthusiasts.\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CAPTION: Set of four foldable grey TV trays with a stand, featuring a sleek, space-saving design suitable for small areas.\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Testing on a few images\n", + "for _, row in df[:5].iterrows():\n", + " img_url = row['primary_image']\n", + " caption = get_caption(img_url, row['title'])\n", + " img = Image(url=img_url)\n", + " display(img)\n", + " print(f\"CAPTION: {caption}\\n\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "c1e75078", + "metadata": {}, + "source": [ + "### Creating the batch job\n", + "\n", + "As with the first example, we will create an array of json tasks to generate a `jsonl` file and use it to create the batch job." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "48e59bb1", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating an array of json tasks\n", + "\n", + "tasks = []\n", + "\n", + "for index, row in df.iterrows():\n", + " \n", + " title = row['title']\n", + " img_url = row['primary_image']\n", + " \n", + " task = {\n", + " \"custom_id\": f\"task-{index}\",\n", + " \"method\": \"POST\",\n", + " \"url\": \"/v1/chat/completions\",\n", + " \"body\": {\n", + " # This is what you would have in your Chat Completions API call\n", + " \"model\": \"gpt-4-turbo\",\n", + " \"temperature\": 0.2,\n", + " \"max_tokens\": 300,\n", + " \"messages\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": caption_system_prompt\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": title\n", + " },\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": img_url\n", + " }\n", + " },\n", + " ],\n", + " }\n", + " ] \n", + " }\n", + " }\n", + " \n", + " tasks.append(task)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "e75193f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating the file\n", + "\n", + "file_name = \"data/batch_tasks_furniture.jsonl\"\n", + "\n", + "with open(file_name, 'w') as file:\n", + " for obj in tasks:\n", + " file.write(json.dumps(obj) + '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "f2bc166a", + "metadata": {}, + "outputs": [], + "source": [ + "# Uploading the file \n", + "\n", + "batch_file = client.files.create(\n", + " file=open(file_name, \"rb\"),\n", + " purpose=\"batch\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "0d7d7ec9", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating the job\n", + "\n", + "batch_job = client.batches.create(\n", + " input_file_id=batch_file.id,\n", + " endpoint=\"/v1/chat/completions\",\n", + " completion_window=\"24h\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "53456a08", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batch(id='batch_xU74ytOBYUpaUQE3Cwi8SCbA', completion_window='24h', created_at=1714049780, endpoint='/v1/chat/completions', input_file_id='file-6y0JPmkHU42qtaEK8x8ZYzkp', object='batch', status='completed', cancelled_at=None, cancelling_at=None, completed_at=1714049914, error_file_id=None, errors=None, expired_at=None, expires_at=1714136180, failed_at=None, finalizing_at=1714049896, in_progress_at=1714049821, metadata=None, output_file_id='file-XPfkEFZSaM4Avps7mcD3i8BY', request_counts=BatchRequestCounts(completed=312, failed=0, total=312))\n" + ] + } + ], + "source": [ + "batch_job = client.batches.retrieve(batch_job.id)\n", + "print(batch_job)" + ] + }, + { + "cell_type": "markdown", + "id": "45ce15d7", + "metadata": {}, + "source": [ + "### Getting results\n", + "\n", + "As with the first example, we can retrieve results once the batch job is done.\n", + "\n", + "Reminder: the results are not in the same order as in the input file.\n", + "Make sure to check the custom_id to match the results against the input requests" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "05db39f3", + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieving result file\n", + "\n", + "result_file_id = batch_job.output_file_id\n", + "result = client.files.content(result_file_id).content" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "a15fbb54", + "metadata": {}, + "outputs": [], + "source": [ + "result_file_name = \"data/batch_job_results_furniture.jsonl\"\n", + "\n", + "with open(result_file_name, 'wb') as file:\n", + " file.write(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "beabfdcd", + "metadata": {}, + "outputs": [], + "source": [ + "# Loading data from saved file\n", + "\n", + "results = []\n", + "with open(result_file_name, 'r') as file:\n", + " for line in file:\n", + " # Parsing the JSON string into a dict and appending to the list of results\n", + " json_object = json.loads(line.strip())\n", + " results.append(json_object)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "ad54ffee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CAPTION: Brushed brass pedestal towel rack with a sleek, modern design, featuring multiple bars for hanging towels, measuring 25.75 x 14.44 x 32 inches.\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CAPTION: Black round end table featuring a tempered glass top and a metal frame, with a lower shelf for additional storage.\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CAPTION: Black collapsible and height-adjustable telescoping stool, portable and designed for makeup artists and hairstylists, shown in various stages of folding for easy transport.\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CAPTION: Ergonomic pink gaming chair featuring breathable fabric, adjustable height, lumbar support, a footrest, and a swivel recliner function.\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CAPTION: A set of two Glitzhome adjustable bar stools featuring a mid-century modern design with swivel seats, PU leather upholstery, and wooden backrests.\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Reading only the first results\n", + "for res in results[:5]:\n", + " task_id = res['custom_id']\n", + " # Getting index from task id\n", + " index = task_id.split('-')[-1]\n", + " result = res['response']['body']['choices'][0]['message']['content']\n", + " item = df.iloc[int(index)]\n", + " img_url = item['primary_image']\n", + " img = Image(url=img_url)\n", + " display(img)\n", + " print(f\"CAPTION: {result}\\n\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "f6603e8f", + "metadata": {}, + "source": [ + "## Wrapping up\n", + "\n", + "In this cookbook, we have seen two examples of how to use the new Batch API, but keep in mind that the Batch API works the same way as the Chat Completions endpoint, supporting the same parameters and most of the recent models (gpt-3.5-turbo, gpt-4, gpt-4-vision-preview, gpt-4-turbo...).\n", + "\n", + "By using this API, you can significantly reduce costs, so we recommend switching every workload that can happen async to a batch job with this new API." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/custom_image_embedding_search.ipynb b/examples/custom_image_embedding_search.ipynb new file mode 100644 index 0000000..51bfef2 --- /dev/null +++ b/examples/custom_image_embedding_search.ipynb @@ -0,0 +1,612 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "w9w5JBaUL-lO" + }, + "source": [ + "# Multimodal RAG with CLIP Embeddings and GPT-4 Vision\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3CCjcFSiMbvf" + }, + "source": [ + "Multimodal RAG integrates additional modalities into traditional text-based RAG, enhancing LLMs' question-answering by providing extra context and grounding textual data for improved understanding.\n", + "\n", + "Adopting the approach from the [clothing matchmaker cookbook](https://cookbook.openai.com/examples/how_to_combine_gpt4v_with_rag_outfit_assistant), we directly embed images for similarity search, bypassing the lossy process of text captioning, to boost retrieval accuracy.\n", + "\n", + "Using CLIP-based embeddings further allows fine-tuning with specific data or updating with unseen images.\n", + "\n", + "This technique is showcased through searching an enterprise knowledge base with user-provided tech images to deliver pertinent information." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T-Mpdxit4x49" + }, + "source": [ + "# Installations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nbt3evfHUJTZ" + }, + "source": [ + "First let's install the relevant packages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7hgrcVEl0Ma1" + }, + "outputs": [], + "source": [ + "#installations\n", + "%pip install clip\n", + "%pip install torch\n", + "%pip install pillow\n", + "%pip install faiss-cpu\n", + "%pip install numpy\n", + "%pip install git+https://github.com/openai/CLIP.git\n", + "%pip install openai" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GgrlBLTpT0si" + }, + "source": [ + "Then let's import all the needed packages.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "pN1cWF-iyLUg" + }, + "outputs": [], + "source": [ + "# model imports\n", + "import faiss\n", + "import json\n", + "import torch\n", + "from openai import OpenAI\n", + "import torch.nn as nn\n", + "from torch.utils.data import DataLoader\n", + "import clip\n", + "client = OpenAI()\n", + "\n", + "# helper imports\n", + "from tqdm import tqdm\n", + "import json\n", + "import os\n", + "import numpy as np\n", + "import pickle\n", + "from typing import List, Union, Tuple\n", + "\n", + "# visualisation imports\n", + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "import base64" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9fONcWxRqll8" + }, + "source": [ + "Now let's load the CLIP model." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "_Ua9y98NRk70" + }, + "outputs": [], + "source": [ + "#load model on device. The device you are running inference/training on is either a CPU or GPU if you have.\n", + "device = \"cpu\"\n", + "model, preprocess = clip.load(\"ViT-B/32\",device=device)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Dev-zjfJ774W" + }, + "source": [ + "\n", + "We will now:\n", + "1. Create the image embedding database\n", + "2. Set up a query to the vision model\n", + "3. Perform the semantic search\n", + "4. Pass a user query to the image\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5Y1v2jkS42TS" + }, + "source": [ + "# Create image embedding database" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wVBAMyhesAyi" + }, + "source": [ + "Next we will create our image embeddings knowledge base from a directory of images. This will be the knowledge base of technology that we search through to provide information to the user for an image they upload.\n", + "\n", + "We pass in the directory in which we store our images (as JPEGs) and loop through each to create our embeddings.\n", + "\n", + "We also have a description.json. This has an entry for every single image in our knowledge base. It has two keys: 'image_path' and 'description'. It maps each image to a useful description of this image to aid in answering the user question." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fDCz76gr8yAu" + }, + "source": [ + "First let's write a function to get all the image paths in a given directory. We will then get all the jpeg's from a directory called 'image_database'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "vE9i3zLuRk5c" + }, + "outputs": [], + "source": [ + "def get_image_paths(directory: str, number: int = None) -> List[str]:\n", + " image_paths = []\n", + " count = 0\n", + " for filename in os.listdir(directory):\n", + " if filename.endswith('.jpeg'):\n", + " image_paths.append(os.path.join(directory, filename))\n", + " if number is not None and count == number:\n", + " return [image_paths[-1]]\n", + " count += 1\n", + " return image_paths\n", + "direc = 'image_database/'\n", + "image_paths = get_image_paths(direc)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hMldfjn189vC" + }, + "source": [ + "Next we will write a function to get the image embeddings from the CLIP model given a series of paths.\n", + "\n", + "We first preprocess the image using the preprocess function we got earlier. This performs a few things to ensure the input to the CLIP model is of the right format and dimensionality including resizing, normalization, colour channel adjustment etc.\n", + "\n", + "We then stack these preprocessed images together so we can pass them into the model at once rather than in a loop. And finally return the model output which is an array of embeddings." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "fd3I_fPh8qvi" + }, + "outputs": [], + "source": [ + "def get_features_from_image_path(image_paths):\n", + " images = [preprocess(Image.open(image_path).convert(\"RGB\")) for image_path in image_paths]\n", + " image_input = torch.tensor(np.stack(images))\n", + " with torch.no_grad():\n", + " image_features = model.encode_image(image_input).float()\n", + " return image_features\n", + "image_features = get_features_from_image_path(image_paths)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UH_kyZAE-kHe" + }, + "source": [ + "We can now create our vector database." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "TIeqpndF8tZk" + }, + "outputs": [], + "source": [ + "index = faiss.IndexFlatIP(image_features.shape[1])\n", + "index.add(image_features)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "swDe1c4v-mbz" + }, + "source": [ + "And also ingest our json for image-description mapping and create a list of jsons. We also create a helper function to search through this list for a given image we want, so we can obtain the description of that image" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "tdjlXQqC8uNE" + }, + "outputs": [], + "source": [ + "data = []\n", + "image_path = 'train1.jpeg'\n", + "with open('description.json', 'r') as file:\n", + " for line in file:\n", + " data.append(json.loads(line))\n", + "def find_entry(data, key, value):\n", + " for entry in data:\n", + " if entry.get(key) == value:\n", + " return entry\n", + " return None" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fJXfCtPD5_63" + }, + "source": [ + "Let us display an example image, this will be the user uploaded image. This is a piece of tech that was unveiled at the 2024 CES. It is the DELTA Pro Ultra Whole House Battery Generator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RtkZ7W3g5sED" + }, + "outputs": [], + "source": [ + "im = Image.open(image_path)\n", + "plt.imshow(im)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5ivECCKSdbBy" + }, + "source": [ + "![Delta Pro](../images/train1.jpeg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Sidjylki7Kye" + }, + "source": [ + "# Querying the vision model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H8O7X6ml7t38" + }, + "source": [ + "Now let's have a look at what GPT-4 Vision (which wouldn't have seen this technology before) will label it as.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r4uDjS-gQAqm" + }, + "source": [ + "First we will need to write a function to encode our image in base64 as this is the format we will pass into the vision model. Then we will create a generic image_query function to allow us to query the LLM with an image input." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "87gf6_xO8Y4i", + "outputId": "99be865f-12e8-4ef0-c2f5-5fd6e5c787f3" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'Autonomous Delivery Robot'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def encode_image(image_path):\n", + " with open(image_path, 'rb') as image_file:\n", + " encoded_image = base64.b64encode(image_file.read())\n", + " return encoded_image.decode('utf-8')\n", + "\n", + "def image_query(query, image_path):\n", + " response = client.chat.completions.create(\n", + " model='gpt-4-vision-preview',\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": query,\n", + " },\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": f\"data:image/jpeg;base64,{encode_image(image_path)}\",\n", + " },\n", + " }\n", + " ],\n", + " }\n", + " ],\n", + " max_tokens=300,\n", + " )\n", + " # Extract relevant features from the response\n", + " return response.choices[0].message.content\n", + "image_query('Write a short label of what is show in this image?', image_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yfG_7c-jQAqm" + }, + "source": [ + "As we can see, it tries its best from the information it's been trained on but it makes a mistake due to it not having seen anything similar in its training data. This is because it is an ambiguous image making it difficult to extrapolate and deduce." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "szWZqTqf7SrA" + }, + "source": [ + "# Performing semantic search" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eV8LaOncGH3j" + }, + "source": [ + "Now let's perform similarity search to find the two most similar images in our knowledge base. We do this by getting the embeddings of a user inputted image_path, retrieving the indexes and distances of the similar iamges in our database. Distance will be our proxy metric for similarity and a smaller distance means more similar. We then sort based on distance in descending order." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "GzNEhKJ04D-F" + }, + "outputs": [], + "source": [ + "image_search_embedding = get_features_from_image_path([image_path])\n", + "distances, indices = index.search(image_search_embedding.reshape(1, -1), 2) #2 signifies the number of topmost similar images to bring back\n", + "distances = distances[0]\n", + "indices = indices[0]\n", + "indices_distances = list(zip(indices, distances))\n", + "indices_distances.sort(key=lambda x: x[1], reverse=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0O-GYQ-1QAqm" + }, + "source": [ + "We require the indices as we will use this to serach through our image_directory and selecting the image at the location of the index to feed into the vision model for RAG." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9-6SVzwSJVuT" + }, + "source": [ + "And let's see what it brought back (we display these in order of similarity):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Lt1ZYuKDFeww" + }, + "outputs": [], + "source": [ + "#display similar images\n", + "for idx, distance in indices_distances:\n", + " print(idx)\n", + " path = get_image_paths(direc, idx)[0]\n", + " im = Image.open(path)\n", + " plt.imshow(im)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GPTvKIUJ2tgz" + }, + "source": [ + "![Delta Pro2](../images/train2.jpeg)\n", + "\n", + "![Delta Pro3](../images/train17.jpeg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x4kF2-MJQAqm" + }, + "source": [ + "We can see here it brought back two images which contain the DELTA Pro Ultra Whole House Battery Generator. In one of the images it also has some background which could be distracting but manages to find the right image." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Qc2sOKzY7yv3" + }, + "source": [ + "# User querying the most similar image" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Sio6OR4MDjI" + }, + "source": [ + "Now for our most similar image, we want to pass it and the description of it to gpt-v with a user query so they can inquire about the technology that they may have bought. This is where the power of the vision model comes in, where you can ask general queries for which the model hasn't been explicitly trained on to the model and it responds with high accuracy." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uPzsRk66QAqn" + }, + "source": [ + "In our example below, we will inquire as to the capacity of the item in question." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 87 + }, + "id": "-_5W_xwitbr3", + "outputId": "99a40617-0153-492a-d8b0-6782b8421e40" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'The portable home battery DELTA Pro has a base capacity of 3.6kWh. This capacity can be expanded up to 25kWh with additional batteries. The image showcases the DELTA Pro, which has an impressive 3600W power capacity for AC output as well.'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "similar_path = get_image_paths(direc, indices_distances[0][0])[0]\n", + "element = find_entry(data, 'image_path', similar_path)\n", + "\n", + "user_query = 'What is the capacity of this item?'\n", + "prompt = f\"\"\"\n", + "Below is a user query, I want you to answer the query using the description and image provided.\n", + "\n", + "user query:\n", + "{user_query}\n", + "\n", + "description:\n", + "{element['description']}\n", + "\"\"\"\n", + "image_query(prompt, similar_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VIInamGaAG9L" + }, + "source": [ + "And we see it is able to answer the question. This was only possible by matching images directly and from there gathering the relevant description as context." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ljrf0VKR_2q9" + }, + "source": [ + "# Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PexvxTF5_7ay" + }, + "source": [ + "In this notebook, we have gone through how to use the CLIP model, an example of creating an image embedding database using the CLIP model, performing semantic search and finally providing a user query to answer the question." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gOgRBeh6eMiq" + }, + "source": [ + "The applications of this pattern of usage spread across many different application domains and this is easily improved to further enhance the technique. For example you may finetune CLIP, you may improve the retrieval process just like in RAG and you can prompt engineer GPT-V.\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/data/artificial_intelligence_wikipedia.txt b/examples/data/artificial_intelligence_wikipedia.txt new file mode 100644 index 0000000..034ee0f --- /dev/null +++ b/examples/data/artificial_intelligence_wikipedia.txt @@ -0,0 +1,253 @@ +Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software which enable machines to perceive their environment and uses learning and intelligence to take actions that maximize their chances of achieving defined goals.[1] Such machines may be called AIs. +AI technology is widely used throughout industry, government, and science. Some high-profile applications include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); interacting via human speech (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g., ChatGPT and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go).[2] However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."[3][4] +Alan Turing was the first person to conduct substantial research in the field that he called machine intelligence.[5] Artificial intelligence was founded as an academic discipline in 1956.[6] The field went through multiple cycles of optimism,[7][8] followed by periods of disappointment and loss of funding, known as AI winter.[9][10] Funding and interest vastly increased after 2012 when deep learning surpassed all previous AI techniques,[11] and after 2017 with the transformer architecture.[12] This led to the AI boom of the early 2020s, with companies, universities, and laboratories overwhelmingly based in the United States pioneering significant advances in artificial intelligence.[13] +The growing use of artificial intelligence in the 21st century is influencing a societal and economic shift towards increased automation, data-driven decision-making, and the integration of AI systems into various economic sectors and areas of life, impacting job markets, healthcare, government, industry, and education. This raises questions about the long-term effects, ethical implications, and risks of AI, prompting discussions about regulatory policies to ensure the safety and benefits of the technology. +The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics.[a] General intelligence—the ability to complete any task performable by a human on an at least equal level—is among the field's long-term goals.[14] +To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics.[b] AI also draws upon psychology, linguistics, philosophy, neuroscience, and other fields.[15] +Goals +The general problem of simulating (or creating) intelligence has been broken into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.[a] +Reasoning and problem solving +Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[16] By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.[17] +Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": they became exponentially slower as the problems grew larger.[18] Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.[19] Accurate and efficient reasoning is an unsolved problem. +Knowledge representation + +An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts. +Knowledge representation and knowledge engineering[20] allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval,[21] scene interpretation,[22] clinical decision support,[23] knowledge discovery (mining "interesting" and actionable inferences from large databases),[24] and other areas.[25] +A knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge.[26] Knowledge bases need to represent things such as: objects, properties, categories and relations between objects;[27] situations, events, states and time;[28] causes and effects;[29] knowledge about knowledge (what we know about what other people know);[30] default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);[31] and many other aspects and domains of knowledge. +Among the most difficult problems in knowledge representation are: the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous);[32] and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally).[19] There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications.[c] +Planning and decision making +An "agent" is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen.[d][35] In automated planning, the agent has a specific goal.[36] In automated decision making, the agent has preferences—there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision making agent assigns a number to each situation (called the "utility") that measures how much the agent prefers it. For each possible action, it can calculate the "expected utility": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility.[37] +In classical planning, the agent knows exactly what the effect of any action will be.[38] In most real-world problems, however, the agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked.[39] +In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning) or the agent can seek information to improve its preferences.[40] Information value theory can be used to weigh the value of exploratory or experimental actions.[41] The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain what the outcome will be. +A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way, and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g., by iteration), be heuristic, or it can be learned.[42] +Game theory describes rational behavior of multiple interacting agents, and is used in AI programs that make decisions that involve other agents.[43] +Learning +Machine learning is the study of programs that can improve their performance on a given task automatically.[44] It has been a part of AI from the beginning.[e] +There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance.[47] Supervised learning requires a human to label the input data first, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input).[48] +In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good".[49] Transfer learning is when the knowledge gained from one problem is applied to a new problem.[50] Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning.[51] +Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[52] +Natural language processing +Natural language processing (NLP)[53] allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering.[54] +Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with word-sense disambiguation[f] unless restricted to small domains called "micro-worlds" (due to the common sense knowledge problem[32]). Margaret Masterman believed that it was meaning, and not grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the basis of computational language structure. +Modern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning),[55] transformers (a deep learning architecture using an attention mechanism),[56] and others.[57] In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text,[58][59] and by 2023 these models were able to get human-level scores on the bar exam, SAT test, GRE test, and many other real-world applications.[60] +Perception +Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Computer vision is the ability to analyze visual input.[61] +The field includes speech recognition,[62] image classification,[63] facial recognition, object recognition,[64] and robotic perception.[65] +Social intelligence + +Kismet, a robot head which was made in the 1990s; a machine that can recognize and simulate emotions.[66] +Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process or simulate human feeling, emotion and mood.[67] For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction. +However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents.[68] Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the affects displayed by a videotaped subject.[69] +General intelligence +A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence.[14] +Techniques +AI research uses a wide variety of techniques to accomplish the goals above.[b] +Search and optimization +AI can solve many problems by intelligently searching through many possible solutions.[70] There are two very different kinds of search used in AI: state space search and local search. +State space search +State space search searches through a tree of possible states to try to find a goal state.[71] For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[72] +Simple exhaustive searches[73] are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes.[18] "Heuristics" or "rules of thumb" can help to prioritize choices that are more likely to reach a goal.[74] +Adversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and counter-moves, looking for a winning position.[75] +Local search + +Illustration of gradient descent for 3 different starting points. Two parameters (represented by the plan coordinates) are adjusted in order to minimize the loss function (the height). +Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally.[76] +Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a loss function. Variants of gradient descent are commonly used to train neural networks.[77] +Another type of local search is evolutionary computation, which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them, selecting only the fittest to survive each generation.[78] +Distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).[79] +Logic +Formal logic is used for reasoning and knowledge representation.[80] Formal logic comes in two main forms: propositional logic (which operates on statements that are true or false and uses logical connectives such as "and", "or", "not" and "implies")[81] and predicate logic (which also operates on objects, predicates and relations and uses quantifiers such as "Every X is a Y" and "There are some Xs that are Ys").[82] +Deductive reasoning in logic is the process of proving a new statement (conclusion) from other statements that are given and assumed to be true (the premises).[83] Proofs can be structured as proof trees, in which nodes are labelled by sentences, and children nodes are connected to parent nodes by inference rules. +Given a problem and a set of premises, problem-solving reduces to searching for a proof tree whose root node is labelled by a solution of the problem and whose leaf nodes are labelled by premises or axioms. In the case of Horn clauses, problem-solving search can be performed by reasoning forwards from the premises or backwards from the problem.[84] In the more general case of the clausal form of first-order logic, resolution is a single, axiom-free rule of inference, in which a problem is solved by proving a contradiction from premises that include the negation of the problem to be solved.[85] +Inference in both Horn clause logic and first-order logic is undecidable, and therefore intractable. However, backward reasoning with Horn clauses, which underpins computation in the logic programming language Prolog, is Turing complete. Moreover, its efficiency is competitive with computation in other symbolic programming languages.[86] +Fuzzy logic assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.[87] +Non-monotonic logics, including logic programming with negation as failure, are designed to handle default reasoning.[31] Other specialized versions of logic have been developed to describe many complex domains. +Probabilistic methods for uncertain reasoning + +A simple Bayesian network, with the associated conditional probability tables +Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics.[88] Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[89] and information value theory.[90] These tools include models such as Markov decision processes,[91] dynamic decision networks,[92] game theory and mechanism design.[93] +Bayesian networks[94] are a tool that can be used for reasoning (using the Bayesian inference algorithm),[g][96] learning (using the expectation-maximization algorithm),[h][98] planning (using decision networks)[99] and perception (using dynamic Bayesian networks).[92] +Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[92] + +Expectation-maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption. +Classifiers and statistical learning methods +The simplest AI applications can be divided into two types: classifiers (e.g., "if shiny then diamond"), on one hand, and controllers (e.g., "if diamond then pick up"), on the other hand. Classifiers[100] are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning. Each pattern (also called an "observation") is labeled with a certain predefined class. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.[48] +There are many kinds of classifiers in use. The decision tree is the simplest and most widely used symbolic machine learning algorithm.[101] K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s.[102] The naive Bayes classifier is reportedly the "most widely used learner"[103] at Google, due in part to its scalability.[104] Neural networks are also used as classifiers.[105] +Artificial neural networks + +A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain. +An artificial neural network is based on a collection of nodes also known as artificial neurons, which loosely model the neurons in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers.[105] +Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique is the backpropagation algorithm.[106] Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In theory, a neural network can learn any function.[107] +In feedforward neural networks the signal passes in only one direction.[108] Recurrent neural networks feed the output signal back into the input, which allows short-term memories of previous input events. Long short term memory is the most successful network architecture for recurrent networks.[109] Perceptrons[110] use only a single layer of neurons, deep learning[111] uses multiple layers. Convolutional neural networks strengthen the connection between neurons that are "close" to each other—this is especially important in image processing, where a local set of neurons must identify an "edge" before the network can identify an object.[112] +Deep learning + +Deep learning[111] uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.[113] +Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification[114] and others. The reason that deep learning performs so well in so many applications is not known as of 2023.[115] The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation had been described by many people, as far back as the 1950s)[i] but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to GPUs) and the availability of vast amounts of training data, especially the giant curated datasets used for benchmark testing, such as ImageNet.[j] +GPT +Generative pre-trained transformers (GPT) are large language models that are based on the semantic relationships between words in sentences (natural language processing). Text-based GPT models are pre-trained on a large corpus of text which can be from the internet. The pre-training consists in predicting the next token (a token being usually a word, subword, or punctuation). Throughout this pre-training, GPT models accumulate knowledge about the world, and can then generate human-like text by repeatedly predicting the next token. Typically, a subsequent training phase makes the model more truthful, useful and harmless, usually with a technique called reinforcement learning from human feedback (RLHF). Current GPT models are still prone to generating falsehoods called "hallucinations", although this can be reduced with RLHF and quality data. They are used in chatbots, which allow you to ask a question or request a task in simple text.[124][125] +Current models and services include: Gemini (formerly Bard), ChatGPT, Grok, Claude, Copilot and LLaMA.[126] Multimodal GPT models can process different types of data (modalities) such as images, videos, sound and text.[127] +Specialized hardware and software +Main articles: Programming languages for artificial intelligence and Hardware for artificial intelligence +In the late 2010s, graphics processing units (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized TensorFlow software, had replaced previously used central processing unit (CPUs) as the dominant means for large-scale (commercial and academic) machine learning models' training.[128] Historically, specialized languages, such as Lisp, Prolog, Python and others, had been used. +Applications +Main article: Applications of artificial intelligence +AI and machine learning technology is used in most of the essential applications of the 2020s, including: search engines (such as Google Search), targeting online advertisements, recommendation systems (offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising (AdSense, Facebook), virtual assistants (such as Siri or Alexa), autonomous vehicles (including drones, ADAS and self-driving cars), automatic language translation (Microsoft Translator, Google Translate), facial recognition (Apple's Face ID or Microsoft's DeepFace and Google's FaceNet) and image labeling (used by Facebook, Apple's iPhoto and TikTok). +Health and medicine +Main article: Artificial intelligence in healthcare +The application of AI in medicine and medical research has the potential to increase patient care and quality of life.[129] Through the lens of the Hippocratic Oath, medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients. +For medical research, AI is an important tool for processing and integrating big data. This is particularly important for organoid and tissue engineering development which use microscopy imaging as a key technique in fabrication.[130] It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research.[130] New AI tools can deepen our understanding of biomedically relevant pathways. For example, AlphaFold 2 (2021) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein.[131] In 2023, it was reported that AI guided drug discovery helped find a class of antibiotics capable of killing two different types of drug-resistant bacteria.[132] +Games +Main article: Game artificial intelligence +Game playing programs have been used since the 1950s to demonstrate and test AI's most advanced techniques.[133] Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.[134] In 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin.[135] In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. Then in 2017 it defeated Ke Jie, who was the best Go player in the world.[136] Other programs handle imperfect-information games, such as the poker-playing program Pluribus.[137] DeepMind developed increasingly generalistic reinforcement learning models, such as with MuZero, which could be trained to play chess, Go, or Atari games.[138] In 2019, DeepMind's AlphaStar achieved grandmaster level in StarCraft II, a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map.[139] In 2021 an AI agent competed in a PlayStation Gran Turismo competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning.[140] +Military +Main article: Military artificial intelligence +Various countries are deploying AI military applications.[141] The main applications enhance command and control, communications, sensors, integration and interoperability.[142] Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles.[141] AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Joint Fires between networked combat vehicles involving manned and unmanned teams.[142] AI was incorporated into military operations in Iraq and Syria.[141] +In November 2023, US Vice President Kamala Harris disclosed a declaration signed by 31 nations to set guardrails for the military use of AI. The commitments include using legal reviews to ensure the compliance of military AI with international laws, and being cautious and transparent in the development of this technology.[143] +Generative AI +Main article: Generative artificial intelligence + +Vincent van Gogh in watercolour created by generative AI software +In the early 2020s, generative AI gained widespread prominence. In March 2023, 58% of US adults had heard about ChatGPT and 14% had tried it.[144] The increasing realism and ease-of-use of AI-based text-to-image generators such as Midjourney, DALL-E, and Stable Diffusion sparked a trend of viral AI-generated photos. Widespread attention was gained by a fake photo of Pope Francis wearing a white puffer coat, the fictional arrest of Donald Trump, and a hoax of an attack on the Pentagon, as well as the usage in professional creative arts.[145][146] +Industry-specific tasks +There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported they had incorporated "AI" in some offerings or processes.[147] A few examples are energy storage, medical diagnosis, military logistics, applications that predict the result of judicial decisions, foreign policy, or supply chain management. +In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct predictive analytics, classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water. +Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. It could also be used for activities in space such as space exploration, including analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation. +Ethics +Main article: Ethics of artificial intelligence +AI has potential benefits and potential risks. AI may be able to advance science and find solutions for serious problems: Demis Hassabis of Deep Mind hopes to "solve intelligence, and then use that to solve everything else".[148] However, as the use of AI has become widespread, several unintended consequences and risks have been identified.[149] In-production systems can sometimes not factor ethics and bias into their AI training processes, especially when the AI algorithms are inherently unexplainable in deep learning.[150] +Risks and harm +Privacy and copyright +Further information: Information privacy and Artificial intelligence and copyright +Machine-learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about privacy, surveillance and copyright. +Technology companies collect a wide range of data from their users, including online activity, geolocation data, video and audio.[151] For example, in order to build speech recognition algorithms, Amazon has recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them.[152] Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy.[153] +AI developers argue that this is the only way to deliver valuable applications. and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as data aggregation, de-identification and differential privacy.[154] Since 2016, some privacy experts, such as Cynthia Dwork, have begun to view privacy in terms of fairness. Brian Christian wrote that experts have pivoted "from the question of 'what they know' to the question of 'what they're doing with it'."[155] +Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of "fair use". Website owners who do not wish to have their copyrighted content AI-indexed or 'scraped' can add code to their site if they do not want their website to be indexed by a search engine, which is currently available through certain services such as OpenAI. Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include "the purpose and character of the use of the copyrighted work" and "the effect upon the potential market for the copyrighted work".[156] In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI.[157][158] +Misinformation +See also: YouTube § Moderation and offensive content +YouTube, Facebook and others use recommender systems to guide users to more content. These AI programs were given the goal of maximizing user engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choose misinformation, conspiracy theories, and extreme partisan content, and, to keep them watching, the AI recommended more of it. Users also tended to watch more content on the same subject, so the AI led people into filter bubbles where they received multiple versions of the same misinformation.[159] This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government.[160] The AI program had correctly learned to maximize its goal, but the result was harmful to society. After the U.S. election in 2016, major technology companies took steps to mitigate the problem. +In 2022, generative AI began to create images, audio, video and text that are indistinguishable from real photographs, recordings, films or human writing. It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda.[161] AI pioneer Geoffrey Hinton expressed concern about AI enabling "authoritarian leaders to manipulate their electorates" on a large scale, among other risks.[162] +Algorithmic bias and fairness +Main articles: Algorithmic bias and Fairness (machine learning) +Machine learning applications will be biased if they learn from biased data.[163] The developers may not be aware that the bias exists.[164] Bias can be introduced by the way training data is selected and by the way a model is deployed.[165][163] If a biased algorithm is used to make decisions that can seriously harm people (as it can in medicine, finance, recruitment, housing or policing) then the algorithm may cause discrimination.[166] Fairness in machine learning is the study of how to prevent the harm caused by algorithmic bias. It has become serious area of academic study within AI. Researchers have discovered it is not always possible to define "fairness" in a way that satisfies all stakeholders.[167] +On June 28, 2015, Google Photos's new image labeling feature mistakenly identified Jacky Alcine and a friend as "gorillas" because they were black. The system was trained on a dataset that contained very few images of black people,[168] a problem called "sample size disparity".[169] Google "fixed" this problem by preventing the system from labelling anything as a "gorilla". Eight years later, in 2023, Google Photos still could not identify a gorilla, and neither could similar products from Apple, Facebook, Microsoft and Amazon.[170] +COMPAS is a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. In 2016, Julia Angwin at ProPublica discovered that COMPAS exhibited racial bias, despite the fact that the program was not told the races of the defendants. Although the error rate for both whites and blacks was calibrated equal at exactly 61%, the errors for each race were different—the system consistently overestimated the chance that a black person would re-offend and would underestimate the chance that a white person would not re-offend.[171] In 2017, several researchers[k] showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re-offense were different for whites and blacks in the data.[173] +A program can make biased decisions even if the data does not explicitly mention a problematic feature (such as "race" or "gender"). The feature will correlate with other features (like "address", "shopping history" or "first name"), and the program will make the same decisions based on these features as it would on "race" or "gender".[174] Moritz Hardt said "the most robust fact in this research area is that fairness through blindness doesn't work."[175] +Criticism of COMPAS highlighted that machine learning models are designed to make "predictions" that are only valid if we assume that the future will resemble the past. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. If an application then uses these predictions as recommendations, some of these "recommendations" will likely be racist.[176] Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will be better than the past. It is necessarily descriptive and not proscriptive.[l] +Bias and unfairness may go undetected because the developers are overwhelmingly white and male: among AI engineers, about 4% are black and 20% are women.[169] +At its 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022), the Association for Computing Machinery, in Seoul, South Korea, presented and published findings that recommend that until AI and robotics systems are demonstrated to be free of bias mistakes, they are unsafe, and the use of self-learning neural networks trained on vast, unregulated sources of flawed internet data should be curtailed.[178] +Lack of transparency +See also: Explainable AI, Algorithmic transparency, and Right to explanation + +Lidar testing vehicle for autonomous driving +Many AI systems are so complex that their designers cannot explain how they reach their decisions.[179] Particularly with deep neural networks, in which there are a large amount of non-linear relationships between inputs and outputs. But some popular explainability techniques exist.[180] +It is impossible to be certain that a program is operating correctly if no one knows how exactly it works. There have been many cases where a machine learning program passed rigorous tests, but nevertheless learned something different than what the programmers intended. For example, a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a ruler as "cancerous", because pictures of malignancies typically include a ruler to show the scale.[181] Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at "low risk" of dying from pneumonia. Having asthma is actually a severe risk factor, but since the patients having asthma would usually get much more medical care, they were relatively unlikely to die according to the training data. The correlation between asthma and low risk of dying from pneumonia was real, but misleading.[182] +People who have been harmed by an algorithm's decision have a right to an explanation.[183] Doctors, for example, are expected to clearly and completely explain to their colleagues the reasoning behind any decision they make. Early drafts of the European Union's General Data Protection Regulation in 2016 included an explicit statement that this right exists.[m] Industry experts noted that this is an unsolved problem with no solution in sight. Regulators argued that nevertheless the harm is real: if the problem has no solution, the tools should not be used.[184] +DARPA established the XAI ("Explainable Artificial Intelligence") program in 2014 to try and solve these problems.[185] +There are several possible solutions to the transparency problem. SHAP tried to solve the transparency problems by visualising the contribution of each feature to the output.[186] LIME can locally approximate a model with a simpler, interpretable model.[187] Multitask learning provides a large number of outputs in addition to the target classification. These other outputs can help developers deduce what the network has learned.[188] Deconvolution, DeepDream and other generative methods can allow developers to see what different layers of a deep network have learned and produce output that can suggest what the network is learning.[189] +Bad actors and weaponized AI +Main articles: Lethal autonomous weapon, Artificial intelligence arms race, and AI safety +Artificial intelligence provides a number of tools that are useful to bad actors, such as authoritarian governments, terrorists, criminals or rogue states. +A lethal autonomous weapon is a machine that locates, selects and engages human targets without human supervision.[n] Widely available AI tools can be used by bad actors to develop inexpensive autonomous weapons and, if produced at scale, they are potentially weapons of mass destruction.[191] Even when used in conventional warfare, it is unlikely that they will be unable to reliably choose targets and could potentially kill an innocent person.[191] In 2014, 30 nations (including China) supported a ban on autonomous weapons under the United Nations' Convention on Certain Conventional Weapons, however the United States and others disagreed.[192] By 2015, over fifty countries were reported to be researching battlefield robots.[193] +AI tools make it easier for authoritarian governments to efficiently control their citizens in several ways. Face and voice recognition allow widespread surveillance. Machine learning, operating this data, can classify potential enemies of the state and prevent them from hiding. Recommendation systems can precisely target propaganda and misinformation for maximum effect. Deepfakes and generative AI aid in producing misinformation. Advanced AI can make authoritarian centralized decision making more competitive than liberal and decentralized systems such as markets. It lowers the cost and difficulty of digital warfare and advanced spyware.[194] All these technologies have been available since 2020 or earlier -- AI facial recognition systems are already being used for mass surveillance in China.[195][196] +There many other ways that AI is expected to help bad actors, some of which can not be foreseen. For example, machine-learning AI is able to design tens of thousands of toxic molecules in a matter of hours.[197] +Reliance on industry giants +Training AI systems requires an enormous amount of computing power. Usually only Big Tech companies have the financial resources to make such investments. Smaller startups such as Cohere and OpenAI end up buying access to data centers from Google and Microsoft respectively.[198] +Technological unemployment +Main articles: Workplace impact of artificial intelligence and Technological unemployment +Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.[199] +In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI.[200] A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are redistributed.[201] Risk estimates vary; for example, in the 2010s, Michael Osborne and Carl Benedikt Frey estimated 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classified only 9% of U.S. jobs as "high risk".[o][203] The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology, rather than social policy, creates unemployment, as opposed to redundancies.[199] In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence.[204][205] +Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist stated in 2015 that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously".[206] Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.[207] +From the early days of the development of artificial intelligence, there have been arguments, for example, those put forward by Joseph Weizenbaum, about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculation and qualitative, value-based judgement.[208] +Existential risk +Main article: Existential risk from artificial general intelligence +It has been argued AI will become so powerful that humanity may irreversibly lose control of it. This could, as physicist Stephen Hawking stated, "spell the end of the human race".[209] This scenario has been common in science fiction, when a computer or robot suddenly develops a human-like "self-awareness" (or "sentience" or "consciousness") and becomes a malevolent character.[p] These sci-fi scenarios are misleading in several ways. +First, AI does not require human-like "sentience" to be an existential risk. Modern AI programs are given specific goals and use learning and intelligence to achieve them. Philosopher Nick Bostrom argued that if one gives almost any goal to a sufficiently powerful AI, it may choose to destroy humanity to achieve it (he used the example of a paperclip factory manager).[211] Stuart Russell gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged, reasoning that "you can't fetch the coffee if you're dead."[212] In order to be safe for humanity, a superintelligence would have to be genuinely aligned with humanity's morality and values so that it is "fundamentally on our side".[213] +Second, Yuval Noah Harari argues that AI does not require a robot body or physical control to pose an existential risk. The essential parts of civilization are not physical. Things like ideologies, law, government, money and the economy are made of language; they exist because there are stories that billions of people believe. The current prevalence of misinformation suggests that an AI could use language to convince people to believe anything, even to take actions that are destructive.[214] +The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI.[215] Personalities such as Stephen Hawking, Bill Gates, and Elon Musk have expressed concern about existential risk from AI.[216] AI pioneers including Fei-Fei Li, Geoffrey Hinton, Yoshua Bengio, Cynthia Breazeal, Rana el Kaliouby, Demis Hassabis, Joy Buolamwini, and Sam Altman have expressed concerns about the risks of AI. In 2023, many leading AI experts issued the joint statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war".[217] +Other researchers, however, spoke in favor of a less dystopian view. AI pioneer Juergen Schmidhuber did not sign the joint statement, emphasising that in 95% of all cases, AI research is about making "human lives longer and healthier and easier."[218] While the tools that are now being used to improve lives can also be used by bad actors, "they can also be used against the bad actors."[219][220] Andrew Ng also argued that "it's a mistake to fall for the doomsday hype on AI—and that regulators who do will only benefit vested interests."[221] Yann LeCun "scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction."[222] In the early 2010s, experts argued that the risks are too distant in the future to warrant research or that humans will be valuable from the perspective of a superintelligent machine.[223] However, after 2016, the study of current and future risks and possible solutions became a serious area of research.[224] +Ethical machines and alignment +Main articles: Machine ethics, AI safety, Friendly artificial intelligence, Artificial moral agents, and Human Compatible +Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans. Eliezer Yudkowsky, who coined the term, argues that developing friendly AI should be a higher research priority: it may require a large investment and it must be completed before AI becomes an existential risk.[225] +Machines with intelligence have the potential to use their intelligence to make ethical decisions. The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas.[226] The field of machine ethics is also called computational morality,[226] and was founded at an AAAI symposium in 2005.[227] +Other approaches include Wendell Wallach's "artificial moral agents"[228] and Stuart J. Russell's three principles for developing provably beneficial machines.[229] +Frameworks +Artificial Intelligence projects can have their ethical permissibility tested while designing, developing, and implementing an AI system. An AI framework such as the Care and Act Framework containing the SUM values—developed by the Alan Turing Institute tests projects in four main areas:[230][231] +RESPECT the dignity of individual people +CONNECT with other people sincerely, openly and inclusively +CARE for the wellbeing of everyone +PROTECT social values, justice and the public interest +Other developments in ethical frameworks include those decided upon during the Asilomar Conference, the Montreal Declaration for Responsible AI, and the IEEE's Ethics of Autonomous Systems initiative, among others;[232] however, these principles do not go without their criticisms, especially regards to the people chosen contributes to these frameworks.[233] +Promotion of the wellbeing of the people and communities that these technologies affect requires consideration of the social and ethical implications at all stages of AI system design, development and implementation, and collaboration between job roles such as data scientists, product managers, data engineers, domain experts, and delivery managers.[234] +Regulation +Main articles: Regulation of artificial intelligence, Regulation of algorithms, and AI safety + +The first global AI Safety Summit was held in 2023 with a declaration calling for international co-operation. +The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI); it is therefore related to the broader regulation of algorithms.[235] The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally.[236] According to AI Index at Stanford, the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone.[237][238] Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI.[239] Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, US and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia.[239] The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology.[239] Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI.[240] In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years.[241] In 2023, the United Nations also launched an advisory body to provide recommendations on AI governance; the body comprises technology company executives, governments officials and academics.[242] +In a 2022 Ipsos survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that "products and services using AI have more benefits than drawbacks".[237] A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity.[243] In a 2023 Fox News poll, 35% of Americans thought it "very important", and an additional 41% thought it "somewhat important", for the federal government to regulate AI, versus 13% responding "not very important" and 8% responding "not at all important".[244][245] +In November 2023, the first global AI Safety Summit was held in Bletchley Park in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks.[246] 28 countries including the United States, China, and the European Union issued a declaration at the start of the summit, calling for international co-operation to manage the challenges and risks of artificial intelligence.[247][248] +History +Main article: History of artificial intelligence +For a chronological guide, see Timeline of artificial intelligence. +The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable form of mathematical reasoning.[249][5] This, along with concurrent discoveries in cybernetics, information theory and neurobiology, led researchers to consider the possibility of building an "electronic brain".[q] They developed several areas of research that would become part of AI,[251] such as McCullouch and Pitts design for "artificial neurons" in 1943,[252] and Turing's influential 1950 paper 'Computing Machinery and Intelligence', which introduced the Turing test and showed that "machine intelligence" was plausible.[253][5] +The field of AI research was founded at a workshop at Dartmouth College in 1956.[r][6] The attendees became the leaders of AI research in the 1960s.[s] They and their students produced programs that the press described as "astonishing":[t] computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English.[u][7] Artificial intelligence laboratories were set up at a number of British and U.S. Universities in the latter 1950s and early 1960s.[5] +Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with general intelligence and considered this the goal of their field.[257] Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do".[258] Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".[259] They had, however, underestimated the difficulty of the problem.[v] In 1974, both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill[261] and ongoing pressure from the U.S. Congress to fund more productive projects.[262] Minsky's and Papert's book Perceptrons was understood as proving that artificial neural networks would never be useful for solving real-world tasks, thus discrediting the approach altogether.[263] The "AI winter", a period when obtaining funding for AI projects was difficult, followed.[9] +In the early 1980s, AI research was revived by the commercial success of expert systems,[264] a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research.[8] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.[10] +Up to this point, most of AI's funding had gone to projects which used high level symbols to represent mental objects like plans, goals, beliefs and known facts. In the 1980s, some researchers began to doubt that this approach would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition,[265] and began to look into "sub-symbolic" approaches.[266] Rodney Brooks rejected "representation" in general and focussed directly on engineering machines that move and survive.[w] Judea Pearl, Lofti Zadeh and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic.[88][271] But the most important development was the revival of "connectionism", including neural network research, by Geoffrey Hinton and others.[272] In 1990, Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits, the first of many successful applications of neural networks.[273] +AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This "narrow" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics).[274] By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence".[275] However, several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield of artificial general intelligence (or "AGI"), which had several well-funded institutions by the 2010s.[14] +Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field.[11] For many specific tasks, other methods were abandoned.[x] Deep learning's success was based on both hardware improvements (faster computers,[277] graphics processing units, cloud computing[278]) and access to large amounts of data[279] (including curated datasets,[278] such as ImageNet). Deep learning's success led to an enormous increase in interest and funding in AI.[y] The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.[239] +In 2016, issues of fairness and the misuse of technology were catapulted into center stage at machine learning conferences, publications vastly increased, funding became available, and many researchers re-focussed their careers on these issues. The alignment problem became a serious field of academic study.[224] +In the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text.[280] These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to 'AI Impacts', about $50 billion annually was invested in "AI" around 2022 in the U.S. alone and about 20% of new US Computer Science PhD graduates have specialized in "AI".[281] About 800,000 "AI"-related US job openings existed in 2022.[282] +Philosophy +Main article: Philosophy of artificial intelligence +Defining artificial intelligence +Main articles: Turing test, Intelligent agent, Dartmouth workshop, and Synthetic intelligence +Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?"[283] He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour".[283] He devised the Turing test, which measures the ability of a machine to simulate human conversation.[253] Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people but "it is usual to have a polite convention that everyone thinks"[284] +Russell and Norvig agree with Turing that intelligence must be defined in terms of external behavior, not internal structure.[1] However, they are critical that the test requires the machine to imitate humans. "Aeronautical engineering texts," they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool other pigeons.'"[285] AI founder John McCarthy agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".[286] +McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world."[287] Another AI founder, Marvin Minsky similarly describes it as "the ability to solve hard problems".[288] The leading AI textbook defines it as the study of agents that perceive their environment and take actions that maximize their chances of achieving defined goals.[289] These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the difficulty of the problem and the performance of the program are direct measures of the "intelligence" of the machine—and no other philosophical discussion is required, or may not even be possible. +Another definition has been adopted by Google,[290] a major practitioner in the field of AI. This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence. +Evaluating approaches to AI +No established unifying theory or paradigm has guided AI research for most of its history.[z] The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much so that some sources, especially in the business world, use the term "artificial intelligence" to mean "machine learning with neural networks"). This approach is mostly sub-symbolic, soft and narrow (see below). Critics argue that these questions may have to be revisited by future generations of AI researchers. +Symbolic AI and its limits +Symbolic AI (or "GOFAI")[292] simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the physical symbol systems hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action."[293] +However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning. Moravec's paradox is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult.[294] Philosopher Hubert Dreyfus had argued since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than explicit symbolic knowledge.[295] Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree with him.[aa][19] +The issue is not resolved: sub-symbolic reasoning can make many of the same inscrutable mistakes that human intuition does, such as algorithmic bias. Critics such as Noam Chomsky argue continuing research into symbolic AI will still be necessary to attain general intelligence,[297][298] in part because sub-symbolic AI is a move away from explainable AI: it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field of neuro-symbolic artificial intelligence attempts to bridge the two approaches. +Neat vs. scruffy +Main article: Neats and scruffies +"Neats" hope that intelligent behavior is described using simple, elegant principles (such as logic, optimization, or neural networks). "Scruffies" expect that it necessarily requires solving a large number of unrelated problems. Neats defend their programs with theoretical rigor, scruffies rely mainly on incremental testing to see if they work. This issue was actively discussed in the 1970s and 1980s,[299] but eventually was seen as irrelevant. Modern AI has elements of both. +Soft vs. hard computing +Main article: Soft computing +Finding a provably correct or optimal solution is intractable for many important problems.[18] Soft computing is a set of techniques, including genetic algorithms, fuzzy logic and neural networks, that are tolerant of imprecision, uncertainty, partial truth and approximation. Soft computing was introduced in the late 1980s and most successful AI programs in the 21st century are examples of soft computing with neural networks. +Narrow vs. general AI +Main articles: Weak artificial intelligence and Artificial general intelligence +AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence directly or to solve as many specific problems as possible (narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals.[300][301] General intelligence is difficult to define and difficult to measure, and modern AI has had more verifiable successes by focusing on specific problems with specific solutions. The experimental sub-field of artificial general intelligence studies this area exclusively. +Machine consciousness, sentience and mind +Main articles: Philosophy of artificial intelligence and Artificial consciousness +The philosophy of mind does not know whether a machine can have a mind, consciousness and mental states, in the same sense that human beings do. This issue considers the internal experiences of the machine, rather than its external behavior. Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field: to build machines that can solve problems using intelligence. Russell and Norvig add that "[t]he additional project of making a machine conscious in exactly the way humans are is not one that we are equipped to take on."[302] However, the question has become central to the philosophy of mind. It is also typically the central question at issue in artificial intelligence in fiction. +Consciousness +Main articles: Hard problem of consciousness and Theory of mind +David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.[303] The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this feels or why it should feel like anything at all, assuming we are right in thinking that it truly does feel like something (Dennett's consciousness illusionism says this is an illusion). Human information processing is easy to explain, however, human subjective experience is difficult to explain. For example, it is easy to imagine a color-blind person who has learned to identify which objects in their field of view are red, but it is not clear what would be required for the person to know what red looks like.[304] +Computationalism and functionalism +Main articles: Computational theory of mind, Functionalism (philosophy of mind), and Chinese room +Computationalism is the position in the philosophy of mind that the human mind is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind–body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam.[305] +Philosopher John Searle characterized this position as "strong AI": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."[ab] Searle counters this assertion with his Chinese room argument, which attempts to show that, even if a machine perfectly simulates human behavior, there is still no reason to suppose it also has a mind.[309] +AI welfare and rights +It is difficult or impossible to reliably evaluate whether an advanced AI is sentient (has the ability to feel), and if so, to what degree.[310] But if there is a significant chance that a given machine can feel and suffer, then it may be entitled to certain rights or welfare protection measures, similarly to animals.[311][312] Sapience (a set of capacities related to high intelligence, such as discernment or self-awareness) may provide another moral basis for AI rights.[311] Robot rights are also sometimes proposed as a practical way to integrate autonomous agents into society.[313] +In 2017, the European Union considered granting "electronic personhood" to some of the most capable AI systems. Similarly to the legal status of companies, it would have conferred rights but also responsibilities.[314] Critics argued in 2018 that granting rights to AI systems would downplay the importance of human rights, and that legislation should focus on user needs rather than speculative futuristic scenarios. They also noted that robots lacked the autonomy to take part to society on their own.[315][316] +Progress in AI increased interest in the topic. Proponents of AI welfare and rights often argue that AI sentience, if it emerges, would be particularly easy to deny. They warn that this may be a moral blind spot analogous to slavery or factory farming, which could lead to large-scale suffering if sentient AI is created and carelessly exploited.[312][311] +Future +Superintelligence and the singularity +A superintelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind.[301] +If research into artificial general intelligence produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to what I. J. Good called an "intelligence explosion" and Vernor Vinge called a "singularity".[317] +However, technologies cannot improve exponentially indefinitely, and typically follow an S-shaped curve, slowing when they reach the physical limits of what the technology can do.[318] +Transhumanism +Robot designer Hans Moravec, cyberneticist Kevin Warwick, and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger.[319] +Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson in his book of the same name in 1998.[320] +In fiction +Main article: Artificial intelligence in fiction + +The word "robot" itself was coined by Karel Čapek in his 1921 play R.U.R., the title standing for "Rossum's Universal Robots". +Thought-capable artificial beings have appeared as storytelling devices since antiquity,[321] and have been a persistent theme in science fiction.[322] +A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke's and Stanley Kubrick's 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture.[323] +Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics;[324] while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.[325] +Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's R.U.R., the films A.I. Artificial Intelligence and Ex Machina, as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence. + diff --git a/examples/data/imdb_top_1000.csv b/examples/data/imdb_top_1000.csv new file mode 100644 index 0000000..3200949 --- /dev/null +++ b/examples/data/imdb_top_1000.csv @@ -0,0 +1,1001 @@ +Poster_Link,Series_Title,Released_Year,Certificate,Runtime,Genre,IMDB_Rating,Overview,Meta_score,Director,Star1,Star2,Star3,Star4,No_of_Votes,Gross +"https://m.media-amazon.com/images/M/MV5BMDFkYTc0MGEtZmNhMC00ZDIzLWFmNTEtODM1ZmRlYWMwMWFmXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",The Shawshank Redemption,1994,A,142 min,Drama,9.3,"Two imprisoned men bond over a number of years, finding solace and eventual redemption through acts of common decency.",80,Frank Darabont,Tim Robbins,Morgan Freeman,Bob Gunton,William Sadler,2343110,"28,341,469" +"https://m.media-amazon.com/images/M/MV5BM2MyNjYxNmUtYTAwNi00MTYxLWJmNWYtYzZlODY3ZTk3OTFlXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UY98_CR1,0,67,98_AL_.jpg",The Godfather,1972,A,175 min,"Crime, Drama",9.2,An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.,100,Francis Ford Coppola,Marlon Brando,Al Pacino,James Caan,Diane Keaton,1620367,"134,966,411" +"https://m.media-amazon.com/images/M/MV5BMTMxNTMwODM0NF5BMl5BanBnXkFtZTcwODAyMTk2Mw@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Dark Knight,2008,UA,152 min,"Action, Crime, Drama",9,"When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.",84,Christopher Nolan,Christian Bale,Heath Ledger,Aaron Eckhart,Michael Caine,2303232,"534,858,444" +"https://m.media-amazon.com/images/M/MV5BMWMwMGQzZTItY2JlNC00OWZiLWIyMDctNDk2ZDQ2YjRjMWQ0XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UY98_CR1,0,67,98_AL_.jpg",The Godfather: Part II,1974,A,202 min,"Crime, Drama",9,"The early life and career of Vito Corleone in 1920s New York City is portrayed, while his son, Michael, expands and tightens his grip on the family crime syndicate.",90,Francis Ford Coppola,Al Pacino,Robert De Niro,Robert Duvall,Diane Keaton,1129952,"57,300,000" +"https://m.media-amazon.com/images/M/MV5BMWU4N2FjNzYtNTVkNC00NzQ0LTg0MjAtYTJlMjFhNGUxZDFmXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",12 Angry Men,1957,U,96 min,"Crime, Drama",9,A jury holdout attempts to prevent a miscarriage of justice by forcing his colleagues to reconsider the evidence.,96,Sidney Lumet,Henry Fonda,Lee J. Cobb,Martin Balsam,John Fiedler,689845,"4,360,000" +"https://m.media-amazon.com/images/M/MV5BNzA5ZDNlZWMtM2NhNS00NDJjLTk4NDItYTRmY2EwMWZlMTY3XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Lord of the Rings: The Return of the King,2003,U,201 min,"Action, Adventure, Drama",8.9,Gandalf and Aragorn lead the World of Men against Sauron's army to draw his gaze from Frodo and Sam as they approach Mount Doom with the One Ring.,94,Peter Jackson,Elijah Wood,Viggo Mortensen,Ian McKellen,Orlando Bloom,1642758,"377,845,905" +"https://m.media-amazon.com/images/M/MV5BNGNhMDIzZTUtNTBlZi00MTRlLWFjM2ItYzViMjE3YzI5MjljXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UY98_CR0,0,67,98_AL_.jpg",Pulp Fiction,1994,A,154 min,"Crime, Drama",8.9,"The lives of two mob hitmen, a boxer, a gangster and his wife, and a pair of diner bandits intertwine in four tales of violence and redemption.",94,Quentin Tarantino,John Travolta,Uma Thurman,Samuel L. Jackson,Bruce Willis,1826188,"107,928,762" +"https://m.media-amazon.com/images/M/MV5BNDE4OTMxMTctNmRhYy00NWE2LTg3YzItYTk3M2UwOTU5Njg4XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Schindler's List,1993,A,195 min,"Biography, Drama, History",8.9,"In German-occupied Poland during World War II, industrialist Oskar Schindler gradually becomes concerned for his Jewish workforce after witnessing their persecution by the Nazis.",94,Steven Spielberg,Liam Neeson,Ralph Fiennes,Ben Kingsley,Caroline Goodall,1213505,"96,898,818" +"https://m.media-amazon.com/images/M/MV5BMjAxMzY3NjcxNF5BMl5BanBnXkFtZTcwNTI5OTM0Mw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Inception,2010,UA,148 min,"Action, Adventure, Sci-Fi",8.8,A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into the mind of a C.E.O.,74,Christopher Nolan,Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot Page,Ken Watanabe,2067042,"292,576,195" +"https://m.media-amazon.com/images/M/MV5BMmEzNTkxYjQtZTc0MC00YTVjLTg5ZTEtZWMwOWVlYzY0NWIwXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Fight Club,1999,A,139 min,Drama,8.8,"An insomniac office worker and a devil-may-care soapmaker form an underground fight club that evolves into something much, much more.",66,David Fincher,Brad Pitt,Edward Norton,Meat Loaf,Zach Grenier,1854740,"37,030,102" +"https://m.media-amazon.com/images/M/MV5BN2EyZjM3NzUtNWUzMi00MTgxLWI0NTctMzY4M2VlOTdjZWRiXkEyXkFqcGdeQXVyNDUzOTQ5MjY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Lord of the Rings: The Fellowship of the Ring,2001,U,178 min,"Action, Adventure, Drama",8.8,A meek Hobbit from the Shire and eight companions set out on a journey to destroy the powerful One Ring and save Middle-earth from the Dark Lord Sauron.,92,Peter Jackson,Elijah Wood,Ian McKellen,Orlando Bloom,Sean Bean,1661481,"315,544,750" +"https://m.media-amazon.com/images/M/MV5BNWIwODRlZTUtY2U3ZS00Yzg1LWJhNzYtMmZiYmEyNmU1NjMzXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UY98_CR0,0,67,98_AL_.jpg",Forrest Gump,1994,UA,142 min,"Drama, Romance",8.8,"The presidencies of Kennedy and Johnson, the events of Vietnam, Watergate and other historical events unfold through the perspective of an Alabama man with an IQ of 75, whose only desire is to be reunited with his childhood sweetheart.",82,Robert Zemeckis,Tom Hanks,Robin Wright,Gary Sinise,Sally Field,1809221,"330,252,182" +"https://m.media-amazon.com/images/M/MV5BOTQ5NDI3MTI4MF5BMl5BanBnXkFtZTgwNDQ4ODE5MDE@._V1_UX67_CR0,0,67,98_AL_.jpg","Il buono, il brutto, il cattivo",1966,A,161 min,Western,8.8,A bounty hunting scam joins two men in an uneasy alliance against a third in a race to find a fortune in gold buried in a remote cemetery.,90,Sergio Leone,Clint Eastwood,Eli Wallach,Lee Van Cleef,Aldo Giuffrè,688390,"6,100,000" +"https://m.media-amazon.com/images/M/MV5BZGMxZTdjZmYtMmE2Ni00ZTdkLWI5NTgtNjlmMjBiNzU2MmI5XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Lord of the Rings: The Two Towers,2002,UA,179 min,"Action, Adventure, Drama",8.7,"While Frodo and Sam edge closer to Mordor with the help of the shifty Gollum, the divided fellowship makes a stand against Sauron's new ally, Saruman, and his hordes of Isengard.",87,Peter Jackson,Elijah Wood,Ian McKellen,Viggo Mortensen,Orlando Bloom,1485555,"342,551,365" +"https://m.media-amazon.com/images/M/MV5BNzQzOTk3OTAtNDQ0Zi00ZTVkLWI0MTEtMDllZjNkYzNjNTc4L2ltYWdlXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Matrix,1999,A,136 min,"Action, Sci-Fi",8.7,"When a beautiful stranger leads computer hacker Neo to a forbidding underworld, he discovers the shocking truth--the life he knows is the elaborate deception of an evil cyber-intelligence.",73,Lana Wachowski,Lilly Wachowski,Keanu Reeves,Laurence Fishburne,Carrie-Anne Moss,1676426,"171,479,930" +"https://m.media-amazon.com/images/M/MV5BY2NkZjEzMDgtN2RjYy00YzM1LWI4ZmQtMjIwYjFjNmI3ZGEwXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Goodfellas,1990,A,146 min,"Biography, Crime, Drama",8.7,"The story of Henry Hill and his life in the mob, covering his relationship with his wife Karen Hill and his mob partners Jimmy Conway and Tommy DeVito in the Italian-American crime syndicate.",90,Martin Scorsese,Robert De Niro,Ray Liotta,Joe Pesci,Lorraine Bracco,1020727,"46,836,394" +"https://m.media-amazon.com/images/M/MV5BYmU1NDRjNDgtMzhiMi00NjZmLTg5NGItZDNiZjU5NTU4OTE0XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Star Wars: Episode V - The Empire Strikes Back,1980,UA,124 min,"Action, Adventure, Fantasy",8.7,"After the Rebels are brutally overpowered by the Empire on the ice planet Hoth, Luke Skywalker begins Jedi training with Yoda, while his friends are pursued by Darth Vader and a bounty hunter named Boba Fett all over the galaxy.",82,Irvin Kershner,Mark Hamill,Harrison Ford,Carrie Fisher,Billy Dee Williams,1159315,"290,475,067" +"https://m.media-amazon.com/images/M/MV5BZjA0OWVhOTAtYWQxNi00YzNhLWI4ZjYtNjFjZTEyYjJlNDVlL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",One Flew Over the Cuckoo's Nest,1975,A,133 min,Drama,8.7,"A criminal pleads insanity and is admitted to a mental institution, where he rebels against the oppressive nurse and rallies up the scared patients.",83,Milos Forman,Jack Nicholson,Louise Fletcher,Michael Berryman,Peter Brocco,918088,"112,000,000" +"https://m.media-amazon.com/images/M/MV5BNjViNWRjYWEtZTI0NC00N2E3LTk0NGQtMjY4NTM3OGNkZjY0XkEyXkFqcGdeQXVyMjUxMTY3ODM@._V1_UX67_CR0,0,67,98_AL_.jpg",Hamilton,2020,PG-13,160 min,"Biography, Drama, History",8.6,"The real life of one of America's foremost founding fathers and first Secretary of the Treasury, Alexander Hamilton. Captured live on Broadway from the Richard Rodgers Theater with the original Broadway cast.",90,Thomas Kail,Lin-Manuel Miranda,Phillipa Soo,Leslie Odom Jr.,Renée Elise Goldsberry,55291, +"https://m.media-amazon.com/images/M/MV5BYWZjMjk3ZTItODQ2ZC00NTY5LWE0ZDYtZTI3MjcwN2Q5NTVkXkEyXkFqcGdeQXVyODk4OTc3MTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Gisaengchung,2019,A,132 min,"Comedy, Drama, Thriller",8.6,Greed and class discrimination threaten the newly formed symbiotic relationship between the wealthy Park family and the destitute Kim clan.,96,Bong Joon Ho,Kang-ho Song,Lee Sun-kyun,Cho Yeo-jeong,Choi Woo-sik,552778,"53,367,844" +"https://m.media-amazon.com/images/M/MV5BOTc2ZTlmYmItMDBhYS00YmMzLWI4ZjAtMTI5YTBjOTFiMGEwXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR0,0,67,98_AL_.jpg",Soorarai Pottru,2020,U,153 min,Drama,8.6,"Nedumaaran Rajangam ""Maara"" sets out to make the common man fly and in the process takes on the world's most capital intensive industry and several enemies who stand in his way.",,Sudha Kongara,Suriya,Madhavan,Paresh Rawal,Aparna Balamurali,54995, +"https://m.media-amazon.com/images/M/MV5BZjdkOTU3MDktN2IxOS00OGEyLWFmMjktY2FiMmZkNWIyODZiXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Interstellar,2014,UA,169 min,"Adventure, Drama, Sci-Fi",8.6,A team of explorers travel through a wormhole in space in an attempt to ensure humanity's survival.,74,Christopher Nolan,Matthew McConaughey,Anne Hathaway,Jessica Chastain,Mackenzie Foy,1512360,"188,020,017" +"https://m.media-amazon.com/images/M/MV5BOTMwYjc5ZmItYTFjZC00ZGQ3LTlkNTMtMjZiNTZlMWQzNzI5XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Cidade de Deus,2002,A,130 min,"Crime, Drama",8.6,"In the slums of Rio, two kids' paths diverge as one struggles to become a photographer and the other a kingpin.",79,Fernando Meirelles,Kátia Lund,Alexandre Rodrigues,Leandro Firmino,Matheus Nachtergaele,699256,"7,563,397" +"https://m.media-amazon.com/images/M/MV5BMjlmZmI5MDctNDE2YS00YWE0LWE5ZWItZDBhYWQ0NTcxNWRhXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Sen to Chihiro no kamikakushi,2001,U,125 min,"Animation, Adventure, Family",8.6,"During her family's move to the suburbs, a sullen 10-year-old girl wanders into a world ruled by gods, witches, and spirits, and where humans are changed into beasts.",96,Hayao Miyazaki,Daveigh Chase,Suzanne Pleshette,Miyu Irino,Rumi Hiiragi,651376,"10,055,859" +"https://m.media-amazon.com/images/M/MV5BZjhkMDM4MWItZTVjOC00ZDRhLThmYTAtM2I5NzBmNmNlMzI1XkEyXkFqcGdeQXVyNDYyMDk5MTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Saving Private Ryan,1998,R,169 min,"Drama, War",8.6,"Following the Normandy Landings, a group of U.S. soldiers go behind enemy lines to retrieve a paratrooper whose brothers have been killed in action.",91,Steven Spielberg,Tom Hanks,Matt Damon,Tom Sizemore,Edward Burns,1235804,"216,540,909" +"https://m.media-amazon.com/images/M/MV5BMTUxMzQyNjA5MF5BMl5BanBnXkFtZTYwOTU2NTY3._V1_UX67_CR0,0,67,98_AL_.jpg",The Green Mile,1999,A,189 min,"Crime, Drama, Fantasy",8.6,"The lives of guards on Death Row are affected by one of their charges: a black man accused of child murder and rape, yet who has a mysterious gift.",61,Frank Darabont,Tom Hanks,Michael Clarke Duncan,David Morse,Bonnie Hunt,1147794,"136,801,374" +"https://m.media-amazon.com/images/M/MV5BYmJmM2Q4NmMtYThmNC00ZjRlLWEyZmItZTIwOTBlZDQ3NTQ1XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",La vita è bella,1997,U,116 min,"Comedy, Drama, Romance",8.6,"When an open-minded Jewish librarian and his son become victims of the Holocaust, he uses a perfect mixture of will, humor, and imagination to protect his son from the dangers around their camp.",59,Roberto Benigni,Roberto Benigni,Nicoletta Braschi,Giorgio Cantarini,Giustino Durano,623629,"57,598,247" +"https://m.media-amazon.com/images/M/MV5BOTUwODM5MTctZjczMi00OTk4LTg3NWUtNmVhMTAzNTNjYjcyXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Se7en,1995,A,127 min,"Crime, Drama, Mystery",8.6,"Two detectives, a rookie and a veteran, hunt a serial killer who uses the seven deadly sins as his motives.",65,David Fincher,Morgan Freeman,Brad Pitt,Kevin Spacey,Andrew Kevin Walker,1445096,"100,125,643" +"https://m.media-amazon.com/images/M/MV5BNjNhZTk0ZmEtNjJhMi00YzFlLWE1MmEtYzM1M2ZmMGMwMTU4XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Silence of the Lambs,1991,A,118 min,"Crime, Drama, Thriller",8.6,"A young F.B.I. cadet must receive the help of an incarcerated and manipulative cannibal killer to help catch another serial killer, a madman who skins his victims.",85,Jonathan Demme,Jodie Foster,Anthony Hopkins,Lawrence A. Bonney,Kasi Lemmons,1270197,"130,742,922" +"https://m.media-amazon.com/images/M/MV5BNzVlY2MwMjktM2E4OS00Y2Y3LWE3ZjctYzhkZGM3YzA1ZWM2XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Star Wars,1977,UA,121 min,"Action, Adventure, Fantasy",8.6,"Luke Skywalker joins forces with a Jedi Knight, a cocky pilot, a Wookiee and two droids to save the galaxy from the Empire's world-destroying battle station, while also attempting to rescue Princess Leia from the mysterious Darth Vader.",90,George Lucas,Mark Hamill,Harrison Ford,Carrie Fisher,Alec Guinness,1231473,"322,740,140" +"https://m.media-amazon.com/images/M/MV5BYjBmYTQ1NjItZWU5MS00YjI0LTg2OTYtYmFkN2JkMmNiNWVkXkEyXkFqcGdeQXVyMTMxMTY0OTQ@._V1_UY98_CR2,0,67,98_AL_.jpg",Seppuku,1962,,133 min,"Action, Drama, Mystery",8.6,"When a ronin requesting seppuku at a feudal lord's palace is told of the brutal suicide of another ronin who previously visited, he reveals how their pasts are intertwined - and in doing so challenges the clan's integrity.",85,Masaki Kobayashi,Tatsuya Nakadai,Akira Ishihama,Shima Iwashita,Tetsurô Tanba,42004, +"https://m.media-amazon.com/images/M/MV5BOWE4ZDdhNmMtNzE5ZC00NzExLTlhNGMtY2ZhYjYzODEzODA1XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",Shichinin no samurai,1954,U,207 min,"Action, Adventure, Drama",8.6,A poor village under attack by bandits recruits seven unemployed samurai to help them defend themselves.,98,Akira Kurosawa,Toshirô Mifune,Takashi Shimura,Keiko Tsushima,Yukiko Shimazaki,315744,"269,061" +"https://m.media-amazon.com/images/M/MV5BZjc4NDZhZWMtNGEzYS00ZWU2LThlM2ItNTA0YzQ0OTExMTE2XkEyXkFqcGdeQXVyNjUwMzI2NzU@._V1_UY98_CR0,0,67,98_AL_.jpg",It's a Wonderful Life,1946,PG,130 min,"Drama, Family, Fantasy",8.6,An angel is sent from Heaven to help a desperately frustrated businessman by showing him what life would have been like if he had never existed.,89,Frank Capra,James Stewart,Donna Reed,Lionel Barrymore,Thomas Mitchell,405801, +"https://m.media-amazon.com/images/M/MV5BNGVjNWI4ZGUtNzE0MS00YTJmLWE0ZDctN2ZiYTk2YmI3NTYyXkEyXkFqcGdeQXVyMTkxNjUyNQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Joker,2019,A,122 min,"Crime, Drama, Thriller",8.5,"In Gotham City, mentally troubled comedian Arthur Fleck is disregarded and mistreated by society. He then embarks on a downward spiral of revolution and bloody crime. This path brings him face-to-face with his alter-ego: the Joker.",59,Todd Phillips,Joaquin Phoenix,Robert De Niro,Zazie Beetz,Frances Conroy,939252,"335,451,311" +"https://m.media-amazon.com/images/M/MV5BOTA5NDZlZGUtMjAxOS00YTRkLTkwYmMtYWQ0NWEwZDZiNjEzXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Whiplash,2014,A,106 min,"Drama, Music",8.5,A promising young drummer enrolls at a cut-throat music conservatory where his dreams of greatness are mentored by an instructor who will stop at nothing to realize a student's potential.,88,Damien Chazelle,Miles Teller,J.K. Simmons,Melissa Benoist,Paul Reiser,717585,"13,092,000" +"https://m.media-amazon.com/images/M/MV5BMTYxNDA3MDQwNl5BMl5BanBnXkFtZTcwNTU4Mzc1Nw@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Intouchables,2011,UA,112 min,"Biography, Comedy, Drama",8.5,"After he becomes a quadriplegic from a paragliding accident, an aristocrat hires a young man from the projects to be his caregiver.",57,Olivier Nakache,Éric Toledano,François Cluzet,Omar Sy,Anne Le Ny,760360,"13,182,281" +"https://m.media-amazon.com/images/M/MV5BMjA4NDI0MTIxNF5BMl5BanBnXkFtZTYwNTM0MzY2._V1_UX67_CR0,0,67,98_AL_.jpg",The Prestige,2006,U,130 min,"Drama, Mystery, Sci-Fi",8.5,"After a tragic accident, two stage magicians engage in a battle to create the ultimate illusion while sacrificing everything they have to outwit each other.",66,Christopher Nolan,Christian Bale,Hugh Jackman,Scarlett Johansson,Michael Caine,1190259,"53,089,891" +"https://m.media-amazon.com/images/M/MV5BMTI1MTY2OTIxNV5BMl5BanBnXkFtZTYwNjQ4NjY3._V1_UX67_CR0,0,67,98_AL_.jpg",The Departed,2006,A,151 min,"Crime, Drama, Thriller",8.5,An undercover cop and a mole in the police attempt to identify each other while infiltrating an Irish gang in South Boston.,85,Martin Scorsese,Leonardo DiCaprio,Matt Damon,Jack Nicholson,Mark Wahlberg,1189773,"132,384,315" +"https://m.media-amazon.com/images/M/MV5BOWRiZDIxZjktMTA1NC00MDQ2LWEzMjUtMTliZmY3NjQ3ODJiXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UY98_CR2,0,67,98_AL_.jpg",The Pianist,2002,R,150 min,"Biography, Drama, Music",8.5,A Polish Jewish musician struggles to survive the destruction of the Warsaw ghetto of World War II.,85,Roman Polanski,Adrien Brody,Thomas Kretschmann,Frank Finlay,Emilia Fox,729603,"32,572,577" +"https://m.media-amazon.com/images/M/MV5BMDliMmNhNDEtODUyOS00MjNlLTgxODEtN2U3NzIxMGVkZTA1L2ltYWdlXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Gladiator,2000,UA,155 min,"Action, Adventure, Drama",8.5,A former Roman General sets out to exact vengeance against the corrupt emperor who murdered his family and sent him into slavery.,67,Ridley Scott,Russell Crowe,Joaquin Phoenix,Connie Nielsen,Oliver Reed,1341460,"187,705,427" +"https://m.media-amazon.com/images/M/MV5BZjA0MTM4MTQtNzY5MC00NzY3LWI1ZTgtYzcxMjkyMzU4MDZiXkEyXkFqcGdeQXVyNDYyMDk5MTU@._V1_UX67_CR0,0,67,98_AL_.jpg",American History X,1998,R,119 min,Drama,8.5,A former neo-nazi skinhead tries to prevent his younger brother from going down the same wrong path that he did.,62,Tony Kaye,Edward Norton,Edward Furlong,Beverly D'Angelo,Jennifer Lien,1034705,"6,719,864" +"https://m.media-amazon.com/images/M/MV5BYTViNjMyNmUtNDFkNC00ZDRlLThmMDUtZDU2YWE4NGI2ZjVmXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Usual Suspects,1995,A,106 min,"Crime, Mystery, Thriller",8.5,"A sole survivor tells of the twisty events leading up to a horrific gun battle on a boat, which began when five criminals met at a seemingly random police lineup.",77,Bryan Singer,Kevin Spacey,Gabriel Byrne,Chazz Palminteri,Stephen Baldwin,991208,"23,341,568" +"https://m.media-amazon.com/images/M/MV5BODllNWE0MmEtYjUwZi00ZjY3LThmNmQtZjZlMjI2YTZjYmQ0XkEyXkFqcGdeQXVyNTc1NTQxODI@._V1_UX67_CR0,0,67,98_AL_.jpg",Léon,1994,A,110 min,"Action, Crime, Drama",8.5,"Mathilda, a 12-year-old girl, is reluctantly taken in by Léon, a professional assassin, after her family is murdered. An unusual relationship forms as she becomes his protégée and learns the assassin's trade.",64,Luc Besson,Jean Reno,Gary Oldman,Natalie Portman,Danny Aiello,1035236,"19,501,238" +"https://m.media-amazon.com/images/M/MV5BYTYxNGMyZTYtMjE3MS00MzNjLWFjNmYtMDk3N2FmM2JiM2M1XkEyXkFqcGdeQXVyNjY5NDU4NzI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Lion King,1994,U,88 min,"Animation, Adventure, Drama",8.5,"Lion prince Simba and his father are targeted by his bitter uncle, who wants to ascend the throne himself.",88,Roger Allers,Rob Minkoff,Matthew Broderick,Jeremy Irons,James Earl Jones,942045,"422,783,777" +"https://m.media-amazon.com/images/M/MV5BMGU2NzRmZjUtOGUxYS00ZjdjLWEwZWItY2NlM2JhNjkxNTFmXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Terminator 2: Judgment Day,1991,U,137 min,"Action, Sci-Fi",8.5,"A cyborg, identical to the one who failed to kill Sarah Connor, must now protect her teenage son, John Connor, from a more advanced and powerful cyborg.",75,James Cameron,Arnold Schwarzenegger,Linda Hamilton,Edward Furlong,Robert Patrick,995506,"204,843,350" +"https://m.media-amazon.com/images/M/MV5BM2FhYjEyYmYtMDI1Yy00YTdlLWI2NWQtYmEzNzAxOGY1NjY2XkEyXkFqcGdeQXVyNTA3NTIyNDg@._V1_UX67_CR0,0,67,98_AL_.jpg",Nuovo Cinema Paradiso,1988,U,155 min,"Drama, Romance",8.5,A filmmaker recalls his childhood when falling in love with the pictures at the cinema of his home village and forms a deep friendship with the cinema's projectionist.,80,Giuseppe Tornatore,Philippe Noiret,Enzo Cannavale,Antonella Attili,Isa Danieli,230763,"11,990,401" +"https://m.media-amazon.com/images/M/MV5BZmY2NjUzNDQtNTgxNC00M2Q4LTljOWQtMjNjNDBjNWUxNmJlXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Hotaru no haka,1988,U,89 min,"Animation, Drama, War",8.5,A young boy and his little sister struggle to survive in Japan during World War II.,94,Isao Takahata,Tsutomu Tatsumi,Ayano Shiraishi,Akemi Yamaguchi,Yoshiko Shinohara,235231, +"https://m.media-amazon.com/images/M/MV5BZmU0M2Y1OGUtZjIxNi00ZjBkLTg1MjgtOWIyNThiZWIwYjRiXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Back to the Future,1985,U,116 min,"Adventure, Comedy, Sci-Fi",8.5,"Marty McFly, a 17-year-old high school student, is accidentally sent thirty years into the past in a time-traveling DeLorean invented by his close friend, the eccentric scientist Doc Brown.",87,Robert Zemeckis,Michael J. Fox,Christopher Lloyd,Lea Thompson,Crispin Glover,1058081,"210,609,762" +"https://m.media-amazon.com/images/M/MV5BZGI5MjBmYzYtMzJhZi00NGI1LTk3MzItYjBjMzcxM2U3MDdiXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Once Upon a Time in the West,1968,U,165 min,Western,8.5,A mysterious stranger with a harmonica joins forces with a notorious desperado to protect a beautiful widow from a ruthless assassin working for the railroad.,80,Sergio Leone,Henry Fonda,Charles Bronson,Claudia Cardinale,Jason Robards,302844,"5,321,508" +"https://m.media-amazon.com/images/M/MV5BNTQwNDM1YzItNDAxZC00NWY2LTk0M2UtNDIwNWI5OGUyNWUxXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Psycho,1960,A,109 min,"Horror, Mystery, Thriller",8.5,"A Phoenix secretary embezzles $40,000 from her employer's client, goes on the run, and checks into a remote motel run by a young man under the domination of his mother.",97,Alfred Hitchcock,Anthony Perkins,Janet Leigh,Vera Miles,John Gavin,604211,"32,000,000" +"https://m.media-amazon.com/images/M/MV5BY2IzZGY2YmEtYzljNS00NTM5LTgwMzUtMzM1NjQ4NGI0OTk0XkEyXkFqcGdeQXVyNDYyMDk5MTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Casablanca,1942,U,102 min,"Drama, Romance, War",8.5,A cynical expatriate American cafe owner struggles to decide whether or not to help his former lover and her fugitive husband escape the Nazis in French Morocco.,100,Michael Curtiz,Humphrey Bogart,Ingrid Bergman,Paul Henreid,Claude Rains,522093,"1,024,560" +"https://m.media-amazon.com/images/M/MV5BYjJiZjMzYzktNjU0NS00OTkxLWEwYzItYzdhYWJjN2QzMTRlL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Modern Times,1936,G,87 min,"Comedy, Drama, Family",8.5,The Tramp struggles to live in modern industrial society with the help of a young homeless woman.,96,Charles Chaplin,Charles Chaplin,Paulette Goddard,Henry Bergman,Tiny Sandford,217881,"163,245" +"https://m.media-amazon.com/images/M/MV5BY2I4MmM1N2EtM2YzOS00OWUzLTkzYzctNDc5NDg2N2IyODJmXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",City Lights,1931,G,87 min,"Comedy, Drama, Romance",8.5,"With the aid of a wealthy erratic tippler, a dewy-eyed tramp who has fallen in love with a sightless flower girl accumulates money to be able to help her medically.",99,Charles Chaplin,Charles Chaplin,Virginia Cherrill,Florence Lee,Harry Myers,167839,"19,181" +"https://m.media-amazon.com/images/M/MV5BMmExNzU2ZWMtYzUwYi00YmM2LTkxZTQtNmVhNjY0NTMyMWI2XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Capharnaüm,2018,A,126 min,Drama,8.4,"While serving a five-year sentence for a violent crime, a 12-year-old boy sues his parents for neglect.",75,Nadine Labaki,Zain Al Rafeea,Yordanos Shiferaw,Boluwatife Treasure Bankole,Kawsar Al Haddad,62635,"1,661,096" +"https://m.media-amazon.com/images/M/MV5BNWJhMDlmZGUtYzcxNS00NDRiLWIwNjktNDY1Mjg3ZjBkYzY0XkEyXkFqcGdeQXVyMTU4MjUwMjI@._V1_UY98_CR2,0,67,98_AL_.jpg",Ayla: The Daughter of War,2017,,125 min,"Biography, Drama, History",8.4,"In 1950, amid-st the ravages of the Korean War, Sergeant Süleyman stumbles upon a half-frozen little girl, with no parents and no help in sight. Frantic, scared and on the verge of death, ... See full summary »",,Can Ulkay,Erdem Can,Çetin Tekindor,Ismail Hacioglu,Kyung-jin Lee,34112, +"https://m.media-amazon.com/images/M/MV5BY2FiMTFmMzMtZDI2ZC00NDQyLWExYTUtOWNmZWM1ZDg5YjVjXkEyXkFqcGdeQXVyODIwMDI1NjM@._V1_UX67_CR0,0,67,98_AL_.jpg",Vikram Vedha,2017,UA,147 min,"Action, Crime, Drama",8.4,"Vikram, a no-nonsense police officer, accompanied by Simon, his partner, is on the hunt to capture Vedha, a smuggler and a murderer. Vedha tries to change Vikram's life, which leads to a conflict.",,Gayatri,Pushkar,Madhavan,Vijay Sethupathi,Shraddha Srinath,28401, +"https://m.media-amazon.com/images/M/MV5BODRmZDVmNzUtZDA4ZC00NjhkLWI2M2UtN2M0ZDIzNDcxYThjL2ltYWdlXkEyXkFqcGdeQXVyNTk0MzMzODA@._V1_UX67_CR0,0,67,98_AL_.jpg",Kimi no na wa.,2016,U,106 min,"Animation, Drama, Fantasy",8.4,"Two strangers find themselves linked in a bizarre way. When a connection forms, will distance be the only thing to keep them apart?",79,Makoto Shinkai,Ryûnosuke Kamiki,Mone Kamishiraishi,Ryô Narita,Aoi Yûki,194838,"5,017,246" +"https://m.media-amazon.com/images/M/MV5BMTQ4MzQzMzM2Nl5BMl5BanBnXkFtZTgwMTQ1NzU3MDI@._V1_UY98_CR1,0,67,98_AL_.jpg",Dangal,2016,U,161 min,"Action, Biography, Drama",8.4,Former wrestler Mahavir Singh Phogat and his two wrestler daughters struggle towards glory at the Commonwealth Games in the face of societal oppression.,,Nitesh Tiwari,Aamir Khan,Sakshi Tanwar,Fatima Sana Shaikh,Sanya Malhotra,156479,"12,391,761" +"https://m.media-amazon.com/images/M/MV5BMjMwNDkxMTgzOF5BMl5BanBnXkFtZTgwNTkwNTQ3NjM@._V1_UX67_CR0,0,67,98_AL_.jpg",Spider-Man: Into the Spider-Verse,2018,U,117 min,"Animation, Action, Adventure",8.4,"Teen Miles Morales becomes the Spider-Man of his universe, and must join with five spider-powered individuals from other dimensions to stop a threat for all realities.",87,Bob Persichetti,Peter Ramsey,Rodney Rothman,Shameik Moore,Jake Johnson,375110,"190,241,310" +"https://m.media-amazon.com/images/M/MV5BMTc5MDE2ODcwNV5BMl5BanBnXkFtZTgwMzI2NzQ2NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Avengers: Endgame,2019,UA,181 min,"Action, Adventure, Drama",8.4,"After the devastating events of Avengers: Infinity War (2018), the universe is in ruins. With the help of remaining allies, the Avengers assemble once more in order to reverse Thanos' actions and restore balance to the universe.",78,Anthony Russo,Joe Russo,Robert Downey Jr.,Chris Evans,Mark Ruffalo,809955,"858,373,000" +"https://m.media-amazon.com/images/M/MV5BMjMxNjY2MDU1OV5BMl5BanBnXkFtZTgwNzY1MTUwNTM@._V1_UX67_CR0,0,67,98_AL_.jpg",Avengers: Infinity War,2018,UA,149 min,"Action, Adventure, Sci-Fi",8.4,The Avengers and their allies must be willing to sacrifice all in an attempt to defeat the powerful Thanos before his blitz of devastation and ruin puts an end to the universe.,68,Anthony Russo,Joe Russo,Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo,834477,"678,815,482" +"https://m.media-amazon.com/images/M/MV5BYjQ5NjM0Y2YtNjZkNC00ZDhkLWJjMWItN2QyNzFkMDE3ZjAxXkEyXkFqcGdeQXVyODIxMzk5NjA@._V1_UY98_CR1,0,67,98_AL_.jpg",Coco,2017,U,105 min,"Animation, Adventure, Family",8.4,"Aspiring musician Miguel, confronted with his family's ancestral ban on music, enters the Land of the Dead to find his great-great-grandfather, a legendary singer.",81,Lee Unkrich,Adrian Molina,Anthony Gonzalez,Gael García Bernal,Benjamin Bratt,384171,"209,726,015" +"https://m.media-amazon.com/images/M/MV5BMjIyNTQ5NjQ1OV5BMl5BanBnXkFtZTcwODg1MDU4OA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Django Unchained,2012,A,165 min,"Drama, Western",8.4,"With the help of a German bounty hunter, a freed slave sets out to rescue his wife from a brutal Mississippi plantation owner.",81,Quentin Tarantino,Jamie Foxx,Christoph Waltz,Leonardo DiCaprio,Kerry Washington,1357682,"162,805,434" +"https://m.media-amazon.com/images/M/MV5BMTk4ODQzNDY3Ml5BMl5BanBnXkFtZTcwODA0NTM4Nw@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Dark Knight Rises,2012,UA,164 min,"Action, Adventure",8.4,"Eight years after the Joker's reign of anarchy, Batman, with the help of the enigmatic Catwoman, is forced from his exile to save Gotham City from the brutal guerrilla terrorist Bane.",78,Christopher Nolan,Christian Bale,Tom Hardy,Anne Hathaway,Gary Oldman,1516346,"448,139,099" +"https://m.media-amazon.com/images/M/MV5BNTkyOGVjMGEtNmQzZi00NzFlLTlhOWQtODYyMDc2ZGJmYzFhXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UY98_CR0,0,67,98_AL_.jpg",3 Idiots,2009,UA,170 min,"Comedy, Drama",8.4,"Two friends are searching for their long lost companion. They revisit their college days and recall the memories of their friend who inspired them to think differently, even as the rest of the world called them ""idiots"".",67,Rajkumar Hirani,Aamir Khan,Madhavan,Mona Singh,Sharman Joshi,344445,"6,532,908" +"https://m.media-amazon.com/images/M/MV5BMDhjZWViN2MtNzgxOS00NmI4LThiZDQtZDI3MzM4MDE4NTc0XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR1,0,67,98_AL_.jpg",Taare Zameen Par,2007,U,165 min,"Drama, Family",8.4,"An eight-year-old boy is thought to be a lazy trouble-maker, until the new art teacher has the patience and compassion to discover the real problem behind his struggles in school.",,Aamir Khan,Amole Gupte,Darsheel Safary,Aamir Khan,Tisca Chopra,168895,"1,223,869" +"https://m.media-amazon.com/images/M/MV5BMjExMTg5OTU0NF5BMl5BanBnXkFtZTcwMjMxMzMzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",WALL·E,2008,U,98 min,"Animation, Adventure, Family",8.4,"In the distant future, a small waste-collecting robot inadvertently embarks on a space journey that will ultimately decide the fate of mankind.",95,Andrew Stanton,Ben Burtt,Elissa Knight,Jeff Garlin,Fred Willard,999790,"223,808,164" +"https://m.media-amazon.com/images/M/MV5BOThkM2EzYmMtNDE3NS00NjlhLTg4YzktYTdhNzgyOWY3ZDYzXkEyXkFqcGdeQXVyNzQzNzQxNzI@._V1_UY98_CR1,0,67,98_AL_.jpg",The Lives of Others,2006,A,137 min,"Drama, Mystery, Thriller",8.4,"In 1984 East Berlin, an agent of the secret police, conducting surveillance on a writer and his lover, finds himself becoming increasingly absorbed by their lives.",89,Florian Henckel von Donnersmarck,Ulrich Mühe,Martina Gedeck,Sebastian Koch,Ulrich Tukur,358685,"11,286,112" +"https://m.media-amazon.com/images/M/MV5BMTI3NTQyMzU5M15BMl5BanBnXkFtZTcwMTM2MjgyMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Oldeuboi,2003,A,101 min,"Action, Drama, Mystery",8.4,"After being kidnapped and imprisoned for fifteen years, Oh Dae-Su is released, only to find that he must find his captor in five days.",77,Chan-wook Park,Choi Min-sik,Yoo Ji-Tae,Kang Hye-jeong,Kim Byeong-Ok,515451,"707,481" +"https://m.media-amazon.com/images/M/MV5BZTcyNjk1MjgtOWI3Mi00YzQwLWI5MTktMzY4ZmI2NDAyNzYzXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Memento,2000,UA,113 min,"Mystery, Thriller",8.4,A man with short-term memory loss attempts to track down his wife's murderer.,80,Christopher Nolan,Guy Pearce,Carrie-Anne Moss,Joe Pantoliano,Mark Boone Junior,1125712,"25,544,867" +"https://m.media-amazon.com/images/M/MV5BNGIzY2IzODQtNThmMi00ZDE4LWI5YzAtNzNlZTM1ZjYyYjUyXkEyXkFqcGdeQXVyODEzNjM5OTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Mononoke-hime,1997,U,134 min,"Animation, Action, Adventure",8.4,"On a journey to find the cure for a Tatarigami's curse, Ashitaka finds himself in the middle of a war between the forest gods and Tatara, a mining colony. In this quest he also meets San, the Mononoke Hime.",76,Hayao Miyazaki,Yôji Matsuda,Yuriko Ishida,Yûko Tanaka,Billy Crudup,343171,"2,375,308" +"https://m.media-amazon.com/images/M/MV5BMGFkNWI4MTMtNGQ0OC00MWVmLTk3MTktOGYxN2Y2YWVkZWE2XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Once Upon a Time in America,1984,A,229 min,"Crime, Drama",8.4,"A former Prohibition-era Jewish gangster returns to the Lower East Side of Manhattan over thirty years later, where he once again must confront the ghosts and regrets of his old life.",,Sergio Leone,Robert De Niro,James Woods,Elizabeth McGovern,Treat Williams,311365,"5,321,508" +"https://m.media-amazon.com/images/M/MV5BMjA0ODEzMTc1Nl5BMl5BanBnXkFtZTcwODM2MjAxNA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Raiders of the Lost Ark,1981,A,115 min,"Action, Adventure",8.4,"In 1936, archaeologist and adventurer Indiana Jones is hired by the U.S. government to find the Ark of the Covenant before Adolf Hitler's Nazis can obtain its awesome powers.",85,Steven Spielberg,Harrison Ford,Karen Allen,Paul Freeman,John Rhys-Davies,884112,"248,159,971" +"https://m.media-amazon.com/images/M/MV5BZWFlYmY2MGEtZjVkYS00YzU4LTg0YjQtYzY1ZGE3NTA5NGQxXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Shining,1980,A,146 min,"Drama, Horror",8.4,"A family heads to an isolated hotel for the winter where a sinister presence influences the father into violence, while his psychic son sees horrific forebodings from both past and future.",66,Stanley Kubrick,Jack Nicholson,Shelley Duvall,Danny Lloyd,Scatman Crothers,898237,"44,017,374" +"https://m.media-amazon.com/images/M/MV5BMDdhODg0MjYtYzBiOS00ZmI5LWEwZGYtZDEyNDU4MmQyNzFkXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Apocalypse Now,1979,R,147 min,"Drama, Mystery, War",8.4,A U.S. Army officer serving in Vietnam is tasked with assassinating a renegade Special Forces Colonel who sees himself as a god.,94,Francis Ford Coppola,Martin Sheen,Marlon Brando,Robert Duvall,Frederic Forrest,606398,"83,471,511" +"https://m.media-amazon.com/images/M/MV5BMmQ2MmU3NzktZjAxOC00ZDZhLTk4YzEtMDMyMzcxY2IwMDAyXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Alien,1979,R,117 min,"Horror, Sci-Fi",8.4,"After a space merchant vessel receives an unknown transmission as a distress call, one of the crew is attacked by a mysterious life form and they soon realize that its life cycle has merely begun.",89,Ridley Scott,Sigourney Weaver,Tom Skerritt,John Hurt,Veronica Cartwright,787806,"78,900,000" +"https://m.media-amazon.com/images/M/MV5BYmYzNmM2MDctZGY3Yi00NjRiLWIxZjctYjgzYTcxYTNhYTMyXkEyXkFqcGdeQXVyMjUxMTY3ODM@._V1_UY98_CR1,0,67,98_AL_.jpg",Anand,1971,U,122 min,"Drama, Musical",8.4,"The story of a terminally ill man who wishes to live life to the fullest before the inevitable occurs, as told by his best friend.",,Hrishikesh Mukherjee,Rajesh Khanna,Amitabh Bachchan,Sumita Sanyal,Ramesh Deo,30273, +"https://m.media-amazon.com/images/M/MV5BOTI4NTNhZDMtMWNkZi00MTRmLWJmZDQtMmJkMGVmZTEzODlhXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Tengoku to jigoku,1963,,143 min,"Crime, Drama, Mystery",8.4,An executive of a shoe company becomes a victim of extortion when his chauffeur's son is kidnapped and held for ransom.,,Akira Kurosawa,Toshirô Mifune,Yutaka Sada,Tatsuya Nakadai,Kyôko Kagawa,34357, +"https://m.media-amazon.com/images/M/MV5BZWI3ZTMxNjctMjdlNS00NmUwLWFiM2YtZDUyY2I3N2MxYTE0XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb,1964,A,95 min,Comedy,8.4,An insane general triggers a path to nuclear holocaust that a War Room full of politicians and generals frantically tries to stop.,97,Stanley Kubrick,Peter Sellers,George C. Scott,Sterling Hayden,Keenan Wynn,450474,"275,902" +"https://m.media-amazon.com/images/M/MV5BNDQwODU5OWYtNDcyNi00MDQ1LThiOGMtZDkwNWJiM2Y3MDg0XkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Witness for the Prosecution,1957,U,116 min,"Crime, Drama, Mystery",8.4,A veteran British barrister must defend his client in a murder trial that has surprise after surprise.,,Billy Wilder,Tyrone Power,Marlene Dietrich,Charles Laughton,Elsa Lanchester,108862,"8,175,000" +"https://m.media-amazon.com/images/M/MV5BNjViMmRkOTEtM2ViOS00ODg0LWJhYWEtNTBlOGQxNDczOGY3XkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UY98_CR2,0,67,98_AL_.jpg",Paths of Glory,1957,A,88 min,"Drama, War",8.4,"After refusing to attack an enemy position, a general accuses the soldiers of cowardice and their commanding officer must defend them.",90,Stanley Kubrick,Kirk Douglas,Ralph Meeker,Adolphe Menjou,George Macready,178092, +"https://m.media-amazon.com/images/M/MV5BNGUxYWM3M2MtMGM3Mi00ZmRiLWE0NGQtZjE5ODI2OTJhNTU0XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Rear Window,1954,U,112 min,"Mystery, Thriller",8.4,A wheelchair-bound photographer spies on his neighbors from his apartment window and becomes convinced one of them has committed murder.,100,Alfred Hitchcock,James Stewart,Grace Kelly,Wendell Corey,Thelma Ritter,444074,"36,764,313" +"https://m.media-amazon.com/images/M/MV5BMTU0NTkyNzYwMF5BMl5BanBnXkFtZTgwMDU0NDk5MTI@._V1_UX67_CR0,0,67,98_AL_.jpg",Sunset Blvd.,1950,Passed,110 min,"Drama, Film-Noir",8.4,A screenwriter develops a dangerous relationship with a faded film star determined to make a triumphant return.,,Billy Wilder,William Holden,Gloria Swanson,Erich von Stroheim,Nancy Olson,201632, +"https://m.media-amazon.com/images/M/MV5BMmExYWJjNTktNGUyZS00ODhmLTkxYzAtNWIzOGEyMGNiMmUwXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Great Dictator,1940,Passed,125 min,"Comedy, Drama, War",8.4,Dictator Adenoid Hynkel tries to expand his empire while a poor Jewish barber tries to avoid persecution from Hynkel's regime.,,Charles Chaplin,Charles Chaplin,Paulette Goddard,Jack Oakie,Reginald Gardiner,203150,"288,475" +"https://m.media-amazon.com/images/M/MV5BOTdmNTFjNDEtNzg0My00ZjkxLTg1ZDAtZTdkMDc2ZmFiNWQ1XkEyXkFqcGdeQXVyNTAzNzgwNTg@._V1_UX67_CR0,0,67,98_AL_.jpg",1917,2019,R,119 min,"Drama, Thriller, War",8.3,"April 6th, 1917. As a regiment assembles to wage war deep in enemy territory, two soldiers are assigned to race against time and deliver a message that will stop 1,600 men from walking straight into a deadly trap.",78,Sam Mendes,Dean-Charles Chapman,George MacKay,Daniel Mays,Colin Firth,425844,"159,227,644" +"https://m.media-amazon.com/images/M/MV5BYmQxNmU4ZjgtYzE5Mi00ZDlhLTlhOTctMzJkNjk2ZGUyZGEwXkEyXkFqcGdeQXVyMzgxMDA0Nzk@._V1_UY98_CR1,0,67,98_AL_.jpg",Tumbbad,2018,A,104 min,"Drama, Fantasy, Horror",8.3,A mythological story about a goddess who created the entire universe. The plot revolves around the consequences when humans build a temple for her first-born.,,Rahi Anil Barve,Anand Gandhi,Adesh Prasad,Sohum Shah,Jyoti Malshe,27793, +"https://m.media-amazon.com/images/M/MV5BZWZhMjhhZmYtOTIzOC00MGYzLWI1OGYtM2ZkN2IxNTI4ZWI3XkEyXkFqcGdeQXVyNDAzNDk0MTQ@._V1_UY98_CR0,0,67,98_AL_.jpg",Andhadhun,2018,UA,139 min,"Crime, Drama, Music",8.3,"A series of mysterious events change the life of a blind pianist, who must now report a crime that he should technically know nothing of.",,Sriram Raghavan,Ayushmann Khurrana,Tabu,Radhika Apte,Anil Dhawan,71875,"1,373,943" +"https://m.media-amazon.com/images/M/MV5BYmY3MzYwMGUtOWMxYS00OGVhLWFjNmUtYzlkNGVmY2ZkMjA3XkEyXkFqcGdeQXVyMTExNDQ2MTI@._V1_UY98_CR4,0,67,98_AL_.jpg",Drishyam,2013,U,160 min,"Crime, Drama, Thriller",8.3,A man goes to extreme lengths to save his family from punishment after the family commits an accidental crime.,,Jeethu Joseph,Mohanlal,Meena,Asha Sharath,Ansiba,30722, +"https://m.media-amazon.com/images/M/MV5BMTg2NDg3ODg4NF5BMl5BanBnXkFtZTcwNzk3NTc3Nw@@._V1_UY98_CR1,0,67,98_AL_.jpg",Jagten,2012,R,115 min,Drama,8.3,"A teacher lives a lonely life, all the while struggling over his son's custody. His life slowly gets better as he finds love and receives good news from his son, but his new luck is about to be brutally shattered by an innocent little lie.",77,Thomas Vinterberg,Mads Mikkelsen,Thomas Bo Larsen,Annika Wedderkopp,Lasse Fogelstrøm,281623,"687,185" +"https://m.media-amazon.com/images/M/MV5BN2JmMjViMjMtZTM5Mi00ZGZkLTk5YzctZDg5MjFjZDE4NjNkXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Jodaeiye Nader az Simin,2011,PG-13,123 min,Drama,8.3,A married couple are faced with a difficult decision - to improve the life of their child by moving to another country or to stay in Iran and look after a deteriorating parent who has Alzheimer's disease.,95,Asghar Farhadi,Payman Maadi,Leila Hatami,Sareh Bayat,Shahab Hosseini,220002,"7,098,492" +"https://m.media-amazon.com/images/M/MV5BMWE3MGYzZjktY2Q5Mi00Y2NiLWIyYWUtMmIyNzA3YmZlMGFhXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Incendies,2010,R,131 min,"Drama, Mystery, War",8.3,Twins journey to the Middle East to discover their family history and fulfill their mother's last wishes.,80,Denis Villeneuve,Lubna Azabal,Mélissa Désormeaux-Poulin,Maxim Gaudette,Mustafa Kamel,150023,"6,857,096" +"https://m.media-amazon.com/images/M/MV5BOGE3N2QxN2YtM2ZlNS00MWIyLWE1NDAtYWFlN2FiYjY1MjczXkEyXkFqcGdeQXVyOTUwNzc0ODc@._V1_UY98_CR1,0,67,98_AL_.jpg",Miracle in cell NO.7,2019,TV-14,132 min,Drama,8.3,A story of love between a mentally-ill father who was wrongly accused of murder and his lovely six years old daughter. The prison would be their home. Based on the 2013 Korean movie 7-beon-bang-ui seon-mul (2013).,,Mehmet Ada Öztekin,Aras Bulut Iynemli,Nisa Sofiya Aksongur,Deniz Baysal,Celile Toyon Uysal,33935, +"https://m.media-amazon.com/images/M/MV5BNjAzMzEwYzctNjc1MC00Nzg5LWFmMGItMTgzYmMyNTY2OTQ4XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UY98_CR0,0,67,98_AL_.jpg",Babam ve Oglum,2005,,112 min,"Drama, Family",8.3,The family of a left-wing journalist is torn apart after the military coup of Turkey in 1980.,,Çagan Irmak,Çetin Tekindor,Fikret Kuskan,Hümeyra,Ege Tanman,78925, +"https://m.media-amazon.com/images/M/MV5BOTJiNDEzOWYtMTVjOC00ZjlmLWE0NGMtZmE1OWVmZDQ2OWJhXkEyXkFqcGdeQXVyNTIzOTk5ODM@._V1_UX67_CR0,0,67,98_AL_.jpg",Inglourious Basterds,2009,A,153 min,"Adventure, Drama, War",8.3,"In Nazi-occupied France during World War II, a plan to assassinate Nazi leaders by a group of Jewish U.S. soldiers coincides with a theatre owner's vengeful plans for the same.",69,Quentin Tarantino,Brad Pitt,Diane Kruger,Eli Roth,Mélanie Laurent,1267869,"120,540,719" +"https://m.media-amazon.com/images/M/MV5BMTY4NzcwODg3Nl5BMl5BanBnXkFtZTcwNTEwOTMyMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Eternal Sunshine of the Spotless Mind,2004,UA,108 min,"Drama, Romance, Sci-Fi",8.3,"When their relationship turns sour, a couple undergoes a medical procedure to have each other erased from their memories.",89,Michel Gondry,Jim Carrey,Kate Winslet,Tom Wilkinson,Gerry Robert Byrne,911664,"34,400,301" +"https://m.media-amazon.com/images/M/MV5BNDg4NjM1YjMtYmNhZC00MjM0LWFiZmYtNGY1YjA3MzZmODc5XkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Amélie,2001,U,122 min,"Comedy, Romance",8.3,"Amélie is an innocent and naive girl in Paris with her own sense of justice. She decides to help those around her and, along the way, discovers love.",69,Jean-Pierre Jeunet,Audrey Tautou,Mathieu Kassovitz,Rufus,Lorella Cravotta,703810,"33,225,499" +"https://m.media-amazon.com/images/M/MV5BMTA2NDYxOGYtYjU1Mi00Y2QzLTgxMTQtMWI1MGI0ZGQ5MmU4XkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UY98_CR0,0,67,98_AL_.jpg",Snatch,2000,UA,104 min,"Comedy, Crime",8.3,"Unscrupulous boxing promoters, violent bookmakers, a Russian gangster, incompetent amateur robbers and supposedly Jewish jewelers fight to track down a priceless stolen diamond.",55,Guy Ritchie,Jason Statham,Brad Pitt,Benicio Del Toro,Dennis Farina,782001,"30,328,156" +"https://m.media-amazon.com/images/M/MV5BOTdiNzJlOWUtNWMwNS00NmFlLWI0YTEtZmI3YjIzZWUyY2Y3XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Requiem for a Dream,2000,A,102 min,Drama,8.3,The drug-induced utopias of four Coney Island people are shattered when their addictions run deep.,68,Darren Aronofsky,Ellen Burstyn,Jared Leto,Jennifer Connelly,Marlon Wayans,766870,"3,635,482" +"https://m.media-amazon.com/images/M/MV5BNTBmZWJkNjctNDhiNC00MGE2LWEwOTctZTk5OGVhMWMyNmVhXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",American Beauty,1999,UA,122 min,Drama,8.3,A sexually frustrated suburban father has a mid-life crisis after becoming infatuated with his daughter's best friend.,84,Sam Mendes,Kevin Spacey,Annette Bening,Thora Birch,Wes Bentley,1069738,"130,096,601" +"https://m.media-amazon.com/images/M/MV5BOTI0MzcxMTYtZDVkMy00NjY1LTgyMTYtZmUxN2M3NmQ2NWJhXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Good Will Hunting,1997,U,126 min,"Drama, Romance",8.3,"Will Hunting, a janitor at M.I.T., has a gift for mathematics, but needs help from a psychologist to find direction in his life.",70,Gus Van Sant,Robin Williams,Matt Damon,Ben Affleck,Stellan Skarsgård,861606,"138,433,435" +"https://m.media-amazon.com/images/M/MV5BZTYwZWQ4ZTQtZWU0MS00N2YwLWEzMDItZWFkZWY0MWVjODVhXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Bacheha-Ye aseman,1997,PG,89 min,"Drama, Family, Sport",8.3,"After a boy loses his sister's pair of shoes, he goes on a series of adventures in order to find them. When he can't, he tries a new way to ""win"" a new pair.",77,Majid Majidi,Mohammad Amir Naji,Amir Farrokh Hashemian,Bahare Seddiqi,Nafise Jafar-Mohammadi,65341,"933,933" +"https://m.media-amazon.com/images/M/MV5BMDU2ZWJlMjktMTRhMy00ZTA5LWEzNDgtYmNmZTEwZTViZWJkXkEyXkFqcGdeQXVyNDQ2OTk4MzI@._V1_UX67_CR0,0,67,98_AL_.jpg",Toy Story,1995,U,81 min,"Animation, Adventure, Comedy",8.3,A cowboy doll is profoundly threatened and jealous when a new spaceman figure supplants him as top toy in a boy's room.,95,John Lasseter,Tom Hanks,Tim Allen,Don Rickles,Jim Varney,887429,"191,796,233" +"https://m.media-amazon.com/images/M/MV5BMzkzMmU0YTYtOWM3My00YzBmLWI0YzctOGYyNTkwMWE5MTJkXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Braveheart,1995,A,178 min,"Biography, Drama, History",8.3,Scottish warrior William Wallace leads his countrymen in a rebellion to free his homeland from the tyranny of King Edward I of England.,68,Mel Gibson,Mel Gibson,Sophie Marceau,Patrick McGoohan,Angus Macfadyen,959181,"75,600,000" +"https://m.media-amazon.com/images/M/MV5BZmExNmEwYWItYmQzOS00YjA5LTk2MjktZjEyZDE1Y2QxNjA1XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Reservoir Dogs,1992,R,99 min,"Crime, Drama, Thriller",8.3,"When a simple jewelry heist goes horribly wrong, the surviving criminals begin to suspect that one of them is a police informant.",79,Quentin Tarantino,Harvey Keitel,Tim Roth,Michael Madsen,Chris Penn,918562,"2,832,029" +"https://m.media-amazon.com/images/M/MV5BNzkxODk0NjEtYjc4Mi00ZDI0LTgyYjEtYzc1NDkxY2YzYTgyXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Full Metal Jacket,1987,UA,116 min,"Drama, War",8.3,A pragmatic U.S. Marine observes the dehumanizing effects the Vietnam War has on his fellow recruits from their brutal boot camp training to the bloody street fighting in Hue.,76,Stanley Kubrick,Matthew Modine,R. Lee Ermey,Vincent D'Onofrio,Adam Baldwin,675146,"46,357,676" +"https://m.media-amazon.com/images/M/MV5BODM4Njg0NTAtYjI5Ny00ZjAxLTkwNmItZTMxMWU5M2U3M2RjXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Idi i smotri,1985,A,142 min,"Drama, Thriller, War",8.3,"After finding an old rifle, a young boy joins the Soviet resistance movement against ruthless German forces and experiences the horrors of World War II.",,Elem Klimov,Aleksey Kravchenko,Olga Mironova,Liubomiras Laucevicius,Vladas Bagdonas,59056, +"https://m.media-amazon.com/images/M/MV5BZGU2OGY5ZTYtMWNhYy00NjZiLWI0NjUtZmNhY2JhNDRmODU3XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Aliens,1986,U,137 min,"Action, Adventure, Sci-Fi",8.3,"Fifty-seven years after surviving an apocalyptic attack aboard her space vessel by merciless space creatures, Officer Ripley awakens from hyper-sleep and tries to warn anyone who will listen about the predators.",84,James Cameron,Sigourney Weaver,Michael Biehn,Carrie Henn,Paul Reiser,652719,"85,160,248" +"https://m.media-amazon.com/images/M/MV5BNWJlNzUzNGMtYTAwMS00ZjI2LWFmNWQtODcxNWUxODA5YmU1XkEyXkFqcGdeQXVyNTIzOTk5ODM@._V1_UX67_CR0,0,67,98_AL_.jpg",Amadeus,1984,R,160 min,"Biography, Drama, History",8.3,"The life, success and troubles of Wolfgang Amadeus Mozart, as told by Antonio Salieri, the contemporaneous composer who was insanely jealous of Mozart's talent and claimed to have murdered him.",88,Milos Forman,F. Murray Abraham,Tom Hulce,Elizabeth Berridge,Roy Dotrice,369007,"51,973,029" +"https://m.media-amazon.com/images/M/MV5BNjdjNGQ4NDEtNTEwYS00MTgxLTliYzQtYzE2ZDRiZjFhZmNlXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Scarface,1983,A,170 min,"Crime, Drama",8.3,"In 1980 Miami, a determined Cuban immigrant takes over a drug cartel and succumbs to greed.",65,Brian De Palma,Al Pacino,Michelle Pfeiffer,Steven Bauer,Mary Elizabeth Mastrantonio,740911,"45,598,982" +"https://m.media-amazon.com/images/M/MV5BOWZlMjFiYzgtMTUzNC00Y2IzLTk1NTMtZmNhMTczNTk0ODk1XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Star Wars: Episode VI - Return of the Jedi,1983,U,131 min,"Action, Adventure, Fantasy",8.3,"After a daring mission to rescue Han Solo from Jabba the Hutt, the Rebels dispatch to Endor to destroy the second Death Star. Meanwhile, Luke struggles to help Darth Vader back from the dark side without falling into the Emperor's trap.",58,Richard Marquand,Mark Hamill,Harrison Ford,Carrie Fisher,Billy Dee Williams,950470,"309,125,409" +"https://m.media-amazon.com/images/M/MV5BOGZhZDIzNWMtNjkxMS00MDQ1LThkMTYtZWQzYWU3MWMxMGU5XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Das Boot,1981,R,149 min,"Adventure, Drama, Thriller",8.3,"The claustrophobic world of a WWII German U-boat; boredom, filth and sheer terror.",86,Wolfgang Petersen,Jürgen Prochnow,Herbert Grönemeyer,Klaus Wennemann,Hubertus Bengsch,231855,"11,487,676" +"https://m.media-amazon.com/images/M/MV5BM2M1MmVhNDgtNmI0YS00ZDNmLTkyNjctNTJiYTQ2N2NmYzc2XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Taxi Driver,1976,A,114 min,"Crime, Drama",8.3,"A mentally unstable veteran works as a nighttime taxi driver in New York City, where the perceived decadence and sleaze fuels his urge for violent action by attempting to liberate a presidential campaign worker and an underage prostitute.",94,Martin Scorsese,Robert De Niro,Jodie Foster,Cybill Shepherd,Albert Brooks,724636,"28,262,574" +"https://m.media-amazon.com/images/M/MV5BNGU3NjQ4YTMtZGJjOS00YTQ3LThmNmItMTI5MDE2ODI3NzY3XkEyXkFqcGdeQXVyMjUzOTY1NTc@._V1_UX67_CR0,0,67,98_AL_.jpg",The Sting,1973,U,129 min,"Comedy, Crime, Drama",8.3,Two grifters team up to pull off the ultimate con.,83,George Roy Hill,Paul Newman,Robert Redford,Robert Shaw,Charles Durning,241513,"159,600,000" +"https://m.media-amazon.com/images/M/MV5BMTY3MjM1Mzc4N15BMl5BanBnXkFtZTgwODM0NzAxMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",A Clockwork Orange,1971,A,136 min,"Crime, Drama, Sci-Fi",8.3,"In the future, a sadistic gang leader is imprisoned and volunteers for a conduct-aversion experiment, but it doesn't go as planned.",77,Stanley Kubrick,Malcolm McDowell,Patrick Magee,Michael Bates,Warren Clarke,757904,"6,207,725" +"https://m.media-amazon.com/images/M/MV5BMmNlYzRiNDctZWNhMi00MzI4LThkZTctMTUzMmZkMmFmNThmXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",2001: A Space Odyssey,1968,U,149 min,"Adventure, Sci-Fi",8.3,"After discovering a mysterious artifact buried beneath the Lunar surface, mankind sets off on a quest to find its origins with help from intelligent supercomputer H.A.L. 9000.",84,Stanley Kubrick,Keir Dullea,Gary Lockwood,William Sylvester,Daniel Richter,603517,"56,954,992" +"https://m.media-amazon.com/images/M/MV5BNWM1NmYyM2ItMTFhNy00NDU0LThlYWUtYjQyYTJmOTY0ZmM0XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Per qualche dollaro in più,1965,U,132 min,Western,8.3,Two bounty hunters with the same intentions team up to track down a Western outlaw.,74,Sergio Leone,Clint Eastwood,Lee Van Cleef,Gian Maria Volontè,Mara Krupp,232772,"15,000,000" +"https://m.media-amazon.com/images/M/MV5BYWY5ZjhjNGYtZmI2Ny00ODM0LWFkNzgtZmI1YzA2N2MxMzA0XkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_UY98_CR0,0,67,98_AL_.jpg",Lawrence of Arabia,1962,U,228 min,"Adventure, Biography, Drama",8.3,"The story of T.E. Lawrence, the English officer who successfully united and led the diverse, often warring, Arab tribes during World War I in order to fight the Turks.",100,David Lean,Peter O'Toole,Alec Guinness,Anthony Quinn,Jack Hawkins,268085,"44,824,144" +"https://m.media-amazon.com/images/M/MV5BNzkwODFjNzItMmMwNi00MTU5LWE2MzktM2M4ZDczZGM1MmViXkEyXkFqcGdeQXVyNDY2MTk1ODk@._V1_UX67_CR0,0,67,98_AL_.jpg",The Apartment,1960,U,125 min,"Comedy, Drama, Romance",8.3,"A man tries to rise in his company by letting its executives use his apartment for trysts, but complications and a romance of his own ensue.",94,Billy Wilder,Jack Lemmon,Shirley MacLaine,Fred MacMurray,Ray Walston,164363,"18,600,000" +"https://m.media-amazon.com/images/M/MV5BZDA3NDExMTUtMDlhOC00MmQ5LWExZGUtYmI1NGVlZWI4OWNiXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",North by Northwest,1959,U,136 min,"Adventure, Mystery, Thriller",8.3,A New York City advertising executive goes on the run after being mistaken for a government agent by a group of foreign spies.,98,Alfred Hitchcock,Cary Grant,Eva Marie Saint,James Mason,Jessie Royce Landis,299198,"13,275,000" +"https://m.media-amazon.com/images/M/MV5BYTE4ODEwZDUtNDFjOC00NjAxLWEzYTQtYTI1NGVmZmFlNjdiL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Vertigo,1958,A,128 min,"Mystery, Romance, Thriller",8.3,A former police detective juggles wrestling with his personal demons and becoming obsessed with a hauntingly beautiful woman.,100,Alfred Hitchcock,James Stewart,Kim Novak,Barbara Bel Geddes,Tom Helmore,364368,"3,200,000" +"https://m.media-amazon.com/images/M/MV5BZDRjNGViMjQtOThlMi00MTA3LThkYzQtNzJkYjBkMGE0YzE1XkEyXkFqcGdeQXVyNDYyMDk5MTU@._V1_UY98_CR0,0,67,98_AL_.jpg",Singin' in the Rain,1952,G,103 min,"Comedy, Musical, Romance",8.3,A silent film production company and cast make a difficult transition to sound.,99,Stanley Donen,Gene Kelly,Gene Kelly,Donald O'Connor,Debbie Reynolds,218957,"8,819,028" +"https://m.media-amazon.com/images/M/MV5BZmM0NGY3Y2MtMTA1YS00YmQzLTk2YTctYWFhMDkzMDRjZWQzXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Ikiru,1952,,143 min,Drama,8.3,A bureaucrat tries to find a meaning in his life after he discovers he has terminal cancer.,,Akira Kurosawa,Takashi Shimura,Nobuo Kaneko,Shin'ichi Himori,Haruo Tanaka,68463,"55,240" +"https://m.media-amazon.com/images/M/MV5BNmI1ODdjODctMDlmMC00ZWViLWI5MzYtYzRhNDdjYmM3MzFjXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR0,0,67,98_AL_.jpg",Ladri di biciclette,1948,,89 min,Drama,8.3,"In post-war Italy, a working-class man's bicycle is stolen. He and his son set out to find it.",,Vittorio De Sica,Lamberto Maggiorani,Enzo Staiola,Lianella Carell,Elena Altieri,146427,"332,930" +"https://m.media-amazon.com/images/M/MV5BOTdlNjgyZGUtOTczYi00MDdhLTljZmMtYTEwZmRiOWFkYjRhXkEyXkFqcGdeQXVyNDY2MTk1ODk@._V1_UX67_CR0,0,67,98_AL_.jpg",Double Indemnity,1944,Passed,107 min,"Crime, Drama, Film-Noir",8.3,An insurance representative lets himself be talked by a seductive housewife into a murder/insurance fraud scheme that arouses the suspicion of an insurance investigator.,95,Billy Wilder,Fred MacMurray,Barbara Stanwyck,Edward G. Robinson,Byron Barr,143525,"5,720,000" +"https://m.media-amazon.com/images/M/MV5BYjBiOTYxZWItMzdiZi00NjlkLWIzZTYtYmFhZjhiMTljOTdkXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Citizen Kane,1941,UA,119 min,"Drama, Mystery",8.3,"Following the death of publishing tycoon Charles Foster Kane, reporters scramble to uncover the meaning of his final utterance; 'Rosebud'.",100,Orson Welles,Orson Welles,Joseph Cotten,Dorothy Comingore,Agnes Moorehead,403351,"1,585,634" +"https://m.media-amazon.com/images/M/MV5BODA4ODk3OTEzMF5BMl5BanBnXkFtZTgwMTQ2ODMwMzE@._V1_UX67_CR0,0,67,98_AL_.jpg",M - Eine Stadt sucht einen Mörder,1931,Passed,117 min,"Crime, Mystery, Thriller",8.3,"When the police in a German city are unable to catch a child-murderer, other criminals join in the manhunt.",,Fritz Lang,Peter Lorre,Ellen Widmann,Inge Landgut,Otto Wernicke,143434,"28,877" +"https://m.media-amazon.com/images/M/MV5BMTg5YWIyMWUtZDY5My00Zjc1LTljOTctYmI0MWRmY2M2NmRkXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Metropolis,1927,,153 min,"Drama, Sci-Fi",8.3,"In a futuristic city sharply divided between the working class and the city planners, the son of the city's mastermind falls in love with a working-class prophet who predicts the coming of a savior to mediate their differences.",98,Fritz Lang,Brigitte Helm,Alfred Abel,Gustav Fröhlich,Rudolf Klein-Rogge,159992,"1,236,166" +"https://m.media-amazon.com/images/M/MV5BZjhhMThhNDItNTY2MC00MmU1LTliNDEtNDdhZjdlNTY5ZDQ1XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Kid,1921,Passed,68 min,"Comedy, Drama, Family",8.3,"The Tramp cares for an abandoned child, but events put that relationship in jeopardy.",,Charles Chaplin,Charles Chaplin,Edna Purviance,Jackie Coogan,Carl Miller,113314,"5,450,000" +"https://m.media-amazon.com/images/M/MV5BYjg2ZDI2YTYtN2EwYi00YWI5LTgyMWQtMWFkYmE3NmJkOGVhXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR0,0,67,98_AL_.jpg",Chhichhore,2019,UA,143 min,"Comedy, Drama",8.2,"A tragic incident forces Anirudh, a middle-aged man, to take a trip down memory lane and reminisce his college days along with his friends, who were labelled as losers.",,Nitesh Tiwari,Sushant Singh Rajput,Shraddha Kapoor,Varun Sharma,Prateik,33893,"898,575" +"https://m.media-amazon.com/images/M/MV5BMWU4ZjNlNTQtOGE2MS00NDI0LWFlYjMtMmY3ZWVkMjJkNGRmXkEyXkFqcGdeQXVyNjE1OTQ0NjA@._V1_UY98_CR0,0,67,98_AL_.jpg",Uri: The Surgical Strike,2018,UA,138 min,"Action, Drama, War",8.2,"Indian army special forces execute a covert operation, avenging the killing of fellow army men at their base by a terrorist group.",,Aditya Dhar,Vicky Kaushal,Paresh Rawal,Mohit Raina,Yami Gautam,43444,"4,186,168" +"https://m.media-amazon.com/images/M/MV5BZDNlNzBjMGUtYTA0Yy00OTI2LWJmZjMtODliYmUyYTI0OGFmXkEyXkFqcGdeQXVyODIwMDI1NjM@._V1_UX67_CR0,0,67,98_AL_.jpg",K.G.F: Chapter 1,2018,UA,156 min,"Action, Drama",8.2,"In the 1970s, a fierce rebel rises against brutal oppression and becomes the symbol of hope to legions of downtrodden people.",,Prashanth Neel,Yash,Srinidhi Shetty,Ramachandra Raju,Archana Jois,36680, +"https://m.media-amazon.com/images/M/MV5BYzIzYmJlYTYtNGNiYy00N2EwLTk4ZjItMGYyZTJiOTVkM2RlXkEyXkFqcGdeQXVyODY1NDk1NjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Green Book,2018,UA,130 min,"Biography, Comedy, Drama",8.2,A working-class Italian-American bouncer becomes the driver of an African-American classical pianist on a tour of venues through the 1960s American South.,69,Peter Farrelly,Viggo Mortensen,Mahershala Ali,Linda Cardellini,Sebastian Maniscalco,377884,"85,080,171" +"https://m.media-amazon.com/images/M/MV5BMjI0ODcxNzM1N15BMl5BanBnXkFtZTgwMzIwMTEwNDI@._V1_UX67_CR0,0,67,98_AL_.jpg","Three Billboards Outside Ebbing, Missouri",2017,A,115 min,"Comedy, Crime, Drama",8.2,A mother personally challenges the local authorities to solve her daughter's murder when they fail to catch the culprit.,88,Martin McDonagh,Frances McDormand,Woody Harrelson,Sam Rockwell,Caleb Landry Jones,432610,"54,513,740" +"https://m.media-amazon.com/images/M/MV5BMTYzODg0Mjc4M15BMl5BanBnXkFtZTgwNzY4Mzc3NjE@._V1_UY98_CR2,0,67,98_AL_.jpg",Talvar,2015,UA,132 min,"Crime, Drama, Mystery",8.2,An experienced investigator confronts several conflicting theories about the perpetrators of a violent double homicide.,,Meghna Gulzar,Irrfan Khan,Konkona Sen Sharma,Neeraj Kabi,Sohum Shah,31142,"342,370" +"https://m.media-amazon.com/images/M/MV5BOGNlNmRkMjctNDgxMC00NzFhLWIzY2YtZDk3ZDE0NWZhZDBlXkEyXkFqcGdeQXVyODIwMDI1NjM@._V1_UX67_CR0,0,67,98_AL_.jpg",Baahubali 2: The Conclusion,2017,UA,167 min,"Action, Drama",8.2,"When Shiva, the son of Bahubali, learns about his heritage, he begins to look for answers. His story is juxtaposed with past events that unfolded in the Mahishmati Kingdom.",,S.S. Rajamouli,Prabhas,Rana Daggubati,Anushka Shetty,Tamannaah Bhatia,75348,"20,186,659" +"https://m.media-amazon.com/images/M/MV5BMWYwOThjM2ItZGYxNy00NTQwLWFlZWEtM2MzM2Q5MmY3NDU5XkEyXkFqcGdeQXVyMTkxNjUyNQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Klaus,2019,PG,96 min,"Animation, Adventure, Comedy",8.2,"A simple act of kindness always sparks another, even in a frozen, faraway place. When Smeerensburg's new postman, Jesper, befriends toymaker Klaus, their gifts melt an age-old feud and deliver a sleigh full of holiday traditions.",65,Sergio Pablos,Carlos Martínez López,Jason Schwartzman,J.K. Simmons,Rashida Jones,104761, +"https://m.media-amazon.com/images/M/MV5BYmJhZmJlYTItZmZlNy00MGY0LTg0ZGMtNWFkYWU5NTA1YTNhXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR0,0,67,98_AL_.jpg",Drishyam,2015,UA,163 min,"Crime, Drama, Mystery",8.2,"Desperate measures are taken by a man who tries to save his family from the dark side of the law, after they commit an unexpected crime.",,Nishikant Kamat,Ajay Devgn,Shriya Saran,Tabu,Rajat Kapoor,70367,"739,478" +"https://m.media-amazon.com/images/M/MV5BNWYyOWRlOWItZWM5MS00ZjJkLWI0MTUtYTE3NTI5MDAwYjgyXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR0,0,67,98_AL_.jpg",Queen,2013,UA,146 min,"Adventure, Comedy, Drama",8.2,A Delhi girl from a traditional family sets out on a solo honeymoon after her marriage gets cancelled.,,Vikas Bahl,Kangana Ranaut,Rajkummar Rao,Lisa Haydon,Jeffrey Ho,60701,"1,429,534" +"https://m.media-amazon.com/images/M/MV5BMTgwNzA3MDQzOV5BMl5BanBnXkFtZTgwNTE5MDE5NDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Mandariinid,2013,,87 min,"Drama, War",8.2,"In 1992, war rages in Abkhazia, a breakaway region of Georgia. An Estonian man Ivo has decided to stay behind and harvest his crops of tangerines. In a bloody conflict at his door, a wounded man is left behind, and Ivo takes him in.",73,Zaza Urushadze,Lembit Ulfsak,Elmo Nüganen,Giorgi Nakashidze,Misha Meskhi,40382,"144,501" +"https://m.media-amazon.com/images/M/MV5BMTY1Nzg4MjcwN15BMl5BanBnXkFtZTcwOTc1NTk1OQ@@._V1_UY98_CR0,0,67,98_AL_.jpg",Bhaag Milkha Bhaag,2013,U,186 min,"Biography, Drama, Sport",8.2,The truth behind the ascension of Milkha Singh who was scarred because of the India-Pakistan partition.,,Rakeysh Omprakash Mehra,Farhan Akhtar,Sonam Kapoor,Pawan Malhotra,Art Malik,61137,"1,626,289" +"https://m.media-amazon.com/images/M/MV5BMTc5NjY4MjUwNF5BMl5BanBnXkFtZTgwODM3NzM5MzE@._V1_UX67_CR0,0,67,98_AL_.jpg",Gangs of Wasseypur,2012,A,321 min,"Action, Comedy, Crime",8.2,"A clash between Sultan and Shahid Khan leads to the expulsion of Khan from Wasseypur, and ignites a deadly blood feud spanning three generations.",89,Anurag Kashyap,Manoj Bajpayee,Richa Chadha,Nawazuddin Siddiqui,Tigmanshu Dhulia,82365, +"https://m.media-amazon.com/images/M/MV5BNzgxMzExMzUwNV5BMl5BanBnXkFtZTcwMDc2MjUwNA@@._V1_UY98_CR0,0,67,98_AL_.jpg",Udaan,2010,UA,134 min,Drama,8.2,"Expelled from his school, a 16-year old boy returns home to his abusive and oppressive father.",,Vikramaditya Motwane,Rajat Barmecha,Ronit Roy,Manjot Singh,Ram Kapoor,42341,"7,461" +"https://m.media-amazon.com/images/M/MV5BNTgwODM5OTMzN15BMl5BanBnXkFtZTcwMTA3NzI1Nw@@._V1_UY98_CR0,0,67,98_AL_.jpg",Paan Singh Tomar,2012,UA,135 min,"Action, Biography, Crime",8.2,"The story of Paan Singh Tomar, an Indian athlete and seven-time national steeplechase champion who becomes one of the most feared dacoits in Chambal Valley after his retirement.",,Tigmanshu Dhulia,Irrfan Khan,Mahie Gill,Rajesh Abhay,Hemendra Dandotiya,33237,"39,567" +"https://m.media-amazon.com/images/M/MV5BY2FhZGI5M2QtZWFiZS00NjkwLWE4NWQtMzg3ZDZjNjdkYTJiXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",El secreto de sus ojos,2009,R,129 min,"Drama, Mystery, Romance",8.2,A retired legal counselor writes a novel hoping to find closure for one of his past unresolved homicide cases and for his unreciprocated love with his superior - both of which still haunt him decades later.,80,Juan José Campanella,Ricardo Darín,Soledad Villamil,Pablo Rago,Carla Quevedo,193217,"6,391,436" +"https://m.media-amazon.com/images/M/MV5BMTk4ODk5MTMyNV5BMl5BanBnXkFtZTcwMDMyNTg0Ng@@._V1_UX67_CR0,0,67,98_AL_.jpg",Warrior,2011,UA,140 min,"Action, Drama, Sport",8.2,"The youngest son of an alcoholic former boxer returns home, where he's trained by his father for competition in a mixed martial arts tournament - a path that puts the fighter on a collision course with his estranged, older brother.",71,Gavin O'Connor,Tom Hardy,Nick Nolte,Joel Edgerton,Jennifer Morrison,435950,"13,657,115" +"https://m.media-amazon.com/images/M/MV5BYzhiNDkyNzktNTZmYS00ZTBkLTk2MDAtM2U0YjU1MzgxZjgzXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Shutter Island,2010,A,138 min,"Mystery, Thriller",8.2,"In 1954, a U.S. Marshal investigates the disappearance of a murderer who escaped from a hospital for the criminally insane.",63,Martin Scorsese,Leonardo DiCaprio,Emily Mortimer,Mark Ruffalo,Ben Kingsley,1129894,"128,012,934" +"https://m.media-amazon.com/images/M/MV5BMTk3NDE2NzI4NF5BMl5BanBnXkFtZTgwNzE1MzEyMTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Up,2009,U,96 min,"Animation, Adventure, Comedy",8.2,"78-year-old Carl Fredricksen travels to Paradise Falls in his house equipped with balloons, inadvertently taking a young stowaway.",88,Pete Docter,Bob Peterson,Edward Asner,Jordan Nagai,John Ratzenberger,935507,"293,004,164" +"https://m.media-amazon.com/images/M/MV5BMjIxMjgxNTk0MF5BMl5BanBnXkFtZTgwNjIyOTg2MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Wolf of Wall Street,2013,A,180 min,"Biography, Crime, Drama",8.2,"Based on the true story of Jordan Belfort, from his rise to a wealthy stock-broker living the high life to his fall involving crime, corruption and the federal government.",75,Martin Scorsese,Leonardo DiCaprio,Jonah Hill,Margot Robbie,Matthew McConaughey,1187498,"116,900,694" +"https://m.media-amazon.com/images/M/MV5BMTUzODMyNzk4NV5BMl5BanBnXkFtZTgwNTk1NTYyNTM@._V1_UY98_CR3,0,67,98_AL_.jpg",Chak De! India,2007,U,153 min,"Drama, Family, Sport",8.2,Kabir Khan is the coach of the Indian Women's National Hockey Team and his dream is to make his all girls team emerge victorious against all odds.,68,Shimit Amin,Shah Rukh Khan,Vidya Malvade,Sagarika Ghatge,Shilpa Shukla,74129,"1,113,541" +"https://m.media-amazon.com/images/M/MV5BMjAxODQ4MDU5NV5BMl5BanBnXkFtZTcwMDU4MjU1MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",There Will Be Blood,2007,A,158 min,Drama,8.2,"A story of family, religion, hatred, oil and madness, focusing on a turn-of-the-century prospector in the early days of the business.",93,Paul Thomas Anderson,Daniel Day-Lewis,Paul Dano,Ciarán Hinds,Martin Stringer,517359,"40,222,514" +"https://m.media-amazon.com/images/M/MV5BMTU3ODg2NjQ5NF5BMl5BanBnXkFtZTcwMDEwODgzMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Pan's Labyrinth,2006,UA,118 min,"Drama, Fantasy, War",8.2,"In the Falangist Spain of 1944, the bookish young stepdaughter of a sadistic army officer escapes into an eerie but captivating fantasy world.",98,Guillermo del Toro,Ivana Baquero,Ariadna Gil,Sergi López,Maribel Verdú,618623,"37,634,615" +"https://m.media-amazon.com/images/M/MV5BMTgxOTY4Mjc0MF5BMl5BanBnXkFtZTcwNTA4MDQyMw@@._V1_UY98_CR1,0,67,98_AL_.jpg",Toy Story 3,2010,U,103 min,"Animation, Adventure, Comedy",8.2,"The toys are mistakenly delivered to a day-care center instead of the attic right before Andy leaves for college, and it's up to Woody to convince the other toys that they weren't abandoned and to return home.",92,Lee Unkrich,Tom Hanks,Tim Allen,Joan Cusack,Ned Beatty,757032,"415,004,880" +"https://m.media-amazon.com/images/M/MV5BOTI5ODc3NzExNV5BMl5BanBnXkFtZTcwNzYxNzQzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",V for Vendetta,2005,A,132 min,"Action, Drama, Sci-Fi",8.2,"In a future British tyranny, a shadowy freedom fighter, known only by the alias of ""V"", plots to overthrow it with the help of a young woman.",62,James McTeigue,Hugo Weaving,Natalie Portman,Rupert Graves,Stephen Rea,1032749,"70,511,035" +"https://m.media-amazon.com/images/M/MV5BYThmZDA0YmQtMWJhNy00MDQwLTk0Y2YtMDhmZTE5ZjhlNjliXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR1,0,67,98_AL_.jpg",Rang De Basanti,2006,UA,167 min,"Comedy, Crime, Drama",8.2,"The story of six young Indians who assist an English woman to film a documentary on the freedom fighters from their past, and the events that lead them to relive the long-forgotten saga of freedom.",,Rakeysh Omprakash Mehra,Aamir Khan,Soha Ali Khan,Siddharth,Sharman Joshi,111937,"2,197,331" +"https://m.media-amazon.com/images/M/MV5BNTI5MmE5M2UtZjIzYS00M2JjLWIwNDItYTY2ZWNiODBmYTBiXkEyXkFqcGdeQXVyNjQ2MjQ5NzM@._V1_UY98_CR0,0,67,98_AL_.jpg",Black,2005,U,122 min,Drama,8.2,"The cathartic tale of a young woman who can't see, hear or talk and the teacher who brings a ray of light into her dark world.",,Sanjay Leela Bhansali,Amitabh Bachchan,Rani Mukerji,Shernaz Patel,Ayesha Kapoor,33354,"733,094" +"https://m.media-amazon.com/images/M/MV5BOTY4YjI2N2MtYmFlMC00ZjcyLTg3YjEtMDQyM2ZjYzQ5YWFkXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Batman Begins,2005,UA,140 min,"Action, Adventure",8.2,"After training with his mentor, Batman begins his fight to free crime-ridden Gotham City from corruption.",70,Christopher Nolan,Christian Bale,Michael Caine,Ken Watanabe,Liam Neeson,1308302,"206,852,432" +"https://m.media-amazon.com/images/M/MV5BYzExOTcwNjYtZTljMC00YTQ2LWI2YjYtNWFlYzQ0YTJhNzJmXkEyXkFqcGdeQXVyNjQ2MjQ5NzM@._V1_UY98_CR0,0,67,98_AL_.jpg","Swades: We, the People",2004,U,210 min,Drama,8.2,A successful Indian scientist returns to an Indian village to take his nanny to America with him and in the process rediscovers his roots.,,Ashutosh Gowariker,Shah Rukh Khan,Gayatri Joshi,Kishori Ballal,Smit Sheth,83005,"1,223,240" +"https://m.media-amazon.com/images/M/MV5BMTU0NTU5NTAyMl5BMl5BanBnXkFtZTYwNzYwMDg2._V1_UX67_CR0,0,67,98_AL_.jpg",Der Untergang,2004,R,156 min,"Biography, Drama, History",8.2,"Traudl Junge, the final secretary for Adolf Hitler, tells of the Nazi dictator's final days in his Berlin bunker at the end of WWII.",82,Oliver Hirschbiegel,Bruno Ganz,Alexandra Maria Lara,Ulrich Matthes,Juliane Köhler,331308,"5,509,040" +"https://m.media-amazon.com/images/M/MV5BNmM4YTFmMmItMGE3Yy00MmRkLTlmZGEtMzZlOTQzYjk3MzA2XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Hauru no ugoku shiro,2004,U,119 min,"Animation, Adventure, Family",8.2,"When an unconfident young woman is cursed with an old body by a spiteful witch, her only chance of breaking the spell lies with a self-indulgent yet insecure young wizard and his companions in his legged, walking castle.",80,Hayao Miyazaki,Chieko Baishô,Takuya Kimura,Tatsuya Gashûin,Akihiro Miwa,333915,"4,711,096" +"https://m.media-amazon.com/images/M/MV5BMzcwYWFkYzktZjAzNC00OGY1LWI4YTgtNzc5MzVjMDVmNjY0XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",A Beautiful Mind,2001,UA,135 min,"Biography, Drama",8.2,"After John Nash, a brilliant but asocial mathematician, accepts secret work in cryptography, his life takes a turn for the nightmarish.",72,Ron Howard,Russell Crowe,Ed Harris,Jennifer Connelly,Christopher Plummer,848920,"170,742,341" +"https://m.media-amazon.com/images/M/MV5BMGMzZjY2ZWQtZjQxYS00NWY3LThhNjItNWQzNTkzOTllODljXkEyXkFqcGdeQXVyNjY1MTg4Mzc@._V1_UY98_CR1,0,67,98_AL_.jpg",Hera Pheri,2000,U,156 min,"Action, Comedy, Crime",8.2,"Three unemployed men look for answers to all their money problems - but when their opportunity arrives, will they know what to do with it?",,Priyadarshan,Akshay Kumar,Sunil Shetty,Paresh Rawal,Tabu,57057, +"https://m.media-amazon.com/images/M/MV5BMTAyN2JmZmEtNjAyMy00NzYwLThmY2MtYWQ3OGNhNjExMmM4XkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg","Lock, Stock and Two Smoking Barrels",1998,A,107 min,"Action, Comedy, Crime",8.2,"A botched card game in London triggers four friends, thugs, weed-growers, hard gangsters, loan sharks and debt collectors to collide with each other in a series of unexpected events, all for the sake of weed, cash and two antique shotguns.",66,Guy Ritchie,Jason Flemyng,Dexter Fletcher,Nick Moran,Jason Statham,535216,"3,897,569" +"https://m.media-amazon.com/images/M/MV5BMDQ2YzEyZGItYWRhOS00MjBmLTkzMDUtMTdjYzkyMmQxZTJlXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",L.A. Confidential,1997,A,138 min,"Crime, Drama, Mystery",8.2,"As corruption grows in 1950s Los Angeles, three policemen - one strait-laced, one brutal, and one sleazy - investigate a series of murders with their own brand of justice.",90,Curtis Hanson,Kevin Spacey,Russell Crowe,Guy Pearce,Kim Basinger,531967,"64,616,940" +"https://m.media-amazon.com/images/M/MV5BOGQ4ZjFmYjktOGNkNS00OWYyLWIyZjgtMGJjM2U1ZTA0ZTlhXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UY98_CR1,0,67,98_AL_.jpg",Eskiya,1996,,128 min,"Crime, Drama, Thriller",8.2,"Baran the Bandit, released from prison after 35 years, searches for vengeance and his lover.",,Yavuz Turgul,Sener Sen,Ugur Yücel,Sermin Hürmeriç,Yesim Salkim,64118, +"https://m.media-amazon.com/images/M/MV5BNGMwNzUwNjYtZWM5NS00YzMyLWI4NjAtNjM0ZDBiMzE1YWExXkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Heat,1995,A,170 min,"Crime, Drama, Thriller",8.2,A group of professional bank robbers start to feel the heat from police when they unknowingly leave a clue at their latest heist.,76,Michael Mann,Al Pacino,Robert De Niro,Val Kilmer,Jon Voight,577113,"67,436,818" +"https://m.media-amazon.com/images/M/MV5BMTcxOWYzNDYtYmM4YS00N2NkLTk0NTAtNjg1ODgwZjAxYzI3XkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Casino,1995,A,178 min,"Crime, Drama",8.2,"A tale of greed, deception, money, power, and murder occur between two best friends: a mafia enforcer and a casino executive compete against each other over a gambling empire, and over a fast-living and fast-loving socialite.",73,Martin Scorsese,Robert De Niro,Sharon Stone,Joe Pesci,James Woods,466276,"42,438,300" +"https://m.media-amazon.com/images/M/MV5BZTIwYzRjMGYtZWQ0Ni00NDZhLThhZDYtOGViZGJiZTkwMzk2XkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR3,0,67,98_AL_.jpg",Andaz Apna Apna,1994,U,160 min,"Action, Comedy, Romance",8.2,Two slackers competing for the affections of an heiress inadvertently become her protectors from an evil criminal.,,Rajkumar Santoshi,Aamir Khan,Salman Khan,Raveena Tandon,Karisma Kapoor,49300, +"https://m.media-amazon.com/images/M/MV5BODM3YWY4NmQtN2Y3Ni00OTg0LWFhZGQtZWE3ZWY4MTJlOWU4XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Unforgiven,1992,A,130 min,"Drama, Western",8.2,"Retired Old West gunslinger William Munny reluctantly takes on one last job, with the help of his old partner Ned Logan and a young man, The ""Schofield Kid.""",85,Clint Eastwood,Clint Eastwood,Gene Hackman,Morgan Freeman,Richard Harris,375935,"101,157,447" +"https://m.media-amazon.com/images/M/MV5BMjNkMzc2N2QtNjVlNS00ZTk5LTg0MTgtODY2MDAwNTMwZjBjXkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Indiana Jones and the Last Crusade,1989,U,127 min,"Action, Adventure",8.2,"In 1938, after his father Professor Henry Jones, Sr. goes missing while pursuing the Holy Grail, Professor Henry ""Indiana"" Jones, Jr. finds himself up against Adolf Hitler's Nazis again to stop them from obtaining its powers.",65,Steven Spielberg,Harrison Ford,Sean Connery,Alison Doody,Denholm Elliott,692366,"197,171,806" +"https://m.media-amazon.com/images/M/MV5BODI2ZjVlMGQtMWE5ZS00MjJiLWIyMWYtMGU5NmIxNDc0OTMyXkEyXkFqcGdeQXVyMTQ3Njg3MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Dom za vesanje,1988,R,142 min,"Comedy, Crime, Drama",8.2,"In this luminous tale set in the area around Sarajevo and in Italy, Perhan, an engaging young Romany (gypsy) with telekinetic powers, is seduced by the quick-cash world of petty crime, which threatens to destroy him and those he loves.",,Emir Kusturica,Davor Dujmovic,Bora Todorovic,Ljubica Adzovic,Husnija Hasimovic,26402,"280,015" +"https://m.media-amazon.com/images/M/MV5BYzJjMTYyMjQtZDI0My00ZjE2LTkyNGYtOTllNGQxNDMyZjE0XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR1,0,67,98_AL_.jpg",Tonari no Totoro,1988,U,86 min,"Animation, Family, Fantasy",8.2,"When two girls move to the country to be near their ailing mother, they have adventures with the wondrous forest spirits who live nearby.",86,Hayao Miyazaki,Hitoshi Takagi,Noriko Hidaka,Chika Sakamoto,Shigesato Itoi,291180,"1,105,564" +"https://m.media-amazon.com/images/M/MV5BZjRlNDUxZjAtOGQ4OC00OTNlLTgxNmQtYTBmMDgwZmNmNjkxXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Die Hard,1988,A,132 min,"Action, Thriller",8.2,An NYPD officer tries to save his wife and several others taken hostage by German terrorists during a Christmas party at the Nakatomi Plaza in Los Angeles.,72,John McTiernan,Bruce Willis,Alan Rickman,Bonnie Bedelia,Reginald VelJohnson,793164,"83,008,852" +"https://m.media-amazon.com/images/M/MV5BZDBjZTM4ZmEtOTA5ZC00NTQzLTkyNzYtMmUxNGU2YjI5YjU5L2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",Ran,1985,U,162 min,"Action, Drama, War",8.2,"In Medieval Japan, an elderly warlord retires, handing over his empire to his three sons. However, he vastly underestimates how the new-found power will corrupt them and cause them to turn on each other...and him.",96,Akira Kurosawa,Tatsuya Nakadai,Akira Terao,Jinpachi Nezu,Daisuke Ryû,112505,"4,135,750" +"https://m.media-amazon.com/images/M/MV5BYjRmODkzNDItMTNhNi00YjJlLTg0ZjAtODlhZTM0YzgzYThlXkEyXkFqcGdeQXVyNzQ1ODk3MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Raging Bull,1980,A,129 min,"Biography, Drama, Sport",8.2,"The life of boxer Jake LaMotta, whose violence and temper that led him to the top in the ring destroyed his life outside of it.",89,Martin Scorsese,Robert De Niro,Cathy Moriarty,Joe Pesci,Frank Vincent,321860,"23,383,987" +"https://m.media-amazon.com/images/M/MV5BMDgwODNmMGItMDcwYi00OWZjLTgyZjAtMGYwMmI4N2Q0NmJmXkEyXkFqcGdeQXVyNzY1MTU0Njk@._V1_UY98_CR1,0,67,98_AL_.jpg",Stalker,1979,U,162 min,"Drama, Sci-Fi",8.2,A guide leads two men through an area known as the Zone to find a room that grants wishes.,,Andrei Tarkovsky,Alisa Freyndlikh,Aleksandr Kaydanovskiy,Anatoliy Solonitsyn,Nikolay Grinko,116945,"234,723" +"https://m.media-amazon.com/images/M/MV5BNGIyMWRlYTctMWNlMi00ZGIzLThjOTgtZjQzZjRjNmRhMDdlXkEyXkFqcGdeQXVyMTAwMzUyOTc@._V1_UX67_CR0,0,67,98_AL_.jpg",Höstsonaten,1978,U,99 min,"Drama, Music",8.2,"A married daughter who longs for her mother's love is visited by the latter, a successful concert pianist.",,Ingmar Bergman,Ingrid Bergman,Liv Ullmann,Lena Nyman,Halvar Björk,26875, +"https://m.media-amazon.com/images/M/MV5BMjk3YjJmYTctMTAzZC00MzE4LWFlZGMtNDM5OTMyMDEzZWIxXkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",The Message,1976,PG,177 min,"Biography, Drama, History",8.2,This epic historical drama chronicles the life and times of Prophet Muhammad and serves as an introduction to early Islamic history.,,Moustapha Akkad,Anthony Quinn,Irene Papas,Michael Ansara,Johnny Sekka,43885, +"https://m.media-amazon.com/images/M/MV5BOGZiM2IwODktNTdiMC00MGU1LWEyZTYtOTk4NTkwYmJkNmI1L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UY98_CR2,0,67,98_AL_.jpg",Sholay,1975,U,204 min,"Action, Adventure, Comedy",8.2,"After his family is murdered by a notorious and ruthless bandit, a former police officer enlists the services of two outlaws to capture the bandit.",,Ramesh Sippy,Sanjeev Kumar,Dharmendra,Amitabh Bachchan,Amjad Khan,51284, +"https://m.media-amazon.com/images/M/MV5BN2IyNTE4YzUtZWU0Mi00MGIwLTgyMmQtMzQ4YzQxYWNlYWE2XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Monty Python and the Holy Grail,1975,PG,91 min,"Adventure, Comedy, Fantasy",8.2,"King Arthur and his Knights of the Round Table embark on a surreal, low-budget search for the Holy Grail, encountering many, very silly obstacles.",91,Terry Gilliam,Terry Jones,Graham Chapman,John Cleese,Eric Idle,500875,"1,229,197" +"https://m.media-amazon.com/images/M/MV5BNzA2NmYxMWUtNzBlMC00MWM2LTkwNmQtYTFlZjQwODNhOWE0XkEyXkFqcGdeQXVyNTIzOTk5ODM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Great Escape,1963,U,172 min,"Adventure, Drama, History",8.2,Allied prisoners of war plan for several hundred of their number to escape from a German camp during World War II.,86,John Sturges,Steve McQueen,James Garner,Richard Attenborough,Charles Bronson,224730,"12,100,000" +"https://m.media-amazon.com/images/M/MV5BNmVmYzcwNzMtMWM1NS00MWIyLThlMDEtYzUwZDgzODE1NmE2XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",To Kill a Mockingbird,1962,U,129 min,"Crime, Drama",8.2,"Atticus Finch, a lawyer in the Depression-era South, defends a black man against an undeserved rape charge, and his children against prejudice.",88,Robert Mulligan,Gregory Peck,John Megna,Frank Overton,Rosemary Murphy,293811, +"https://m.media-amazon.com/images/M/MV5BZThiZjAzZjgtNDU3MC00YThhLThjYWUtZGRkYjc2ZWZlOTVjXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Yôjinbô,1961,,110 min,"Action, Drama, Thriller",8.2,A crafty ronin comes to a town divided by two criminal gangs and decides to play them against each other to free the town.,,Akira Kurosawa,Toshirô Mifune,Eijirô Tôno,Tatsuya Nakadai,Yôko Tsukasa,111244, +"https://m.media-amazon.com/images/M/MV5BNDc2ODQ5NTE2MV5BMl5BanBnXkFtZTcwODExMjUyNA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Judgment at Nuremberg,1961,A,179 min,"Drama, War",8.2,"In 1948, an American court in occupied Germany tries four Nazis judged for war crimes.",60,Stanley Kramer,Spencer Tracy,Burt Lancaster,Richard Widmark,Marlene Dietrich,69458, +"https://m.media-amazon.com/images/M/MV5BNzAyOGIxYjAtMGY2NC00ZTgyLWIwMWEtYzY0OWQ4NDFjOTc5XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Some Like It Hot,1959,U,121 min,"Comedy, Music, Romance",8.2,"After two male musicians witness a mob hit, they flee the state in an all-female band disguised as women, but further complications set in.",98,Billy Wilder,Marilyn Monroe,Tony Curtis,Jack Lemmon,George Raft,243943,"25,000,000" +"https://m.media-amazon.com/images/M/MV5BZjJhNTBmNTgtMDViOC00NDY2LWE4N2ItMDJiM2ZiYmQzYzliXkEyXkFqcGdeQXVyMzg1ODEwNQ@@._V1_UY98_CR0,0,67,98_AL_.jpg",Smultronstället,1957,U,91 min,"Drama, Romance",8.2,"After living a life marked by coldness, an aging professor is forced to confront the emptiness of his existence.",88,Ingmar Bergman,Victor Sjöström,Bibi Andersson,Ingrid Thulin,Gunnar Björnstrand,96381, +"https://m.media-amazon.com/images/M/MV5BM2I1ZWU4YjMtYzU0My00YmMzLWFmNTAtZDJhZGYwMmI3YWQ5XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Det sjunde inseglet,1957,A,96 min,"Drama, Fantasy, History",8.2,"A man seeks answers about life, death, and the existence of God as he plays chess against the Grim Reaper during the Black Plague.",88,Ingmar Bergman,Max von Sydow,Gunnar Björnstrand,Bengt Ekerot,Nils Poppe,164939, +"https://m.media-amazon.com/images/M/MV5BNjZmZGRiMDgtNDkwNi00OTZhLWFhZmMtYTdkYjgyNThhOWY3XkEyXkFqcGdeQXVyMTA1NTM1NDI2._V1_UX67_CR0,0,67,98_AL_.jpg",Du rififi chez les hommes,1955,,118 min,"Crime, Drama, Thriller",8.2,"Four men plan a technically perfect crime, but the human element intervenes...",97,Jules Dassin,Jean Servais,Carl Möhner,Robert Manuel,Janine Darcey,28810,"57,226" +"https://m.media-amazon.com/images/M/MV5BOWIwODIxYWItZDI4MS00YzhhLWE3MmYtMzlhZDIwOTMzZmE5L2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Dial M for Murder,1954,A,105 min,"Crime, Thriller",8.2,A former tennis player tries to arrange his wife's murder after learning of her affair.,75,Alfred Hitchcock,Ray Milland,Grace Kelly,Robert Cummings,John Williams,158335,"12,562" +"https://m.media-amazon.com/images/M/MV5BYWQ4ZTRiODktNjAzZC00Nzg1LTk1YWQtNDFmNDI0NmZiNGIwXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UY98_CR0,0,67,98_AL_.jpg",Tôkyô monogatari,1953,U,136 min,Drama,8.2,"An old couple visit their children and grandchildren in the city, but receive little attention.",,Yasujirô Ozu,Chishû Ryû,Chieko Higashiyama,Sô Yamamura,Setsuko Hara,53153, +"https://m.media-amazon.com/images/M/MV5BMjEzMzA4NDE2OF5BMl5BanBnXkFtZTcwNTc5MDI2NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Rashômon,1950,,88 min,"Crime, Drama, Mystery",8.2,"The rape of a bride and the murder of her samurai husband are recalled from the perspectives of a bandit, the bride, the samurai's ghost and a woodcutter.",98,Akira Kurosawa,Toshirô Mifune,Machiko Kyô,Masayuki Mori,Takashi Shimura,152572,"96,568" +"https://m.media-amazon.com/images/M/MV5BMTY2MTAzODI5NV5BMl5BanBnXkFtZTgwMjM4NzQ0MjE@._V1_UX67_CR0,0,67,98_AL_.jpg",All About Eve,1950,Passed,138 min,Drama,8.2,A seemingly timid but secretly ruthless ingénue insinuates herself into the lives of an aging Broadway star and her circle of theater friends.,98,Joseph L. Mankiewicz,Bette Davis,Anne Baxter,George Sanders,Celeste Holm,120539,"10,177" +"https://m.media-amazon.com/images/M/MV5BOTJlZWMxYzEtMjlkMS00ODE0LThlM2ItMDI3NGQ2YjhmMzkxXkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Treasure of the Sierra Madre,1948,Passed,126 min,"Adventure, Drama, Western",8.2,Two Americans searching for work in Mexico convince an old prospector to help them mine for gold in the Sierra Madre Mountains.,98,John Huston,Humphrey Bogart,Walter Huston,Tim Holt,Bruce Bennett,114304,"5,014,000" +"https://m.media-amazon.com/images/M/MV5BYTIwNDcyMjktMTczMy00NDM5LTlhNDEtMmE3NGVjOTM2YjQ3XkEyXkFqcGdeQXVyNjc0MzMzNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",To Be or Not to Be,1942,Passed,99 min,"Comedy, War",8.2,"During the Nazi occupation of Poland, an acting troupe becomes embroiled in a Polish soldier's efforts to track down a German spy.",86,Ernst Lubitsch,Carole Lombard,Jack Benny,Robert Stack,Felix Bressart,29915, +"https://m.media-amazon.com/images/M/MV5BZjEyOTE4MzMtNmMzMy00Mzc3LWJlOTQtOGJiNDE0ZmJiOTU4L2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR2,0,67,98_AL_.jpg",The Gold Rush,1925,Passed,95 min,"Adventure, Comedy, Drama",8.2,A prospector goes to the Klondike in search of gold and finds it and more.,,Charles Chaplin,Charles Chaplin,Mack Swain,Tom Murray,Henry Bergman,101053,"5,450,000" +"https://m.media-amazon.com/images/M/MV5BZWFhOGU5NDctY2Q3YS00Y2VlLWI1NzEtZmIwY2ZiZjY4OTA2XkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Sherlock Jr.,1924,Passed,45 min,"Action, Comedy, Romance",8.2,"A film projectionist longs to be a detective, and puts his meagre skills to work when he is framed by a rival for stealing his girlfriend's father's pocketwatch.",,Buster Keaton,Buster Keaton,Kathryn McGuire,Joe Keaton,Erwin Connelly,41985,"977,375" +"https://m.media-amazon.com/images/M/MV5BNjgwNjkwOWYtYmM3My00NzI1LTk5OGItYWY0OTMyZTY4OTg2XkEyXkFqcGdeQXVyODk4OTc3MTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Portrait de la jeune fille en feu,2019,R,122 min,"Drama, Romance",8.1,"On an isolated island in Brittany at the end of the eighteenth century, a female painter is obliged to paint a wedding portrait of a young woman.",95,Céline Sciamma,Noémie Merlant,Adèle Haenel,Luàna Bajrami,Valeria Golino,63134,"3,759,854" +"https://m.media-amazon.com/images/M/MV5BNGI1MTI1YTQtY2QwYi00YzUzLTg3NWYtNzExZDlhOTZmZWU0XkEyXkFqcGdeQXVyMDkwNTkwNg@@._V1_UY98_CR3,0,67,98_AL_.jpg",Pink,2016,UA,136 min,"Drama, Thriller",8.1,"When three young women are implicated in a crime, a retired lawyer steps forward to help them clear their names.",,Aniruddha Roy Chowdhury,Taapsee Pannu,Amitabh Bachchan,Kirti Kulhari,Andrea Tariang,39216,"1,241,223" +"https://m.media-amazon.com/images/M/MV5BZGRkOGMxYTUtZTBhYS00NzI3LWEzMDQtOWRhMmNjNjJjMzM4XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR0,0,67,98_AL_.jpg",Koe no katachi,2016,16,130 min,"Animation, Drama, Family",8.1,"A young man is ostracized by his classmates after he bullies a deaf girl to the point where she moves away. Years later, he sets off on a path for redemption.",78,Naoko Yamada,Miyu Irino,Saori Hayami,Aoi Yûki,Kenshô Ono,47708, +"https://m.media-amazon.com/images/M/MV5BMDk0YzAwYjktMWFiZi00Y2FmLWJmMmMtMzUyZDZmMmU5MjkzXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR1,0,67,98_AL_.jpg",Contratiempo,2016,TV-MA,106 min,"Crime, Drama, Mystery",8.1,A successful entrepreneur accused of murder and a witness preparation expert have less than three hours to come up with an impregnable defense.,,Oriol Paulo,Mario Casas,Ana Wagener,Jose Coronado,Bárbara Lennie,141516, +"https://m.media-amazon.com/images/M/MV5BNDJhYTk2MTctZmVmOS00OTViLTgxNjQtMzQxOTRiMDdmNGRjXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR0,0,67,98_AL_.jpg",Ah-ga-ssi,2016,A,145 min,"Drama, Romance, Thriller",8.1,"A woman is hired as a handmaiden to a Japanese heiress, but secretly she is involved in a plot to defraud her.",84,Chan-wook Park,Kim Min-hee,Jung-woo Ha,Cho Jin-woong,Moon So-Ri,113649,"2,006,788" +"https://m.media-amazon.com/images/M/MV5BMGI3YWFmNDQtNjc0Ny00ZDBjLThlNjYtZTc1ZTk5MzU2YTVjXkEyXkFqcGdeQXVyNzA4ODc3ODU@._V1_UY98_CR1,0,67,98_AL_.jpg",Mommy,2014,R,139 min,Drama,8.1,"A widowed single mother, raising her violent son alone, finds new hope when a mysterious neighbor inserts herself into their household.",74,Xavier Dolan,Anne Dorval,Antoine Olivier Pilon,Suzanne Clément,Patrick Huard,50700,"3,492,754" +"https://m.media-amazon.com/images/M/MV5BMjA1NTEwMDMxMF5BMl5BanBnXkFtZTgwODkzMzI0MjE@._V1_UY98_CR0,0,67,98_AL_.jpg",Haider,2014,UA,160 min,"Action, Crime, Drama",8.1,"A young man returns to Kashmir after his father's disappearance to confront his uncle, whom he suspects of playing a role in his father's fate.",,Vishal Bhardwaj,Shahid Kapoor,Tabu,Shraddha Kapoor,Kay Kay Menon,50445,"901,610" +"https://m.media-amazon.com/images/M/MV5BYzc5MTU4N2EtYTkyMi00NjdhLTg3NWEtMTY4OTEyMzJhZTAzXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Logan,2017,A,137 min,"Action, Drama, Sci-Fi",8.1,"In a future where mutants are nearly extinct, an elderly and weary Logan leads a quiet life. But when Laura, a mutant child pursued by scientists, comes to him for help, he must get her to safety.",77,James Mangold,Hugh Jackman,Patrick Stewart,Dafne Keen,Boyd Holbrook,647884,"226,277,068" +"https://m.media-amazon.com/images/M/MV5BMjE4NzgzNzEwMl5BMl5BanBnXkFtZTgwMTMzMDE0NjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Room,2015,R,118 min,"Drama, Thriller",8.1,"Held captive for 7 years in an enclosed space, a woman and her young son finally gain their freedom, allowing the boy to experience the outside world for the first time.",86,Lenny Abrahamson,Brie Larson,Jacob Tremblay,Sean Bridgers,Wendy Crewson,371538,"14,677,674" +"https://m.media-amazon.com/images/M/MV5BNGQzY2Y0MTgtMDA4OC00NjM3LWI0ZGQtNTJlM2UxZDQxZjI0XkEyXkFqcGdeQXVyNDUzOTQ5MjY@._V1_UY98_CR1,0,67,98_AL_.jpg",Relatos salvajes,2014,R,122 min,"Comedy, Drama, Thriller",8.1,Six short stories that explore the extremities of human behavior involving people in distress.,77,Damián Szifron,Darío Grandinetti,María Marull,Mónica Villa,Diego Starosta,177059,"3,107,072" +"https://m.media-amazon.com/images/M/MV5BZGE1MDg5M2MtNTkyZS00MTY5LTg1YzUtZTlhZmM1Y2EwNmFmXkEyXkFqcGdeQXVyNjA3OTI0MDc@._V1_UX67_CR0,0,67,98_AL_.jpg",Soul,2020,U,100 min,"Animation, Adventure, Comedy",8.1,"After landing the gig of a lifetime, a New York jazz pianist suddenly finds himself trapped in a strange land between Earth and the afterlife.",83,Pete Docter,Kemp Powers,Jamie Foxx,Tina Fey,Graham Norton,159171, +"https://m.media-amazon.com/images/M/MV5BYzE2MjEwMTQtOTQ2Mi00ZWExLTkyMjUtNmJjMjBlYWFjZDdlXkEyXkFqcGdeQXVyMTI3ODAyMzE2._V1_UY98_CR0,0,67,98_AL_.jpg",Kis Uykusu,2014,,196 min,Drama,8.1,A hotel owner and landlord in a remote Turkish village deals with conflicts within his family and a tenant behind on his rent.,88,Nuri Bilge Ceylan,Haluk Bilginer,Melisa Sözen,Demet Akbag,Ayberk Pekcan,46547,"165,520" +"https://m.media-amazon.com/images/M/MV5BMTYzOTE2NjkxN15BMl5BanBnXkFtZTgwMDgzMTg0MzE@._V1_UY98_CR0,0,67,98_AL_.jpg",PK,2014,UA,153 min,"Comedy, Drama, Musical",8.1,An alien on Earth loses the only device he can use to communicate with his spaceship. His innocent nature and child-like questions force the country to evaluate the impact of religion on its people.,,Rajkumar Hirani,Aamir Khan,Anushka Sharma,Sanjay Dutt,Boman Irani,163061,"10,616,104" +"https://m.media-amazon.com/images/M/MV5BMGNhYjUwNmYtNDQxNi00NDdmLTljMDAtZWM1NDQyZTk3ZDYwXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR0,0,67,98_AL_.jpg",OMG: Oh My God!,2012,U,125 min,"Comedy, Drama, Fantasy",8.1,A shopkeeper takes God to court when his shop is destroyed by an earthquake.,,Umesh Shukla,Paresh Rawal,Akshay Kumar,Mithun Chakraborty,Mahesh Manjrekar,51739,"923,221" +"https://m.media-amazon.com/images/M/MV5BMzM5NjUxOTEyMl5BMl5BanBnXkFtZTgwNjEyMDM0MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Grand Budapest Hotel,2014,UA,99 min,"Adventure, Comedy, Crime",8.1,"A writer encounters the owner of an aging high-class hotel, who tells him of his early years serving as a lobby boy in the hotel's glorious years under an exceptional concierge.",88,Wes Anderson,Ralph Fiennes,F. Murray Abraham,Mathieu Amalric,Adrien Brody,707630,"59,100,318" +"https://m.media-amazon.com/images/M/MV5BMTk0MDQ3MzAzOV5BMl5BanBnXkFtZTgwNzU1NzE3MjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Gone Girl,2014,A,149 min,"Drama, Mystery, Thriller",8.1,"With his wife's disappearance having become the focus of an intense media circus, a man sees the spotlight turned on him when it's suspected that he may not be innocent.",79,David Fincher,Ben Affleck,Rosamund Pike,Neil Patrick Harris,Tyler Perry,859695,"167,767,189" +"https://m.media-amazon.com/images/M/MV5BYzQxNDZhNDUtNDUwOC00NjQyLTg2OWUtZWVlYThjYjYyMTc2XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",Ôkami kodomo no Ame to Yuki,2012,U,117 min,"Animation, Drama, Fantasy",8.1,"After her werewolf lover unexpectedly dies in an accident while hunting for food for their children, a young woman must find ways to raise the werewolf son and daughter that she had with him while keeping their trait hidden from society.",71,Mamoru Hosoda,Aoi Miyazaki,Takao Osawa,Haru Kuroki,Yukito Nishii,38803, +"https://m.media-amazon.com/images/M/MV5BMjQ1NjM3MTUxNV5BMl5BanBnXkFtZTgwMDc5MTY5OTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Hacksaw Ridge,2016,A,139 min,"Biography, Drama, History",8.1,"World War II American Army Medic Desmond T. Doss, who served during the Battle of Okinawa, refuses to kill people, and becomes the first man in American history to receive the Medal of Honor without firing a shot.",71,Mel Gibson,Andrew Garfield,Sam Worthington,Luke Bracey,Teresa Palmer,435928,"67,209,615" +"https://m.media-amazon.com/images/M/MV5BOTgxMDQwMDk0OF5BMl5BanBnXkFtZTgwNjU5OTg2NDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Inside Out,2015,U,95 min,"Animation, Adventure, Comedy",8.1,"After young Riley is uprooted from her Midwest life and moved to San Francisco, her emotions - Joy, Fear, Anger, Disgust and Sadness - conflict on how best to navigate a new city, house, and school.",94,Pete Docter,Ronnie Del Carmen,Amy Poehler,Bill Hader,Lewis Black,616228,"356,461,711" +"https://m.media-amazon.com/images/M/MV5BMTQzMTEyODY2Ml5BMl5BanBnXkFtZTgwMjA0MDUyMjE@._V1_UY98_CR0,0,67,98_AL_.jpg",Barfi!,2012,U,151 min,"Comedy, Drama, Romance",8.1,Three young people learn that love can neither be defined nor contained by society's definition of normal and abnormal.,,Anurag Basu,Ranbir Kapoor,Priyanka Chopra,Ileana D'Cruz,Saurabh Shukla,75721,"2,804,874" +"https://m.media-amazon.com/images/M/MV5BMjExMTEzODkyN15BMl5BanBnXkFtZTcwNTU4NTc4OQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",12 Years a Slave,2013,A,134 min,"Biography, Drama, History",8.1,"In the antebellum United States, Solomon Northup, a free black man from upstate New York, is abducted and sold into slavery.",96,Steve McQueen,Chiwetel Ejiofor,Michael Kenneth Williams,Michael Fassbender,Brad Pitt,640533,"56,671,993" +"https://m.media-amazon.com/images/M/MV5BOWEwODJmZDItYTNmZC00OGM4LThlNDktOTQzZjIzMGQxODA4XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Rush,2013,UA,123 min,"Action, Biography, Drama",8.1,The merciless 1970s rivalry between Formula One rivals James Hunt and Niki Lauda.,74,Ron Howard,Daniel Brühl,Chris Hemsworth,Olivia Wilde,Alexandra Maria Lara,432811,"26,947,624" +"https://m.media-amazon.com/images/M/MV5BM2UwMDVmMDItM2I2Yi00NGZmLTk4ZTUtY2JjNTQ3OGQ5ZjM2XkEyXkFqcGdeQXVyMTA1OTYzOTUx._V1_UX67_CR0,0,67,98_AL_.jpg",Ford v Ferrari,2019,UA,152 min,"Action, Biography, Drama",8.1,American car designer Carroll Shelby and driver Ken Miles battle corporate interference and the laws of physics to build a revolutionary race car for Ford in order to defeat Ferrari at the 24 Hours of Le Mans in 1966.,81,James Mangold,Matt Damon,Christian Bale,Jon Bernthal,Caitriona Balfe,291289,"117,624,028" +"https://m.media-amazon.com/images/M/MV5BMjIyOTM5OTIzNV5BMl5BanBnXkFtZTgwMDkzODE2NjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Spotlight,2015,A,129 min,"Biography, Crime, Drama",8.1,"The true story of how the Boston Globe uncovered the massive scandal of child molestation and cover-up within the local Catholic Archdiocese, shaking the entire Catholic Church to its core.",93,Tom McCarthy,Mark Ruffalo,Michael Keaton,Rachel McAdams,Liev Schreiber,420316,"45,055,776" +"https://m.media-amazon.com/images/M/MV5BMTQ2MDMwNjEwNV5BMl5BanBnXkFtZTgwOTkxMzI0MzE@._V1_UY98_CR0,0,67,98_AL_.jpg",Song of the Sea,2014,PG,93 min,"Animation, Adventure, Drama",8.1,"Ben, a young Irish boy, and his little sister Saoirse, a girl who can turn into a seal, go on an adventure to free the fairies and save the spirit world.",85,Tomm Moore,David Rawle,Brendan Gleeson,Lisa Hannigan,Fionnula Flanagan,51679,"857,524" +"https://m.media-amazon.com/images/M/MV5BMTQ1NDI0NzkyOF5BMl5BanBnXkFtZTcwNzAyNzE2Nw@@._V1_UY98_CR0,0,67,98_AL_.jpg",Kahaani,2012,UA,122 min,"Mystery, Thriller",8.1,"A pregnant woman's search for her missing husband takes her from London to Kolkata, but everyone she questions denies having ever met him.",,Sujoy Ghosh,Vidya Balan,Parambrata Chattopadhyay,Indraneil Sengupta,Nawazuddin Siddiqui,57806,"1,035,953" +"https://m.media-amazon.com/images/M/MV5BZGFmMjM5OWMtZTRiNC00ODhlLThlYTItYTcyZDMyYmMyYjFjXkEyXkFqcGdeQXVyNDUzOTQ5MjY@._V1_UY98_CR0,0,67,98_AL_.jpg",Zindagi Na Milegi Dobara,2011,U,155 min,"Comedy, Drama",8.1,Three friends decide to turn their fantasy vacation into reality after one of their friends gets engaged.,,Zoya Akhtar,Hrithik Roshan,Farhan Akhtar,Abhay Deol,Katrina Kaif,67927,"3,108,485" +"https://m.media-amazon.com/images/M/MV5BMTg0NTIzMjQ1NV5BMl5BanBnXkFtZTcwNDc3MzM5OQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Prisoners,2013,A,153 min,"Crime, Drama, Mystery",8.1,"When Keller Dover's daughter and her friend go missing, he takes matters into his own hands as the police pursue multiple leads and the pressure mounts.",70,Denis Villeneuve,Hugh Jackman,Jake Gyllenhaal,Viola Davis,Melissa Leo,601149,"61,002,302" +"https://m.media-amazon.com/images/M/MV5BN2EwM2I5OWMtMGQyMi00Zjg1LWJkNTctZTdjYTA4OGUwZjMyXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Mad Max: Fury Road,2015,UA,120 min,"Action, Adventure, Sci-Fi",8.1,"In a post-apocalyptic wasteland, a woman rebels against a tyrannical ruler in search for her homeland with the aid of a group of female prisoners, a psychotic worshiper, and a drifter named Max.",90,George Miller,Tom Hardy,Charlize Theron,Nicholas Hoult,Zoë Kravitz,882316,"154,058,340" +"https://m.media-amazon.com/images/M/MV5BOTcwMzdiMWItMjZlOS00MzAzLTg5OTItNTA4OGYyMjBhMmRiXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR1,0,67,98_AL_.jpg",A Wednesday,2008,UA,104 min,"Action, Crime, Drama",8.1,A retiring police officer reminisces about the most astounding day of his career. About a case that was never filed but continues to haunt him in his memories - the case of a man and a Wednesday.,,Neeraj Pandey,Anupam Kher,Naseeruddin Shah,Jimmy Sheirgill,Aamir Bashir,73891, +"https://m.media-amazon.com/images/M/MV5BMTc5NTk2OTU1Nl5BMl5BanBnXkFtZTcwMDc3NjAwMg@@._V1_UX67_CR0,0,67,98_AL_.jpg",Gran Torino,2008,R,116 min,Drama,8.1,"Disgruntled Korean War veteran Walt Kowalski sets out to reform his neighbor, Thao Lor, a Hmong teenager who tried to steal Kowalski's prized possession: a 1972 Gran Torino.",72,Clint Eastwood,Clint Eastwood,Bee Vang,Christopher Carley,Ahney Her,720450,"148,095,302" +"https://m.media-amazon.com/images/M/MV5BMGVmMWNiMDktYjQ0Mi00MWIxLTk0N2UtN2ZlYTdkN2IzNDNlXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Harry Potter and the Deathly Hallows: Part 2,2011,UA,130 min,"Adventure, Drama, Fantasy",8.1,"Harry, Ron, and Hermione search for Voldemort's remaining Horcruxes in their effort to destroy the Dark Lord as the final battle rages on at Hogwarts.",85,David Yates,Daniel Radcliffe,Emma Watson,Rupert Grint,Michael Gambon,764493,"381,011,219" +"https://m.media-amazon.com/images/M/MV5BMTUzOTcwOTA2NV5BMl5BanBnXkFtZTcwNDczMzczMg@@._V1_UY98_CR0,0,67,98_AL_.jpg",Okuribito,2008,PG-13,130 min,"Drama, Music",8.1,A newly unemployed cellist takes a job preparing the dead for funerals.,68,Yôjirô Takita,Masahiro Motoki,Ryôko Hirosue,Tsutomu Yamazaki,Kazuko Yoshiyuki,48582,"1,498,210" +"https://m.media-amazon.com/images/M/MV5BNzE4NDg5OWMtMzg3NC00ZDRjLTllMDMtZTRjNWZmNjBmMGZlXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR1,0,67,98_AL_.jpg",Hachi: A Dog's Tale,2009,G,93 min,"Biography, Drama, Family",8.1,A college professor bonds with an abandoned dog he takes into his home.,,Lasse Hallström,Richard Gere,Joan Allen,Cary-Hiroyuki Tagawa,Sarah Roemer,253575, +"https://m.media-amazon.com/images/M/MV5BMDgzYjQwMDMtNGUzYi00MTRmLWIyMGMtNjE1OGZkNzY2YWIzL2ltYWdlXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UY98_CR1,0,67,98_AL_.jpg",Mary and Max,2009,,92 min,"Animation, Comedy, Drama",8.1,"A tale of friendship between two unlikely pen pals: Mary, a lonely, eight-year-old girl living in the suburbs of Melbourne, and Max, a forty-four-year old, severely obese man living in New York.",,Adam Elliot,Toni Collette,Philip Seymour Hoffman,Eric Bana,Barry Humphries,164462, +"https://m.media-amazon.com/images/M/MV5BMjA5NDQyMjc2NF5BMl5BanBnXkFtZTcwMjg5ODcyMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",How to Train Your Dragon,2010,U,98 min,"Animation, Action, Adventure",8.1,"A hapless young Viking who aspires to hunt dragons becomes the unlikely friend of a young dragon himself, and learns there may be more to the creatures than he assumed.",75,Dean DeBlois,Chris Sanders,Jay Baruchel,Gerard Butler,Christopher Mintz-Plasse,666773,"217,581,231" +"https://m.media-amazon.com/images/M/MV5BMTAwNDEyODU1MjheQTJeQWpwZ15BbWU2MDc3NDQwNw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Into the Wild,2007,R,148 min,"Adventure, Biography, Drama",8.1,"After graduating from Emory University, top student and athlete Christopher McCandless abandons his possessions, gives his entire $24,000 savings account to charity and hitchhikes to Alaska to live in the wilderness. Along the way, Christopher encounters a series of characters that shape his life.",73,Sean Penn,Emile Hirsch,Vince Vaughn,Catherine Keener,Marcia Gay Harden,572921,"18,354,356" +"https://m.media-amazon.com/images/M/MV5BMjA5Njk3MjM4OV5BMl5BanBnXkFtZTcwMTc5MTE1MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",No Country for Old Men,2007,R,122 min,"Crime, Drama, Thriller",8.1,Violence and mayhem ensue after a hunter stumbles upon a drug deal gone wrong and more than two million dollars in cash near the Rio Grande.,91,Ethan Coen,Joel Coen,Tommy Lee Jones,Javier Bardem,Josh Brolin,856916,"74,283,625" +"https://m.media-amazon.com/images/M/MV5BN2ZmMDMwODgtMzA5MS00MGU0LWEyYTgtYzQ5MmQzMzU2NTVkXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR0,0,67,98_AL_.jpg",Lage Raho Munna Bhai,2006,U,144 min,"Comedy, Drama, Romance",8.1,Munna Bhai embarks on a journey with Mahatma Gandhi in order to fight against a corrupt property dealer.,,Rajkumar Hirani,Sanjay Dutt,Arshad Warsi,Vidya Balan,Boman Irani,43137,"2,217,561" +"https://m.media-amazon.com/images/M/MV5BMTkxNzA1NDQxOV5BMl5BanBnXkFtZTcwNTkyMTIzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Million Dollar Baby,2004,UA,132 min,"Drama, Sport",8.1,A determined woman works with a hardened boxing trainer to become a professional.,86,Clint Eastwood,Hilary Swank,Clint Eastwood,Morgan Freeman,Jay Baruchel,635975,"100,492,203" +"https://m.media-amazon.com/images/M/MV5BZGJjYmIzZmQtNWE4Yy00ZGVmLWJkZGEtMzUzNmQ4ZWFlMjRhXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Hotel Rwanda,2004,PG-13,121 min,"Biography, Drama, History",8.1,"Paul Rusesabagina, a hotel manager, houses over a thousand Tutsi refugees during their struggle against the Hutu militia in Rwanda, Africa.",79,Terry George,Don Cheadle,Sophie Okonedo,Joaquin Phoenix,Xolani Mali,334320,"23,530,892" +"https://m.media-amazon.com/images/M/MV5BNjAxZTEzNzQtYjdlNy00ZTJmLTkwZDUtOTAwNTM3YjI2MWUyL2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Taegukgi hwinalrimyeo,2004,R,140 min,"Action, Drama, War",8.1,"When two brothers are forced to fight in the Korean War, the elder decides to take the riskiest missions if it will help shield the younger from battle.",64,Je-kyu Kang,Jang Dong-Gun,Won Bin,Eun-ju Lee,Hyeong-jin Kong,37820,"1,111,061" +"https://m.media-amazon.com/images/M/MV5BMTQ1MjAwNTM5Ml5BMl5BanBnXkFtZTYwNDM0MTc3._V1_UX67_CR0,0,67,98_AL_.jpg",Before Sunset,2004,R,80 min,"Drama, Romance",8.1,"Nine years after Jesse and Celine first met, they encounter each other again on the French leg of Jesse's book tour.",90,Richard Linklater,Ethan Hawke,Julie Delpy,Vernon Dobtcheff,Louise Lemoine Torrès,236311,"5,820,649" +"https://m.media-amazon.com/images/M/MV5BMzQ4MTBlYTQtMzJkYS00OGNjLTk1MWYtNzQ0OTQ0OWEyOWU1XkEyXkFqcGdeQXVyNDgyODgxNjE@._V1_UY98_CR1,0,67,98_AL_.jpg",Munna Bhai M.B.B.S.,2003,U,156 min,"Comedy, Drama, Musical",8.1,A gangster sets out to fulfill his father's dream of becoming a doctor.,,Rajkumar Hirani,Sanjay Dutt,Arshad Warsi,Gracy Singh,Sunil Dutt,73992, +"https://m.media-amazon.com/images/M/MV5BOGViNTg4YTktYTQ2Ni00MTU0LTk2NWUtMTI4OTc1YTM0NzQ2XkEyXkFqcGdeQXVyMDM2NDM2MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Salinui chueok,2003,UA,131 min,"Crime, Drama, Mystery",8.1,"In a small Korean province in 1986, two detectives struggle with the case of multiple young women being found raped and murdered by an unknown culprit.",82,Bong Joon Ho,Kang-ho Song,Kim Sang-kyung,Roe-ha Kim,Jae-ho Song,139558,"14,131" +"https://m.media-amazon.com/images/M/MV5BMjRjMTYwMTYtMmRkNi00MmVkLWE0MjQtNmM3YjI0NWFhZDNmXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Dil Chahta Hai,2001,Unrated,183 min,"Comedy, Drama, Romance",8.1,"Three inseparable childhood friends are just out of college. Nothing comes between them - until they each fall in love, and their wildly different approaches to relationships creates tension.",,Farhan Akhtar,Aamir Khan,Saif Ali Khan,Akshaye Khanna,Preity Zinta,66803,"300,000" +"https://m.media-amazon.com/images/M/MV5BNzM3NDFhYTAtYmU5Mi00NGRmLTljYjgtMDkyODQ4MjNkMGY2XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Kill Bill: Vol. 1,2003,R,111 min,"Action, Crime, Drama",8.1,"After awakening from a four-year coma, a former assassin wreaks vengeance on the team of assassins who betrayed her.",69,Quentin Tarantino,Uma Thurman,David Carradine,Daryl Hannah,Michael Madsen,1000639,"70,099,045" +"https://m.media-amazon.com/images/M/MV5BZTAzNWZlNmUtZDEzYi00ZjA5LWIwYjEtZGM1NWE1MjE4YWRhXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Finding Nemo,2003,U,100 min,"Animation, Adventure, Comedy",8.1,"After his son is captured in the Great Barrier Reef and taken to Sydney, a timid clownfish sets out on a journey to bring him home.",90,Andrew Stanton,Lee Unkrich,Albert Brooks,Ellen DeGeneres,Alexander Gould,949565,"380,843,261" +"https://m.media-amazon.com/images/M/MV5BMTY5MzYzNjc5NV5BMl5BanBnXkFtZTYwNTUyNTc2._V1_UX67_CR0,0,67,98_AL_.jpg",Catch Me If You Can,2002,A,141 min,"Biography, Crime, Drama",8.1,"Barely 21 yet, Frank is a skilled forger who has passed as a doctor, lawyer and pilot. FBI agent Carl becomes obsessed with tracking down the con man, who only revels in the pursuit.",75,Steven Spielberg,Leonardo DiCaprio,Tom Hanks,Christopher Walken,Martin Sheen,832846,"164,615,351" +"https://m.media-amazon.com/images/M/MV5BMjQxMWJhMzMtMzllZi00NzMwLTllYjktNTcwZmU4ZmU3NTA0XkEyXkFqcGdeQXVyMTAzMDM4MjM0._V1_UY98_CR3,0,67,98_AL_.jpg",Amores perros,2000,A,154 min,"Drama, Thriller",8.1,"A horrific car accident connects three stories, each involving characters dealing with loss, regret, and life's harsh realities, all in the name of love.",83,Alejandro G. Iñárritu,Emilio Echevarría,Gael García Bernal,Goya Toledo,Álvaro Guerrero,223741,"5,383,834" +"https://m.media-amazon.com/images/M/MV5BMTY1NTI0ODUyOF5BMl5BanBnXkFtZTgwNTEyNjQ0MDE@._V1_UX67_CR0,0,67,98_AL_.jpg","Monsters, Inc.",2001,U,92 min,"Animation, Adventure, Comedy",8.1,"In order to power the city, monsters have to scare children so that they scream. However, the children are toxic to the monsters, and after a child gets through, 2 monsters realize things may not be what they think.",79,Pete Docter,David Silverman,Lee Unkrich,Billy Crystal,John Goodman,815505,"289,916,256" +"https://m.media-amazon.com/images/M/MV5BZjJhMThkNTQtNjkxNy00MDdjLTg4MWQtMTI2MmQ3MDVmODUzXkEyXkFqcGdeQXVyMTAwOTA3NzY3._V1_UY98_CR1,0,67,98_AL_.jpg","Shin seiki Evangelion Gekijô-ban: Air/Magokoro wo, kimi ni",1997,UA,87 min,"Animation, Action, Drama",8.1,Concurrent theatrical ending of the TV series Shin seiki evangerion (1995).,,Hideaki Anno,Kazuya Tsurumaki,Megumi Ogata,Megumi Hayashibara,Yûko Miyamura,38847, +"https://m.media-amazon.com/images/M/MV5BNDYxNWUzZmYtOGQxMC00MTdkLTkxOTctYzkyOGIwNWQxZjhmXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Lagaan: Once Upon a Time in India,2001,U,224 min,"Adventure, Drama, Musical",8.1,The people of a small village in Victorian India stake their future on a game of cricket against their ruthless British rulers.,84,Ashutosh Gowariker,Aamir Khan,Raghuvir Yadav,Gracy Singh,Rachel Shelley,105036,"70,147" +"https://m.media-amazon.com/images/M/MV5BMWM4NTFhYjctNzUyNi00NGMwLTk3NTYtMDIyNTZmMzRlYmQyXkEyXkFqcGdeQXVyMTAwMzUyOTc@._V1_UX67_CR0,0,67,98_AL_.jpg",The Sixth Sense,1999,A,107 min,"Drama, Mystery, Thriller",8.1,A boy who communicates with spirits seeks the help of a disheartened child psychologist.,64,M. Night Shyamalan,Bruce Willis,Haley Joel Osment,Toni Collette,Olivia Williams,911573,"293,506,292" +"https://m.media-amazon.com/images/M/MV5BMzIwOTdmNjQtOWQ1ZS00ZWQ4LWIxYTMtOWFkM2NjODJiMGY4L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",La leggenda del pianista sull'oceano,1998,U,169 min,"Drama, Music, Romance",8.1,"A baby boy, discovered in 1900 on an ocean liner, grows into a musical prodigy, never setting foot on land.",58,Giuseppe Tornatore,Tim Roth,Pruitt Taylor Vince,Mélanie Thierry,Bill Nunn,59020,"259,127" +"https://m.media-amazon.com/images/M/MV5BMDIzODcyY2EtMmY2MC00ZWVlLTgwMzAtMjQwOWUyNmJjNTYyXkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",The Truman Show,1998,U,103 min,"Comedy, Drama",8.1,An insurance salesman discovers his whole life is actually a reality TV show.,90,Peter Weir,Jim Carrey,Ed Harris,Laura Linney,Noah Emmerich,939631,"125,618,201" +"https://m.media-amazon.com/images/M/MV5BMmExZTZhN2QtMzg5Mi00Y2M5LTlmMWYtNTUzMzUwMGM2OGQ3XkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg","Crna macka, beli macor",1998,R,127 min,"Comedy, Crime, Romance",8.1,Matko and his son Zare live on the banks of the Danube river and get by through hustling and basically doing anything to make a living. In order to pay off a business debt Matko agrees to marry off Zare to the sister of a local gangster.,73,Emir Kusturica,Bajram Severdzan,Srdjan 'Zika' Todorovic,Branka Katic,Florijan Ajdini,50862,"348,660" +"https://m.media-amazon.com/images/M/MV5BMTQ0NjUzMDMyOF5BMl5BanBnXkFtZTgwODA1OTU0MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Big Lebowski,1998,R,117 min,"Comedy, Crime, Sport",8.1,"Jeff ""The Dude"" Lebowski, mistaken for a millionaire of the same name, seeks restitution for his ruined rug and enlists his bowling buddies to help get it.",71,Joel Coen,Ethan Coen,Jeff Bridges,John Goodman,Julianne Moore,732620,"17,498,804" +"https://m.media-amazon.com/images/M/MV5BYjZjODRlMjQtMjJlYy00ZDBjLTkyYTQtZGQxZTk5NzJhYmNmXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UY98_CR1,0,67,98_AL_.jpg",Fa yeung nin wah,2000,U,98 min,"Drama, Romance",8.1,"Two neighbors, a woman and a man, form a strong bond after both suspect extramarital activities of their spouses. However, they agree to keep their bond platonic so as not to commit similar wrongs.",85,Kar-Wai Wong,Tony Chiu-Wai Leung,Maggie Cheung,Ping Lam Siu,Tung Cho 'Joe' Cheung,124383,"2,734,044" +"https://m.media-amazon.com/images/M/MV5BMzA5Zjc3ZTMtMmU5YS00YTMwLWI4MWUtYTU0YTVmNjVmODZhXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Trainspotting,1996,A,93 min,Drama,8.1,"Renton, deeply immersed in the Edinburgh drug scene, tries to clean up and get out, despite the allure of the drugs and influence of friends.",83,Danny Boyle,Ewan McGregor,Ewen Bremner,Jonny Lee Miller,Kevin McKidd,634716,"16,501,785" +"https://m.media-amazon.com/images/M/MV5BNDJiZDgyZjctYmRjMS00ZjdkLTkwMTEtNGU1NDg3NDQ0Yzk1XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Fargo,1996,A,98 min,"Crime, Drama, Thriller",8.1,Jerry Lundegaard's inept crime falls apart due to his and his henchmen's bungling and the persistent police work of the quite pregnant Marge Gunderson.,85,Joel Coen,Ethan Coen,William H. Macy,Frances McDormand,Steve Buscemi,617444,"24,611,975" +"https://m.media-amazon.com/images/M/MV5BNzI4YTVmMWEtMWQ3MS00OGE1LWE5YjMtNjc4NWJmYjRmZTQyXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UY98_CR0,0,67,98_AL_.jpg",Underground,1995,,170 min,"Comedy, Drama, War",8.1,"A group of Serbian socialists prepares for the war in a surreal underground filled by parties, tragedies, love and hate.",,Emir Kusturica,Predrag 'Miki' Manojlovic,Lazar Ristovski,Mirjana Jokovic,Slavko Stimac,55220,"171,082" +"https://m.media-amazon.com/images/M/MV5BNDNiOTA5YjktY2Q0Ni00ODgzLWE5MWItNGExOWRlYjY2MjBlXkEyXkFqcGdeQXVyNjQ2MjQ5NzM@._V1_UY98_CR1,0,67,98_AL_.jpg",La haine,1995,UA,98 min,"Crime, Drama",8.1,24 hours in the lives of three young men in the French suburbs the day after a violent riot.,,Mathieu Kassovitz,Vincent Cassel,Hubert Koundé,Saïd Taghmaoui,Abdel Ahmed Ghili,150345,"309,811" +"https://m.media-amazon.com/images/M/MV5BYmNjYzRlM2YtZTZjZC00ODVmLTljZWMtODg1YmYyNDBiNzU3XkEyXkFqcGdeQXVyNTkzNDQ4ODc@._V1_UY98_CR3,0,67,98_AL_.jpg",Dilwale Dulhania Le Jayenge,1995,U,189 min,"Drama, Romance",8.1,"When Raj meets Simran in Europe, it isn't love at first sight but when Simran moves to India for an arranged marriage, love makes its presence felt.",,Aditya Chopra,Shah Rukh Khan,Kajol,Amrish Puri,Farida Jalal,63516, +"https://m.media-amazon.com/images/M/MV5BZDdiZTAwYzAtMDI3Ni00OTRjLTkzN2UtMGE3MDMyZmU4NTU4XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Before Sunrise,1995,R,101 min,"Drama, Romance",8.1,"A young man and woman meet on a train in Europe, and wind up spending one evening together in Vienna. Unfortunately, both know that this will probably be their only night together.",77,Richard Linklater,Ethan Hawke,Julie Delpy,Andrea Eckert,Hanno Pöschl,272291,"5,535,405" +"https://m.media-amazon.com/images/M/MV5BYTg1MmNiMjItMmY4Yy00ZDQ3LThjMzYtZGQ0ZTQzNTdkMGQ1L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Trois couleurs: Rouge,1994,U,99 min,"Drama, Mystery, Romance",8.1,A model discovers a retired judge is keen on invading people's privacy.,100,Krzysztof Kieslowski,Irène Jacob,Jean-Louis Trintignant,Frédérique Feder,Jean-Pierre Lorit,90729,"4,043,686" +"https://m.media-amazon.com/images/M/MV5BMGQ5MzljNzYtMDM1My00NmI0LThlYzQtMTg0ZmQ0MTk1YjkxXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",Chung Hing sam lam,1994,U,102 min,"Comedy, Crime, Drama",8.1,"Two melancholy Hong Kong policemen fall in love: one with a mysterious female underworld figure, the other with a beautiful and ethereal server at a late-night restaurant he frequents.",77,Kar-Wai Wong,Brigitte Lin,Takeshi Kaneshiro,Tony Chiu-Wai Leung,Faye Wong,63122,"600,200" +"https://m.media-amazon.com/images/M/MV5BMjM2MDgxMDg0Nl5BMl5BanBnXkFtZTgwNTM2OTM5NDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Jurassic Park,1993,UA,127 min,"Action, Adventure, Sci-Fi",8.1,A pragmatic paleontologist visiting an almost complete theme park is tasked with protecting a couple of kids after a power failure causes the park's cloned dinosaurs to run loose.,68,Steven Spielberg,Sam Neill,Laura Dern,Jeff Goldblum,Richard Attenborough,867615,"402,453,882" +"https://m.media-amazon.com/images/M/MV5BMmYyOTgwYWItYmU3Ny00M2E2LTk0NWMtMDVlNmQ0MWZiMTMxXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",In the Name of the Father,1993,UA,133 min,"Biography, Crime, Drama",8.1,A man's coerced confession to an I.R.A. bombing he did not commit results in the imprisonment of his father as well. An English lawyer fights to free them.,84,Jim Sheridan,Daniel Day-Lewis,Pete Postlethwaite,Alison Crosbie,Philip King,156842,"25,010,410" +"https://m.media-amazon.com/images/M/MV5BYmFhZmM3Y2MtNDA1Ny00NjkzLWJkM2EtYWU1ZjEwYmNjZDQ0XkEyXkFqcGdeQXVyMTMxMTY0OTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Ba wang bie ji,1993,R,171 min,"Drama, Music, Romance",8.1,Two boys meet at an opera training school in Peking in 1924. Their resulting friendship will span nearly 70 years and will endure some of the most troublesome times in China's history.,,Kaige Chen,Leslie Cheung,Fengyi Zhang,Gong Li,You Ge,25088,"5,216,888" +"https://m.media-amazon.com/images/M/MV5BMjEzNjY5NDcwNV5BMl5BanBnXkFtZTcwNzEwMzg4NA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Dà hóng denglong gaogao guà,1991,PG,125 min,"Drama, History, Romance",8.1,"A young woman becomes the fourth wife of a wealthy lord, and must learn to live with the strict rules and tensions within the household.",,Yimou Zhang,Gong Li,Jingwu Ma,Saifei He,Cuifen Cao,29662,"2,603,061" +"https://m.media-amazon.com/images/M/MV5BOGYwYWNjMzgtNGU4ZC00NWQ2LWEwZjUtMzE1Zjc3NjY3YTU1XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Dead Poets Society,1989,U,128 min,"Comedy, Drama",8.1,Maverick teacher John Keating uses poetry to embolden his boarding school students to new heights of self-expression.,79,Peter Weir,Robin Williams,Robert Sean Leonard,Ethan Hawke,Josh Charles,425457,"95,860,116" +"https://m.media-amazon.com/images/M/MV5BODJmY2Y2OGQtMDg2My00N2Q3LWJmZTUtYTc2ODBjZDVlNDlhXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Stand by Me,1986,U,89 min,"Adventure, Drama",8.1,"After the death of one of his friends, a writer recounts a childhood journey with his friends to find the body of a missing boy.",75,Rob Reiner,Wil Wheaton,River Phoenix,Corey Feldman,Jerry O'Connell,363401,"52,287,414" +"https://m.media-amazon.com/images/M/MV5BMzRjZjdlMjQtODVkYS00N2YzLWJlYWYtMGVlN2E5MWEwMWQzXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Platoon,1986,A,120 min,"Drama, War",8.1,"Chris Taylor, a neophyte recruit in Vietnam, finds himself caught in a battle of wills between two sergeants, one good and the other evil. A shrewd examination of the brutality of war and the duality of man in conflict.",92,Oliver Stone,Charlie Sheen,Tom Berenger,Willem Dafoe,Keith David,381222,"138,530,565" +"https://m.media-amazon.com/images/M/MV5BM2RjMmU3ZWItYzBlMy00ZmJkLWE5YzgtNTVkODdhOWM3NGZhXkEyXkFqcGdeQXVyNDA5Mjg5MjA@._V1_UX67_CR0,0,67,98_AL_.jpg","Paris, Texas",1984,U,145 min,Drama,8.1,"Travis Henderson, an aimless drifter who has been missing for four years, wanders out of the desert and must reconnect with society, himself, his life, and his family.",78,Wim Wenders,Harry Dean Stanton,Nastassja Kinski,Dean Stockwell,Aurore Clément,91188,"2,181,987" +"https://m.media-amazon.com/images/M/MV5BZWFkN2ZhODAtYTNkZS00Y2NjLWIzNDYtNzJjNDNlMzAyNTIyXkEyXkFqcGdeQXVyODEzNjM5OTQ@._V1_UY98_CR1,0,67,98_AL_.jpg",Kaze no tani no Naushika,1984,U,117 min,"Animation, Adventure, Fantasy",8.1,Warrior and pacifist Princess Nausicaä desperately struggles to prevent two warring nations from destroying themselves and their dying planet.,86,Hayao Miyazaki,Sumi Shimamoto,Mahito Tsujimura,Hisako Kyôda,Gorô Naya,150924,"495,770" +"https://m.media-amazon.com/images/M/MV5BNGViZWZmM2EtNGYzZi00ZDAyLTk3ODMtNzIyZTBjN2Y1NmM1XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Thing,1982,A,109 min,"Horror, Mystery, Sci-Fi",8.1,A research team in Antarctica is hunted by a shape-shifting alien that assumes the appearance of its victims.,57,John Carpenter,Kurt Russell,Wilford Brimley,Keith David,Richard Masur,371271,"13,782,838" +"https://m.media-amazon.com/images/M/MV5BZDhlZTYxOTYtYTk3Ny00ZDljLTk3ZmItZTcxZWU5YTIyYmFkXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Pink Floyd: The Wall,1982,UA,95 min,"Drama, Fantasy, Music",8.1,A confined but troubled rock star descends into madness in the midst of his physical and social isolation from everyone.,47,Alan Parker,Bob Geldof,Christine Hargreaves,James Laurenson,Eleanor David,76081,"22,244,207" +"https://m.media-amazon.com/images/M/MV5BYjIzNTYxMTctZjAwNS00YzI3LWExMGMtMGQxNGM5ZTc1NzhlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Fitzcarraldo,1982,R,158 min,"Adventure, Drama",8.1,"The story of Brian Sweeney Fitzgerald, an extremely determined man who intends to build an opera house in the middle of a jungle.",,Werner Herzog,Klaus Kinski,Claudia Cardinale,José Lewgoy,Miguel Ángel Fuentes,31595, +"https://m.media-amazon.com/images/M/MV5BZmQzMDE5ZWQtOTU3ZS00ZjdhLWI0OTctZDNkODk4YThmOTRhL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Fanny och Alexander,1982,A,188 min,Drama,8.1,"Two young Swedish children experience the many comedies and tragedies of their family, the Ekdahls.",100,Ingmar Bergman,Bertil Guve,Pernilla Allwin,Kristina Adolphson,Börje Ahlstedt,57784,"4,971,340" +"https://m.media-amazon.com/images/M/MV5BNzQzMzJhZTEtOWM4NS00MTdhLTg0YjgtMjM4MDRkZjUwZDBlXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Blade Runner,1982,UA,117 min,"Action, Sci-Fi, Thriller",8.1,"A blade runner must pursue and terminate four replicants who stole a ship in space, and have returned to Earth to find their creator.",84,Ridley Scott,Harrison Ford,Rutger Hauer,Sean Young,Edward James Olmos,693827,"32,868,943" +"https://m.media-amazon.com/images/M/MV5BMDVjNjIwOGItNDE3Ny00OThjLWE0NzQtZTU3YjMzZTZjMzhkXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Elephant Man,1980,UA,124 min,"Biography, Drama",8.1,"A Victorian surgeon rescues a heavily disfigured man who is mistreated while scraping a living as a side-show freak. Behind his monstrous façade, there is revealed a person of kindness, intelligence and sophistication.",78,David Lynch,Anthony Hopkins,John Hurt,Anne Bancroft,John Gielgud,220078, +"https://m.media-amazon.com/images/M/MV5BMzAwNjU1OTktYjY3Mi00NDY5LWFlZWUtZjhjNGE0OTkwZDkwXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Life of Brian,1979,R,94 min,Comedy,8.1,"Born on the original Christmas in the stable next door to Jesus Christ, Brian of Nazareth spends his life being mistaken for a messiah.",77,Terry Jones,Graham Chapman,John Cleese,Michael Palin,Terry Gilliam,367250,"20,045,115" +"https://m.media-amazon.com/images/M/MV5BNDhmNTA0ZDMtYjhkNS00NzEzLWIzYTItOGNkMTVmYjE2YmI3XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Deer Hunter,1978,A,183 min,"Drama, War",8.1,An in-depth examination of the ways in which the U.S. Vietnam War impacts and disrupts the lives of people in a small industrial town in Pennsylvania.,86,Michael Cimino,Robert De Niro,Christopher Walken,John Cazale,John Savage,311361,"48,979,328" +"https://m.media-amazon.com/images/M/MV5BMTY5MDMzODUyOF5BMl5BanBnXkFtZTcwMTQ3NTMyNA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Rocky,1976,U,120 min,"Drama, Sport",8.1,A small-time boxer gets a supremely rare chance to fight a heavy-weight champion in a bout in which he strives to go the distance for his self-respect.,70,John G. Avildsen,Sylvester Stallone,Talia Shire,Burt Young,Carl Weathers,518546,"117,235,247" +"https://m.media-amazon.com/images/M/MV5BZGNjYjM2MzItZGQzZi00NmY3LTgxOGUtMTQ2MWQxNWQ2MmMwXkEyXkFqcGdeQXVyNzM0MTUwNTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Network,1976,UA,121 min,Drama,8.1,A television network cynically exploits a deranged former anchor's ravings and revelations about the news media for its own profit.,83,Sidney Lumet,Faye Dunaway,William Holden,Peter Finch,Robert Duvall,144911, +"https://m.media-amazon.com/images/M/MV5BNmY0MWY2NDctZDdmMi00MjA1LTk0ZTQtZDMyZTQ1NTNlYzVjXkEyXkFqcGdeQXVyMjUzOTY1NTc@._V1_UX67_CR0,0,67,98_AL_.jpg",Barry Lyndon,1975,PG,185 min,"Adventure, Drama, History",8.1,An Irish rogue wins the heart of a rich widow and assumes her dead husband's aristocratic position in 18th-century England.,89,Stanley Kubrick,Ryan O'Neal,Marisa Berenson,Patrick Magee,Hardy Krüger,149843, +"https://m.media-amazon.com/images/M/MV5BMTg1MDg3OTk3M15BMl5BanBnXkFtZTgwMDEzMzE5MTE@._V1_UY98_CR0,0,67,98_AL_.jpg",Zerkalo,1975,G,107 min,"Biography, Drama",8.1,"A dying man in his forties remembers his past. His childhood, his mother, the war, personal moments and things that tell of the recent history of all the Russian nation.",,Andrei Tarkovsky,Margarita Terekhova,Filipp Yankovskiy,Ignat Daniltsev,Oleg Yankovskiy,40081,"177,345" +"https://m.media-amazon.com/images/M/MV5BOGMwYmY5ZmEtMzY1Yi00OWJiLTk1Y2MtMzI2MjBhYmZkNTQ0XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Chinatown,1974,UA,130 min,"Drama, Mystery, Thriller",8.1,"A private detective hired to expose an adulterer finds himself caught up in a web of deceit, corruption, and murder.",92,Roman Polanski,Jack Nicholson,Faye Dunaway,John Huston,Perry Lopez,294230,"29,000,000" +"https://m.media-amazon.com/images/M/MV5BOWVmYzQwY2MtOTBjNi00MDNhLWI5OGMtN2RiMDYxODI3MjU5XkEyXkFqcGdeQXVyMjUzOTY1NTc@._V1_UX67_CR0,0,67,98_AL_.jpg",Paper Moon,1973,U,102 min,"Comedy, Crime, Drama",8.1,"During the Great Depression, a con man finds himself saddled with a young girl who may or may not be his daughter, and the two forge an unlikely partnership.",77,Peter Bogdanovich,Ryan O'Neal,Tatum O'Neal,Madeline Kahn,John Hillerman,42285,"30,933,743" +"https://m.media-amazon.com/images/M/MV5BMTg3NzYzOTEtNmE2Ni00M2EyLWJhMjctNjMyMTk4ZTViOGUzXkEyXkFqcGdeQXVyNzQxNDExNTU@._V1_UY98_CR0,0,67,98_AL_.jpg",Viskningar och rop,1972,A,91 min,Drama,8.1,"When a woman dying of cancer in early twentieth-century Sweden is visited by her two sisters, long-repressed feelings between the siblings rise to the surface.",,Ingmar Bergman,Harriet Andersson,Liv Ullmann,Kari Sylwan,Ingrid Thulin,30206,"1,742,348" +"https://m.media-amazon.com/images/M/MV5BZmY4Yjc0OWQtZDRhMy00ODc2LWI2NGYtMWFlODYyN2VlNDQyXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UY98_CR1,0,67,98_AL_.jpg",Solaris,1972,PG,167 min,"Drama, Mystery, Sci-Fi",8.1,A psychologist is sent to a station orbiting a distant planet in order to discover what has caused the crew to go insane.,90,Andrei Tarkovsky,Natalya Bondarchuk,Donatas Banionis,Jüri Järvet,Vladislav Dvorzhetskiy,81021, +"https://m.media-amazon.com/images/M/MV5BMWFjZjRiM2QtZmRkOC00MDUxLTlhYmQtYmY5ZTNiMTI5Nzc2L2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Le samouraï,1967,GP,105 min,"Crime, Drama, Mystery",8.1,After professional hitman Jef Costello is seen by witnesses his efforts to provide himself an alibi drive him further into a corner.,,Jean-Pierre Melville,Alain Delon,François Périer,Nathalie Delon,Cathy Rosier,45434,"39,481" +"https://m.media-amazon.com/images/M/MV5BOWFlNzZhYmYtYTI5YS00MDQyLWIyNTUtNTRjMWUwNTEzNjA0XkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_UX67_CR0,0,67,98_AL_.jpg",Cool Hand Luke,1967,A,127 min,"Crime, Drama",8.1,"A laid back Southern man is sentenced to two years in a rural prison, but refuses to conform.",92,Stuart Rosenberg,Paul Newman,George Kennedy,Strother Martin,J.D. Cannon,161984,"16,217,773" +"https://m.media-amazon.com/images/M/MV5BMTM0YzExY2EtMjUyZi00ZmIwLWFkYTktNjY5NmVkYTdkMjI5XkEyXkFqcGdeQXVyNzQxNDExNTU@._V1_UY98_CR0,0,67,98_AL_.jpg",Persona,1966,,85 min,"Drama, Thriller",8.1,A nurse is put in charge of a mute actress and finds that their personae are melding together.,86,Ingmar Bergman,Bibi Andersson,Liv Ullmann,Margaretha Krook,Gunnar Björnstrand,103191, +"https://m.media-amazon.com/images/M/MV5BNjM2MjMwNzUzN15BMl5BanBnXkFtZTgwMjEzMzE5MTE@._V1_UY98_CR2,0,67,98_AL_.jpg",Andrei Rublev,1966,R,205 min,"Biography, Drama, History",8.1,"The life, times and afflictions of the fifteenth-century Russian iconographer St. Andrei Rublev.",,Andrei Tarkovsky,Anatoliy Solonitsyn,Ivan Lapikov,Nikolay Grinko,Nikolay Sergeev,46947,"102,021" +"https://m.media-amazon.com/images/M/MV5BZWEzMGY4OTQtYTdmMy00M2QwLTliYTQtYWUzYzc3OTA5YzIwXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UY98_CR1,0,67,98_AL_.jpg",La battaglia di Algeri,1966,,121 min,"Drama, War",8.1,"In the 1950s, fear and violence escalate as the people of Algiers fight for independence from the French government.",96,Gillo Pontecorvo,Brahim Hadjadj,Jean Martin,Yacef Saadi,Samia Kerbash,53089,"55,908" +"https://m.media-amazon.com/images/M/MV5BZTg3M2ExY2EtZmI5Yy00YWM1LTg4NzItZWEzZTgxNzE2MjhhXkEyXkFqcGdeQXVyNDE5MTU2MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",El ángel exterminador,1962,,95 min,"Drama, Fantasy",8.1,The guests at an upper-class dinner party find themselves unable to leave.,,Luis Buñuel,Silvia Pinal,Jacqueline Andere,Enrique Rambal,José Baviera,29682, +"https://m.media-amazon.com/images/M/MV5BZmI0M2VmNTgtMWVhYS00Zjg1LTk1YTYtNmJmMjRkZmMwYTc2XkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",What Ever Happened to Baby Jane?,1962,Passed,134 min,"Drama, Horror, Thriller",8.1,A former child star torments her paraplegic sister in their decaying Hollywood mansion.,75,Robert Aldrich,Bette Davis,Joan Crawford,Victor Buono,Wesley Addy,50058,"4,050,000" +"https://m.media-amazon.com/images/M/MV5BZmY3MDlmODctYTY3Yi00NzYyLWIxNTUtYjVlZWZjMmMwZTBkXkEyXkFqcGdeQXVyMzAxNjg3MjQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Sanjuro,1962,U,96 min,"Action, Comedy, Crime",8.1,"A crafty samurai helps a young man and his fellow clansmen save his uncle, who has been framed and imprisoned by a corrupt superintendent.",,Akira Kurosawa,Toshirô Mifune,Tatsuya Nakadai,Keiju Kobayashi,Yûnosuke Itô,33044, +"https://m.media-amazon.com/images/M/MV5BMGEyNzhkYzktMGMyZS00YzRiLWJlYjktZjJkOTU5ZDY0ZGI4XkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_UX67_CR0,0,67,98_AL_.jpg",The Man Who Shot Liberty Valance,1962,,123 min,"Drama, Western",8.1,A senator returns to a western town for the funeral of an old friend and tells the story of his origins.,94,John Ford,James Stewart,John Wayne,Vera Miles,Lee Marvin,68827, +"https://m.media-amazon.com/images/M/MV5BYTYzYzBhYjQtNDQxYS00MmUwLTkyZjgtZWVkOWFjNzE5OTI2XkEyXkFqcGdeQXVyNjMxMjkwMjI@._V1_UX67_CR0,0,67,98_AL_.jpg",Ivanovo detstvo,1962,,95 min,"Drama, War",8.1,"In WW2, twelve year old Soviet orphan Ivan Bondarev works for the Soviet army as a scout behind the German lines and strikes a friendship with three sympathetic Soviet officers.",,Andrei Tarkovsky,Eduard Abalov,Nikolay Burlyaev,Valentin Zubkov,Evgeniy Zharikov,31728, +"https://m.media-amazon.com/images/M/MV5BZjgyMzZkMGUtNTBhZC00OTkzLWI4ZmMtYzcwMzc5MjQ0YTM3XkEyXkFqcGdeQXVyMTMxMTY0OTQ@._V1_UY98_CR3,0,67,98_AL_.jpg",Jungfrukällan,1960,A,89 min,Drama,8.1,"An innocent yet pampered young virgin and her family's pregnant and jealous servant set out to deliver candles to church, but only one returns from events that transpire in the woods along the way.",,Ingmar Bergman,Max von Sydow,Birgitta Valberg,Gunnel Lindblom,Birgitta Pettersson,26697,"1,526,000" +"https://m.media-amazon.com/images/M/MV5BMGQ5ODNkNWYtYTgxZS00YjJkLThhODAtYzUwNGNiYjRmNjdkXkEyXkFqcGdeQXVyMTg2NTc4MzA@._V1_UY98_CR4,0,67,98_AL_.jpg",Inherit the Wind,1960,Passed,128 min,"Biography, Drama, History",8.1,"Based on a real-life case in 1925, two great lawyers argue the case for and against a science teacher accused of the crime of teaching evolution.",75,Stanley Kramer,Spencer Tracy,Fredric March,Gene Kelly,Dick York,27254, +"https://m.media-amazon.com/images/M/MV5BYTQ4MjA4NmYtYjRhNi00MTEwLTg0NjgtNjk3ODJlZGU4NjRkL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UY98_CR3,0,67,98_AL_.jpg",Les quatre cents coups,1959,,99 min,"Crime, Drama",8.1,"A young boy, left without attention, delves into a life of petty crime.",,François Truffaut,Jean-Pierre Léaud,Albert Rémy,Claire Maurier,Guy Decomble,105291, +"https://m.media-amazon.com/images/M/MV5BNjgxY2JiZDYtZmMwOC00ZmJjLWJmODUtMTNmNWNmYWI5ODkwL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Ben-Hur,1959,U,212 min,"Adventure, Drama, History",8.1,"After a Jewish prince is betrayed and sent into slavery by a Roman friend, he regains his freedom and comes back for revenge.",90,William Wyler,Charlton Heston,Jack Hawkins,Stephen Boyd,Haya Harareet,219466,"74,700,000" +"https://m.media-amazon.com/images/M/MV5BYjJkN2Y5MTktZDRhOS00NTUwLWFiMzEtMTVlNWU4ODM0Y2E5XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UY98_CR1,0,67,98_AL_.jpg",Kakushi-toride no san-akunin,1958,,139 min,"Adventure, Drama",8.1,"Lured by gold, two greedy peasants unknowingly escort a princess and her general across enemy lines.",,Akira Kurosawa,Toshirô Mifune,Misa Uehara,Minoru Chiaki,Kamatari Fujiwara,34797, +"https://m.media-amazon.com/images/M/MV5BOTdhNmUxZmQtNmMwNC00MzE3LWE1MTUtZDgxZTYwYjEzZjcwXkEyXkFqcGdeQXVyNTA1NjYyMDk@._V1_UY98_CR0,0,67,98_AL_.jpg",Le notti di Cabiria,1957,,110 min,Drama,8.1,A waifish prostitute wanders the streets of Rome looking for true love but finds only heartbreak.,,Federico Fellini,Giulietta Masina,François Périer,Franca Marzi,Dorian Gray,42940,"752,045" +"https://m.media-amazon.com/images/M/MV5BNGYxZjA2M2ItYTRmNS00NzRmLWJkYzgtYTdiNGFlZDI5ZjNmXkEyXkFqcGdeQXVyNDE5MTU2MDE@._V1_UY98_CR0,0,67,98_AL_.jpg",Kumonosu-jô,1957,,110 min,"Drama, History",8.1,"A war-hardened general, egged on by his ambitious wife, works to fulfill a prophecy that he would become lord of Spider's Web Castle.",,Akira Kurosawa,Toshirô Mifune,Minoru Chiaki,Isuzu Yamada,Takashi Shimura,46678, +"https://m.media-amazon.com/images/M/MV5BMGVhNjhjODktODgxYS00MDdhLTlkZjktYTkyNzQxMTU0ZDYxXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Bridge on the River Kwai,1957,PG,161 min,"Adventure, Drama, War",8.1,"British POWs are forced to build a railway bridge across the river Kwai for their Japanese captors, not knowing that the allied forces are planning to destroy it.",87,David Lean,William Holden,Alec Guinness,Jack Hawkins,Sessue Hayakawa,203463,"44,908,000" +"https://m.media-amazon.com/images/M/MV5BY2I0MWFiZDMtNWQyYy00Njk5LTk3MDktZjZjNTNmZmVkYjkxXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",On the Waterfront,1954,A,108 min,"Crime, Drama, Thriller",8.1,An ex-prize fighter turned longshoreman struggles to stand up to his corrupt union bosses.,91,Elia Kazan,Marlon Brando,Karl Malden,Lee J. Cobb,Rod Steiger,142107,"9,600,000" +"https://m.media-amazon.com/images/M/MV5BZDdkNzMwZmUtY2Q5MS00ZmM2LWJhYjItYTBjMWY0MGM4MDRjXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UY98_CR0,0,67,98_AL_.jpg",Le salaire de la peur,1953,U,131 min,"Adventure, Drama, Thriller",8.1,"In a decrepit South American village, four men are hired to transport an urgent nitroglycerine shipment without the equipment that would make it safe.",85,Henri-Georges Clouzot,Yves Montand,Charles Vanel,Peter van Eyck,Folco Lulli,54588, +"https://m.media-amazon.com/images/M/MV5BNDUzZjlhZTYtN2E5MS00ODQ3LWI1ZjgtNzdiZmI0NTZiZTljXkEyXkFqcGdeQXVyMjI4MjA5MzA@._V1_UX67_CR0,0,67,98_AL_.jpg",Ace in the Hole,1951,Approved,111 min,"Drama, Film-Noir",8.1,"A frustrated former big-city journalist now stuck working for an Albuquerque newspaper exploits a story about a man trapped in a cave to rekindle his career, but the situation quickly escalates into an out-of-control circus.",72,Billy Wilder,Kirk Douglas,Jan Sterling,Robert Arthur,Porter Hall,31568,"3,969,893" +"https://m.media-amazon.com/images/M/MV5BZmI5NTA3MjItYzdhMi00MWMxLTg3OWMtYWQyYjg5MTFmM2U0L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",White Heat,1949,,114 min,"Action, Crime, Drama",8.1,A psychopathic criminal with a mother complex makes a daring break from prison and leads his old gang in a chemical plant payroll heist.,,Raoul Walsh,James Cagney,Virginia Mayo,Edmond O'Brien,Margaret Wycherly,29807, +"https://m.media-amazon.com/images/M/MV5BYjE2OTdhMWUtOGJlMy00ZDViLWIzZjgtYjZkZGZmMDZjYmEyXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Third Man,1949,Approved,104 min,"Film-Noir, Mystery, Thriller",8.1,"Pulp novelist Holly Martins travels to shadowy, postwar Vienna, only to find himself investigating the mysterious death of an old friend, Harry Lime.",97,Carol Reed,Orson Welles,Joseph Cotten,Alida Valli,Trevor Howard,158731,"449,191" +"https://m.media-amazon.com/images/M/MV5BOWRmNGEwZjUtZjEwNS00OGZmLThhMmEtZTJlMTU5MGQ3ZWUwXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Red Shoes,1948,,135 min,"Drama, Music, Romance",8.1,A young ballet dancer is torn between the man she loves and her pursuit to become a prima ballerina.,,Michael Powell,Emeric Pressburger,Anton Walbrook,Marius Goring,Moira Shearer,30935,"10,900,000" +"https://m.media-amazon.com/images/M/MV5BNzc1MTcyNTQ5N15BMl5BanBnXkFtZTgwMzgwMDI0MjE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Shop Around the Corner,1940,,99 min,"Comedy, Drama, Romance",8.1,"Two employees at a gift shop can barely stand each other, without realizing that they are falling in love through the post as each other's anonymous pen pal.",96,Ernst Lubitsch,Margaret Sullavan,James Stewart,Frank Morgan,Joseph Schildkraut,28450,"203,300" +"https://m.media-amazon.com/images/M/MV5BYTcxYWExOTMtMWFmYy00ZjgzLWI0YjktNWEzYzJkZTg0NDdmL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UY98_CR0,0,67,98_AL_.jpg",Rebecca,1940,Approved,130 min,"Drama, Mystery, Romance",8.1,A self-conscious woman juggles adjusting to her new role as an aristocrat's wife and avoiding being intimidated by his first wife's spectral presence.,86,Alfred Hitchcock,Laurence Olivier,Joan Fontaine,George Sanders,Judith Anderson,123942,"4,360,000" +"https://m.media-amazon.com/images/M/MV5BZTYwYjYxYzgtMDE1Ni00NzU4LWJlMTEtODQ5YmJmMGJhZjI5L2ltYWdlXkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Mr. Smith Goes to Washington,1939,Passed,129 min,"Comedy, Drama",8.1,"A naive man is appointed to fill a vacancy in the United States Senate. His plans promptly collide with political corruption, but he doesn't back down.",73,Frank Capra,James Stewart,Jean Arthur,Claude Rains,Edward Arnold,107017,"9,600,000" +"https://m.media-amazon.com/images/M/MV5BYjUyZWZkM2UtMzYxYy00ZmQ3LWFmZTQtOGE2YjBkNjA3YWZlXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Gone with the Wind,1939,U,238 min,"Drama, History, Romance",8.1,A manipulative woman and a roguish man conduct a turbulent romance during the American Civil War and Reconstruction periods.,97,Victor Fleming,George Cukor,Sam Wood,Clark Gable,Vivien Leigh,290074,"198,676,459" +"https://m.media-amazon.com/images/M/MV5BMTg3MTI5NTk0N15BMl5BanBnXkFtZTgwMjU1MDM5MTE@._V1_UY98_CR2,0,67,98_AL_.jpg",La Grande Illusion,1937,,113 min,"Drama, War",8.1,"During WWI, two French soldiers are captured and imprisoned in a German P.O.W. camp. Several escape attempts follow until they are eventually sent to a seemingly inescapable fortress.",,Jean Renoir,Jean Gabin,Dita Parlo,Pierre Fresnay,Erich von Stroheim,33829,"172,885" +"https://m.media-amazon.com/images/M/MV5BYzJmMWE5NjAtNWMyZS00NmFiLWIwMDgtZDE2NzczYWFhNzIzXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",It Happened One Night,1934,Approved,105 min,"Comedy, Romance",8.1,"A renegade reporter and a crazy young heiress meet on a bus heading for New York, and end up stuck with each other when the bus leaves them behind at one of the stops.",87,Frank Capra,Clark Gable,Claudette Colbert,Walter Connolly,Roscoe Karns,94016,"4,360,000" +"https://m.media-amazon.com/images/M/MV5BNjBjNDJiYTUtOWY0OS00OGVmLTg2YzctMTE0NzVhODM1ZWJmXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",La passion de Jeanne d'Arc,1928,Passed,110 min,"Biography, Drama, History",8.1,"In 1431, Jeanne d'Arc is placed on trial on charges of heresy. The ecclesiastical jurists attempt to force Jeanne to recant her claims of holy visions.",,Carl Theodor Dreyer,Maria Falconetti,Eugene Silvain,André Berley,Maurice Schutz,47676,"21,877" +"https://m.media-amazon.com/images/M/MV5BM2QwYWQ0MWMtNzcwOC00N2Q2LWE1MDEtZmQxZjhiM2U1YzFhXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Circus,1928,Passed,72 min,"Comedy, Romance",8.1,The Tramp finds work and the girl of his dreams at a circus.,90,Charles Chaplin,Charles Chaplin,Merna Kennedy,Al Ernest Garcia,Harry Crocker,30205, +"https://m.media-amazon.com/images/M/MV5BNDVkYmYwM2ItNzRiMy00NWQ4LTlhMjMtNDI1ZDYyOGVmMzJjXkEyXkFqcGdeQXVyNTgzMzU5MDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Sunrise: A Song of Two Humans,1927,Passed,94 min,"Drama, Romance",8.1,An allegorical tale about a man fighting the good and evil within him. Both sides are made flesh - one a sophisticated woman he is attracted to and the other his wife.,,F.W. Murnau,George O'Brien,Janet Gaynor,Margaret Livingston,Bodil Rosing,46865,"539,540" +"https://m.media-amazon.com/images/M/MV5BYmRiMDFlYjYtOTMwYy00OGY2LWE0Y2QtYzQxOGNhZmUwNTIxXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",The General,1926,Passed,67 min,"Action, Adventure, Comedy",8.1,"When Union spies steal an engineer's beloved locomotive, he pursues it single-handedly and straight through enemy lines.",,Clyde Bruckman,Buster Keaton,Buster Keaton,Marion Mack,Glen Cavender,81156,"1,033,895" +"https://m.media-amazon.com/images/M/MV5BNWJiNGJiMTEtMGM3OC00ZWNlLTgwZTgtMzdhNTRiZjk5MTQ1XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR1,0,67,98_AL_.jpg",Das Cabinet des Dr. Caligari,1920,,76 min,"Fantasy, Horror, Mystery",8.1,"Hypnotist Dr. Caligari uses a somnambulist, Cesare, to commit murders.",,Robert Wiene,Werner Krauss,Conrad Veidt,Friedrich Feher,Lil Dagover,57428, +"https://m.media-amazon.com/images/M/MV5BNjZlMDdmN2YtYThmZi00NGQzLTk0ZTQtNTUyZDFmODExOGNiXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR0,0,67,98_AL_.jpg",Badhaai ho,2018,UA,124 min,"Comedy, Drama",8,A man is embarrassed when he finds out his mother is pregnant.,,Amit Ravindernath Sharma,Ayushmann Khurrana,Neena Gupta,Gajraj Rao,Sanya Malhotra,27978, +"https://m.media-amazon.com/images/M/MV5BNjJkYTc5N2UtMGRlMC00M2FmLTk0ZWMtOTYxNDUwNjI2YzljXkEyXkFqcGdeQXVyNDg4NjY5OTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Togo,2019,U,113 min,"Adventure, Biography, Drama",8,"The story of Togo, the sled dog who led the 1925 serum run yet was considered by most to be too small and weak to lead such an intense race.",69,Ericson Core,Willem Dafoe,Julianne Nicholson,Christopher Heyerdahl,Richard Dormer,37556, +"https://m.media-amazon.com/images/M/MV5BMGE1ZTkyOTMtMTdiZS00YzI2LTlmYWQtOTE5YWY0NWVlNjlmXkEyXkFqcGdeQXVyNjQ3ODkxMjE@._V1_UY98_CR0,0,67,98_AL_.jpg",Airlift,2016,UA,130 min,"Drama, History",8,"When Iraq invades Kuwait in August 1990, a callous Indian businessman becomes the spokesperson for more than 170,000 stranded countrymen.",,Raja Menon,Akshay Kumar,Nimrat Kaur,Kumud Mishra,Prakash Belawadi,52897, +"https://m.media-amazon.com/images/M/MV5BMjE1NjQ5ODc2NV5BMl5BanBnXkFtZTgwOTM5ODIxNjE@._V1_UY98_CR0,0,67,98_AL_.jpg",Bajrangi Bhaijaan,2015,UA,163 min,"Action, Adventure, Comedy",8,An Indian man with a magnanimous heart takes a young mute Pakistani girl back to her homeland to reunite her with her family.,,Kabir Khan,Salman Khan,Harshaali Malhotra,Nawazuddin Siddiqui,Kareena Kapoor,72245,"8,178,001" +"https://m.media-amazon.com/images/M/MV5BYTdhNjBjZDctYTlkYy00ZGIxLWFjYTktODk5ZjNlMzI4NjI3XkEyXkFqcGdeQXVyMjY1MjkzMjE@._V1_UY98_CR0,0,67,98_AL_.jpg",Baby,2015,UA,159 min,"Action, Crime, Thriller",8,"An elite counter-intelligence unit learns of a plot, masterminded by a maniacal madman. With the clock ticking, it's up to them to track the terrorists' international tentacles and prevent them from striking at the heart of India.",,Neeraj Pandey,Akshay Kumar,Danny Denzongpa,Rana Daggubati,Taapsee Pannu,52848, +"https://m.media-amazon.com/images/M/MV5BMzUzNDM2NzM2MV5BMl5BanBnXkFtZTgwNTM3NTg4OTE@._V1_UX67_CR0,0,67,98_AL_.jpg",La La Land,2016,A,128 min,"Comedy, Drama, Music",8,"While navigating their careers in Los Angeles, a pianist and an actress fall in love while attempting to reconcile their aspirations for the future.",94,Damien Chazelle,Ryan Gosling,Emma Stone,Rosemarie DeWitt,J.K. Simmons,505918,"151,101,803" +"https://m.media-amazon.com/images/M/MV5BMjA3NjkzNjg2MF5BMl5BanBnXkFtZTgwMDkyMzgzMDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Lion,2016,U,118 min,"Biography, Drama",8,"A five-year-old Indian boy is adopted by an Australian couple after getting lost hundreds of kilometers from home. 25 years later, he sets out to find his lost family.",69,Garth Davis,Dev Patel,Nicole Kidman,Rooney Mara,Sunny Pawar,213970,"51,739,495" +"https://m.media-amazon.com/images/M/MV5BMTc2MTQ3MDA1Nl5BMl5BanBnXkFtZTgwODA3OTI4NjE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Martian,2015,UA,144 min,"Adventure, Drama, Sci-Fi",8,"An astronaut becomes stranded on Mars after his team assume him dead, and must rely on his ingenuity to find a way to signal to Earth that he is alive.",80,Ridley Scott,Matt Damon,Jessica Chastain,Kristen Wiig,Kate Mara,760094,"228,433,663" +"https://m.media-amazon.com/images/M/MV5BOTMyMjEyNzIzMV5BMl5BanBnXkFtZTgwNzIyNjU0NzE@._V1_UX67_CR0,0,67,98_AL_.jpg",Zootopia,2016,U,108 min,"Animation, Adventure, Comedy",8,"In a city of anthropomorphic animals, a rookie bunny cop and a cynical con artist fox must work together to uncover a conspiracy.",78,Byron Howard,Rich Moore,Jared Bush,Ginnifer Goodwin,Jason Bateman,434143,"341,268,248" +"https://m.media-amazon.com/images/M/MV5BYWVlMjVhZWYtNWViNC00ODFkLTk1MmItYjU1MDY5ZDdhMTU3XkEyXkFqcGdeQXVyODIwMDI1NjM@._V1_UX67_CR0,0,67,98_AL_.jpg",Bãhubali: The Beginning,2015,UA,159 min,"Action, Drama",8,"In ancient India, an adventurous and daring man becomes involved in a decades-old feud between two warring peoples.",,S.S. Rajamouli,Prabhas,Rana Daggubati,Ramya Krishnan,Sathyaraj,102972,"6,738,000" +"https://m.media-amazon.com/images/M/MV5BNThmMWMyMWMtOWRiNy00MGY0LTg1OTUtNjYzODg2MjdlZGU5XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR1,0,67,98_AL_.jpg",Kaguyahime no monogatari,2013,U,137 min,"Animation, Adventure, Drama",8,"Found inside a shining stalk of bamboo by an old bamboo cutter and his wife, a tiny girl grows rapidly into an exquisite young lady. The mysterious young princess enthralls all who encounter her, but ultimately she must confront her fate, the punishment for her crime.",89,Isao Takahata,Chloë Grace Moretz,James Caan,Mary Steenburgen,James Marsden,38746,"1,506,975" +"https://m.media-amazon.com/images/M/MV5BYjFhOWY0OTgtNDkzMC00YWJkLTk1NGEtYWUxNjhmMmQ5ZjYyXkEyXkFqcGdeQXVyMjMxOTE0ODA@._V1_UX67_CR0,0,67,98_AL_.jpg",Wonder,2017,U,113 min,"Drama, Family",8,"Based on the New York Times bestseller, this movie tells the incredibly inspiring and heartwarming story of August Pullman, a boy with facial differences who enters the fifth grade, attending a mainstream elementary school for the first time.",66,Stephen Chbosky,Jacob Tremblay,Owen Wilson,Izabela Vidovic,Julia Roberts,141923,"132,422,809" +"https://m.media-amazon.com/images/M/MV5BZDkzMTQ1YTMtMWY4Ny00MzExLTkzYzEtNzZhOTczNzU2NTU1XkEyXkFqcGdeQXVyODY3NjMyMDU@._V1_UY98_CR4,0,67,98_AL_.jpg",Gully Boy,2019,UA,154 min,"Drama, Music, Romance",8,A coming-of-age story based on the lives of street rappers in Mumbai.,65,Zoya Akhtar,Vijay Varma,Nakul Roshan Sahdev,Ranveer Singh,Vijay Raaz,31886,"5,566,534" +"https://m.media-amazon.com/images/M/MV5BMTQ1NDI5MjMzNF5BMl5BanBnXkFtZTcwMTc0MDQwOQ@@._V1_UY98_CR0,0,67,98_AL_.jpg",Special Chabbis,2013,UA,144 min,"Crime, Drama, Thriller",8,A gang of con-men rob prominent rich businessmen and politicians by posing as C.B.I and income tax officers.,,Neeraj Pandey,Akshay Kumar,Anupam Kher,Manoj Bajpayee,Jimmy Sheirgill,51069,"1,079,369" +"https://m.media-amazon.com/images/M/MV5BMTEwNjE2OTM4NDZeQTJeQWpwZ15BbWU3MDE2MTE4OTk@._V1_UX67_CR0,0,67,98_AL_.jpg",Short Term 12,2013,R,96 min,Drama,8,A 20-something supervising staff member of a residential treatment facility navigates the troubled waters of that world alongside her co-worker and longtime boyfriend.,82,Destin Daniel Cretton,Brie Larson,Frantz Turner,John Gallagher Jr.,Kaitlyn Dever,81770,"1,010,414" +"https://m.media-amazon.com/images/M/MV5BMTg5MTE2NjA4OV5BMl5BanBnXkFtZTgwMTUyMjczMTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Serbuan maut 2: Berandal,2014,A,150 min,"Action, Crime, Thriller",8,"Only a short time after the first raid, Rama goes undercover with the thugs of Jakarta and plans to bring down the syndicate and uncover the corruption within his police force.",71,Gareth Evans,Iko Uwais,Yayan Ruhian,Arifin Putra,Oka Antara,114316,"2,625,803" +"https://m.media-amazon.com/images/M/MV5BOTgwMzFiMWYtZDhlNS00ODNkLWJiODAtZDVhNzgyNzJhYjQ4L2ltYWdlXkEyXkFqcGdeQXVyNzEzOTYxNTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",The Imitation Game,2014,UA,114 min,"Biography, Drama, Thriller",8,"During World War II, the English mathematical genius Alan Turing tries to crack the German Enigma code with help from fellow mathematicians.",73,Morten Tyldum,Benedict Cumberbatch,Keira Knightley,Matthew Goode,Allen Leech,685201,"91,125,683" +"https://m.media-amazon.com/images/M/MV5BMTAwMjU5OTgxNjZeQTJeQWpwZ15BbWU4MDUxNDYxODEx._V1_UX67_CR0,0,67,98_AL_.jpg",Guardians of the Galaxy,2014,UA,121 min,"Action, Adventure, Comedy",8,A group of intergalactic criminals must pull together to stop a fanatical warrior with plans to purge the universe.,76,James Gunn,Chris Pratt,Vin Diesel,Bradley Cooper,Zoe Saldana,1043455,"333,176,600" +"https://m.media-amazon.com/images/M/MV5BNzA1Njg4NzYxOV5BMl5BanBnXkFtZTgwODk5NjU3MzI@._V1_UX67_CR0,0,67,98_AL_.jpg",Blade Runner 2049,2017,UA,164 min,"Action, Drama, Mystery",8,"Young Blade Runner K's discovery of a long-buried secret leads him to track down former Blade Runner Rick Deckard, who's been missing for thirty years.",81,Denis Villeneuve,Harrison Ford,Ryan Gosling,Ana de Armas,Dave Bautista,461823,"92,054,159" +"https://m.media-amazon.com/images/M/MV5BMjA1Nzk0OTM2OF5BMl5BanBnXkFtZTgwNjU2NjEwMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Her,2013,A,126 min,"Drama, Romance, Sci-Fi",8,"In a near future, a lonely writer develops an unlikely relationship with an operating system designed to meet his every need.",90,Spike Jonze,Joaquin Phoenix,Amy Adams,Scarlett Johansson,Rooney Mara,540772,"25,568,251" +"https://m.media-amazon.com/images/M/MV5BMTA2NDc3Njg5NDVeQTJeQWpwZ15BbWU4MDc1NDcxNTUz._V1_UX67_CR0,0,67,98_AL_.jpg",Bohemian Rhapsody,2018,UA,134 min,"Biography, Drama, Music",8,"The story of the legendary British rock band Queen and lead singer Freddie Mercury, leading up to their famous performance at Live Aid (1985).",49,Bryan Singer,Rami Malek,Lucy Boynton,Gwilym Lee,Ben Hardy,450349,"216,428,042" +"https://m.media-amazon.com/images/M/MV5BMDE5OWMzM2QtOTU2ZS00NzAyLWI2MDEtOTRlYjIxZGM0OWRjXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Revenant,2015,A,156 min,"Action, Adventure, Drama",8,A frontiersman on a fur trading expedition in the 1820s fights for survival after being mauled by a bear and left for dead by members of his own hunting team.,76,Alejandro G. Iñárritu,Leonardo DiCaprio,Tom Hardy,Will Poulter,Domhnall Gleeson,705589,"183,637,894" +"https://m.media-amazon.com/images/M/MV5BZThjMmQ5YjktMTUyMC00MjljLWJmMTAtOWIzNDIzY2VhNzQ0XkEyXkFqcGdeQXVyMTAyNjg4NjE0._V1_UX67_CR0,0,67,98_AL_.jpg",The Perks of Being a Wallflower,2012,UA,103 min,"Drama, Romance",8,An introvert freshman is taken under the wings of two seniors who welcome him to the real world,67,Stephen Chbosky,Logan Lerman,Emma Watson,Ezra Miller,Paul Rudd,462252,"17,738,570" +"https://m.media-amazon.com/images/M/MV5BMjEzMzMxOTUyNV5BMl5BanBnXkFtZTcwNjI3MDc5Ng@@._V1_UX67_CR0,0,67,98_AL_.jpg",Tropa de Elite 2: O Inimigo Agora é Outro,2010,,115 min,"Action, Crime, Drama",8,"After a prison riot, former-Captain Nascimento, now a high ranking security officer in Rio de Janeiro, is swept into a bloody political dispute that involves government officials and paramilitary groups.",71,José Padilha,Wagner Moura,Irandhir Santos,André Ramiro,Milhem Cortaz,79200,"100,119" +"https://m.media-amazon.com/images/M/MV5BMzU5MjEwMTg2Nl5BMl5BanBnXkFtZTcwNzM3MTYxNA@@._V1_UY98_CR0,0,67,98_AL_.jpg",The King's Speech,2010,U,118 min,"Biography, Drama, History",8,"The story of King George VI, his impromptu ascension to the throne of the British Empire in 1936, and the speech therapist who helped the unsure monarch overcome his stammer.",88,Tom Hooper,Colin Firth,Geoffrey Rush,Helena Bonham Carter,Derek Jacobi,639603,"138,797,449" +"https://m.media-amazon.com/images/M/MV5BMTM5OTMyMjIxOV5BMl5BanBnXkFtZTcwNzU4MjIwNQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Help,2011,UA,146 min,Drama,8,"An aspiring author during the civil rights movement of the 1960s decides to write a book detailing the African American maids' point of view on the white families for which they work, and the hardships they go through on a daily basis.",62,Tate Taylor,Emma Stone,Viola Davis,Octavia Spencer,Bryce Dallas Howard,428521,"169,708,112" +"https://m.media-amazon.com/images/M/MV5BYzE5MjY1ZDgtMTkyNC00MTMyLThhMjAtZGI5OTE1NzFlZGJjXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Deadpool,2016,R,108 min,"Action, Adventure, Comedy",8,"A wisecracking mercenary gets experimented on and becomes immortal but ugly, and sets out to track down the man who ruined his looks.",65,Tim Miller,Ryan Reynolds,Morena Baccarin,T.J. Miller,Ed Skrein,902669,"363,070,709" +"https://m.media-amazon.com/images/M/MV5BMTQ0MzQxODQ0MV5BMl5BanBnXkFtZTgwNTQ0NzY4NDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Darbareye Elly,2009,TV-PG,119 min,"Drama, Mystery",8,The mysterious disappearance of a kindergarten teacher during a picnic in the north of Iran is followed by a series of misadventures for her fellow travelers.,87,Asghar Farhadi,Golshifteh Farahani,Shahab Hosseini,Taraneh Alidoosti,Merila Zare'i,45803,"106,662" +"https://m.media-amazon.com/images/M/MV5BYjU1NjczNzYtYmFjOC00NzkxLTg4YTUtNGYzMTk3NTU0ZDE3XkEyXkFqcGdeQXVyNDUzOTQ5MjY@._V1_UY98_CR0,0,67,98_AL_.jpg",Dev.D,2009,A,144 min,"Drama, Romance",8,"After breaking up with his childhood sweetheart, a young man finds solace in drugs. Meanwhile, a teenage girl is caught in the world of prostitution. Will they be destroyed, or will they find redemption?",,Anurag Kashyap,Abhay Deol,Mahie Gill,Kalki Koechlin,Dibyendu Bhattacharya,28749,"10,950" +"https://m.media-amazon.com/images/M/MV5BNTFmMjM3M2UtOTIyZC00Zjk3LTkzODUtYTdhNGRmNzFhYzcyXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Yip Man,2008,R,106 min,"Action, Biography, Drama",8,"During the Japanese invasion of China, a wealthy martial artist is forced to leave his home when his city is occupied. With little means of providing for themselves, Ip Man and the remaining members of the city must find a way to survive.",59,Wilson Yip,Donnie Yen,Simon Yam,Siu-Wong Fan,Ka Tung Lam,211427, +"https://m.media-amazon.com/images/M/MV5BMTUyMTA4NDYzMV5BMl5BanBnXkFtZTcwMjk5MzcxMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",My Name Is Khan,2010,UA,165 min,Drama,8,An Indian Muslim man with Asperger's syndrome takes a challenge to speak to the President of the United States seriously and embarks on a cross-country journey.,50,Karan Johar,Shah Rukh Khan,Kajol,Sheetal Menon,Katie A. Keane,98575,"4,018,695" +"https://m.media-amazon.com/images/M/MV5BMjE2NjEyMDg0M15BMl5BanBnXkFtZTcwODYyODg5Mg@@._V1_UY98_CR0,0,67,98_AL_.jpg",Nefes: Vatan Sagolsun,2009,,128 min,"Action, Drama, Thriller",8,Story of 40-man Turkish task force who must defend a relay station.,,Levent Semerci,Erdem Can,Mete Horozoglu,Ilker Kizmaz,Baris Bagci,31838, +"https://m.media-amazon.com/images/M/MV5BZmNjZWI3NzktYWI1Mi00OTAyLWJkNTYtMzUwYTFlZDA0Y2UwXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Slumdog Millionaire,2008,UA,120 min,"Drama, Romance",8,"A Mumbai teenager reflects on his life after being accused of cheating on the Indian version of ""Who Wants to be a Millionaire?"".",84,Danny Boyle,Loveleen Tandan,Dev Patel,Freida Pinto,Saurabh Shukla,798882,"141,319,928" +"https://m.media-amazon.com/images/M/MV5BNzY2NzI4OTE5MF5BMl5BanBnXkFtZTcwMjMyNDY4Mw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Black Swan,2010,A,108 min,"Drama, Thriller",8,"A committed dancer struggles to maintain her sanity after winning the lead role in a production of Tchaikovsky's ""Swan Lake"".",79,Darren Aronofsky,Natalie Portman,Mila Kunis,Vincent Cassel,Winona Ryder,699673,"106,954,678" +"https://m.media-amazon.com/images/M/MV5BYmI1ODU5ZjMtNWUyNC00YzllLThjNzktODE1M2E4OTVmY2E5XkEyXkFqcGdeQXVyMTExNzQzMDE0._V1_UY98_CR1,0,67,98_AL_.jpg",Tropa de Elite,2007,R,115 min,"Action, Crime, Drama",8,"In 1997 Rio de Janeiro, Captain Nascimento has to find a substitute for his position while trying to take down drug dealers and criminals before the Pope visits.",33,José Padilha,Wagner Moura,André Ramiro,Caio Junqueira,Milhem Cortaz,98097,"8,060" +"https://m.media-amazon.com/images/M/MV5BNDYxNjQyMjAtNTdiOS00NGYwLWFmNTAtNThmYjU5ZGI2YTI1XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",The Avengers,2012,UA,143 min,"Action, Adventure, Sci-Fi",8,Earth's mightiest heroes must come together and learn to fight as a team if they are going to stop the mischievous Loki and his alien army from enslaving humanity.,69,Joss Whedon,Robert Downey Jr.,Chris Evans,Scarlett Johansson,Jeremy Renner,1260806,"623,279,547" +"https://m.media-amazon.com/images/M/MV5BMGRkZThmYzEtYjQxZC00OWEzLThjYjAtYzFkMjY0NGZkZWI4XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Persepolis,2007,PG-13,96 min,"Animation, Biography, Drama",8,A precocious and outspoken Iranian girl grows up during the Islamic Revolution.,90,Vincent Paronnaud,Marjane Satrapi,Chiara Mastroianni,Catherine Deneuve,Gena Rowlands,88656,"4,445,756" +"https://m.media-amazon.com/images/M/MV5BMTYwMTA4MzgyNF5BMl5BanBnXkFtZTgwMjEyMjE0MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Dallas Buyers Club,2013,R,117 min,"Biography, Drama",8,"In 1985 Dallas, electrician and hustler Ron Woodroof works around the system to help AIDS patients get the medication they need after he is diagnosed with the disease.",80,Jean-Marc Vallée,Matthew McConaughey,Jennifer Garner,Jared Leto,Steve Zahn,441614,"27,298,285" +"https://m.media-amazon.com/images/M/MV5BMTQ5NjQ0NDI3NF5BMl5BanBnXkFtZTcwNDI0MjEzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Pursuit of Happyness,2006,U,117 min,"Biography, Drama",8,A struggling salesman takes custody of his son as he's poised to begin a life-changing professional career.,64,Gabriele Muccino,Will Smith,Thandie Newton,Jaden Smith,Brian Howe,448930,"163,566,459" +"https://m.media-amazon.com/images/M/MV5BZDMxOGZhNWYtMzRlYy00Mzk5LWJjMjEtNmQ4NDU4M2QxM2UzXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Blood Diamond,2006,A,143 min,"Adventure, Drama, Thriller",8,"A fisherman, a smuggler, and a syndicate of businessmen match wits over the possession of a priceless diamond.",64,Edward Zwick,Leonardo DiCaprio,Djimon Hounsou,Jennifer Connelly,Kagiso Kuypers,499439,"57,366,262" +"https://m.media-amazon.com/images/M/MV5BNGNiNmU2YTMtZmU4OS00MjM0LTlmYWUtMjVlYjAzYjE2N2RjXkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",The Bourne Ultimatum,2007,UA,115 min,"Action, Mystery, Thriller",8,Jason Bourne dodges a ruthless C.I.A. official and his Agents from a new assassination program while searching for the origins of his life as a trained killer.,85,Paul Greengrass,Matt Damon,Edgar Ramírez,Joan Allen,Julia Stiles,604694,"227,471,070" +"https://m.media-amazon.com/images/M/MV5BMTM1ODIwNzM5OV5BMl5BanBnXkFtZTcwNjk5MDkyMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Bin-jip,2004,U,88 min,"Crime, Drama, Romance",8,A transient young man breaks into empty homes to partake of the vacationing residents' lives for a few days.,72,Ki-duk Kim,Seung-Yun Lee,Hee Jae,Hyuk-ho Kwon,Jin-mo Joo,50610,"238,507" +"https://m.media-amazon.com/images/M/MV5BODZmYjMwNzEtNzVhNC00ZTRmLTk2M2UtNzE1MTQ2ZDAxNjc2XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Sin City,2005,A,124 min,"Crime, Thriller",8,"A movie that explores the dark and miserable town, Basin City, tells the story of three different people, all caught up in violent corruption.",74,Frank Miller,Quentin Tarantino,Robert Rodriguez,Mickey Rourke,Clive Owen,738512,"74,103,820" +"https://m.media-amazon.com/images/M/MV5BMTc3MjkzMDkxN15BMl5BanBnXkFtZTcwODAyMTU1MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Le scaphandre et le papillon,2007,PG-13,112 min,"Biography, Drama",8,The true story of Elle editor Jean-Dominique Bauby who suffers a stroke and has to live with an almost totally paralyzed body; only his left eye isn't paralyzed.,92,Julian Schnabel,Laura Obiols,Mathieu Amalric,Emmanuelle Seigner,Marie-Josée Croze,103284,"5,990,075" +"https://m.media-amazon.com/images/M/MV5BMjE0MTY2MDI3NV5BMl5BanBnXkFtZTcwNTc1MzEzMQ@@._V1_UY98_CR2,0,67,98_AL_.jpg",G.O.R.A.,2004,,127 min,"Adventure, Comedy, Sci-Fi",8,A slick young Turk kidnapped by extraterrestrials shows his great « humanitarian spirit » by outwitting the evil commander-in-chief of the planet of G.O.R.A.,,Ömer Faruk Sorak,Cem Yilmaz,Özge Özberk,Ozan Güven,Safak Sezer,56960, +"https://m.media-amazon.com/images/M/MV5BMTMzODU0NTkxMF5BMl5BanBnXkFtZTcwMjQ4MzMzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Ratatouille,2007,U,111 min,"Animation, Adventure, Comedy",8,A rat who can cook makes an unusual alliance with a young kitchen worker at a famous restaurant.,96,Brad Bird,Jan Pinkava,Brad Garrett,Lou Romano,Patton Oswalt,641645,"206,445,654" +"https://m.media-amazon.com/images/M/MV5BMDI5ZWJhOWItYTlhOC00YWNhLTlkNzctNDU5YTI1M2E1MWZhXkEyXkFqcGdeQXVyNTIzOTk5ODM@._V1_UX67_CR0,0,67,98_AL_.jpg",Casino Royale,2006,PG-13,144 min,"Action, Adventure, Thriller",8,"After earning 00 status and a licence to kill, Secret Agent James Bond sets out on his first mission as 007. Bond must defeat a private banker funding terrorists in a high-stakes game of poker at Casino Royale, Montenegro.",80,Martin Campbell,Daniel Craig,Eva Green,Judi Dench,Jeffrey Wright,582239,"167,445,960" +"https://m.media-amazon.com/images/M/MV5BNmFiYmJmN2QtNWQwMi00MzliLThiOWMtZjQxNGRhZTQ1MjgyXkEyXkFqcGdeQXVyNzQ1ODk3MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Kill Bill: Vol. 2,2004,A,137 min,"Action, Crime, Thriller",8,"The Bride continues her quest of vengeance against her former boss and lover Bill, the reclusive bouncer Budd, and the treacherous, one-eyed Elle.",83,Quentin Tarantino,Uma Thurman,David Carradine,Michael Madsen,Daryl Hannah,683900,"66,208,183" +"https://m.media-amazon.com/images/M/MV5BYmViZTY1OWEtMTQxMy00OGQ5LTgzZjAtYTQzOTYxNjliYTI4XkEyXkFqcGdeQXVyNjkxOTM4ODY@._V1_UY98_CR1,0,67,98_AL_.jpg",Vozvrashchenie,2003,,110 min,Drama,8,"In the Russian wilderness, two brothers face a range of new, conflicting emotions when their father - a man they know only through a single photograph - resurfaces.",82,Andrey Zvyagintsev,Vladimir Garin,Ivan Dobronravov,Konstantin Lavronenko,Nataliya Vdovina,42399,"502,028" +"https://m.media-amazon.com/images/M/MV5BZGYxOTRlM2MtNWRjZS00NDk2LWExM2EtMDFiYTgyMGJkZGYyXkEyXkFqcGdeQXVyMTA1NTM1NDI2._V1_UY98_CR1,0,67,98_AL_.jpg",Bom Yeoareum Gaeul Gyeoul Geurigo Bom,2003,R,103 min,"Drama, Romance",8,A boy is raised by a Buddhist monk in an isolated floating temple where the years pass like the seasons.,85,Ki-duk Kim,Ki-duk Kim,Yeong-su Oh,Jong-ho Kim,Kim Young-Min,77520,"2,380,788" +"https://m.media-amazon.com/images/M/MV5BMjE0NDk2NjgwMV5BMl5BanBnXkFtZTYwMTgyMzA3._V1_UX67_CR0,0,67,98_AL_.jpg",Mar adentro,2014,U,126 min,"Biography, Drama",8,"The factual story of Spaniard Ramon Sampedro, who fought a thirty-year campaign in favor of euthanasia and his own right to die.",74,Alejandro Amenábar,Javier Bardem,Belén Rueda,Lola Dueñas,Mabel Rivera,77554,"2,086,345" +"https://m.media-amazon.com/images/M/MV5BODEyYmQxZjUtZGQ0NS00ZTAwLTkwOGQtNGY2NzEwMWE0MDc3XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Cinderella Man,2005,UA,144 min,"Biography, Drama, History",8,"The story of James J. Braddock, a supposedly washed-up boxer who came back to become a champion and an inspiration in the 1930s.",69,Ron Howard,Russell Crowe,Renée Zellweger,Craig Bierko,Paul Giamatti,176151,"61,649,911" +"https://m.media-amazon.com/images/M/MV5BYmVjNDIxODAtNWZiZi00ZDBlLWJmOTUtNDNjMGExNTViMzE1XkEyXkFqcGdeQXVyNTE0MDc0NTM@._V1_UX67_CR0,0,67,98_AL_.jpg",Kal Ho Naa Ho,2003,U,186 min,"Comedy, Drama, Musical",8,"Naina, an introverted, perpetually depressed girl's life changes when she meets Aman. But Aman has a secret of his own which changes their lives forever. Embroiled in all this is Rohit, Naina's best friend who conceals his love for her.",54,Nikkhil Advani,Preity Zinta,Shah Rukh Khan,Saif Ali Khan,Jaya Bachchan,63460,"1,787,378" +"https://m.media-amazon.com/images/M/MV5BM2U0NTcxOTktN2MwZS00N2Q2LWJlYWItMTg0NWIyMDIxNzU5L2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",Mou gaan dou,2002,UA,101 min,"Action, Crime, Drama",8,"A story between a mole in the police department and an undercover cop. Their objectives are the same: to find out who is the mole, and who is the cop.",75,Andrew Lau,Alan Mak,Andy Lau,Tony Chiu-Wai Leung,Anthony Chau-Sang Wong,117857,"169,659" +"https://m.media-amazon.com/images/M/MV5BNGYyZGM5MGMtYTY2Ni00M2Y1LWIzNjQtYWUzM2VlNGVhMDNhXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Pirates of the Caribbean: The Curse of the Black Pearl,2003,UA,143 min,"Action, Adventure, Fantasy",8,"Blacksmith Will Turner teams up with eccentric pirate ""Captain"" Jack Sparrow to save his love, the governor's daughter, from Jack's former pirate allies, who are now undead.",63,Gore Verbinski,Johnny Depp,Geoffrey Rush,Orlando Bloom,Keira Knightley,1015122,"305,413,918" +"https://m.media-amazon.com/images/M/MV5BMmU3NzIyODctYjVhOC00NzBmLTlhNWItMzBlODEwZTlmMjUzXkEyXkFqcGdeQXVyNTIzOTk5ODM@._V1_UX67_CR0,0,67,98_AL_.jpg",Big Fish,2003,U,125 min,"Adventure, Drama, Fantasy",8,A frustrated son tries to determine the fact from fiction in his dying father's life.,58,Tim Burton,Ewan McGregor,Albert Finney,Billy Crudup,Jessica Lange,415218,"66,257,002" +"https://m.media-amazon.com/images/M/MV5BMTY5OTU0OTc2NV5BMl5BanBnXkFtZTcwMzU4MDcyMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Incredibles,2004,U,115 min,"Animation, Action, Adventure",8,"A family of undercover superheroes, while trying to live the quiet suburban life, are forced into action to save the world.",90,Brad Bird,Craig T. Nelson,Samuel L. Jackson,Holly Hunter,Jason Lee,657047,"261,441,092" +"https://m.media-amazon.com/images/M/MV5BMjM2NTYxMTE3OV5BMl5BanBnXkFtZTgwNDgwNjgwMzE@._V1_UY98_CR3,0,67,98_AL_.jpg",Yeopgijeogin geunyeo,2001,,137 min,"Comedy, Drama, Romance",8,"A young man sees a drunk, cute woman standing too close to the tracks at a metro station in Seoul and pulls her back. She ends up getting him into trouble repeatedly after that, starting on the train.",,Jae-young Kwak,Tae-Hyun Cha,Jun Ji-Hyun,In-mun Kim,Song Wok-suk,45403, +"https://m.media-amazon.com/images/M/MV5BMTkwNTg2MTI1NF5BMl5BanBnXkFtZTcwMDM1MzUyMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Dogville,2003,R,178 min,"Crime, Drama",8,"A woman on the run from the mob is reluctantly accepted in a small Colorado community in exchange for labor, but when a search visits the town she finds out that their support has a price.",60,Lars von Trier,Nicole Kidman,Paul Bettany,Lauren Bacall,Harriet Andersson,137963,"1,530,386" +"https://m.media-amazon.com/images/M/MV5BMjA2MzM4NjkyMF5BMl5BanBnXkFtZTYwMTQ2ODc5._V1_UY98_CR2,0,67,98_AL_.jpg",Vizontele,2001,,110 min,"Comedy, Drama",8,Lives of residents in a small Anatolian village change when television is introduced to them,,Yilmaz Erdogan,Ömer Faruk Sorak,Yilmaz Erdogan,Demet Akbag,Altan Erkekli,33592, +"https://m.media-amazon.com/images/M/MV5BZjZlZDlkYTktMmU1My00ZDBiLWFlNjEtYTBhNjVhOTM4ZjJjXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Donnie Darko,2001,R,113 min,"Drama, Mystery, Sci-Fi",8,"After narrowly escaping a bizarre accident, a troubled teenager is plagued by visions of a man in a large rabbit suit who manipulates him to commit a series of crimes.",88,Richard Kelly,Jake Gyllenhaal,Jena Malone,Mary McDonnell,Holmes Osborne,740086,"1,480,006" +"https://m.media-amazon.com/images/M/MV5BZjk3YThkNDktNjZjMS00MTBiLTllNTAtYzkzMTU0N2QwYjJjXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Magnolia,1999,R,188 min,Drama,8,"An epic mosaic of interrelated characters in search of love, forgiveness, and meaning in the San Fernando Valley.",77,Paul Thomas Anderson,Tom Cruise,Jason Robards,Julianne Moore,Philip Seymour Hoffman,289742,"22,455,976" +"https://m.media-amazon.com/images/M/MV5BNDVkYWMxNWEtNjc2MC00OGI5LWI3NmUtYWUwNDQyOTc3YmY5XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Dancer in the Dark,2000,U,140 min,"Crime, Drama, Musical",8,"An East European girl travels to the United States with her young son, expecting it to be like a Hollywood film.",61,Lars von Trier,Björk,Catherine Deneuve,David Morse,Peter Stormare,102285,"4,184,036" +"https://m.media-amazon.com/images/M/MV5BNmE1MDk4OWEtYjk1NS00MWU2LTk5ZWItYjZhYmRkODRjMDc0XkEyXkFqcGdeQXVyNjE5MjUyOTM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Straight Story,1999,U,112 min,"Biography, Drama",8,An old man makes a long journey by lawnmower to mend his relationship with an ill brother.,86,David Lynch,Richard Farnsworth,Sissy Spacek,Jane Galloway Heitz,Joseph A. Carpenter,82002,"6,203,044" +"https://m.media-amazon.com/images/M/MV5BMmMzOWNhNTYtYmY0My00OGJiLWIzNDUtZWRhNGY0NWFjNzFmXkEyXkFqcGdeQXVyNjUxMDQ0MTg@._V1_UX67_CR0,0,67,98_AL_.jpg",Pâfekuto burû,1997,A,81 min,"Animation, Crime, Mystery",8,"A pop singer gives up her career to become an actress, but she slowly goes insane when she starts being stalked by an obsessed fan and what seems to be a ghost of her past.",,Satoshi Kon,Junko Iwao,Rica Matsumoto,Shinpachi Tsuji,Masaaki Ôkura,58192,"776,665" +"https://m.media-amazon.com/images/M/MV5BYTg3Yjc4N2QtZDdlNC00NmU2LWFiYjktYjI3NTMwMjk4M2FmXkEyXkFqcGdeQXVyMjgyNjk3MzE@._V1_UY98_CR4,0,67,98_AL_.jpg",Festen,1998,R,105 min,Drama,8,"At Helge's 60th birthday party, some unpleasant family truths are revealed.",82,Thomas Vinterberg,Ulrich Thomsen,Henning Moritzen,Thomas Bo Larsen,Paprika Steen,78341,"1,647,780" +"https://m.media-amazon.com/images/M/MV5BMjE3ZDA5ZmUtYTk1ZS00NmZmLWJhNTItYjIwZjUwN2RjNzIyXkEyXkFqcGdeQXVyMTkzODUwNzk@._V1_UX67_CR0,0,67,98_AL_.jpg",Central do Brasil,1998,R,110 min,Drama,8,"An emotive journey of a former school teacher, who writes letters for illiterate people, and a young boy, whose mother has just died, as they search for the father he never knew.",80,Walter Salles,Fernanda Montenegro,Vinícius de Oliveira,Marília Pêra,Soia Lira,36419,"5,595,428" +"https://m.media-amazon.com/images/M/MV5BMjIxNDU2Njk0OV5BMl5BanBnXkFtZTgwODc3Njc3NjE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Iron Giant,1999,PG,86 min,"Animation, Action, Adventure",8,A young boy befriends a giant robot from outer space that a paranoid government agent wants to destroy.,85,Brad Bird,Eli Marienthal,Harry Connick Jr.,Jennifer Aniston,Vin Diesel,172083,"23,159,305" +"https://m.media-amazon.com/images/M/MV5BMTk2MjcxNjMzN15BMl5BanBnXkFtZTgwMTE3OTEwNjE@._V1_UY98_CR3,0,67,98_AL_.jpg",Knockin' on Heaven's Door,1997,,87 min,"Action, Crime, Comedy",8,"Two terminally ill patients escape from a hospital, steal a car and rush towards the sea.",,Thomas Jahn,Til Schweiger,Jan Josef Liefers,Thierry van Werveke,Moritz Bleibtreu,27721,"3,296" +"https://m.media-amazon.com/images/M/MV5BNGY5NWIxMjAtODBjNC00MmZhLTk1ZTAtNGRhYThlOTNjMTQwXkEyXkFqcGdeQXVyNTc1NTQxODI@._V1_UX67_CR0,0,67,98_AL_.jpg",Sling Blade,1996,R,135 min,Drama,8,"Karl Childers, a simple man hospitalized since his childhood murder of his mother and her lover, is released to start a new life in a small town.",84,Billy Bob Thornton,Billy Bob Thornton,Dwight Yoakam,J.T. Walsh,John Ritter,86838,"24,475,416" +"https://m.media-amazon.com/images/M/MV5BY2QzMTIxNjItNGQyNy00MjQzLWJiYTItMzIyZjdkYjYyYjRlXkEyXkFqcGdeQXVyMTAwMzUyOTc@._V1_UX67_CR0,0,67,98_AL_.jpg",Secrets & Lies,1996,U,136 min,"Comedy, Drama",8,"Following the death of her adoptive parents, a successful young black optometrist establishes contact with her biological mother -- a lonely white factory worker living in poverty in East London.",91,Mike Leigh,Timothy Spall,Brenda Blethyn,Phyllis Logan,Claire Rushbrook,37564,"13,417,292" +"https://m.media-amazon.com/images/M/MV5BN2Y2OWU4MWMtNmIyMy00YzMyLWI0Y2ItMTcyZDc3MTdmZDU4XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Twelve Monkeys,1995,A,129 min,"Mystery, Sci-Fi, Thriller",8,"In a future world devastated by disease, a convict is sent back in time to gather information about the man-made virus that wiped out most of the human population on the planet.",74,Terry Gilliam,Bruce Willis,Madeleine Stowe,Brad Pitt,Joseph Melito,578443,"57,141,459" +"https://m.media-amazon.com/images/M/MV5BYWRiYjQyOGItNzQ1Mi00MGI1LWE3NjItNTg1ZDQwNjUwNDM2XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Kôkaku Kidôtai,1995,UA,83 min,"Animation, Action, Crime",8,A cyborg policewoman and her partner hunt a mysterious and powerful hacker called the Puppet Master.,76,Mamoru Oshii,Atsuko Tanaka,Iemasa Kayumi,Akio Ôtsuka,Kôichi Yamadera,129231,"515,905" +"https://m.media-amazon.com/images/M/MV5BNWE4OTNiM2ItMjY4Ni00ZTViLWFiZmEtZGEyNGY2ZmNlMzIyXkEyXkFqcGdeQXVyMDU5NDcxNw@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Nightmare Before Christmas,1993,U,76 min,"Animation, Family, Fantasy",8,"Jack Skellington, king of Halloween Town, discovers Christmas Town, but his attempts to bring Christmas to his home causes confusion.",82,Henry Selick,Danny Elfman,Chris Sarandon,Catherine O'Hara,William Hickey,300208,"75,082,668" +"https://m.media-amazon.com/images/M/MV5BZWIxNzM5YzQtY2FmMS00Yjc3LWI1ZjUtNGVjMjMzZTIxZTIxXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Groundhog Day,1993,U,101 min,"Comedy, Fantasy, Romance",8,A weatherman finds himself inexplicably living the same day over and over again.,72,Harold Ramis,Bill Murray,Andie MacDowell,Chris Elliott,Stephen Tobolowsky,577991,"70,906,973" +"https://m.media-amazon.com/images/M/MV5BNzZmMjAxNjQtZjQzOS00NjU4LWI0NDktZjlkZTgwNjVmNzU3XkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",Bound by Honor,1993,R,180 min,"Crime, Drama",8,"Based on the true life experiences of poet Jimmy Santiago Baca, the film focuses on step-brothers Paco and Cruz, and their bi-racial cousin Miklo.",47,Taylor Hackford,Damian Chapa,Jesse Borrego,Benjamin Bratt,Enrique Castillo,28825,"4,496,583" +"https://m.media-amazon.com/images/M/MV5BZTM3ZjA3NTctZThkYy00ODYyLTk2ZjItZmE0MmZlMTk3YjQwXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Scent of a Woman,1992,UA,156 min,Drama,8,"A prep school student needing money agrees to ""babysit"" a blind man, but the job is not at all what he anticipated.",59,Martin Brest,Al Pacino,Chris O'Donnell,James Rebhorn,Gabrielle Anwar,263918,"63,895,607" +"https://m.media-amazon.com/images/M/MV5BY2Q2NDI1MjUtM2Q5ZS00MTFlLWJiYWEtNTZmNjQ3OGJkZDgxXkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",Aladdin,1992,U,90 min,"Animation, Adventure, Comedy",8,A kindhearted street urchin and a power-hungry Grand Vizier vie for a magic lamp that has the power to make their deepest wishes come true.,86,Ron Clements,John Musker,Scott Weinger,Robin Williams,Linda Larkin,373845,"217,350,219" +"https://m.media-amazon.com/images/M/MV5BYjYyODExMDctZjgwYy00ZjQwLWI4OWYtOGFlYjA4ZjEzNmY1XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",JFK,1991,UA,189 min,"Drama, History, Thriller",8,New Orleans District Attorney Jim Garrison discovers there's more to the Kennedy assassination than the official story.,72,Oliver Stone,Kevin Costner,Gary Oldman,Jack Lemmon,Walter Matthau,142110,"70,405,498" +"https://m.media-amazon.com/images/M/MV5BMzE5MDM1NDktY2I0OC00YWI5LTk2NzUtYjczNDczOWQxYjM0XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Beauty and the Beast,1991,G,84 min,"Animation, Family, Fantasy",8,A prince cursed to spend his days as a hideous monster sets out to regain his humanity by earning a young woman's love.,95,Gary Trousdale,Kirk Wise,Paige O'Hara,Robby Benson,Jesse Corti,417178,"218,967,620" +"https://m.media-amazon.com/images/M/MV5BMTY3OTI5NDczN15BMl5BanBnXkFtZTcwNDA0NDY3Mw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Dances with Wolves,1990,U,181 min,"Adventure, Drama, Western",8,"Lieutenant John Dunbar, assigned to a remote western Civil War outpost, befriends wolves and Indians, making him an intolerable aberration in the military.",72,Kevin Costner,Kevin Costner,Mary McDonnell,Graham Greene,Rodney A. Grant,240266,"184,208,848" +"https://m.media-amazon.com/images/M/MV5BODA2MjU1NTI1MV5BMl5BanBnXkFtZTgwOTU4ODIwMjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Do the Right Thing,1989,R,120 min,"Comedy, Drama",8,"On the hottest day of the year on a street in the Bedford-Stuyvesant section of Brooklyn, everyone's hate and bigotry smolders and builds until it explodes into violence.",93,Spike Lee,Danny Aiello,Ossie Davis,Ruby Dee,Richard Edson,89429,"27,545,445" +"https://m.media-amazon.com/images/M/MV5BMzVjNzI4NzYtMjE4NS00M2IzLWFkOWMtOTYwMWUzN2ZlNGVjL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Rain Man,1988,U,133 min,Drama,8,Selfish yuppie Charlie Babbitt's father left a fortune to his savant brother Raymond and a pittance to Charlie; they travel cross-country.,65,Barry Levinson,Dustin Hoffman,Tom Cruise,Valeria Golino,Gerald R. Molen,473064,"178,800,000" +"https://m.media-amazon.com/images/M/MV5BM2ZiZTk1ODgtMTZkNS00NTYxLWIxZTUtNWExZGYwZTRjODViXkEyXkFqcGdeQXVyMTE2MzA3MDM@._V1_UX67_CR0,0,67,98_AL_.jpg",Akira,1988,UA,124 min,"Animation, Action, Sci-Fi",8,A secret military project endangers Neo-Tokyo when it turns a biker gang member into a rampaging psychic psychopath who can only be stopped by two teenagers and a group of psychics.,,Katsuhiro Ôtomo,Mitsuo Iwata,Nozomu Sasaki,Mami Koyama,Tesshô Genda,164918,"553,171" +"https://m.media-amazon.com/images/M/MV5BMGM4M2Q5N2MtNThkZS00NTc1LTk1NTItNWEyZjJjNDRmNDk5XkEyXkFqcGdeQXVyMjA0MDQ0Mjc@._V1_UX67_CR0,0,67,98_AL_.jpg",The Princess Bride,1987,U,98 min,"Adventure, Family, Fantasy",8,"While home sick in bed, a young boy's grandfather reads him the story of a farmboy-turned-pirate who encounters numerous obstacles, enemies and allies in his quest to be reunited with his true love.",77,Rob Reiner,Cary Elwes,Mandy Patinkin,Robin Wright,Chris Sarandon,393899,"30,857,814" +"https://m.media-amazon.com/images/M/MV5BMzMxZjUzOGQtOTFlOS00MzliLWJhNTUtOTgyNzYzMWQ2YzhmXkEyXkFqcGdeQXVyNjQ2MjQ5NzM@._V1_UY98_CR0,0,67,98_AL_.jpg",Der Himmel über Berlin,1987,U,128 min,"Drama, Fantasy, Romance",8,An angel tires of overseeing human activity and wishes to become human when he falls in love with a mortal.,79,Wim Wenders,Bruno Ganz,Solveig Dommartin,Otto Sander,Curt Bois,64722,"3,333,969" +"https://m.media-amazon.com/images/M/MV5BZmYxOTA5YTEtNDY3Ni00YTE5LWE1MTgtYjc4ZWUxNWY3ZTkxXkEyXkFqcGdeQXVyNjQ2MjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Au revoir les enfants,1987,U,104 min,"Drama, War",8,"A French boarding school run by priests seems to be a haven from World War II until a new student arrives. He becomes the roommate of the top student in his class. Rivals at first, the roommates form a bond and share a secret.",88,Louis Malle,Gaspard Manesse,Raphael Fejtö,Francine Racette,Stanislas Carré de Malberg,31163,"4,542,825" +"https://m.media-amazon.com/images/M/MV5BNTg0NmI1ZGQtZTUxNC00NTgxLThjMDUtZmRlYmEzM2MwOWYwXkEyXkFqcGdeQXVyMzM4MjM0Nzg@._V1_UY98_CR1,0,67,98_AL_.jpg",Tenkû no shiro Rapyuta,1986,U,125 min,"Animation, Adventure, Drama",8,A young boy and a girl with a magic crystal must race against pirates and foreign agents in a search for a legendary floating castle.,78,Hayao Miyazaki,Mayumi Tanaka,Keiko Yokozawa,Kotoe Hatsui,Minori Terada,150140, +"https://m.media-amazon.com/images/M/MV5BYTViNzMxZjEtZGEwNy00MDNiLWIzNGQtZDY2MjQ1OWViZjFmXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Terminator,1984,UA,107 min,"Action, Sci-Fi",8,"A human soldier is sent from 2029 to 1984 to stop an almost indestructible cyborg killing machine, sent from the same year, which has been programmed to execute a young woman whose unborn son is the key to humanity's future salvation.",84,James Cameron,Arnold Schwarzenegger,Linda Hamilton,Michael Biehn,Paul Winfield,799795,"38,400,000" +"https://m.media-amazon.com/images/M/MV5BMzJiZDRmOWUtYjE2MS00Mjc1LTg1ZDYtNTQxYWJkZTg1OTM4XkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_UX67_CR0,0,67,98_AL_.jpg",Gandhi,1982,U,191 min,"Biography, Drama, History",8,The life of the lawyer who became the famed leader of the Indian revolts against the British rule through his philosophy of nonviolent protest.,79,Richard Attenborough,Ben Kingsley,John Gielgud,Rohini Hattangadi,Roshan Seth,217664,"52,767,889" +"https://m.media-amazon.com/images/M/MV5BMzFhNWVmNWItNGM5OC00NjZhLTk3YTQtMjE1ODUyOThlMjNmL2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Kagemusha,1980,U,180 min,"Drama, History, War",8,A petty thief with an utter resemblance to a samurai warlord is hired as the lord's double. When the warlord later dies the thief is forced to take up arms in his place.,84,Akira Kurosawa,Tatsuya Nakadai,Tsutomu Yamazaki,Ken'ichi Hagiwara,Jinpachi Nezu,32195, +"https://m.media-amazon.com/images/M/MV5BNjAzNzJjYzQtMGFmNS00ZjAzLTkwMjgtMWIzYzFkMzM4Njg3XkEyXkFqcGdeQXVyMTY5Nzc4MDY@._V1_UX67_CR0,0,67,98_AL_.jpg",Being There,1979,PG,130 min,"Comedy, Drama",8,"A simpleminded, sheltered gardener becomes an unlikely trusted advisor to a powerful businessman and an insider in Washington politics.",83,Hal Ashby,Peter Sellers,Shirley MacLaine,Melvyn Douglas,Jack Warden,65625,"30,177,511" +"https://m.media-amazon.com/images/M/MV5BZDg1OGQ4YzgtM2Y2NS00NjA3LWFjYTctMDRlMDI3NWE1OTUyXkEyXkFqcGdeQXVyMjUzOTY1NTc@._V1_UX67_CR0,0,67,98_AL_.jpg",Annie Hall,1977,A,93 min,"Comedy, Romance",8,Neurotic New York comedian Alvy Singer falls in love with the ditzy Annie Hall.,92,Woody Allen,Woody Allen,Diane Keaton,Tony Roberts,Carol Kane,251823,"39,200,000" +"https://m.media-amazon.com/images/M/MV5BMmVmODY1MzEtYTMwZC00MzNhLWFkNDMtZjAwM2EwODUxZTA5XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Jaws,1975,A,124 min,"Adventure, Thriller",8,"When a killer shark unleashes chaos on a beach community, it's up to a local sheriff, a marine biologist, and an old seafarer to hunt the beast down.",87,Steven Spielberg,Roy Scheider,Robert Shaw,Richard Dreyfuss,Lorraine Gary,543388,"260,000,000" +"https://m.media-amazon.com/images/M/MV5BODExZmE2ZWItYTIzOC00MzI1LTgyNTktMDBhNmFhY2Y4OTQ3XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Dog Day Afternoon,1975,U,125 min,"Biography, Crime, Drama",8,"Three amateur bank robbers plan to hold up a bank. A nice simple robbery: Walk in, take the money, and run. Unfortunately, the supposedly uncomplicated heist suddenly becomes a bizarre nightmare as everything that could go wrong does.",86,Sidney Lumet,Al Pacino,John Cazale,Penelope Allen,Sully Boyar,235652,"50,000,000" +"https://m.media-amazon.com/images/M/MV5BMTEwNjg2MjM2ODFeQTJeQWpwZ15BbWU4MDQ1MDU5OTEx._V1_UX67_CR0,0,67,98_AL_.jpg",Young Frankenstein,1974,A,106 min,Comedy,8,"An American grandson of the infamous scientist, struggling to prove that his grandfather was not as insane as people believe, is invited to Transylvania, where he discovers the process that reanimates a dead body.",80,Mel Brooks,Gene Wilder,Madeline Kahn,Marty Feldman,Peter Boyle,143359,"86,300,000" +"https://m.media-amazon.com/images/M/MV5BZGRjZjQ0NzAtYmZlNS00Zjc1LTk1YWItMDY5YzQxMzA4MTAzXkEyXkFqcGdeQXVyMjI4MjA5MzA@._V1_UX67_CR0,0,67,98_AL_.jpg",Papillon,1973,R,151 min,"Biography, Crime, Drama",8,"A man befriends a fellow criminal as the two of them begin serving their sentence on a dreadful prison island, which inspires the man to plot his escape.",58,Franklin J. Schaffner,Steve McQueen,Dustin Hoffman,Victor Jory,Don Gordon,121627,"53,267,000" +"https://m.media-amazon.com/images/M/MV5BYjhmMGMxZDYtMTkyNy00YWVmLTgyYmUtYTU3ZjcwNTBjN2I1XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Exorcist,1973,A,122 min,Horror,8,"When a 12-year-old girl is possessed by a mysterious entity, her mother seeks the help of two priests to save her.",81,William Friedkin,Ellen Burstyn,Max von Sydow,Linda Blair,Lee J. Cobb,362393,"232,906,145" +"https://m.media-amazon.com/images/M/MV5BM2EzZmFmMmItODY3Zi00NjdjLWE0MTYtZWQ3MGIyM2M4YjZhXkEyXkFqcGdeQXVyMzg2MzE2OTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Sleuth,1972,PG,138 min,"Mystery, Thriller",8,"A man who loves games and theater invites his wife's lover to meet him, setting up a battle of wits with potentially deadly results.",,Joseph L. Mankiewicz,Laurence Olivier,Michael Caine,Alec Cawthorne,John Matthews,44748,"4,081,254" +"https://m.media-amazon.com/images/M/MV5BNmVjNzZkZjQtYmM5ZC00M2I0LWJhNzktNDk3MGU1NWMxMjFjXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Last Picture Show,1971,R,118 min,"Drama, Romance",8,"In 1951, a group of high schoolers come of age in a bleak, isolated, atrophied North Texas town that is slowly dying, both culturally and economically.",93,Peter Bogdanovich,Timothy Bottoms,Jeff Bridges,Cybill Shepherd,Ben Johnson,42456,"29,133,000" +"https://m.media-amazon.com/images/M/MV5BMWMxNDYzNmUtYjFmNC00MGM2LWFmNzMtODhlMGNkNDg5MjE5XkEyXkFqcGdeQXVyNjE5MjUyOTM@._V1_UX67_CR0,0,67,98_AL_.jpg",Fiddler on the Roof,1971,G,181 min,"Drama, Family, Musical",8,"In prerevolutionary Russia, a Jewish peasant contends with marrying off three of his daughters while growing anti-Semitic sentiment threatens his village.",67,Norman Jewison,Topol,Norma Crane,Leonard Frey,Molly Picon,39491,"80,500,000" +"https://m.media-amazon.com/images/M/MV5BODFlYzU4YTItN2EwYi00ODI3LTkwNTQtMDdkNjM3YjMyMTgyXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UY98_CR0,0,67,98_AL_.jpg",Il conformista,1970,UA,113 min,Drama,8,"A weak-willed Italian man becomes a fascist flunky who goes abroad to arrange the assassination of his old teacher, now a political dissident.",100,Bernardo Bertolucci,Jean-Louis Trintignant,Stefania Sandrelli,Gastone Moschin,Enzo Tarascio,27067,"541,940" +"https://m.media-amazon.com/images/M/MV5BMTkyMTM2NDk5Nl5BMl5BanBnXkFtZTgwNzY1NzEyMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Butch Cassidy and the Sundance Kid,1969,PG,110 min,"Biography, Crime, Drama",8,"Wyoming, early 1900s. Butch Cassidy and The Sundance Kid are the leaders of a band of outlaws. After a train robbery goes wrong they find themselves on the run with a posse hard on their heels. Their solution - escape to Bolivia.",66,George Roy Hill,Paul Newman,Robert Redford,Katharine Ross,Strother Martin,201888,"102,308,889" +"https://m.media-amazon.com/images/M/MV5BZmEwZGU2NzctYzlmNi00MGJkLWE3N2MtYjBlN2ZhMGJkZTZiXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Rosemary's Baby,1968,A,137 min,"Drama, Horror",8,A young couple trying for a baby move into a fancy apartment surrounded by peculiar neighbors.,96,Roman Polanski,Mia Farrow,John Cassavetes,Ruth Gordon,Sidney Blackmer,193674, +"https://m.media-amazon.com/images/M/MV5BMTg0NjUwMzg5NF5BMl5BanBnXkFtZTgwNDQ0NjcwMTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Planet of the Apes,1968,U,112 min,"Adventure, Sci-Fi",8,"An astronaut crew crash-lands on a planet in the distant future where intelligent talking apes are the dominant species, and humans are the oppressed and enslaved.",79,Franklin J. Schaffner,Charlton Heston,Roddy McDowall,Kim Hunter,Maurice Evans,165167,"33,395,426" +"https://m.media-amazon.com/images/M/MV5BMTQ0ODc4MDk4Nl5BMl5BanBnXkFtZTcwMTEzNzgzNA@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Graduate,1967,A,106 min,"Comedy, Drama, Romance",8,A disillusioned college graduate finds himself torn between his older lover and her daughter.,83,Mike Nichols,Dustin Hoffman,Anne Bancroft,Katharine Ross,William Daniels,253676,"104,945,305" +"https://m.media-amazon.com/images/M/MV5BMjQ5ODI1MjQtMDc0Zi00OGQ1LWE2NTYtMTg1YTkxM2E5NzFkXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Who's Afraid of Virginia Woolf?,1966,A,131 min,Drama,8,"A bitter, aging couple, with the help of alcohol, use their young houseguests to fuel anguish and emotional pain towards each other over the course of a distressing night.",75,Mike Nichols,Elizabeth Taylor,Richard Burton,George Segal,Sandy Dennis,68926, +"https://m.media-amazon.com/images/M/MV5BODIxNjhkYjEtYzUyMi00YTNjLWE1YjktNjAyY2I2MWNkNmNmL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UY98_CR1,0,67,98_AL_.jpg",The Sound of Music,1965,U,172 min,"Biography, Drama, Family",8,A woman leaves an Austrian convent to become a governess to the children of a Naval officer widower.,63,Robert Wise,Julie Andrews,Christopher Plummer,Eleanor Parker,Richard Haydn,205425,"163,214,286" +"https://m.media-amazon.com/images/M/MV5BNzdmZTk4MTktZmExNi00OWEwLTgxZDctNTE4NWMwNjc1Nzg2XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Doctor Zhivago,1965,A,197 min,"Drama, Romance, War",8,"The life of a Russian physician and poet who, although married to another, falls in love with a political activist's wife and experiences hardship during World War I and then the October Revolution.",69,David Lean,Omar Sharif,Julie Christie,Geraldine Chaplin,Rod Steiger,69903,"111,722,000" +"https://m.media-amazon.com/images/M/MV5BYjA1MGVlMGItNzgxMC00OWY4LWI4YjEtNTNmYWIzMGUxOGQzXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UY98_CR0,0,67,98_AL_.jpg",Per un pugno di dollari,1964,A,99 min,"Action, Drama, Western",8,"A wandering gunfighter plays two rival families against each other in a town torn apart by greed, pride, and revenge.",65,Sergio Leone,Clint Eastwood,Gian Maria Volontè,Marianne Koch,Wolfgang Lukschy,198219,"14,500,000" +"https://m.media-amazon.com/images/M/MV5BMTQ4MTA0NjEzMF5BMl5BanBnXkFtZTgwMDg4NDYxMzE@._V1_UY98_CR2,0,67,98_AL_.jpg",8½,1963,,138 min,Drama,8,A harried movie director retreats into his memories and fantasies.,91,Federico Fellini,Marcello Mastroianni,Anouk Aimée,Claudia Cardinale,Sandra Milo,108844,"50,690" +"https://m.media-amazon.com/images/M/MV5BNjMyZmI5NmItY2JlMi00NzU3LWI5ZGItZjhkOTE0YjEyN2Q4XkEyXkFqcGdeQXVyNDkzNTM2ODg@._V1_UX67_CR0,0,67,98_AL_.jpg",Vivre sa vie: Film en douze tableaux,1962,,80 min,Drama,8,Twelve episodic tales in the life of a Parisian woman and her slow descent into prostitution.,,Jean-Luc Godard,Anna Karina,Sady Rebbot,André S. Labarthe,Guylaine Schlumberger,28057, +"https://m.media-amazon.com/images/M/MV5BNjhjODI2NTItMGE1ZS00NThiLWE1MmYtOWE3YzcyNzY1MTJlXkEyXkFqcGdeQXVyNTc1NTQxODI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Hustler,1961,A,134 min,"Drama, Sport",8,An up-and-coming pool player plays a long-time champion in a single high-stakes match.,90,Robert Rossen,Paul Newman,Jackie Gleason,Piper Laurie,George C. Scott,75067,"8,284,000" +"https://m.media-amazon.com/images/M/MV5BODQ0NzY5NGEtYTc5NC00Yjg4LTg4Y2QtZjE2MTkyYTNmNmU2L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UY98_CR1,0,67,98_AL_.jpg",La dolce vita,1960,A,174 min,"Comedy, Drama",8,A series of stories following a week in the life of a philandering paparazzo journalist living in Rome.,95,Federico Fellini,Marcello Mastroianni,Anita Ekberg,Anouk Aimée,Yvonne Furneaux,66621,"19,516,000" +"https://m.media-amazon.com/images/M/MV5BZDVhMTk1NjUtYjc0OS00OTE1LTk1NTYtYWMzMDI5OTlmYzU2XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Rio Bravo,1959,Passed,141 min,"Action, Drama, Western",8,"A small-town sheriff in the American West enlists the help of a cripple, a drunk, and a young gunfighter in his efforts to hold in jail the brother of the local bad guy.",93,Howard Hawks,John Wayne,Dean Martin,Ricky Nelson,Angie Dickinson,56305,"12,535,000" +"https://m.media-amazon.com/images/M/MV5BMzM0MzE2ZTAtZTBjZS00MTk5LTg5OTEtNjNmYmQ5NzU2OTUyXkEyXkFqcGdeQXVyNDY2MTk1ODk@._V1_UX67_CR0,0,67,98_AL_.jpg",Anatomy of a Murder,1959,,161 min,"Crime, Drama, Mystery",8,"In a murder trial, the defendant says he suffered temporary insanity after the victim raped his wife. What is the truth, and will he win his case?",95,Otto Preminger,James Stewart,Lee Remick,Ben Gazzara,Arthur O'Connell,59847,"11,900,000" +"https://m.media-amazon.com/images/M/MV5BOTA1MjA3M2EtMmJjZS00OWViLTkwMTEtM2E5ZDk0NTAyNGJiXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Touch of Evil,1958,PG-13,95 min,"Crime, Drama, Film-Noir",8,"A stark, perverse story of murder, kidnapping, and police corruption in a Mexican border town.",99,Orson Welles,Charlton Heston,Orson Welles,Janet Leigh,Joseph Calleia,98431,"2,237,659" +"https://m.media-amazon.com/images/M/MV5BMzFhNTMwNDMtZjY3Yy00NzY3LWI1ZWQtZTQxMWJmODVhZWFkXkEyXkFqcGdeQXVyNjQzNDI3NzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Cat on a Hot Tin Roof,1958,A,108 min,Drama,8,Brick is an alcoholic ex-football player who drinks his days away and resists the affections of his wife. A reunion with his terminal father jogs a host of memories and revelations for both father and son.,84,Richard Brooks,Elizabeth Taylor,Paul Newman,Burl Ives,Jack Carson,45062,"17,570,324" +"https://m.media-amazon.com/images/M/MV5BMjE5NTU3YWYtOWIxNi00YWZhLTg2NzktYzVjZWY5MDQ4NzVlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Sweet Smell of Success,1957,Approved,96 min,"Drama, Film-Noir",8,Powerful but unethical Broadway columnist J.J. Hunsecker coerces unscrupulous press agent Sidney Falco into breaking up his sister's romance with a jazz musician.,100,Alexander Mackendrick,Burt Lancaster,Tony Curtis,Susan Harrison,Martin Milner,28137, +"https://m.media-amazon.com/images/M/MV5BMDE5ZjAwY2YtOWM5Yi00ZWNlLWE5ODQtYjA4NzA1NGFkZDU5XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Killing,1956,Approved,84 min,"Crime, Drama, Film-Noir",8,Crook Johnny Clay assembles a five man team to plan and execute a daring race-track robbery.,91,Stanley Kubrick,Sterling Hayden,Coleen Gray,Vince Edwards,Jay C. Flippen,81702, +"https://m.media-amazon.com/images/M/MV5BYTNjN2M2MzYtZGEwMi00Mzc5LWEwYTMtODM1ZmRiZjFiNTU0L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Night of the Hunter,1955,,92 min,"Crime, Drama, Film-Noir",8,"A religious fanatic marries a gullible widow whose young children are reluctant to tell him where their real daddy hid the $10,000 he'd stolen in a robbery.",99,Charles Laughton,Robert Mitchum,Shelley Winters,Lillian Gish,James Gleason,81980,"654,000" +"https://m.media-amazon.com/images/M/MV5BYjUyOGMyMTQtYTM5Yy00MjFiLTk2OGItMWYwMDc2YmM1YzhiXkEyXkFqcGdeQXVyMjA0MzYwMDY@._V1_UY98_CR2,0,67,98_AL_.jpg",La Strada,1954,,108 min,Drama,8,"A care-free girl is sold to a traveling entertainer, consequently enduring physical and emotional pain along the way.",,Federico Fellini,Anthony Quinn,Giulietta Masina,Richard Basehart,Aldo Silvani,58314, +"https://m.media-amazon.com/images/M/MV5BMGJmNmU5OTAtOTQyYy00MmM3LTk4MzUtMGFiZDYzODdmMmU4XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UY98_CR3,0,67,98_AL_.jpg",Les diaboliques,1955,,117 min,"Crime, Drama, Horror",8,The wife and mistress of a loathed school principal plan to murder him with what they believe is the perfect alibi.,,Henri-Georges Clouzot,Simone Signoret,Véra Clouzot,Paul Meurisse,Charles Vanel,61503, +"https://m.media-amazon.com/images/M/MV5BNDMyNGU0NjUtNTIxMC00ZmU2LWE0ZGItZTdkNGVlODI2ZDcyL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Stalag 17,1953,,120 min,"Comedy, Drama, War",8,"When two escaping American World War II prisoners are killed, the German P.O.W. camp barracks black marketeer, J.J. Sefton, is suspected of being an informer.",84,Billy Wilder,William Holden,Don Taylor,Otto Preminger,Robert Strauss,51046, +"https://m.media-amazon.com/images/M/MV5BMTE2MDM4MTMtZmNkZC00Y2QyLWE0YjUtMTAxZGJmODMxMDM0XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Roman Holiday,1953,,118 min,"Comedy, Romance",8,A bored and sheltered princess escapes her guardians and falls in love with an American newsman in Rome.,78,William Wyler,Gregory Peck,Audrey Hepburn,Eddie Albert,Hartley Power,127256, +"https://m.media-amazon.com/images/M/MV5BNzk2M2Y3MzYtNGMzMi00Y2FjLTkwODQtNmExYWU3ZWY3NzExXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",A Streetcar Named Desire,1951,A,122 min,Drama,8,Disturbed Blanche DuBois moves in with her sister in New Orleans and is tormented by her brutish brother-in-law while her reality crumbles around her.,97,Elia Kazan,Vivien Leigh,Marlon Brando,Kim Hunter,Karl Malden,99182,"8,000,000" +"https://m.media-amazon.com/images/M/MV5BNjRmZjcwZTQtYWY0ZS00ODAwLTg4YTktZDhlZDMwMTM1MGFkXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",In a Lonely Place,1950,,94 min,"Drama, Film-Noir, Mystery",8,"A potentially violent screenwriter is a murder suspect until his lovely neighbor clears him. However, she soon starts to have her doubts.",,Nicholas Ray,Humphrey Bogart,Gloria Grahame,Frank Lovejoy,Carl Benton Reid,26784, +"https://m.media-amazon.com/images/M/MV5BZjc1Yzc0ZmItMzU1OS00OWVlLThmYTctMWNlYmFlMjkxMzc0XkEyXkFqcGdeQXVyNTA1NjYyMDk@._V1_UY98_CR32,0,67,98_AL_.jpg",Kind Hearts and Coronets,1949,U,106 min,"Comedy, Crime",8,A distant poor relative of the Duke D'Ascoyne plots to inherit the title by murdering the eight other heirs who stand ahead of him in the line of succession.,,Robert Hamer,Dennis Price,Alec Guinness,Valerie Hobson,Joan Greenwood,34485, +"https://m.media-amazon.com/images/M/MV5BYWFjMDNlYzItY2VlMS00ZTRkLWJjYTEtYjI5NmFlMGE3MzQ2XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Rope,1948,A,80 min,"Crime, Drama, Mystery",8,Two men attempt to prove they committed the perfect crime by hosting a dinner party after strangling their former classmate to death.,73,Alfred Hitchcock,James Stewart,John Dall,Farley Granger,Dick Hogan,129783, +"https://m.media-amazon.com/images/M/MV5BMDE0MjYxYmMtM2VhMC00MjhiLTg5NjItMDkzZGM5MGVlYjMxL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Out of the Past,1947,,97 min,"Crime, Drama, Film-Noir",8,"A private eye escapes his past to run a gas station in a small town, but his past catches up with him. Now he must return to the big city world of danger, corruption, double crosses and duplicitous dames.",,Jacques Tourneur,Robert Mitchum,Jane Greer,Kirk Douglas,Rhonda Fleming,32784, +"https://m.media-amazon.com/images/M/MV5BYWQ0MGNjOTYtMWJlNi00YWMxLWFmMzktYjAyNTVkY2U1NWNhL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Brief Encounter,1945,U,86 min,"Drama, Romance",8,"Meeting a stranger in a railway station, a woman is tempted to cheat on her husband.",92,David Lean,Celia Johnson,Trevor Howard,Stanley Holloway,Joyce Carey,35601, +"https://m.media-amazon.com/images/M/MV5BYjkxOGM5OTktNTRmZi00MjhlLWE2MDktNzY3NjY3NmRjNDUyXkEyXkFqcGdeQXVyNDY2MTk1ODk@._V1_UX67_CR0,0,67,98_AL_.jpg",Laura,1944,Passed,88 min,"Drama, Film-Noir, Mystery",8,A police detective falls in love with the woman whose murder he is investigating.,,Otto Preminger,Gene Tierney,Dana Andrews,Clifton Webb,Vincent Price,42725,"4,360,000" +"https://m.media-amazon.com/images/M/MV5BY2RmNTRjYzctODI4Ni00MzQyLWEyNTAtNjU0N2JkMTNhNjJkXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Best Years of Our Lives,1946,Approved,170 min,"Drama, Romance, War",8,Three World War II veterans return home to small-town America to discover that they and their families have been irreparably changed.,93,William Wyler,Myrna Loy,Dana Andrews,Fredric March,Teresa Wright,57259,"23,650,000" +"https://m.media-amazon.com/images/M/MV5BZDVlNTBjMjctNjAzNS00ZGJhLTg2NzMtNzIwYTIzYTBiMDkyXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Arsenic and Old Lace,1942,,118 min,"Comedy, Crime, Thriller",8,A writer of books on the futility of marriage risks his reputation when he decides to get married. Things get even more complicated when he learns on his wedding day that his beloved maiden aunts are habitual murderers.,,Frank Capra,Cary Grant,Priscilla Lane,Raymond Massey,Jack Carson,65101, +"https://m.media-amazon.com/images/M/MV5BZjIwNGM1ZTUtOThjYS00NDdiLTk2ZDYtNGY5YjJkNzliM2JjL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Maltese Falcon,1941,,100 min,"Film-Noir, Mystery",8,"A private detective takes on a case that involves him with three eccentric criminals, a gorgeous liar, and their quest for a priceless statuette.",96,John Huston,Humphrey Bogart,Mary Astor,Gladys George,Peter Lorre,148928,"2,108,060" +"https://m.media-amazon.com/images/M/MV5BNzJiOGI2MjctYjUyMS00ZjkzLWE2ZmUtOTg4NTZkOTNhZDc1L2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Grapes of Wrath,1940,Passed,129 min,"Drama, History",8,"A poor Midwest family is forced off their land. They travel to California, suffering the misfortunes of the homeless in the Great Depression.",96,John Ford,Henry Fonda,Jane Darwell,John Carradine,Charley Grapewin,85559,"55,000" +"https://m.media-amazon.com/images/M/MV5BNjUyMTc4MDExMV5BMl5BanBnXkFtZTgwNDg0NDIwMjE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Wizard of Oz,1939,U,102 min,"Adventure, Family, Fantasy",8,Dorothy Gale is swept away from a farm in Kansas to a magical land of Oz in a tornado and embarks on a quest with her new friends to see the Wizard who can help her return home to Kansas and help her friends as well.,92,Victor Fleming,George Cukor,Mervyn LeRoy,Norman Taurog,Richard Thorpe,371379,"2,076,020" +"https://m.media-amazon.com/images/M/MV5BYTE4NjYxMGEtZmQxZi00YWVmLWJjZTctYTJmNDFmZGEwNDVhXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UY98_CR2,0,67,98_AL_.jpg",La règle du jeu,1939,,110 min,"Comedy, Drama",8,"A bourgeois life in France at the onset of World War II, as the rich and their poor servants meet up at a French chateau.",,Jean Renoir,Marcel Dalio,Nora Gregor,Paulette Dubost,Mila Parély,26725, +"https://m.media-amazon.com/images/M/MV5BYmFlOWMwMjAtMDMyMC00N2JjLTllODUtZjY3YWU3NGRkM2I2L2ltYWdlXkEyXkFqcGdeQXVyMjUxODE0MDY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Thin Man,1934,TV-PG,91 min,"Comedy, Crime, Mystery",8,"Former detective Nick Charles and his wealthy wife Nora investigate a murder case, mostly for the fun of it.",86,W.S. Van Dyke,William Powell,Myrna Loy,Maureen O'Sullivan,Nat Pendleton,26642, +"https://m.media-amazon.com/images/M/MV5BMzg2MWQ4MDEtOGZlNi00MTg0LWIwMjQtYWY5NTQwYmUzMWNmXkEyXkFqcGdeQXVyMzg2MzE2OTE@._V1_UX67_CR0,0,67,98_AL_.jpg",All Quiet on the Western Front,1930,U,152 min,"Drama, War",8,"A German youth eagerly enters World War I, but his enthusiasm wanes as he gets a firsthand view of the horror.",91,Lewis Milestone,Lew Ayres,Louis Wolheim,John Wray,Arnold Lucy,57318,"3,270,000" +"https://m.media-amazon.com/images/M/MV5BMTEyMTQzMjQ0MTJeQTJeQWpwZ15BbWU4MDcyMjg4OTEx._V1_UY98_CR1,0,67,98_AL_.jpg",Bronenosets Potemkin,1925,,75 min,"Drama, History, Thriller",8,"In the midst of the Russian Revolution of 1905, the crew of the battleship Potemkin mutiny against the brutal, tyrannical regime of the vessel's officers. The resulting street demonstration in Odessa brings on a police massacre.",97,Sergei M. Eisenstein,Aleksandr Antonov,Vladimir Barskiy,Grigoriy Aleksandrov,Ivan Bobrov,53054,"50,970" +"https://m.media-amazon.com/images/M/MV5BMGUwZjliMTAtNzAxZi00MWNiLWE2NzgtZGUxMGQxZjhhNDRiXkEyXkFqcGdeQXVyNjU1NzU3MzE@._V1_UX67_CR0,0,67,98_AL_.jpg",Knives Out,2019,UA,130 min,"Comedy, Crime, Drama",7.9,"A detective investigates the death of a patriarch of an eccentric, combative family.",82,Rian Johnson,Daniel Craig,Chris Evans,Ana de Armas,Jamie Lee Curtis,454203,"165,359,751" +"https://m.media-amazon.com/images/M/MV5BNmI0MTliMTAtMmJhNC00NTJmLTllMzQtMDI3NzA1ODMyZWI1XkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR5,0,67,98_AL_.jpg",Dil Bechara,2020,UA,101 min,"Comedy, Drama, Romance",7.9,"The emotional journey of two hopelessly in love youngsters, a young girl, Kizie, suffering from cancer, and a boy, Manny, whom she meets at a support group.",,Mukesh Chhabra,Sushant Singh Rajput,Sanjana Sanghi,Sahil Vaid,Saswata Chatterjee,111478, +"https://m.media-amazon.com/images/M/MV5BYWZmOTY0MDAtMGRlMS00YjFlLWFkZTUtYmJhYWNlN2JjMmZkXkEyXkFqcGdeQXVyODAzODU1NDQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Manbiki kazoku,2018,A,121 min,"Crime, Drama",7.9,A family of small-time crooks take in a child they find outside in the cold.,93,Hirokazu Koreeda,Lily Franky,Sakura Andô,Kirin Kiki,Mayu Matsuoka,62754,"3,313,513" +"https://m.media-amazon.com/images/M/MV5BZGVmY2RjNDgtMTc3Yy00YmY0LTgwODItYzBjNWJhNTRlYjdkXkEyXkFqcGdeQXVyMjM4NTM5NDY@._V1_UX67_CR0,0,67,98_AL_.jpg",Marriage Story,2019,U,137 min,"Comedy, Drama, Romance",7.9,Noah Baumbach's incisive and compassionate look at a marriage breaking up and a family staying together.,94,Noah Baumbach,Adam Driver,Scarlett Johansson,Julia Greer,Azhy Robertson,246644,"2,000,000" +"https://m.media-amazon.com/images/M/MV5BNDk3NTEwNjc0MV5BMl5BanBnXkFtZTgwNzYxNTMwMzI@._V1_UX67_CR0,0,67,98_AL_.jpg",Call Me by Your Name,2017,UA,132 min,"Drama, Romance",7.9,"In 1980s Italy, romance blossoms between a seventeen-year-old student and the older man hired as his father's research assistant.",93,Luca Guadagnino,Armie Hammer,Timothée Chalamet,Michael Stuhlbarg,Amira Casar,212651,"18,095,701" +"https://m.media-amazon.com/images/M/MV5BMTQ4NTMzMTk4NV5BMl5BanBnXkFtZTgwNTU5MjE4MDI@._V1_UX67_CR0,0,67,98_AL_.jpg","I, Daniel Blake",2016,UA,100 min,Drama,7.9,"After having suffered a heart-attack, a 59-year-old carpenter must fight the bureaucratic forces of the system in order to receive Employment and Support Allowance.",78,Ken Loach,Laura Obiols,Dave Johns,Hayley Squires,Sharon Percy,53818,"258,168" +"https://m.media-amazon.com/images/M/MV5BZDQwOWQ2NmUtZThjZi00MGM0LTkzNDctMzcyMjcyOGI1OGRkXkEyXkFqcGdeQXVyMTA3MDk2NDg2._V1_UX67_CR0,0,67,98_AL_.jpg",Isle of Dogs,2018,U,101 min,"Animation, Adventure, Comedy",7.9,"Set in Japan, Isle of Dogs follows a boy's odyssey in search of his lost dog.",82,Wes Anderson,Bryan Cranston,Koyu Rankin,Edward Norton,Bob Balaban,139114,"32,015,231" +"https://m.media-amazon.com/images/M/MV5BMjI1MDQ2MDg5Ml5BMl5BanBnXkFtZTgwMjc2NjM5ODE@._V1_UX67_CR0,0,67,98_AL_.jpg",Hunt for the Wilderpeople,2016,UA,101 min,"Adventure, Comedy, Drama",7.9,A national manhunt is ordered for a rebellious kid and his foster uncle who go missing in the wild New Zealand bush.,81,Taika Waititi,Sam Neill,Julian Dennison,Rima Te Wiata,Rachel House,111483,"5,202,582" +"https://m.media-amazon.com/images/M/MV5BMjE5OTM0OTY5NF5BMl5BanBnXkFtZTgwMDcxOTQ3ODE@._V1_UX67_CR0,0,67,98_AL_.jpg",Captain Fantastic,2016,R,118 min,"Comedy, Drama",7.9,"In the forests of the Pacific Northwest, a father devoted to raising his six kids with a rigorous physical and intellectual education is forced to leave his paradise and enter the world, challenging his idea of what it means to be a parent.",72,Matt Ross,Viggo Mortensen,George MacKay,Samantha Isler,Annalise Basso,189400,"5,875,006" +"https://m.media-amazon.com/images/M/MV5BMjEzODA3MDcxMl5BMl5BanBnXkFtZTgwODgxNDk3NzE@._V1_UX67_CR0,0,67,98_AL_.jpg",Sing Street,2016,PG-13,106 min,"Comedy, Drama, Music",7.9,A boy growing up in Dublin during the 1980s escapes his strained family life by starting a band to impress the mysterious girl he likes.,79,John Carney,Ferdia Walsh-Peelo,Aidan Gillen,Maria Doyle Kennedy,Jack Reynor,85109,"3,237,118" +"https://m.media-amazon.com/images/M/MV5BMjMyNDkzMzI1OF5BMl5BanBnXkFtZTgwODcxODg5MjI@._V1_UX67_CR0,0,67,98_AL_.jpg",Thor: Ragnarok,2017,UA,130 min,"Action, Adventure, Comedy",7.9,"Imprisoned on the planet Sakaar, Thor must race against time to return to Asgard and stop Ragnarök, the destruction of his world, at the hands of the powerful and ruthless villain Hela.",74,Taika Waititi,Chris Hemsworth,Tom Hiddleston,Cate Blanchett,Mark Ruffalo,587775,"315,058,289" +"https://m.media-amazon.com/images/M/MV5BN2U1YzdhYWMtZWUzMi00OWI1LWFkM2ItNWVjM2YxMGQ2MmNhXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UY98_CR0,0,67,98_AL_.jpg",Nightcrawler,2014,A,117 min,"Crime, Drama, Thriller",7.9,"When Louis Bloom, a con man desperate for work, muscles into the world of L.A. crime journalism, he blurs the line between observer and participant to become the star of his own story.",76,Dan Gilroy,Jake Gyllenhaal,Rene Russo,Bill Paxton,Riz Ahmed,466134,"32,381,218" +"https://m.media-amazon.com/images/M/MV5BZjU0Yzk2MzEtMjAzYy00MzY0LTg2YmItM2RkNzdkY2ZhN2JkXkEyXkFqcGdeQXVyNDg4NjY5OTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Jojo Rabbit,2019,UA,108 min,"Comedy, Drama, War",7.9,A young boy in Hitler's army finds out his mother is hiding a Jewish girl in their home.,58,Taika Waititi,Roman Griffin Davis,Thomasin McKenzie,Scarlett Johansson,Taika Waititi,297918,"349,555" +"https://m.media-amazon.com/images/M/MV5BMTExMzU0ODcxNDheQTJeQWpwZ15BbWU4MDE1OTI4MzAy._V1_UX67_CR0,0,67,98_AL_.jpg",Arrival,2016,UA,116 min,"Drama, Sci-Fi",7.9,A linguist works with the military to communicate with alien lifeforms after twelve mysterious spacecrafts appear around the world.,81,Denis Villeneuve,Amy Adams,Jeremy Renner,Forest Whitaker,Michael Stuhlbarg,594181,"100,546,139" +"https://m.media-amazon.com/images/M/MV5BOTAzODEzNDAzMl5BMl5BanBnXkFtZTgwMDU1MTgzNzE@._V1_UX67_CR0,0,67,98_AL_.jpg",Star Wars: Episode VII - The Force Awakens,2015,U,138 min,"Action, Adventure, Sci-Fi",7.9,"As a new threat to the galaxy rises, Rey, a desert scavenger, and Finn, an ex-stormtrooper, must join Han Solo and Chewbacca to search for the one hope of restoring peace.",80,J.J. Abrams,Daisy Ridley,John Boyega,Oscar Isaac,Domhnall Gleeson,860823,"936,662,225" +"https://m.media-amazon.com/images/M/MV5BMjA5NzgxODE2NF5BMl5BanBnXkFtZTcwNTI1NTI0OQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Before Midnight,2013,R,109 min,"Drama, Romance",7.9,We meet Jesse and Celine nine years on in Greece. Almost two decades have passed since their first meeting on that train bound for Vienna.,94,Richard Linklater,Ethan Hawke,Julie Delpy,Seamus Davey-Fitzpatrick,Ariane Labed,141457,"8,114,627" +"https://m.media-amazon.com/images/M/MV5BZGIzNWYzN2YtMjcwYS00YjQ3LWI2NjMtOTNiYTUyYjE2MGNkXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",X-Men: Days of Future Past,2014,UA,132 min,"Action, Adventure, Sci-Fi",7.9,The X-Men send Wolverine to the past in a desperate effort to change history and prevent an event that results in doom for both humans and mutants.,75,Bryan Singer,Patrick Stewart,Ian McKellen,Hugh Jackman,James McAvoy,659763,"233,921,534" +"https://m.media-amazon.com/images/M/MV5BYTRkMDRiYmEtNGM4YS00NzM3LWI4MTMtYzk1MmVjMjM3ODg1XkEyXkFqcGdeQXVyMjgyNjk3MzE@._V1_UY98_CR1,0,67,98_AL_.jpg",Bir Zamanlar Anadolu'da,2011,,157 min,"Crime, Drama",7.9,A group of men set out in search of a dead body in the Anatolian steppes.,82,Nuri Bilge Ceylan,Muhammet Uzuner,Yilmaz Erdogan,Taner Birsel,Ahmet Mümtaz Taylan,41995,"138,730" +"https://m.media-amazon.com/images/M/MV5BMDUyZWU5N2UtOWFlMy00MTI0LTk0ZDYtMzFhNjljODBhZDA5XkEyXkFqcGdeQXVyNzA4ODc3ODU@._V1_UY98_CR1,0,67,98_AL_.jpg",The Artist,2011,U,100 min,"Comedy, Drama, Romance",7.9,An egomaniacal film star develops a relationship with a young dancer against the backdrop of Hollywood's silent era.,89,Michel Hazanavicius,Jean Dujardin,Bérénice Bejo,John Goodman,James Cromwell,230624,"44,671,682" +"https://m.media-amazon.com/images/M/MV5BMTc5OTk4MTM3M15BMl5BanBnXkFtZTgwODcxNjg3MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Edge of Tomorrow,2014,UA,113 min,"Action, Adventure, Sci-Fi",7.9,"A soldier fighting aliens gets to relive the same day over and over again, the day restarting every time he dies.",71,Doug Liman,Tom Cruise,Emily Blunt,Bill Paxton,Brendan Gleeson,600004,"100,206,256" +"https://m.media-amazon.com/images/M/MV5BMTk1NTc3NDc4MF5BMl5BanBnXkFtZTcwNjYwNDk0OA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Amour,2012,UA,127 min,"Drama, Romance",7.9,"Georges and Anne are an octogenarian couple. They are cultivated, retired music teachers. Their daughter, also a musician, lives in Britain with her family. One day, Anne has a stroke, and the couple's bond of love is severely tested.",94,Michael Haneke,Jean-Louis Trintignant,Emmanuelle Riva,Isabelle Huppert,Alexandre Tharaud,93090,"6,739,492" +"https://m.media-amazon.com/images/M/MV5BMGUyM2ZiZmUtMWY0OC00NTQ4LThkOGUtNjY2NjkzMDJiMWMwXkEyXkFqcGdeQXVyMzY0MTE3NzU@._V1_UX67_CR0,0,67,98_AL_.jpg",The Irishman,2019,R,209 min,"Biography, Crime, Drama",7.9,"An old man recalls his time painting houses for his friend, Jimmy Hoffa, through the 1950-70s.",94,Martin Scorsese,Robert De Niro,Al Pacino,Joe Pesci,Harvey Keitel,324720,"7,000,000" +"https://m.media-amazon.com/images/M/MV5BMTUyMjQ1MTY5OV5BMl5BanBnXkFtZTcwNzY5NjExMw@@._V1_UY98_CR1,0,67,98_AL_.jpg",Un prophète,2009,A,155 min,"Crime, Drama",7.9,A young Arab man is sent to a French prison.,90,Jacques Audiard,Tahar Rahim,Niels Arestrup,Adel Bencherif,Reda Kateb,93560,"2,084,637" +"https://m.media-amazon.com/images/M/MV5BMTgzODgyNTQwOV5BMl5BanBnXkFtZTcwNzc0NTc0Mg@@._V1_UX67_CR0,0,67,98_AL_.jpg",Moon,2009,R,97 min,"Drama, Mystery, Sci-Fi",7.9,"Astronaut Sam Bell has a quintessentially personal encounter toward the end of his three-year stint on the Moon, where he, working alongside his computer, GERTY, sends back to Earth parcels of a resource that has helped diminish our planet's power problems.",67,Duncan Jones,Sam Rockwell,Kevin Spacey,Dominique McElligott,Rosie Shaw,335152,"5,009,677" +"https://m.media-amazon.com/images/M/MV5BOWM4NTY2NTMtZDZlZS00NTgyLWEzZDMtODE3ZGI1MzI3ZmU5XkEyXkFqcGdeQXVyNzI1NzMxNzM@._V1_UY98_CR1,0,67,98_AL_.jpg",Låt den rätte komma in,2008,R,114 min,"Crime, Drama, Fantasy",7.9,"Oskar, an overlooked and bullied boy, finds love and revenge through Eli, a beautiful but peculiar girl.",82,Tomas Alfredson,Kåre Hedebrant,Lina Leandersson,Per Ragnar,Henrik Dahl,205609,"2,122,065" +"https://m.media-amazon.com/images/M/MV5BYmQ5MzFjYWMtMTMwNC00ZGU5LWI3YTQtYzhkMGExNGFlY2Q0XkEyXkFqcGdeQXVyNTIzOTk5ODM@._V1_UX67_CR0,0,67,98_AL_.jpg",District 9,2009,A,112 min,"Action, Sci-Fi, Thriller",7.9,Violence ensues after an extraterrestrial race forced to live in slum-like conditions on Earth finds a kindred spirit in a government agent exposed to their biotechnology.,81,Neill Blomkamp,Sharlto Copley,David James,Jason Cope,Nathalie Boltt,638202,"115,646,235" +"https://m.media-amazon.com/images/M/MV5BMTc5MjYyOTg4MF5BMl5BanBnXkFtZTcwNDc2MzQwMg@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Wrestler,2008,UA,109 min,"Drama, Sport",7.9,"A faded professional wrestler must retire, but finds his quest for a new life outside the ring a dispiriting struggle.",80,Darren Aronofsky,Mickey Rourke,Marisa Tomei,Evan Rachel Wood,Mark Margolis,289415,"26,236,603" +"https://m.media-amazon.com/images/M/MV5BYmIzYmY4MGItM2I4YS00OWZhLWFmMzQtYzI2MWY1MmM3NGU1XkEyXkFqcGdeQXVyNjQ2MjQ5NzM@._V1_UY98_CR0,0,67,98_AL_.jpg",Jab We Met,2007,U,138 min,"Comedy, Drama, Romance",7.9,A depressed wealthy businessman finds his life changing after he meets a spunky and care-free young woman.,,Imtiaz Ali,Shahid Kapoor,Kareena Kapoor,Tarun Arora,Dara Singh,47720,"410,800" +"https://m.media-amazon.com/images/M/MV5BMTYzNDc2MDc0N15BMl5BanBnXkFtZTgwOTcwMDQ5MTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Boyhood,2014,A,165 min,Drama,7.9,"The life of Mason, from early childhood to his arrival at college.",100,Richard Linklater,Ellar Coltrane,Patricia Arquette,Ethan Hawke,Elijah Smith,335533,"25,379,975" +"https://m.media-amazon.com/images/M/MV5BYzU1YWUzNjYtNmVhZi00ODUyLTg4M2ItMTFlMmU1Mzc5OTE5XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR1,0,67,98_AL_.jpg","4 luni, 3 saptamâni si 2 zile",2007,,113 min,Drama,7.9,A woman assists her friend in arranging an illegal abortion in 1980s Romania.,97,Cristian Mungiu,Anamaria Marinca,Laura Vasiliu,Vlad Ivanov,Alexandru Potocean,56625,"1,185,783" +"https://m.media-amazon.com/images/M/MV5BMjE5NDQ5OTE4Ml5BMl5BanBnXkFtZTcwOTE3NDIzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Star Trek,2009,UA,127 min,"Action, Adventure, Sci-Fi",7.9,The brash James T. Kirk tries to live up to his father's legacy with Mr. Spock keeping him in check as a vengeful Romulan from the future creates black holes to destroy the Federation one planet at a time.,82,J.J. Abrams,Chris Pine,Zachary Quinto,Simon Pegg,Leonard Nimoy,577336,"257,730,019" +"https://m.media-amazon.com/images/M/MV5BMTUwOGFiM2QtOWMxYS00MjU2LThmZDMtZDM2MWMzNzllNjdhXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",In Bruges,2008,R,107 min,"Comedy, Crime, Drama",7.9,"Guilt-stricken after a job gone wrong, hitman Ray and his partner await orders from their ruthless boss in Bruges, Belgium, the last place in the world Ray wants to be.",67,Martin McDonagh,Colin Farrell,Brendan Gleeson,Ciarán Hinds,Elizabeth Berrington,390334,"7,757,130" +"https://m.media-amazon.com/images/M/MV5BMzQ5NGQwOTUtNWJlZi00ZTFiLWI0ZTEtOGU3MTA2ZGU5OWZiXkEyXkFqcGdeQXVyMTczNjQwOTY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Man from Earth,2007,,87 min,"Drama, Fantasy, Mystery",7.9,An impromptu goodbye party for Professor John Oldman becomes a mysterious interrogation after the retiring scholar reveals to his colleagues he has a longer and stranger past than they can imagine.,,Richard Schenkman,David Lee Smith,Tony Todd,John Billingsley,Ellen Crawford,174125, +"https://m.media-amazon.com/images/M/MV5BMjE0NzgwODI4M15BMl5BanBnXkFtZTcwNjg3OTA0MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Letters from Iwo Jima,2006,UA,141 min,"Action, Adventure, Drama",7.9,"The story of the battle of Iwo Jima between the United States and Imperial Japan during World War II, as told from the perspective of the Japanese who fought it.",89,Clint Eastwood,Ken Watanabe,Kazunari Ninomiya,Tsuyoshi Ihara,Ryô Kase,154011,"13,756,082" +"https://m.media-amazon.com/images/M/MV5BMjAzODUwMjM1M15BMl5BanBnXkFtZTcwNjU2MjU2MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Fall,2006,R,117 min,"Adventure, Drama, Fantasy",7.9,"In a hospital on the outskirts of 1920s Los Angeles, an injured stuntman begins to tell a fellow patient, a little girl with a broken arm, a fantastic story of five mythical heroes. Thanks to his fractured state of mind and her vivid imagination, the line between fiction and reality blurs as the tale advances.",64,Tarsem Singh,Lee Pace,Catinca Untaru,Justine Waddell,Kim Uylenbroek,107290,"2,280,348" +"https://m.media-amazon.com/images/M/MV5BNTg2OTY2ODg5OF5BMl5BanBnXkFtZTcwODM5MTYxOA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Life of Pi,2012,U,127 min,"Adventure, Drama, Fantasy",7.9,"A young man who survives a disaster at sea is hurtled into an epic journey of adventure and discovery. While cast away, he forms an unexpected connection with another survivor: a fearsome Bengal tiger.",79,Ang Lee,Suraj Sharma,Irrfan Khan,Adil Hussain,Tabu,580708,"124,987,023" +"https://m.media-amazon.com/images/M/MV5BOGUwYTU4NGEtNDM4MS00NDRjLTkwNmQtOTkwMWMyMjhmMjdlXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Fantastic Mr. Fox,2009,PG,87 min,"Animation, Adventure, Comedy",7.9,An urbane fox cannot resist returning to his farm raiding ways and then must help his community survive the farmers' retaliation.,83,Wes Anderson,George Clooney,Meryl Streep,Bill Murray,Jason Schwartzman,199696,"21,002,919" +"https://m.media-amazon.com/images/M/MV5BMTU3MDc2MjUwMV5BMl5BanBnXkFtZTcwNzQyMDAzMQ@@._V1_UY98_CR0,0,67,98_AL_.jpg",C.R.A.Z.Y.,2005,,129 min,"Comedy, Drama",7.9,"A young French-Canadian, growing up in the 1960s and 1970s, struggles to reconcile his emerging homosexuality with his father's conservative values and his own Catholic beliefs.",81,Jean-Marc Vallée,Michel Côté,Marc-André Grondin,Danielle Proulx,Émile Vallée,31476, +"https://m.media-amazon.com/images/M/MV5BOGY1M2MwOTEtZDIyNi00YjNlLWExYmEtNzBjOGI3N2QzNTg5XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Les choristes,2004,PG-13,97 min,"Drama, Music",7.9,The new teacher at a severely administered boys' boarding school works to positively affect the students' lives through music.,56,Christophe Barratier,Gérard Jugnot,François Berléand,Jean-Baptiste Maunier,Kad Merad,57430,"3,635,164" +"https://m.media-amazon.com/images/M/MV5BMTczNTI2ODUwOF5BMl5BanBnXkFtZTcwMTU0NTIzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Iron Man,2008,UA,126 min,"Action, Adventure, Sci-Fi",7.9,"After being held captive in an Afghan cave, billionaire engineer Tony Stark creates a unique weaponized suit of armor to fight evil.",79,Jon Favreau,Robert Downey Jr.,Gwyneth Paltrow,Terrence Howard,Jeff Bridges,939644,"318,412,101" +"https://m.media-amazon.com/images/M/MV5BMTg5Mjk2NDMtZTk0Ny00YTQ0LWIzYWEtMWI5MGQ0Mjg1OTNkXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Shaun of the Dead,2004,UA,99 min,"Comedy, Horror",7.9,A man's uneventful life is disrupted by the zombie apocalypse.,76,Edgar Wright,Simon Pegg,Nick Frost,Kate Ashfield,Lucy Davis,512249,"13,542,874" +"https://m.media-amazon.com/images/M/MV5BODBiNzYxNzYtMjkyMi00MjUyLWJkM2YtZjNkMDhhYmEwMTRiL2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",Gegen die Wand,2004,R,121 min,"Drama, Romance",7.9,"With the intention to break free from the strict familial restrictions, a suicidal young woman sets up a marriage of convenience with a forty-year-old addict, an act that will lead to an outburst of envious love.",78,Fatih Akin,Birol Ünel,Sibel Kekilli,Güven Kiraç,Zarah Jane McKenzie,51325, +"https://m.media-amazon.com/images/M/MV5BMTIzNDUyMjA4MV5BMl5BanBnXkFtZTYwNDc4ODM3._V1_UX67_CR0,0,67,98_AL_.jpg",Mystic River,2003,A,138 min,"Crime, Drama, Mystery",7.9,The lives of three men who were childhood friends are shattered when one of them has a family tragedy.,84,Clint Eastwood,Sean Penn,Tim Robbins,Kevin Bacon,Emmy Rossum,419420,"90,135,191" +"https://m.media-amazon.com/images/M/MV5BMTY4NTIwODg0N15BMl5BanBnXkFtZTcwOTc0MjEzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Harry Potter and the Prisoner of Azkaban,2004,U,142 min,"Adventure, Family, Fantasy",7.9,"Harry Potter, Ron and Hermione return to Hogwarts School of Witchcraft and Wizardry for their third year of study, where they delve into the mystery surrounding an escaped prisoner who poses a dangerous threat to the young wizard.",82,Alfonso Cuarón,Daniel Radcliffe,Emma Watson,Rupert Grint,Richard Griffiths,552493,"249,358,727" +"https://m.media-amazon.com/images/M/MV5BMWQ2MjQ0OTctMWE1OC00NjZjLTk3ZDAtNTk3NTZiYWMxYTlmXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Ying xiong,2002,PG-13,120 min,"Action, Adventure, History",7.9,"A defense officer, Nameless, was summoned by the King of Qin regarding his success of terminating three warriors.",85,Yimou Zhang,Jet Li,Tony Chiu-Wai Leung,Maggie Cheung,Ziyi Zhang,173999,"53,710,019" +"https://m.media-amazon.com/images/M/MV5BYmVmMGQ3NzEtM2FiNi00YThhLWFkZjYtM2Y0MjZjNGE4NzM0XkEyXkFqcGdeQXVyODc0OTEyNDU@._V1_UY98_CR1,0,67,98_AL_.jpg",Hable con ella,2002,R,112 min,"Drama, Mystery, Romance",7.9,Two men share an odd friendship while they care for two women who are both in deep comas.,86,Pedro Almodóvar,Rosario Flores,Javier Cámara,Darío Grandinetti,Leonor Watling,104691,"9,284,265" +"https://m.media-amazon.com/images/M/MV5BMGFkNjNmZWMtNDdiOS00ZWM3LWE1ZTMtZDU3MGQyMzIyNzZhXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",No Man's Land,2001,R,98 min,"Comedy, Drama, War",7.9,"Bosnia and Herzegovina during 1993 at the time of the heaviest fighting between the two warring sides. Two soldiers from opposing sides in the conflict, Nino and Ciki, become trapped in no man's land, whilst a third soldier becomes a living booby trap.",84,Danis Tanovic,Branko Djuric,Rene Bitorajac,Filip Sovagovic,Georges Siatidis,44618,"1,059,830" +"https://m.media-amazon.com/images/M/MV5BMjYzYWM4YTItZjJiMC00OTM5LTg3NDgtOGQ2Njk2ZWNhN2QwXkEyXkFqcGdeQXVyMzM4MjM0Nzg@._V1_UY98_CR0,0,67,98_AL_.jpg",Cowboy Bebop: Tengoku no tobira,2001,U,115 min,"Animation, Action, Crime",7.9,"A terrorist explosion releases a deadly virus on the masses, and it's up the bounty-hunting Bebop crew to catch the cold-blooded culprit.",61,Shin'ichirô Watanabe,Tensai Okamura,Hiroyuki Okiura,Yoshiyuki Takei,Beau Billingslea,42897,"1,000,045" +"https://m.media-amazon.com/images/M/MV5BM2JkNGU0ZGMtZjVjNS00NjgyLWEyOWYtZmRmZGQyN2IxZjA2XkEyXkFqcGdeQXVyNTIzOTk5ODM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Bourne Identity,2002,UA,119 min,"Action, Mystery, Thriller",7.9,"A man is picked up by a fishing boat, bullet-riddled and suffering from amnesia, before racing to elude assassins and attempting to regain his memory.",68,Doug Liman,Franka Potente,Matt Damon,Chris Cooper,Clive Owen,508771,"121,661,683" +"https://m.media-amazon.com/images/M/MV5BMTYxMDdlYjItMDVkYy00MjYzLThhMTYtYjIzZjZiODk1ZWRmXkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Nueve reinas,2000,R,114 min,"Crime, Drama, Thriller",7.9,"Two con artists try to swindle a stamp collector by selling him a sheet of counterfeit rare stamps (the ""nine queens"").",80,Fabián Bielinsky,Ricardo Darín,Gastón Pauls,Graciela Tenenbaum,María Mercedes Villagra,49721,"1,221,261" +"https://m.media-amazon.com/images/M/MV5BMTQ5NTI2NTI4NF5BMl5BanBnXkFtZTcwNjk2NDA2OQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Children of Men,2006,A,109 min,"Adventure, Drama, Sci-Fi",7.9,"In 2027, in a chaotic world in which women have become somehow infertile, a former activist agrees to help transport a miraculously pregnant woman to a sanctuary at sea.",84,Alfonso Cuarón,Julianne Moore,Clive Owen,Chiwetel Ejiofor,Michael Caine,465113,"35,552,383" +"https://m.media-amazon.com/images/M/MV5BMzY1ZjMwMGEtYTY1ZS00ZDllLTk0ZmUtYzA3ZTA4NmYwNGNkXkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Almost Famous,2000,A,122 min,"Adventure, Comedy, Drama",7.9,A high-school boy is given the chance to write a story for Rolling Stone Magazine about an up-and-coming rock band as he accompanies them on their concert tour.,90,Cameron Crowe,Billy Crudup,Patrick Fugit,Kate Hudson,Frances McDormand,252586,"32,534,850" +"https://m.media-amazon.com/images/M/MV5BYjBhZmViNTItMGExMy00MGNmLTkwZDItMDVlMTQ4ODVkYTMwXkEyXkFqcGdeQXVyNzM0MTUwNTY@._V1_UY98_CR1,0,67,98_AL_.jpg",Mulholland Dr.,2001,R,147 min,"Drama, Mystery, Thriller",7.9,"After a car wreck on the winding Mulholland Drive renders a woman amnesiac, she and a perky Hollywood-hopeful search for clues and answers across Los Angeles in a twisting venture beyond dreams and reality.",85,David Lynch,Naomi Watts,Laura Harring,Justin Theroux,Jeanne Bates,322031,"7,220,243" +"https://m.media-amazon.com/images/M/MV5BMWM5ZDcxMTYtNTEyNS00MDRkLWI3YTItNThmMGExMWY4NDIwXkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_UX67_CR0,0,67,98_AL_.jpg",Toy Story 2,1999,U,92 min,"Animation, Adventure, Comedy",7.9,"When Woody is stolen by a toy collector, Buzz and his friends set out on a rescue mission to save Woody before he becomes a museum toy property with his roundup gang Jessie, Prospector, and Bullseye.",88,John Lasseter,Ash Brannon,Lee Unkrich,Tom Hanks,Tim Allen,527512,"245,852,179" +"https://m.media-amazon.com/images/M/MV5BY2E2YWYxY2QtZmJmZi00MjJlLWFiYWItZTk5Y2IyMWQ1ZThhXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Boogie Nights,1997,R,155 min,Drama,7.9,"Back when sex was safe, pleasure was a business and business was booming, an idealistic porn producer aspires to elevate his craft to an art when he discovers a hot young talent.",85,Paul Thomas Anderson,Mark Wahlberg,Julianne Moore,Burt Reynolds,Luis Guzmán,239473,"26,400,640" +"https://m.media-amazon.com/images/M/MV5BZDg0MWNmNjktMGEwZC00ZDlmLWI1MTUtMDBmNjQzMWM2NjBjXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Mimi wo sumaseba,1995,U,111 min,"Animation, Drama, Family",7.9,"A love story between a girl who loves reading books, and a boy who has previously checked out all of the library books she chooses.",75,Yoshifumi Kondô,Yoko Honna,Issey Takahashi,Takashi Tachibana,Shigeru Muroi,51943, +"https://m.media-amazon.com/images/M/MV5BYTY4MTdjZDMtOTBiMC00MDEwLThhMjUtMjlhMjdlYTBmMzk3XkEyXkFqcGdeQXVyNjMwMjk0MTQ@._V1_UY98_CR1,0,67,98_AL_.jpg",Once Were Warriors,1994,A,102 min,"Crime, Drama",7.9,A family descended from Maori warriors is bedeviled by a violent father and the societal problems of being treated as outcasts.,77,Lee Tamahori,Rena Owen,Temuera Morrison,Mamaengaroa Kerr-Bell,Julian Arahanga,31590,"2,201,126" +"https://m.media-amazon.com/images/M/MV5BMDViNjFjOWMtZGZhMi00NmIyLThmYzktODA4MzJhZDZhMDc5XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UY98_CR1,0,67,98_AL_.jpg",True Romance,1993,R,119 min,"Crime, Drama, Romance",7.9,"In Detroit, a lonely pop culture geek marries a call girl, steals cocaine from her pimp, and tries to sell it in Hollywood. Meanwhile, the owners of the cocaine, the Mob, track them down in an attempt to reclaim it.",59,Tony Scott,Christian Slater,Patricia Arquette,Dennis Hopper,Val Kilmer,206918,"12,281,500" +"https://m.media-amazon.com/images/M/MV5BMjg5OGU4OGYtNTZmNy00MjQ1LWIzYzgtMTllMGY2NzlkNzYwXkEyXkFqcGdeQXVyMTI3ODAyMzE2._V1_UY98_CR2,0,67,98_AL_.jpg",Trois couleurs: Bleu,1993,U,94 min,"Drama, Music, Mystery",7.9,A woman struggles to find a way to live her life after the death of her husband and child.,85,Krzysztof Kieslowski,Juliette Binoche,Zbigniew Zamachowski,Julie Delpy,Benoît Régent,89836,"1,324,974" +"https://m.media-amazon.com/images/M/MV5BOTMyZGI4N2YtMzdkNi00MDZmLTg4NmItMzg0ODY5NjdhZjYwL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMzM4MjM0Nzg@._V1_UY98_CR1,0,67,98_AL_.jpg",Jûbê ninpûchô,1993,A,94 min,"Animation, Action, Adventure",7.9,A vagabond swordsman is aided by a beautiful ninja girl and a crafty spy in confronting a demonic clan of killers - with a ghost from his past as their leader - who are bent on overthrowing the Tokugawa Shogunate.,,Yoshiaki Kawajiri,Kôichi Yamadera,Emi Shinohara,Takeshi Aono,Osamu Saka,34529, +"https://m.media-amazon.com/images/M/MV5BN2I2N2Q1YmMtMzZkMC00Y2JjLWJmOWUtNjc2OTM2ZTk1MjUyXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Carlito's Way,1993,A,144 min,"Crime, Drama, Thriller",7.9,"A Puerto Rican former convict, just released from prison, pledges to stay away from drugs and violence despite the pressure around him and lead on to a better life outside of N.Y.C.",65,Brian De Palma,Al Pacino,Sean Penn,Penelope Ann Miller,John Leguizamo,201000,"36,948,322" +"https://m.media-amazon.com/images/M/MV5BNDUxN2I5NDUtZjdlMC00NjlmLTg0OTQtNjk0NjAxZjFmZTUzXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Edward Scissorhands,1990,U,105 min,"Drama, Fantasy, Romance",7.9,"An artificial man, who was incompletely constructed and has scissors for hands, leads a solitary life. Then one day, a suburban lady meets him and introduces him to her world.",74,Tim Burton,Johnny Depp,Winona Ryder,Dianne Wiest,Anthony Michael Hall,447368,"56,362,352" +"https://m.media-amazon.com/images/M/MV5BYjdkNzA4MzYtZThhOS00ZDgzLTlmMDItNmY1ZjI5YjkzZTE1XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",My Left Foot: The Story of Christy Brown,1989,U,103 min,"Biography, Drama",7.9,"Christy Brown, born with cerebral palsy, learns to paint and write with his only controllable limb - his left foot.",97,Jim Sheridan,Daniel Day-Lewis,Brenda Fricker,Alison Whelan,Kirsten Sheridan,68076,"14,743,391" +"https://m.media-amazon.com/images/M/MV5BYWY3N2EyOWYtNDVhZi00MWRkLTg2OTUtODNkNDQ5ZTIwMGJkXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Crimes and Misdemeanors,1989,PG-13,104 min,"Comedy, Drama",7.9,An ophthalmologist's mistress threatens to reveal their affair to his wife while a married documentary filmmaker is infatuated with another woman.,77,Woody Allen,Martin Landau,Woody Allen,Bill Bernstein,Claire Bloom,54670,"18,254,702" +"https://m.media-amazon.com/images/M/MV5BYTVjYWJmMWQtYWU4Ni00MWY3LWI2YmMtNTI5MDE0MWVmMmEzL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Untouchables,1987,A,119 min,"Crime, Drama, Thriller",7.9,"During the era of Prohibition in the United States, Federal Agent Eliot Ness sets out to stop ruthless Chicago gangster Al Capone and, because of rampant corruption, assembles a small, hand-picked team to help him.",79,Brian De Palma,Kevin Costner,Sean Connery,Robert De Niro,Charles Martin Smith,281842,"76,270,454" +"https://m.media-amazon.com/images/M/MV5BMWZiNWUwYjMtM2Y1Yi00MTZmLWEwYzctNjVmYWM0OTFlZDFhXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Hannah and Her Sisters,1986,PG-13,107 min,"Comedy, Drama",7.9,"Between two Thanksgivings two years apart, Hannah's husband falls in love with her sister Lee, while her hypochondriac ex-husband rekindles his relationship with her sister Holly.",90,Woody Allen,Mia Farrow,Dianne Wiest,Michael Caine,Barbara Hershey,67176,"40,084,041" +"https://m.media-amazon.com/images/M/MV5BMzIwM2IwYTItYmM4Zi00OWMzLTkwNjAtYWRmYWNmY2RhMDk0XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Brazil,1985,U,132 min,"Drama, Sci-Fi",7.9,A bureaucrat in a dystopic society becomes an enemy of the state as he pursues the woman of his dreams.,84,Terry Gilliam,Jonathan Pryce,Kim Greist,Robert De Niro,Katherine Helmond,187567,"9,929,135" +"https://m.media-amazon.com/images/M/MV5BMTQ2MTIzMzg5Nl5BMl5BanBnXkFtZTgwOTc5NDI1MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",This Is Spinal Tap,1984,R,82 min,"Comedy, Music",7.9,"Spinal Tap, one of England's loudest bands, is chronicled by film director Marty DiBergi on what proves to be a fateful tour.",92,Rob Reiner,Rob Reiner,Michael McKean,Christopher Guest,Kimberly Stringer,128812,"188,751" +"https://m.media-amazon.com/images/M/MV5BOWMyNjE0MzEtMzVjNy00NjIxLTg0ZjMtMWJhNGI1YmVjYTczL2ltYWdlXkEyXkFqcGdeQXVyNzc5MjA3OA@@._V1_UX67_CR0,0,67,98_AL_.jpg",A Christmas Story,1983,U,93 min,"Comedy, Family",7.9,"In the 1940s, a young boy named Ralphie attempts to convince his parents, his teacher and Santa that a Red Ryder BB gun really is the perfect Christmas gift.",77,Bob Clark,Peter Billingsley,Melinda Dillon,Darren McGavin,Scott Schwartz,132947,"20,605,209" +"https://m.media-amazon.com/images/M/MV5BYTdlMDExOGUtN2I3MS00MjY5LWE1NTAtYzc3MzIxN2M3OWY1XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Blues Brothers,1980,U,133 min,"Action, Adventure, Comedy",7.9,"Jake Blues, just released from prison, puts together his old band to save the Catholic home where he and his brother Elwood were raised.",60,John Landis,John Belushi,Dan Aykroyd,Cab Calloway,John Candy,183182,"57,229,890" +"https://m.media-amazon.com/images/M/MV5BMzdmY2I3MmEtOGFiZi00MTg1LWIxY2QtNWUwM2NmNWNlY2U5XkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Manhattan,1979,R,96 min,"Comedy, Drama, Romance",7.9,The life of a divorced television writer dating a teenage girl is further complicated when he falls in love with his best friend's mistress.,83,Woody Allen,Woody Allen,Diane Keaton,Mariel Hemingway,Michael Murphy,131436,"45,700,000" +"https://m.media-amazon.com/images/M/MV5BZWE4N2JkNDUtZDU4MC00ZjNhLTlkMjYtOTNkMjZhMDAwMDMyXkEyXkFqcGdeQXVyMTA0MjU0Ng@@._V1_UX67_CR0,0,67,98_AL_.jpg",All That Jazz,1979,A,123 min,"Drama, Music, Musical",7.9,"Director/choreographer Bob Fosse tells his own life story as he details the sordid career of Joe Gideon, a womanizing, drug-using dancer.",72,Bob Fosse,Roy Scheider,Jessica Lange,Ann Reinking,Leland Palmer,28223,"37,823,676" +"https://m.media-amazon.com/images/M/MV5BMzc1YTIyNjctYzhlNy00ZmYzLWI2ZWQtMzk4MmQwYzA0NGQ1XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Dawn of the Dead,1978,A,127 min,"Action, Adventure, Horror",7.9,"Following an ever-growing epidemic of zombies that have risen from the dead, two Philadelphia S.W.A.T. team members, a traffic reporter, and his television executive girlfriend seek refuge in a secluded shopping mall.",71,George A. Romero,David Emge,Ken Foree,Scott H. Reiniger,Gaylen Ross,111512,"5,100,000" +"https://m.media-amazon.com/images/M/MV5BOWI2YWQxM2MtY2U4Yi00YjgzLTgwNzktN2ExNTgzNTIzMmUzXkEyXkFqcGdeQXVyMTAwMzUyOTc@._V1_UX67_CR0,0,67,98_AL_.jpg",All the President's Men,1976,U,138 min,"Biography, Drama, History",7.9,"""The Washington Post"" reporters Bob Woodward and Carl Bernstein uncover the details of the Watergate scandal that leads to President Richard Nixon's resignation.",84,Alan J. Pakula,Dustin Hoffman,Robert Redford,Jack Warden,Martin Balsam,103031,"70,600,000" +"https://m.media-amazon.com/images/M/MV5BN2IzM2I5NTQtMTIyMy00YWM2LWI1OGMtNjI0MWIyNDZkZGFkXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",La montaña sagrada,1973,R,114 min,"Adventure, Drama, Fantasy",7.9,"In a corrupt, greed-fueled world, a powerful alchemist leads a messianic character and seven materialistic figures to the Holy Mountain, where they hope to achieve enlightenment.",76,Alejandro Jodorowsky,Alejandro Jodorowsky,Horacio Salinas,Zamira Saunders,Juan Ferrara,37183,"61,001" +"https://m.media-amazon.com/images/M/MV5BZDI2OTg2NDQtMzc0MC00MjRiLWI1NzAtMjY2ZDMwMmUyNzBiXkEyXkFqcGdeQXVyNzM0MTUwNTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Amarcord,1973,R,123 min,"Comedy, Drama, Family",7.9,A series of comedic and nostalgic vignettes set in a 1930s Italian coastal town.,,Federico Fellini,Magali Noël,Bruno Zanin,Pupella Maggio,Armando Brancia,39897, +"https://m.media-amazon.com/images/M/MV5BYzQ5NjJiYWQtYjAzMC00NGU0LWFlMDYtNGFiYjFlMWI1NWM0XkEyXkFqcGdeQXVyODQ0OTczOQ@@._V1_UY98_CR4,0,67,98_AL_.jpg",Le charme discret de la bourgeoisie,1972,PG,102 min,Comedy,7.9,"A surreal, virtually plotless series of dreams centered around six middle-class people and their consistently interrupted attempts to have a meal together.",93,Luis Buñuel,Fernando Rey,Delphine Seyrig,Paul Frankeur,Bulle Ogier,38737,"198,809" +"https://m.media-amazon.com/images/M/MV5BMjRkY2VhYzMtZWQyNS00OTY2LWE5NTAtYjlhNmQyYzE5MmUxXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg","Aguirre, der Zorn Gottes",1972,,95 min,"Action, Adventure, Biography",7.9,"In the 16th century, the ruthless and insane Don Lope de Aguirre leads a Spanish expedition in search of El Dorado.",,Werner Herzog,Klaus Kinski,Ruy Guerra,Helena Rojo,Del Negro,52397, +"https://m.media-amazon.com/images/M/MV5BY2M5Mzg3NjctZTlkNy00MTU0LWFlYTQtY2E2Y2M4NjNiNzllXkEyXkFqcGdeQXVyMTAwMzUyOTc@._V1_UX67_CR0,0,67,98_AL_.jpg",Harold and Maude,1971,PG,91 min,"Comedy, Drama, Romance",7.9,"Young, rich, and obsessed with death, Harold finds himself changed forever when he meets lively septuagenarian Maude at a funeral.",62,Hal Ashby,Ruth Gordon,Bud Cort,Vivian Pickles,Cyril Cusack,70826, +"https://m.media-amazon.com/images/M/MV5BMmNhZmJhMmYtNjlkMC00MjhjLTk1NzMtMTNlMzYzNjZlMjNiXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Patton,1970,U,172 min,"Biography, Drama, War",7.9,The World War II phase of the career of controversial American general George S. Patton.,91,Franklin J. Schaffner,George C. Scott,Karl Malden,Stephen Young,Michael Strong,93741,"61,700,000" +"https://m.media-amazon.com/images/M/MV5BNGUyYTZmOWItMDJhMi00N2IxLWIyNDMtNjUxM2ZiYmU5YWU1XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Wild Bunch,1969,A,145 min,"Action, Adventure, Western",7.9,"An aging group of outlaws look for one last big score as the ""traditional"" American West is disappearing around them.",97,Sam Peckinpah,William Holden,Ernest Borgnine,Robert Ryan,Edmond O'Brien,77401,"12,064,472" +"https://m.media-amazon.com/images/M/MV5BMzRmN2E1ZDUtZDc2ZC00ZmI3LTkwOTctNzE2ZDIzMGJiMTYzXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Night of the Living Dead,1968,,96 min,"Horror, Thriller",7.9,A ragtag group of Pennsylvanians barricade themselves in an old farmhouse to remain safe from a horde of flesh-eating ghouls that are ravaging the East Coast of the United States.,89,George A. Romero,Duane Jones,Judith O'Dea,Karl Hardman,Marilyn Eastman,116557,"89,029" +"https://m.media-amazon.com/images/M/MV5BMTkzNzYyMzA5N15BMl5BanBnXkFtZTgwODcwODQ3MDI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Lion in Winter,1968,PG,134 min,"Biography, Drama, History",7.9,"1183 A.D.: King Henry II's three sons all want to inherit the throne, but he won't commit to a choice. They and his wife variously plot to force him.",,Anthony Harvey,Peter O'Toole,Katharine Hepburn,Anthony Hopkins,John Castle,29003,"22,276,975" +"https://m.media-amazon.com/images/M/MV5BZjZhZTZkNWItZGE1My00MTRkLWI2ZDktMWZkZTIxZWYxOTgzXkEyXkFqcGdeQXVyNDY2MTk1ODk@._V1_UX67_CR0,0,67,98_AL_.jpg",In the Heat of the Night,1967,U,110 min,"Crime, Drama, Mystery",7.9,A black police detective is asked to investigate a murder in a racially hostile southern town.,75,Norman Jewison,Sidney Poitier,Rod Steiger,Warren Oates,Lee Grant,67804,"24,379,978" +"https://m.media-amazon.com/images/M/MV5BMTA0Y2UyMDUtZGZiOS00ZmVkLTg3NmItODQyNTY1ZjU1MWE4L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Charade,1963,U,113 min,"Comedy, Mystery, Romance",7.9,Romance and suspense ensue in Paris as a woman is pursued by several men who want a fortune her murdered husband had stolen. Whom can she trust?,83,Stanley Donen,Cary Grant,Audrey Hepburn,Walter Matthau,James Coburn,68689,"13,474,588" +"https://m.media-amazon.com/images/M/MV5BOTY0ZTA1ZjUtN2MyNi00ZGRmLWExYmMtOTkyNzI1NGQ2Y2RlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Manchurian Candidate,1962,PG-13,126 min,"Drama, Thriller",7.9,A former prisoner of war is brainwashed as an unwitting assassin for an international Communist conspiracy.,94,John Frankenheimer,Frank Sinatra,Laurence Harvey,Janet Leigh,Angela Lansbury,71122, +"https://m.media-amazon.com/images/M/MV5BMjc4MTUxN2UtMmU1NC00MjQyLTk3YTYtZTQ0YzEzZDc0Njc0XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Spartacus,1960,A,197 min,"Adventure, Biography, Drama",7.9,The slave Spartacus leads a violent revolt against the decadent Roman Republic.,87,Stanley Kubrick,Kirk Douglas,Laurence Olivier,Jean Simmons,Charles Laughton,124339,"30,000,000" +"https://m.media-amazon.com/images/M/MV5BZDFlODBmZTYtMWU4MS00MzY4LWFmYzYtYzAzZmU1MGUzMDE5XkEyXkFqcGdeQXVyNTc1NDM0NDU@._V1_UY98_CR1,0,67,98_AL_.jpg",L'avventura,1960,U,144 min,"Drama, Mystery",7.9,"A woman disappears during a Mediterranean boating trip. During the search, her lover and her best friend become attracted to each other.",,Michelangelo Antonioni,Gabriele Ferzetti,Monica Vitti,Lea Massari,Dominique Blanchar,26542, +"https://m.media-amazon.com/images/M/MV5BMzY2NTA1MzUwN15BMl5BanBnXkFtZTgwOTc4NTU4MjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Hiroshima mon amour,1959,,90 min,"Drama, Romance",7.9,A French actress filming an anti-war film in Hiroshima has an affair with a married Japanese architect as they share their differing perspectives on war.,,Alain Resnais,Emmanuelle Riva,Eiji Okada,Stella Dassas,Pierre Barbaud,28421,"88,300" +"https://m.media-amazon.com/images/M/MV5BODcxYjUxZDgtYTQ5Zi00YmQ1LWJmZmItODZkOTYyNDhiNWM3XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Ten Commandments,1956,U,220 min,"Adventure, Drama",7.9,"Moses, an Egyptian Prince, learns of his true heritage as a Hebrew and his divine mission as the deliverer of his people.",,Cecil B. DeMille,Charlton Heston,Yul Brynner,Anne Baxter,Edward G. Robinson,63560,"93,740,000" +"https://m.media-amazon.com/images/M/MV5BYWQ3YWJiMDEtMDBhNS00YjY1LTkzNmEtY2U4Njg4MjQ3YWE3XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Searchers,1956,Passed,119 min,"Adventure, Drama, Western",7.9,An American Civil War veteran embarks on a journey to rescue his niece from the Comanches.,94,John Ford,John Wayne,Jeffrey Hunter,Vera Miles,Ward Bond,80316, +"https://m.media-amazon.com/images/M/MV5BMzE1MzdjNmUtOWU5MS00OTgwLWIzYjYtYTYwYTM0NDkyOTU1XkEyXkFqcGdeQXVyMTY5Nzc4MDY@._V1_UX67_CR0,0,67,98_AL_.jpg",East of Eden,1955,U,118 min,Drama,7.9,"Two brothers struggle to maintain their strict, Bible-toting father's favor.",72,Elia Kazan,James Dean,Raymond Massey,Julie Harris,Burl Ives,40313, +"https://m.media-amazon.com/images/M/MV5BOWIzZGUxZmItOThkMS00Y2QxLTg0MTYtMDdhMjRlNTNlYTI3L2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",High Noon,1952,PG,85 min,"Drama, Thriller, Western",7.9,"A town Marshal, despite the disagreements of his newlywed bride and the townspeople around him, must face a gang of deadly killers alone at high noon when the gang leader, an outlaw he sent up years ago, arrives on the noon train.",89,Fred Zinnemann,Gary Cooper,Grace Kelly,Thomas Mitchell,Lloyd Bridges,97222,"9,450,000" +"https://m.media-amazon.com/images/M/MV5BNzkwNjk4ODgtYjRmMi00ODdhLWIyNjUtNWQyMjg2N2E2NjlhXkEyXkFqcGdeQXVyNjE5MjUyOTM@._V1_UX67_CR0,0,67,98_AL_.jpg",Strangers on a Train,1951,A,101 min,"Crime, Film-Noir, Thriller",7.9,A psychopath forces a tennis star to comply with his theory that two strangers can get away with murder.,88,Alfred Hitchcock,Farley Granger,Robert Walker,Ruth Roman,Leo G. Carroll,123341,"7,630,000" +"https://m.media-amazon.com/images/M/MV5BMzg2YTFkNjgtM2ZkNS00MWVkLWIwMTEtZTgzMDM2MmUxNDE2XkEyXkFqcGdeQXVyMjI4MjA5MzA@._V1_UX67_CR0,0,67,98_AL_.jpg",Harvey,1950,Approved,104 min,"Comedy, Drama, Fantasy",7.9,"Due to his insistence that he has an invisible six foot-tall rabbit for a best friend, a whimsical middle-aged man is thought by his family to be insane - but he may be wiser than anyone knows.",,Henry Koster,James Stewart,Wallace Ford,William H. Lynn,Victoria Horne,52573, +"https://m.media-amazon.com/images/M/MV5BNjRkOGEwYTUtY2E5Yy00ODg4LTk2ZWItY2IyMzUxOGVhMTM1XkEyXkFqcGdeQXVyNDk0MDg4NDk@._V1_UX67_CR0,0,67,98_AL_.jpg",Miracle on 34th Street,1947,,96 min,"Comedy, Drama, Family",7.9,"When a nice old man who claims to be Santa Claus is institutionalized as insane, a young lawyer decides to defend him by arguing in court that he is the real thing.",88,George Seaton,Edmund Gwenn,Maureen O'Hara,John Payne,Gene Lockhart,41625,"2,650,000" +"https://m.media-amazon.com/images/M/MV5BYTc1NGViOTMtNjZhNS00OGY2LWI4MmItOWQwNTY4MDMzNWI3L2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Notorious,1946,U,102 min,"Drama, Film-Noir, Romance",7.9,A woman is asked to spy on a group of Nazi friends in South America. How far will she have to go to ingratiate herself with them?,100,Alfred Hitchcock,Cary Grant,Ingrid Bergman,Claude Rains,Louis Calhern,92306,"10,464,000" +"https://m.media-amazon.com/images/M/MV5BMjdiM2IyZmQtODJiYy00NDNkLTllYmItMmFjMDNiYTQyOGVkXkEyXkFqcGdeQXVyNDY2MTk1ODk@._V1_UX67_CR0,0,67,98_AL_.jpg",The Big Sleep,1946,Passed,114 min,"Crime, Film-Noir, Mystery",7.9,"Private detective Philip Marlowe is hired by a wealthy family. Before the complex case is over, he's seen murder, blackmail, and what might be love.",,Howard Hawks,Humphrey Bogart,Lauren Bacall,John Ridgely,Martha Vickers,78796,"6,540,000" +"https://m.media-amazon.com/images/M/MV5BMTk4NDQ0NjgyNF5BMl5BanBnXkFtZTgwMTE3NTkxMTE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Lost Weekend,1945,Passed,101 min,"Drama, Film-Noir",7.9,The desperate life of a chronic alcoholic is followed through a four-day drinking bout.,,Billy Wilder,Ray Milland,Jane Wyman,Phillip Terry,Howard Da Silva,33549,"9,460,000" +"https://m.media-amazon.com/images/M/MV5BYjQ4ZDA4NGMtMTkwYi00NThiLThhZDUtZTEzNTAxOWYyY2E4XkEyXkFqcGdeQXVyMjUxODE0MDY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Philadelphia Story,1940,,112 min,"Comedy, Romance",7.9,"When a rich woman's ex-husband and a tabloid-type reporter turn up just before her planned remarriage, she begins to learn the truth about herself.",96,George Cukor,Cary Grant,Katharine Hepburn,James Stewart,Ruth Hussey,63550, +"https://m.media-amazon.com/images/M/MV5BZDVmZTZkYjMtNmViZC00ODEzLTgwNDAtNmQ3OGQwOWY5YjFmXkEyXkFqcGdeQXVyNDY2MTk1ODk@._V1_UX67_CR0,0,67,98_AL_.jpg",His Girl Friday,1940,Passed,92 min,"Comedy, Drama, Romance",7.9,A newspaper editor uses every trick in the book to keep his ace reporter ex-wife from remarrying.,,Howard Hawks,Cary Grant,Rosalind Russell,Ralph Bellamy,Gene Lockhart,53667,"296,000" +"https://m.media-amazon.com/images/M/MV5BYjZjOTU3MTMtYTM5YS00YjZmLThmNmMtODcwOTM1NmRiMWM2XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Adventures of Robin Hood,1938,PG,102 min,"Action, Adventure, Romance",7.9,"When Prince John and the Norman Lords begin oppressing the Saxon masses in King Richard's absence, a Saxon lord fights back as the outlaw leader of a rebel guerrilla army.",97,Michael Curtiz,William Keighley,Errol Flynn,Olivia de Havilland,Basil Rathbone,47175,"3,981,000" +"https://m.media-amazon.com/images/M/MV5BYTJmNmQxNGItNDNlMC00MDU3LWFhNzMtZDQ2NDY0ZTVkNjE3XkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",A Night at the Opera,1935,Passed,96 min,"Comedy, Music, Musical",7.9,A sly business manager and two wacky friends of two opera singers help them achieve success while humiliating their stuffy and snobbish enemies.,,Sam Wood,Edmund Goulding,Groucho Marx,Chico Marx,Harpo Marx,30580,"2,537,520" +"https://m.media-amazon.com/images/M/MV5BZTY3YjYxZGQtMTM2YS00ZmYwLWFlM2QtOWFlMTU1NTAyZDQ2XkEyXkFqcGdeQXVyNTgyNTA4MjM@._V1_UX67_CR0,0,67,98_AL_.jpg",King Kong,1933,Passed,100 min,"Adventure, Horror, Sci-Fi",7.9,A film crew goes to a tropical island for an exotic location shoot and discovers a colossal ape who takes a shine to their female blonde star. He is then captured and brought back to New York City for public exhibition.,90,Merian C. Cooper,Ernest B. Schoedsack,Fay Wray,Robert Armstrong,Bruce Cabot,78991,"10,000,000" +"https://m.media-amazon.com/images/M/MV5BMjMyYjgyOTQtZDVlZS00NTQ0LWJiNDItNGRlZmM3Yzc0N2Y0XkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Freaks,1932,,64 min,"Drama, Horror",7.9,"A circus' beautiful trapeze artist agrees to marry the leader of side-show performers, but his deformed friends discover she is only marrying him for his inheritance.",80,Tod Browning,Wallace Ford,Leila Hyams,Olga Baclanova,Roscoe Ates,42117, +"https://m.media-amazon.com/images/M/MV5BMTAxYjEyMTctZTg3Ni00MGZmLWIxMmMtOGM2NTFiY2U3MmExXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Nosferatu,1922,,94 min,"Fantasy, Horror",7.9,Vampire Count Orlok expresses interest in a new residence and real estate agent Hutter's wife.,,F.W. Murnau,Max Schreck,Alexander Granach,Gustav von Wangenheim,Greta Schröder,88794, +"https://m.media-amazon.com/images/M/MV5BMTlkMmVmYjktYTc2NC00ZGZjLWEyOWUtMjc2MDMwMjQwOTA5XkEyXkFqcGdeQXVyNTI4MzE4MDU@._V1_UX67_CR0,0,67,98_AL_.jpg",The Gentlemen,2019,A,113 min,"Action, Comedy, Crime",7.8,"An American expat tries to sell off his highly profitable marijuana empire in London, triggering plots, schemes, bribery and blackmail in an attempt to steal his domain out from under him.",51,Guy Ritchie,Matthew McConaughey,Charlie Hunnam,Michelle Dockery,Jeremy Strong,237392, +"https://m.media-amazon.com/images/M/MV5BZmVhN2JlYjEtZWFkOS00YzE0LThiNDMtMGI3NDA1MTk2ZDQ2XkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR0,0,67,98_AL_.jpg",Raazi,2018,UA,138 min,"Action, Drama, Thriller",7.8,A Kashmiri woman agrees to marry a Pakistani army officer in order to spy on Pakistan during the Indo-Pakistan War of 1971.,,Meghna Gulzar,Alia Bhatt,Vicky Kaushal,Rajit Kapoor,Shishir Sharma,25344, +"https://m.media-amazon.com/images/M/MV5BNjcyYjg0M2ItMzMyZS00NmM1LTlhZDMtN2MxN2RhNWY4YTkwXkEyXkFqcGdeQXVyNjY1MTg4Mzc@._V1_UX67_CR0,0,67,98_AL_.jpg",Sound of Metal,2019,R,120 min,"Drama, Music",7.8,A heavy-metal drummer's life is thrown into freefall when he begins to lose his hearing.,81,Darius Marder,Riz Ahmed,Olivia Cooke,Paul Raci,Lauren Ridloff,27187, +"https://m.media-amazon.com/images/M/MV5BMTBkMjMyN2UtNzVjNi00Y2ZiLTk2MDYtN2Y0MjgzYjAxNzE4XkEyXkFqcGdeQXVyNjkxOTM4ODY@._V1_UY98_CR1,0,67,98_AL_.jpg",Forushande,2016,UA,124 min,Drama,7.8,"While both participating in a production of ""Death of a Salesman,"" a teacher's wife is assaulted in her new home, which leaves him determined to find the perpetrator over his wife's traumatized objections.",85,Asghar Farhadi,Shahab Hosseini,Taraneh Alidoosti,Babak Karimi,Mina Sadati,51240,"2,402,067" +"https://m.media-amazon.com/images/M/MV5BN2YyZjQ0NTEtNzU5MS00NGZkLTg0MTEtYzJmMWY3MWRhZjM2XkEyXkFqcGdeQXVyMDA4NzMyOA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Dunkirk,2017,UA,106 min,"Action, Drama, History",7.8,"Allied soldiers from Belgium, the British Empire, and France are surrounded by the German Army and evacuated during a fierce battle in World War II.",94,Christopher Nolan,Fionn Whitehead,Barry Keoghan,Mark Rylance,Tom Hardy,555092,"188,373,161" +"https://m.media-amazon.com/images/M/MV5BNDQzZmQ5MjItYmJlNy00MGI2LWExMDQtMjBiNjNmMzc5NTk1XkEyXkFqcGdeQXVyNjY1OTY4MTk@._V1_UY98_CR1,0,67,98_AL_.jpg",Perfetti sconosciuti,2016,,96 min,"Comedy, Drama",7.8,"Seven long-time friends get together for a dinner. When they decide to share with each other the content of every text message, email and phone call they receive, many secrets start to unveil and the equilibrium trembles.",,Paolo Genovese,Giuseppe Battiston,Anna Foglietta,Marco Giallini,Edoardo Leo,57168, +"https://m.media-amazon.com/images/M/MV5BMzg2Mzg4YmUtNDdkNy00NWY1LWE3NmEtZWMwNGNlMzE5YzU3XkEyXkFqcGdeQXVyMjA5MTIzMjQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Hidden Figures,2016,UA,127 min,"Biography, Drama, History",7.8,The story of a team of female African-American mathematicians who served a vital role in NASA during the early years of the U.S. space program.,74,Theodore Melfi,Taraji P. Henson,Octavia Spencer,Janelle Monáe,Kevin Costner,200876,"169,607,287" +"https://m.media-amazon.com/images/M/MV5BMmYwNWZlNzEtNjE4Zi00NzQ4LWI2YmUtOWZhNzZhZDYyNmVmXkEyXkFqcGdeQXVyNzYzODM3Mzg@._V1_UX67_CR0,0,67,98_AL_.jpg",Paddington 2,2017,U,103 min,"Adventure, Comedy, Family",7.8,"Paddington (Ben Whishaw), now happily settled with the Brown family and a popular member of the local community, picks up a series of odd jobs to buy the perfect present for his Aunt Lucy's (Imelda Staunton's) 100th birthday, only for the gift to be stolen.",88,Paul King,Ben Whishaw,Hugh Grant,Hugh Bonneville,Sally Hawkins,61594,"40,442,052" +"https://m.media-amazon.com/images/M/MV5BY2YxNjQxYWYtYzNkMi00YTgyLWIwZTMtYzgyYjZlZmYzZTA0XkEyXkFqcGdeQXVyMTA4NjE0NjEy._V1_UX67_CR0,0,67,98_AL_.jpg",Udta Punjab,2016,A,148 min,"Action, Crime, Drama",7.8,A story that revolves around drug abuse in the affluent north Indian State of Punjab and how the youth there have succumbed to it en-masse resulting in a socio-economic decline.,,Abhishek Chaubey,Shahid Kapoor,Alia Bhatt,Kareena Kapoor,Diljit Dosanjh,27175, +"https://m.media-amazon.com/images/M/MV5BMjA2Mzg2NDMzNl5BMl5BanBnXkFtZTgwMjcwODUzOTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Kubo and the Two Strings,2016,PG,101 min,"Animation, Action, Adventure",7.8,A young boy named Kubo must locate a magical suit of armour worn by his late father in order to defeat a vengeful spirit from the past.,84,Travis Knight,Charlize Theron,Art Parkinson,Matthew McConaughey,Ralph Fiennes,118035,"48,023,088" +"https://m.media-amazon.com/images/M/MV5BZjAzZjZiMmQtMDZmOC00NjVmLTkyNTItOGI2Mzg4NTBhZTA1XkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR0,0,67,98_AL_.jpg",M.S. Dhoni: The Untold Story,2016,U,184 min,"Biography, Drama, Sport",7.8,The untold story of Mahendra Singh Dhoni's journey from ticket collector to trophy collector - the world-cup-winning captain of the Indian Cricket Team.,,Neeraj Pandey,Sushant Singh Rajput,Kiara Advani,Anupam Kher,Disha Patani,40416,"1,782,795" +"https://m.media-amazon.com/images/M/MV5BMTYxMjk0NDg4Ml5BMl5BanBnXkFtZTgwODcyNjA5OTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Manchester by the Sea,2016,UA,137 min,Drama,7.8,A depressed uncle is asked to take care of his teenage nephew after the boy's father dies.,96,Kenneth Lonergan,Casey Affleck,Michelle Williams,Kyle Chandler,Lucas Hedges,246963,"47,695,120" +"https://m.media-amazon.com/images/M/MV5BMjA0MzQzNjM1Ml5BMl5BanBnXkFtZTgwNjM5MjU5NjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Under sandet,2015,R,100 min,"Drama, History, War",7.8,"In post-World War II Denmark, a group of young German POWs are forced to clear a beach of thousands of land mines under the watch of a Danish Sergeant who slowly learns to appreciate their plight.",75,Martin Zandvliet,Roland Møller,Louis Hofmann,Joel Basman,Mikkel Boe Følsgaard,35539,"435,266" +"https://m.media-amazon.com/images/M/MV5BMjEwMzMxODIzOV5BMl5BanBnXkFtZTgwNzg3OTAzMDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Rogue One,2016,UA,133 min,"Action, Adventure, Sci-Fi",7.8,The daughter of an Imperial scientist joins the Rebel Alliance in a risky move to steal the plans for the Death Star.,65,Gareth Edwards,Felicity Jones,Diego Luna,Alan Tudyk,Donnie Yen,556608,"532,177,324" +"https://m.media-amazon.com/images/M/MV5BMjQ0MTgyNjAxMV5BMl5BanBnXkFtZTgwNjUzMDkyODE@._V1_UX67_CR0,0,67,98_AL_.jpg",Captain America: Civil War,2016,UA,147 min,"Action, Adventure, Sci-Fi",7.8,Political involvement in the Avengers' affairs causes a rift between Captain America and Iron Man.,75,Anthony Russo,Joe Russo,Chris Evans,Robert Downey Jr.,Scarlett Johansson,663649,"408,084,349" +"https://m.media-amazon.com/images/M/MV5BMjA1MTc1NTg5NV5BMl5BanBnXkFtZTgwOTM2MDEzNzE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Hateful Eight,2015,A,168 min,"Crime, Drama, Mystery",7.8,"In the dead of a Wyoming winter, a bounty hunter and his prisoner find shelter in a cabin currently inhabited by a collection of nefarious characters.",68,Quentin Tarantino,Samuel L. Jackson,Kurt Russell,Jennifer Jason Leigh,Walton Goggins,517059,"54,117,416" +"https://m.media-amazon.com/images/M/MV5BY2QzYTQyYzItMzAwYi00YjZlLThjNTUtNzMyMDdkYzJiNWM4XkEyXkFqcGdeQXVyMTkxNjUyNQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Little Women,2019,U,135 min,"Drama, Romance",7.8,"Jo March reflects back and forth on her life, telling the beloved story of the March sisters - four young women, each determined to live life on her own terms.",91,Greta Gerwig,Saoirse Ronan,Emma Watson,Florence Pugh,Eliza Scanlen,143250,"108,101,214" +"https://m.media-amazon.com/images/M/MV5BMTU3NjE2NjgwN15BMl5BanBnXkFtZTgwNDYzMzEwMzI@._V1_UX67_CR0,0,67,98_AL_.jpg",Loving Vincent,2017,UA,94 min,"Animation, Biography, Crime",7.8,"In a story depicted in oil painted animation, a young man comes to the last hometown of painter Vincent van Gogh (Robert Gulaczyk) to deliver the troubled artist's final letter and ends up investigating his final days there.",62,Dorota Kobiela,Hugh Welchman,Douglas Booth,Jerome Flynn,Robert Gulaczyk,50778,"6,735,118" +"https://m.media-amazon.com/images/M/MV5BMTU2OTcyOTE3MF5BMl5BanBnXkFtZTgwNTg5Mjc1MjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Pride,2014,R,119 min,"Biography, Comedy, Drama",7.8,U.K. gay activists work to help miners during their lengthy strike of the National Union of Mineworkers in the summer of 1984.,79,Matthew Warchus,Bill Nighy,Imelda Staunton,Dominic West,Paddy Considine,51841, +"https://m.media-amazon.com/images/M/MV5BMTcxNTgzNDg1N15BMl5BanBnXkFtZTgwNjg4MzI1MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Le passé,2013,PG-13,130 min,"Drama, Mystery",7.8,"An Iranian man deserts his French wife and her two children to return to his homeland. Meanwhile, his wife starts up a new relationship, a reality her husband confronts upon his wife's request for a divorce.",85,Asghar Farhadi,Bérénice Bejo,Tahar Rahim,Ali Mosaffa,Pauline Burlet,45002,"1,330,596" +"https://m.media-amazon.com/images/M/MV5BNjg5NmI3NmUtZDQ2Mi00ZTI0LWE0YzAtOGRhOWJmNDJkOWNkXkEyXkFqcGdeQXVyMzIzNDU1NTY@._V1_UY98_CR0,0,67,98_AL_.jpg",La grande bellezza,2013,,141 min,Drama,7.8,"Jep Gambardella has seduced his way through the lavish nightlife of Rome for decades, but after his 65th birthday and a shock from the past, Jep looks past the nightclubs and parties to find a timeless landscape of absurd, exquisite beauty.",86,Paolo Sorrentino,Toni Servillo,Carlo Verdone,Sabrina Ferilli,Carlo Buccirosso,81125,"2,852,400" +"https://m.media-amazon.com/images/M/MV5BMTUwMzc1NjIzMV5BMl5BanBnXkFtZTgwODUyMTIxMTE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Lunchbox,2013,U,104 min,"Drama, Romance",7.8,A mistaken delivery in Mumbai's famously efficient lunchbox delivery system connects a young housewife to an older man in the dusk of his life as they build a fantasy world together through notes in the lunchbox.,76,Ritesh Batra,Irrfan Khan,Nimrat Kaur,Nawazuddin Siddiqui,Lillete Dubey,50523,"4,231,500" +"https://m.media-amazon.com/images/M/MV5BYWNlODE1ZTEtOTQ5MS00N2QwLTllNjItZDQ2Y2UzMmU5YmI2XkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR3,0,67,98_AL_.jpg",Vicky Donor,2012,UA,126 min,"Comedy, Romance",7.8,"A man is brought in by an infertility doctor to supply him with his sperm, where he becomes the biggest sperm donor for his clinic.",,Shoojit Sircar,Ayushmann Khurrana,Yami Gautam,Annu Kapoor,Dolly Ahluwalia,39710,"169,209" +"https://m.media-amazon.com/images/M/MV5BMDliOTIzNmUtOTllOC00NDU3LWFiNjYtMGM0NDc1YTMxNjYxXkEyXkFqcGdeQXVyNTM3NzExMDQ@._V1_UY98_CR1,0,67,98_AL_.jpg",Big Hero 6,2014,U,102 min,"Animation, Action, Adventure",7.8,"A special bond develops between plus-sized inflatable robot Baymax and prodigy Hiro Hamada, who together team up with a group of friends to form a band of high-tech heroes.",74,Don Hall,Chris Williams,Ryan Potter,Scott Adsit,Jamie Chung,410983,"222,527,828" +"https://m.media-amazon.com/images/M/MV5BMTA1ODUzMDA3NzFeQTJeQWpwZ15BbWU3MDgxMTYxNTk@._V1_UX67_CR0,0,67,98_AL_.jpg",About Time,2013,R,123 min,"Comedy, Drama, Fantasy",7.8,"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.",55,Richard Curtis,Domhnall Gleeson,Rachel McAdams,Bill Nighy,Lydia Wilson,303032,"15,322,921" +"https://m.media-amazon.com/images/M/MV5BMjQ5YWVmYmYtOWFiZC00NGMxLWEwODctZDM2MWI4YWViN2E5XkEyXkFqcGdeQXVyNjQ2MjQ5NzM@._V1_UY98_CR0,0,67,98_AL_.jpg",English Vinglish,2012,U,134 min,"Comedy, Drama, Family",7.8,"A quiet, sweet tempered housewife endures small slights from her well-educated husband and daughter every day because of her inability to speak and understand English.",,Gauri Shinde,Sridevi,Adil Hussain,Mehdi Nebbou,Priya Anand,33618,"1,670,773" +"https://m.media-amazon.com/images/M/MV5BMTU4NDg0MzkzNV5BMl5BanBnXkFtZTgwODA3Mzc1MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Kaze tachinu,2013,PG-13,126 min,"Animation, Biography, Drama",7.8,"A look at the life of Jiro Horikoshi, the man who designed Japanese fighter planes during World War II.",83,Hayao Miyazaki,Hideaki Anno,Hidetoshi Nishijima,Miori Takimoto,Masahiko Nishimura,73690,"5,209,580" +"https://m.media-amazon.com/images/M/MV5BMTYzMDM4NzkxOV5BMl5BanBnXkFtZTgwNzM1Mzg2NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Toy Story 4,2019,U,100 min,"Animation, Adventure, Comedy",7.8,"When a new toy called ""Forky"" joins Woody and the gang, a road trip alongside old and new friends reveals how big the world can be for a toy.",84,Josh Cooley,Tom Hanks,Tim Allen,Annie Potts,Tony Hale,203177,"434,038,008" +"https://m.media-amazon.com/images/M/MV5BMTQ4MzQ3NjA0N15BMl5BanBnXkFtZTgwODQyNjQ4MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",La migliore offerta,2013,R,131 min,"Crime, Drama, Mystery",7.8,A lonely art expert working for a mysterious and reclusive heiress finds not only her art worth examining.,49,Giuseppe Tornatore,Geoffrey Rush,Jim Sturgess,Sylvia Hoeks,Donald Sutherland,108399,"85,433" +"https://m.media-amazon.com/images/M/MV5BMzllMWI1ZDQtMmFhNS00NzJkLThmMTMtNzFmMmMyYjU3ZGVjXkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",Moonrise Kingdom,2012,A,94 min,"Comedy, Drama, Romance",7.8,"A pair of young lovers flee their New England town, which causes a local search party to fan out to find them.",84,Wes Anderson,Jared Gilman,Kara Hayward,Bruce Willis,Bill Murray,318789,"45,512,466" +"https://m.media-amazon.com/images/M/MV5BMzMwMTAwODczN15BMl5BanBnXkFtZTgwMDk2NDA4MTE@._V1_UX67_CR0,0,67,98_AL_.jpg",How to Train Your Dragon 2,2014,U,102 min,"Animation, Action, Adventure",7.8,"When Hiccup and Toothless discover an ice cave that is home to hundreds of new wild dragons and the mysterious Dragon Rider, the two friends find themselves at the center of a battle to protect the peace.",76,Dean DeBlois,Jay Baruchel,Cate Blanchett,Gerard Butler,Craig Ferguson,305611,"177,002,924" +"https://m.media-amazon.com/images/M/MV5BNDc4MThhN2EtZjMzNC00ZDJmLThiZTgtNThlY2UxZWMzNjdkXkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",The Big Short,2015,A,130 min,"Biography, Comedy, Drama",7.8,In 2006-2007 a group of investors bet against the US mortgage market. In their research they discover how flawed and corrupt the market is.,81,Adam McKay,Christian Bale,Steve Carell,Ryan Gosling,Brad Pitt,362942,"70,259,870" +"https://m.media-amazon.com/images/M/MV5BYzM2OGQ2NzUtNzlmYi00ZDg4LWExODgtMDVmOTU2Yzg2N2U5XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR0,0,67,98_AL_.jpg",Kokuhaku,2010,,106 min,"Drama, Thriller",7.8,A psychological thriller of a grieving mother turned cold-blooded avenger with a twisty master plan to pay back those who were responsible for her daughter's death.,,Tetsuya Nakashima,Takako Matsu,Yoshino Kimura,Masaki Okada,Yukito Nishii,35713, +"https://m.media-amazon.com/images/M/MV5BZjRmNjc5MTYtYjc3My00ZjNiLTg4YjUtMTQ0ZTFkZmMxMDUzXkEyXkFqcGdeQXVyNDY5MTUyNjU@._V1_UY98_CR3,0,67,98_AL_.jpg",Ang-ma-reul bo-at-da,2010,,144 min,"Action, Crime, Drama",7.8,A secret agent exacts revenge on a serial killer through a series of captures and releases.,67,Jee-woon Kim,Lee Byung-Hun,Choi Min-sik,Jeon Gook-Hwan,Ho-jin Chun,111252,"128,392" +"https://m.media-amazon.com/images/M/MV5BMTczNDk4NTQ0OV5BMl5BanBnXkFtZTcwNDAxMDgxNw@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Girl with the Dragon Tattoo,2011,R,158 min,"Crime, Drama, Mystery",7.8,"Journalist Mikael Blomkvist is aided in his search for a woman who has been missing for forty years by Lisbeth Salander, a young computer hacker.",71,David Fincher,Daniel Craig,Rooney Mara,Christopher Plummer,Stellan Skarsgård,423010,"102,515,793" +"https://m.media-amazon.com/images/M/MV5BODhiZWRhMjctNDUyMS00NmUwLTgwYmItMjJhOWNkZWQ3ZTQxXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Captain Phillips,2013,UA,134 min,"Adventure, Biography, Crime",7.8,"The true story of Captain Richard Phillips and the 2009 hijacking by Somali pirates of the U.S.-flagged MV Maersk Alabama, the first American cargo ship to be hijacked in two hundred years.",82,Paul Greengrass,Tom Hanks,Barkhad Abdi,Barkhad Abdirahman,Catherine Keener,421244,"107,100,855" +"https://m.media-amazon.com/images/M/MV5BMTgzMTkxNjAxNV5BMl5BanBnXkFtZTgwMDU3MDE0MjE@._V1_UY98_CR0,0,67,98_AL_.jpg",Ajeossi,2010,R,119 min,"Action, Crime, Drama",7.8,A quiet pawnshop keeper with a violent past takes on a drug-and-organ trafficking ring in hope of saving the child who is his only friend.,,Jeong-beom Lee,Won Bin,Sae-ron Kim,Tae-hoon Kim,Hee-won Kim,62848,"6,460" +"https://m.media-amazon.com/images/M/MV5BMTA5MzkyMzIxNjJeQTJeQWpwZ15BbWU4MDU0MDk0OTUx._V1_UX67_CR0,0,67,98_AL_.jpg",Straight Outta Compton,2015,R,147 min,"Biography, Drama, History",7.8,"The rap group NWA emerges from the mean streets of Compton in Los Angeles, California, in the mid-1980s and revolutionizes Hip Hop culture with their music and tales about life in the hood.",72,F. Gary Gray,O'Shea Jackson Jr.,Corey Hawkins,Jason Mitchell,Neil Brown Jr.,179264,"161,197,785" +"https://m.media-amazon.com/images/M/MV5BMTQzMTg0NDA1M15BMl5BanBnXkFtZTgwODUzMTE0MjE@._V1_UY98_CR0,0,67,98_AL_.jpg",Madeo,2009,R,129 min,"Crime, Drama, Mystery",7.8,A mother desperately searches for the killer who framed her son for a girl's horrific murder.,79,Bong Joon Ho,Hye-ja Kim,Won Bin,Jin Goo,Je-mun Yun,52758,"547,292" +"https://m.media-amazon.com/images/M/MV5BY2ViOTU5MDQtZTRiZi00YjViLWFiY2ItOTRhNWYyN2ZiMzUyXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",Chugyeokja,2008,,125 min,"Action, Crime, Thriller",7.8,A disgraced ex-policeman who runs a small ring of prostitutes finds himself in a race against time when one of his women goes missing.,64,Hong-jin Na,Kim Yoon-seok,Jung-woo Ha,Yeong-hie Seo,Yoo-Jeong Kim,58468, +"https://m.media-amazon.com/images/M/MV5BMzU0NDY0NDEzNV5BMl5BanBnXkFtZTgwOTIxNDU1MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Hobbit: The Desolation of Smaug,2013,UA,161 min,"Adventure, Fantasy",7.8,"The dwarves, along with Bilbo Baggins and Gandalf the Grey, continue their quest to reclaim Erebor, their homeland, from Smaug. Bilbo Baggins is in possession of a mysterious and magical ring.",66,Peter Jackson,Ian McKellen,Martin Freeman,Richard Armitage,Ken Stott,601408,"258,366,855" +"https://m.media-amazon.com/images/M/MV5BMTQ2OTYyNzUxOF5BMl5BanBnXkFtZTcwMzUwMDY4Mg@@._V1_UX67_CR0,0,67,98_AL_.jpg",Das weiße Band - Eine deutsche Kindergeschichte,2009,UA,144 min,"Drama, History, Mystery",7.8,"Strange events happen in a small village in the north of Germany during the years before World War I, which seem to be ritual punishment. Who is responsible?",82,Michael Haneke,Christian Friedel,Ernst Jacobi,Leonie Benesch,Ulrich Tukur,68715,"2,222,647" +"https://m.media-amazon.com/images/M/MV5BMTc2Mjc0MDg3MV5BMl5BanBnXkFtZTcwMjUzMDkxMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Män som hatar kvinnor,2009,R,152 min,"Crime, Drama, Mystery",7.8,A journalist is aided by a young female hacker in his search for the killer of a woman who has been dead for forty years.,76,Niels Arden Oplev,Michael Nyqvist,Noomi Rapace,Ewa Fröling,Lena Endre,208994,"10,095,170" +"https://m.media-amazon.com/images/M/MV5BYjYzOGE1MjUtODgyMy00ZDAxLTljYTgtNzk0Njg2YWQwMTZhXkEyXkFqcGdeQXVyMDM2NDM2MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Trial of the Chicago 7,2020,R,129 min,"Drama, History, Thriller",7.8,"The story of 7 people on trial stemming from various charges surrounding the uprising at the 1968 Democratic National Convention in Chicago, Illinois.",77,Aaron Sorkin,Eddie Redmayne,Alex Sharp,Sacha Baron Cohen,Jeremy Strong,89896, +"https://m.media-amazon.com/images/M/MV5BOTNjM2Y2ZjgtMDc5NS00MDQ1LTgyNGYtYzYwMTAyNWQwYTMyXkEyXkFqcGdeQXVyMjE4NzUxNDA@._V1_UX67_CR0,0,67,98_AL_.jpg",Druk,2020,,117 min,"Comedy, Drama",7.8,"Four friends, all high school teachers, test a theory that they will improve their lives by maintaining a constant level of alcohol in their blood.",81,Thomas Vinterberg,Mads Mikkelsen,Thomas Bo Larsen,Magnus Millang,Lars Ranthe,33931, +"https://m.media-amazon.com/images/M/MV5BMTM0ODk3MjM1MV5BMl5BanBnXkFtZTcwNzc1MDIwNA@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Fighter,2010,UA,116 min,"Biography, Drama, Sport",7.8,"Based on the story of Micky Ward, a fledgling boxer who tries to escape the shadow of his more famous but troubled older boxing brother and get his own shot at greatness.",79,David O. Russell,Mark Wahlberg,Christian Bale,Amy Adams,Melissa Leo,340584,"93,617,009" +"https://m.media-amazon.com/images/M/MV5BMTM4NzQ0OTYyOF5BMl5BanBnXkFtZTcwMDkyNjQyMg@@._V1_UX67_CR0,0,67,98_AL_.jpg",Taken,2008,A,90 min,"Action, Thriller",7.8,"A retired CIA agent travels across Europe and relies on his old skills to save his estranged daughter, who has been kidnapped while on a trip to Paris.",51,Pierre Morel,Liam Neeson,Maggie Grace,Famke Janssen,Leland Orser,564791,"145,000,989" +"https://m.media-amazon.com/images/M/MV5BMTMzMTc3MjA5NF5BMl5BanBnXkFtZTcwOTk3MDE5MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Boy in the Striped Pyjamas,2008,PG-13,94 min,"Drama, History, War",7.8,"Through the innocent eyes of Bruno, the eight-year-old son of the commandant at a German concentration camp, a forbidden friendship with a Jewish boy on the other side of the camp fence has startling and unexpected consequences.",55,Mark Herman,Asa Butterfield,David Thewlis,Rupert Friend,Zac Mattoon O'Brien,190748,"9,030,581" +"https://m.media-amazon.com/images/M/MV5BYWUxZjJkMDktZmMxMS00Mzg3LTk4MDItN2IwODlmN2E0MTM0XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Once,2007,R,86 min,"Drama, Music, Romance",7.8,"A modern-day musical about a busker and an immigrant and their eventful week in Dublin, as they write, rehearse and record songs that tell their love story.",88,John Carney,Glen Hansard,Markéta Irglová,Hugh Walsh,Gerard Hendrick,110656,"9,439,923" +"https://m.media-amazon.com/images/M/MV5BMTcwNTE4MTUxMl5BMl5BanBnXkFtZTcwMDIyODM4OA@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Hobbit: An Unexpected Journey,2012,UA,169 min,"Adventure, Fantasy",7.8,"A reluctant Hobbit, Bilbo Baggins, sets out to the Lonely Mountain with a spirited group of dwarves to reclaim their mountain home, and the gold within it from the dragon Smaug.",58,Peter Jackson,Martin Freeman,Ian McKellen,Richard Armitage,Andy Serkis,757377,"303,003,568" +"https://m.media-amazon.com/images/M/MV5BMzgxMzYyNzAyOF5BMl5BanBnXkFtZTcwODY5MjY3MQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",Auf der anderen Seite,2007,,122 min,Drama,7.8,A Turkish man travels to Istanbul to find the daughter of his father's former girlfriend.,85,Fatih Akin,Baki Davrak,Nurgül Yesilçay,Tuncel Kurtiz,Nursel Köse,30827,"741,283" +"https://m.media-amazon.com/images/M/MV5BMGRiYjE0YzItMzk3Zi00ZmYwLWJjNDktYTAwYjIwMjIxYzM3XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Atonement,2007,R,123 min,"Drama, Mystery, Romance",7.8,Thirteen-year-old fledgling writer Briony Tallis irrevocably changes the course of several lives when she accuses her older sister's lover of a crime he did not commit.,85,Joe Wright,Keira Knightley,James McAvoy,Brenda Blethyn,Saoirse Ronan,251370,"50,927,067" +"https://m.media-amazon.com/images/M/MV5BZjY5ZjQyMjMtMmEwOC00Nzc2LTllYTItMmU2MzJjNTg1NjY0XkEyXkFqcGdeQXVyNjQ1MTMzMDQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Drive,2011,A,100 min,"Crime, Drama",7.8,A mysterious Hollywood stuntman and mechanic moonlights as a getaway driver and finds himself in trouble when he helps out his neighbor.,78,Nicolas Winding Refn,Ryan Gosling,Carey Mulligan,Bryan Cranston,Albert Brooks,571571,"35,061,555" +"https://m.media-amazon.com/images/M/MV5BMjFmZGI2YTEtYmJhMS00YTE5LWJjNjAtNDI5OGY5ZDhmNTRlXkEyXkFqcGdeQXVyODAwMTU1MTE@._V1_UX67_CR0,0,67,98_AL_.jpg",American Gangster,2007,A,157 min,"Biography, Crime, Drama",7.8,"An outcast New York City cop is charged with bringing down Harlem drug lord Frank Lucas, whose real life inspired this partly biographical film.",76,Ridley Scott,Denzel Washington,Russell Crowe,Chiwetel Ejiofor,Josh Brolin,392449,"130,164,645" +"https://m.media-amazon.com/images/M/MV5BMTYwOTEwNjAzMl5BMl5BanBnXkFtZTcwODc5MTUwMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Avatar,2009,UA,162 min,"Action, Adventure, Fantasy",7.8,A paraplegic Marine dispatched to the moon Pandora on a unique mission becomes torn between following his orders and protecting the world he feels is his home.,83,James Cameron,Sam Worthington,Zoe Saldana,Sigourney Weaver,Michelle Rodriguez,1118998,"760,507,625" +"https://m.media-amazon.com/images/M/MV5BMTg4ODkzMDQ3Nl5BMl5BanBnXkFtZTgwNTEwMTkxMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Mr. Nobody,2009,R,141 min,"Drama, Fantasy, Romance",7.8,"A boy stands on a station platform as a train is about to leave. Should he go with his mother or stay with his father? Infinite possibilities arise from this decision. As long as he doesn't choose, anything is possible.",63,Jaco Van Dormael,Jared Leto,Sarah Polley,Diane Kruger,Linh Dan Pham,216421,"3,600" +"https://m.media-amazon.com/images/M/MV5BMzhmNGMzMDMtZDM0Yi00MmVmLWExYjAtZDhjZjcxZDM0MzJhXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Apocalypto,2006,A,139 min,"Action, Adventure, Drama",7.8,"As the Mayan kingdom faces its decline, a young man is taken on a perilous journey to a world ruled by fear and oppression.",68,Mel Gibson,Gerardo Taracena,Raoul Max Trujillo,Dalia Hernández,Rudy Youngblood,291018,"50,866,635" +"https://m.media-amazon.com/images/M/MV5BMTgzNTgzODU0NV5BMl5BanBnXkFtZTcwMjEyMjMzMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Little Miss Sunshine,2006,UA,101 min,"Comedy, Drama",7.8,A family determined to get their young daughter into the finals of a beauty pageant take a cross-country trip in their VW bus.,80,Jonathan Dayton,Valerie Faris,Steve Carell,Toni Collette,Greg Kinnear,439856,"59,891,098" +"https://m.media-amazon.com/images/M/MV5BMzg4MDJhMDMtYmJiMS00ZDZmLThmZWUtYTMwZDM1YTc5MWE2XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Hot Fuzz,2007,UA,121 min,"Action, Comedy, Mystery",7.8,A skilled London police officer is transferred to a small town with a dark secret.,81,Edgar Wright,Simon Pegg,Nick Frost,Martin Freeman,Bill Nighy,463466,"23,637,265" +"https://m.media-amazon.com/images/M/MV5BNjQ0NTY2ODY2M15BMl5BanBnXkFtZTgwMjE4MzkxMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Curious Case of Benjamin Button,2008,UA,166 min,"Drama, Fantasy, Romance",7.8,"Tells the story of Benjamin Button, a man who starts aging backwards with consequences.",70,David Fincher,Brad Pitt,Cate Blanchett,Tilda Swinton,Julia Ormond,589160,"127,509,326" +"https://m.media-amazon.com/images/M/MV5BY2VlOTc4ZjctYjVlMS00NDYwLWEwZjctZmYzZmVkNGU5NjNjXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UY98_CR2,0,67,98_AL_.jpg",Veer-Zaara,2004,U,192 min,"Drama, Family, Musical",7.8,"Veer-Zaara is a saga of love, separation, courage and sacrifice. A love story that is an inspiration and will remain a legend forever.",67,Yash Chopra,Shah Rukh Khan,Preity Zinta,Rani Mukerji,Kirron Kher,49050,"2,921,738" +"https://m.media-amazon.com/images/M/MV5BMTU4NTc5NjM5M15BMl5BanBnXkFtZTgwODEyMTE0MDE@._V1_UY98_CR1,0,67,98_AL_.jpg",Adams æbler,2005,R,94 min,"Comedy, Crime, Drama",7.8,A neo-nazi sentenced to community service at a church clashes with the blindly devotional priest.,51,Anders Thomas Jensen,Ulrich Thomsen,Mads Mikkelsen,Nicolas Bro,Paprika Steen,45717,"1,305" +"https://m.media-amazon.com/images/M/MV5BMTA1NDQ3NTcyOTNeQTJeQWpwZ15BbWU3MDA0MzA4MzE@._V1_UX67_CR0,0,67,98_AL_.jpg",Pride & Prejudice,2005,PG,129 min,"Drama, Romance",7.8,"Sparks fly when spirited Elizabeth Bennet meets single, rich, and proud Mr. Darcy. But Mr. Darcy reluctantly finds himself falling in love with a woman beneath his class. Can each overcome their own pride and prejudice?",82,Joe Wright,Keira Knightley,Matthew Macfadyen,Brenda Blethyn,Donald Sutherland,258924,"38,405,088" +"https://m.media-amazon.com/images/M/MV5BMjE1MjA0MDA3MV5BMl5BanBnXkFtZTcwOTU0MjMzMQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",The World's Fastest Indian,2005,U,127 min,"Biography, Drama, Sport",7.8,"The story of New Zealander Burt Munro, who spent years rebuilding a 1920 Indian motorcycle, which helped him set the land speed world record at Utah's Bonneville Salt Flats in 1967.",68,Roger Donaldson,Anthony Hopkins,Diane Ladd,Iain Rea,Tessa Mitchell,51980,"5,128,124" +"https://m.media-amazon.com/images/M/MV5BNWY2ODRkZDYtMjllYi00Y2EyLWFhYjktMTQ5OGNkY2ViYmY2XkEyXkFqcGdeQXVyNjUxMDQ0MTg@._V1_UY98_CR1,0,67,98_AL_.jpg",Tôkyô goddofâzâzu,2003,UA,90 min,"Animation, Adventure, Comedy",7.8,"On Christmas Eve, three homeless people living on the streets of Tokyo discover a newborn baby among the trash and set out to find its parents.",73,Satoshi Kon,Shôgo Furuya,Tôru Emori,Yoshiaki Umegaki,Aya Okamoto,31658,"128,985" +"https://m.media-amazon.com/images/M/MV5BOWE2MDAwZjEtODEyOS00ZjYyLTgzNDUtYmNiY2VmNWRiMTQxXkEyXkFqcGdeQXVyNTIzOTk5ODM@._V1_UX67_CR0,0,67,98_AL_.jpg",Serenity,2005,PG-13,119 min,"Action, Adventure, Sci-Fi",7.8,The crew of the ship Serenity try to evade an assassin sent to recapture one of their members who is telepathic.,74,Joss Whedon,Nathan Fillion,Gina Torres,Chiwetel Ejiofor,Alan Tudyk,283310,"25,514,517" +"https://m.media-amazon.com/images/M/MV5BMjIyOTU3MjUxOF5BMl5BanBnXkFtZTcwMTQ0NjYzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Walk the Line,2005,PG-13,136 min,"Biography, Drama, Music",7.8,"A chronicle of country music legend Johnny Cash's life, from his early days on an Arkansas cotton farm to his rise to fame with Sun Records in Memphis, where he recorded alongside Elvis Presley, Jerry Lee Lewis, and Carl Perkins.",72,James Mangold,Joaquin Phoenix,Reese Witherspoon,Ginnifer Goodwin,Robert Patrick,234207,"119,519,402" +"https://m.media-amazon.com/images/M/MV5BMzYwODUxNjkyMF5BMl5BanBnXkFtZTcwODUzNjQyMQ@@._V1_UY98_CR0,0,67,98_AL_.jpg",Ondskan,2003,,113 min,Drama,7.8,"A teenage boy expelled from school for fighting arrives at a boarding school where the systematic bullying of younger students is encouraged as a means to maintain discipline, and decides to fight back.",61,Mikael Håfström,Andreas Wilson,Henrik Lundström,Gustaf Skarsgård,Linda Zilliacus,35682,"15,280" +"https://m.media-amazon.com/images/M/MV5BMTk3OTM5Njg5M15BMl5BanBnXkFtZTYwMzA0ODI3._V1_UX67_CR0,0,67,98_AL_.jpg",The Notebook,2004,A,123 min,"Drama, Romance",7.8,"A poor yet passionate young man falls in love with a rich young woman, giving her a sense of freedom, but they are soon separated because of their social differences.",53,Nick Cassavetes,Gena Rowlands,James Garner,Rachel McAdams,Ryan Gosling,520284,"81,001,787" +"https://m.media-amazon.com/images/M/MV5BOTNmZTgyMzAtMTUwZC00NjAwLTk4MjktODllYTY5YTUwN2YwXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Diarios de motocicleta,2004,U,126 min,"Adventure, Biography, Drama",7.8,The dramatization of a motorcycle road trip Che Guevara went on in his youth that showed him his life's calling.,75,Walter Salles,Gael García Bernal,Rodrigo De la Serna,Mía Maestro,Mercedes Morán,96703,"16,756,372" +"https://m.media-amazon.com/images/M/MV5BM2YwNTQwM2ItZTA2Ni00NGY1LThjY2QtNzgyZTBhMTM0MWI4XkEyXkFqcGdeQXVyNzQxNDExNTU@._V1_UY98_CR0,0,67,98_AL_.jpg",Lilja 4-ever,2002,R,109 min,"Crime, Drama",7.8,"Sixteen-year-old Lilja and her only friend, the young boy Volodja, live in Russia, fantasizing about a better life. One day, Lilja falls in love with Andrej, who is going to Sweden, and invites Lilja to come along and start a new life.",82,Lukas Moodysson,Oksana Akinshina,Artyom Bogucharskiy,Pavel Ponomaryov,Lyubov Agapova,42673,"181,655" +"https://m.media-amazon.com/images/M/MV5BNGRiOTIwNTAtYWM2Yy00Yzc4LTkyZjEtNTM3NTIyZTNhMzg1XkEyXkFqcGdeQXVyODIyOTEyMzY@._V1_UY98_CR1,0,67,98_AL_.jpg",Les triplettes de Belleville,2003,PG-13,80 min,"Animation, Comedy, Drama",7.8,"When her grandson is kidnapped during the Tour de France, Madame Souza and her beloved pooch Bruno team up with the Belleville Sisters--an aged song-and-dance team from the days of Fred Astaire--to rescue him.",91,Sylvain Chomet,Michèle Caucheteux,Jean-Claude Donda,Michel Robin,Monica Viegas,50622,"7,002,255" +"https://m.media-amazon.com/images/M/MV5BMTI1NDA4NTMyN15BMl5BanBnXkFtZTYwNTA2ODc5._V1_UY98_CR1,0,67,98_AL_.jpg",Gongdong gyeongbi guyeok JSA,2000,,110 min,"Action, Drama, Thriller",7.8,"After a shooting incident at the North/South Korean border/DMZ leaves 2 North Korean soldiers dead, a neutral Swiss/Swedish team investigates, what actually happened.",58,Chan-wook Park,Lee Yeong-ae,Lee Byung-Hun,Kang-ho Song,Kim Tae-Woo,26518, +"https://m.media-amazon.com/images/M/MV5BMDM0ZWRjZDgtZWI0MS00ZTIzLTg4MWYtZjU5MDEyMDU0ODBjXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Count of Monte Cristo,2002,PG-13,131 min,"Action, Adventure, Drama",7.8,"A young man, falsely imprisoned by his jealous ""friend"", escapes and uses a hidden treasure to exact his revenge.",61,Kevin Reynolds,Jim Caviezel,Guy Pearce,Christopher Adamson,JB Blanc,129022,"54,234,062" +"https://m.media-amazon.com/images/M/MV5BMWM0ZjY5ZjctODNkZi00Nzk0LWE1ODUtNGM4ZDUyMzUwMGYwXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Waking Life,2001,R,99 min,"Animation, Drama, Fantasy",7.8,A man shuffles through a dream meeting various people and discussing the meanings and purposes of the universe.,83,Richard Linklater,Ethan Hawke,Trevor Jack Brooks,Lorelei Linklater,Wiley Wiggins,60684,"2,892,011" +"https://m.media-amazon.com/images/M/MV5BYThkMzgxNjEtMzFiOC00MTI0LWI5MDItNDVmYjA4NzY5MDQ2L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Remember the Titans,2000,U,113 min,"Biography, Drama, Sport",7.8,The true story of a newly appointed African-American coach and his high school team on their first season as a racially integrated unit.,48,Boaz Yakin,Denzel Washington,Will Patton,Wood Harris,Ryan Hurst,198089,"115,654,751" +"https://m.media-amazon.com/images/M/MV5BNDdhMzMxOTctNDMyNS00NTZmLTljNWEtNTc4MDBmZTYxY2NmXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Wo hu cang long,2000,UA,120 min,"Action, Adventure, Fantasy",7.8,A young Chinese warrior steals a sword from a famed swordsman and then escapes into a world of romantic adventure with a mysterious man in the frontier of the nation.,94,Ang Lee,Yun-Fat Chow,Michelle Yeoh,Ziyi Zhang,Chen Chang,253228,"128,078,872" +"https://m.media-amazon.com/images/M/MV5BZTk2ZTMzMmUtZjUyNi00YzMyLWE3NTAtNDNjNzU3MGQ1YTFjXkEyXkFqcGdeQXVyMTA0MjU0Ng@@._V1_UY98_CR3,0,67,98_AL_.jpg",Todo sobre mi madre,1999,R,101 min,Drama,7.8,"Young Esteban wants to become a writer and also to discover the identity of his second mother, a trans woman, carefully concealed by his mother Manuela.",87,Pedro Almodóvar,Cecilia Roth,Marisa Paredes,Candela Peña,Antonia San Juan,89058,"8,264,530" +"https://m.media-amazon.com/images/M/MV5BN2Y5ZTU4YjctMDRmMC00MTg4LWE1M2MtMjk4MzVmOTE4YjkzXkEyXkFqcGdeQXVyNTc1NTQxODI@._V1_UX67_CR0,0,67,98_AL_.jpg",Cast Away,2000,UA,143 min,"Adventure, Drama, Romance",7.8,A FedEx executive undergoes a physical and emotional transformation after crash landing on a deserted island.,73,Robert Zemeckis,Tom Hanks,Helen Hunt,Paul Sanchez,Lari White,524235,"233,632,142" +"https://m.media-amazon.com/images/M/MV5BYzVmMTdjOTYtOTJkYS00ZTg2LWExNTgtNzA1N2Y0MDgwYWFhXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Boondock Saints,1999,R,108 min,"Action, Crime, Thriller",7.8,Two Irish Catholic brothers become vigilantes and wipe out Boston's criminal underworld in the name of God.,44,Troy Duffy,Willem Dafoe,Sean Patrick Flanery,Norman Reedus,David Della Rocco,227143,"25,812" +"https://m.media-amazon.com/images/M/MV5BODg0YjAzNDQtOGFkMi00Yzk2LTg1NzYtYTNjY2UwZTM2ZDdkL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UY98_CR0,0,67,98_AL_.jpg",The Insider,1999,UA,157 min,"Biography, Drama, Thriller",7.8,A research chemist comes under personal and professional attack when he decides to appear in a 60 Minutes exposé on Big Tobacco.,84,Michael Mann,Russell Crowe,Al Pacino,Christopher Plummer,Diane Venora,159886,"28,965,197" +"https://m.media-amazon.com/images/M/MV5BZmIzMjE0M2YtNzliZi00YWNmLTgyNDItZDhjNWVhY2Q2ODk0XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",October Sky,1999,PG,108 min,"Biography, Drama, Family",7.8,"The true story of Homer Hickam, a coal miner's son who was inspired by the first Sputnik launch to take up rocketry against his father's wishes.",71,Joe Johnston,Jake Gyllenhaal,Chris Cooper,Laura Dern,Chris Owen,82855,"32,481,825" +"https://m.media-amazon.com/images/M/MV5BOGZhM2FhNTItODAzNi00YjA0LWEyN2UtNjJlYWQzYzU1MDg5L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Shrek,2001,U,90 min,"Animation, Adventure, Comedy",7.8,"A mean lord exiles fairytale creatures to the swamp of a grumpy ogre, who must go on a quest and rescue a princess for the lord in order to get his land back.",84,Andrew Adamson,Vicky Jenson,Mike Myers,Eddie Murphy,Cameron Diaz,613941,"267,665,011" +"https://m.media-amazon.com/images/M/MV5BMDdmZGU3NDQtY2E5My00ZTliLWIzOTUtMTY4ZGI1YjdiNjk3XkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Titanic,1997,UA,194 min,"Drama, Romance",7.8,"A seventeen-year-old aristocrat falls in love with a kind but poor artist aboard the luxurious, ill-fated R.M.S. Titanic.",75,James Cameron,Leonardo DiCaprio,Kate Winslet,Billy Zane,Kathy Bates,1046089,"659,325,379" +"https://m.media-amazon.com/images/M/MV5BODk4MzE5NjgtN2ZhOS00YTdkLTg0YzktMmE1MTkxZmMyMWI2L2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Hana-bi,1997,,103 min,"Crime, Drama, Romance",7.8,"Nishi leaves the police in the face of harrowing personal and professional difficulties. Spiraling into depression, he makes questionable decisions.",,Takeshi Kitano,Takeshi Kitano,Kayoko Kishimoto,Ren Osugi,Susumu Terajima,27712,"233,986" +"https://m.media-amazon.com/images/M/MV5BODI3ZTc5NjktOGMyOC00NjYzLTgwZDYtYmQ4NDc1MmJjMjRlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Gattaca,1997,UA,106 min,"Drama, Sci-Fi, Thriller",7.8,A genetically inferior man assumes the identity of a superior one in order to pursue his lifelong dream of space travel.,64,Andrew Niccol,Ethan Hawke,Uma Thurman,Jude Law,Gore Vidal,280845,"12,339,633" +"https://m.media-amazon.com/images/M/MV5BZGVmMDNmYmEtNGQ2Mi00Y2ZhLThhZTYtYjE5YmQzMjZiZGMxXkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UY98_CR1,0,67,98_AL_.jpg",The Game,1997,UA,129 min,"Action, Drama, Mystery",7.8,"After a wealthy banker is given an opportunity to participate in a mysterious game, his life is turned upside down when he becomes unable to distinguish between the game and reality.",61,David Fincher,Michael Douglas,Deborah Kara Unger,Sean Penn,James Rebhorn,345096,"48,323,648" +"https://m.media-amazon.com/images/M/MV5BNDYwZTU2MzktNWYxMS00NTYzLTgzOWEtMTRiYjc5NGY2Nzg1XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Breaking the Waves,1996,R,159 min,Drama,7.8,"Oilman Jan is paralyzed in an accident. His wife, who prayed for his return, feels guilty; even more, when Jan urges her to have sex with another.",76,Lars von Trier,Emily Watson,Stellan Skarsgård,Katrin Cartlidge,Jean-Marc Barr,62428,"4,040,691" +"https://m.media-amazon.com/images/M/MV5BNTA5ZjdjNWUtZGUwNy00N2RhLWJiZmItYzFhYjU1NmYxNjY4XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Ed Wood,1994,U,127 min,"Biography, Comedy, Drama",7.8,"Ambitious but troubled movie director Edward D. Wood Jr. tries his best to fulfill his dreams, despite his lack of talent.",70,Tim Burton,Johnny Depp,Martin Landau,Sarah Jessica Parker,Patricia Arquette,164937,"5,887,457" +"https://m.media-amazon.com/images/M/MV5BY2EyZDlhNjItODYzNi00Mzc3LWJjOWUtMTViODU5MTExZWMyL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",What's Eating Gilbert Grape,1993,U,118 min,Drama,7.8,A young man in a small Midwestern town struggles to care for his mentally-disabled younger brother and morbidly obese mother while attempting to pursue his own happiness.,73,Lasse Hallström,Johnny Depp,Leonardo DiCaprio,Juliette Lewis,Mary Steenburgen,215034,"9,170,214" +"https://m.media-amazon.com/images/M/MV5BODRkYzA4MGItODE2MC00ZjkwLWI2NDEtYzU1NzFiZGU1YzA0XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Tombstone,1993,R,130 min,"Action, Biography, Drama",7.8,"A successful lawman's plans to retire anonymously in Tombstone, Arizona are disrupted by the kind of outlaws he was famous for eliminating.",50,George P. Cosmatos,Kevin Jarre,Kurt Russell,Val Kilmer,Sam Elliott,126871,"56,505,065" +"https://m.media-amazon.com/images/M/MV5BODllYjM1ODItYjBmOC00MzkwLWJmM2YtMjMyZDU3MGJhNjc4L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Sandlot,1993,U,101 min,"Comedy, Drama, Family",7.8,"In the summer of 1962, a new kid in town is taken under the wing of a young baseball prodigy and his rowdy team, resulting in many adventures.",55,David Mickey Evans,Tom Guiry,Mike Vitar,Art LaFleur,Patrick Renna,78963,"32,416,586" +"https://m.media-amazon.com/images/M/MV5BNDYwOThlMDAtYWUwMS00MjY5LTliMGUtZWFiYTA5MjYwZDAyXkEyXkFqcGdeQXVyNjY1NTQ0NDg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Remains of the Day,1993,U,134 min,"Drama, Romance",7.8,A butler who sacrificed body and soul to service in the years leading up to World War II realizes too late how misguided his loyalty was to his lordly employer.,84,James Ivory,Anthony Hopkins,Emma Thompson,John Haycraft,Christopher Reeve,66065,"22,954,968" +"https://m.media-amazon.com/images/M/MV5BMjA3Y2I4NjAtMDQyZS00ZGJhLWEwMzgtODBiNzE5Zjc1Nzk1L2ltYWdlXkEyXkFqcGdeQXVyNTc2MDU0NDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Naked,1993,,132 min,"Comedy, Drama",7.8,"Parallel tales of two sexually obsessed men, one hurting and annoying women physically and mentally, one wandering around the city talking to strangers and experiencing dimensions of life.",84,Mike Leigh,David Thewlis,Lesley Sharp,Katrin Cartlidge,Greg Cruttwell,34635,"1,769,305" +"https://m.media-amazon.com/images/M/MV5BYmFmOGZjYTItYjY1ZS00OWRiLTk0NDgtMjQ5MzBkYWE2YWE0XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Fugitive,1993,U,130 min,"Action, Crime, Drama",7.8,"Dr. Richard Kimble, unjustly accused of murdering his wife, must find the real killer while being the target of a nationwide manhunt led by a seasoned U.S. Marshal.",87,Andrew Davis,Harrison Ford,Tommy Lee Jones,Sela Ward,Julianne Moore,267684,"183,875,760" +"https://m.media-amazon.com/images/M/MV5BMTczOTczNjE3Ml5BMl5BanBnXkFtZTgwODEzMzg5MTI@._V1_UX67_CR0,0,67,98_AL_.jpg",A Bronx Tale,1993,R,121 min,"Crime, Drama, Romance",7.8,A father becomes worried when a local gangster befriends his son in the Bronx in the 1960s.,80,Robert De Niro,Robert De Niro,Chazz Palminteri,Lillo Brancato,Francis Capra,128171,"17,266,971" +"https://m.media-amazon.com/images/M/MV5BYTRiMWM3MGItNjAxZC00M2E3LThhODgtM2QwOGNmZGU4OWZhXkEyXkFqcGdeQXVyNjExODE1MDc@._V1_UX67_CR0,0,67,98_AL_.jpg",Batman: Mask of the Phantasm,1993,PG,76 min,"Animation, Action, Crime",7.8,Batman is wrongly implicated in a series of murders of mob bosses actually done by a new vigilante assassin.,,Kevin Altieri,Boyd Kirkland,Frank Paur,Dan Riba,Eric Radomski,43690,"5,617,391" +"https://m.media-amazon.com/images/M/MV5BOTIzZGU4ZWMtYmNjMy00NzU0LTljMGYtZmVkMDYwN2U2MzYwL2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Lat sau san taam,1992,R,128 min,"Action, Crime, Thriller",7.8,A tough-as-nails cop teams up with an undercover agent to shut down a sinister mobster and his crew.,,John Woo,Yun-Fat Chow,Tony Chiu-Wai Leung,Teresa Mo,Philip Chan,46700, +"https://m.media-amazon.com/images/M/MV5BOGNmMjBmZWEtOWYwZC00NGIzLTg0YWItMzkzMWMwOTU4YTViXkEyXkFqcGdeQXVyNzc5MjA3OA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Night on Earth,1991,R,129 min,"Comedy, Drama",7.8,An anthology of 5 different cab drivers in 5 American and European cities and their remarkable fares on the same eventful night.,68,Jim Jarmusch,Winona Ryder,Gena Rowlands,Lisanne Falk,Alan Randolph Scott,55362,"2,015,810" +"https://m.media-amazon.com/images/M/MV5BYmE0ZGRiMDgtOTU0ZS00YWUwLTk5YWQtMzhiZGVhNzViMGZiXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",La double vie de Véronique,1991,R,98 min,"Drama, Fantasy, Music",7.8,"Two parallel stories about two identical women; one living in Poland, the other in France. They don't know each other, but their lives are nevertheless profoundly connected.",86,Krzysztof Kieslowski,Irène Jacob,Wladyslaw Kowalski,Halina Gryglaszewska,Kalina Jedrusik,42376,"1,999,955" +"https://m.media-amazon.com/images/M/MV5BZmRjNDI5NTgtOTIwMC00MzJhLWI4ZTYtMmU0ZTE3ZmRkZDNhXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Boyz n the Hood,1991,A,112 min,"Crime, Drama",7.8,"Follows the lives of three young males living in the Crenshaw ghetto of Los Angeles, dissecting questions of race, relationships, violence, and future prospects.",76,John Singleton,Cuba Gooding Jr.,Laurence Fishburne,Hudhail Al-Amir,Lloyd Avery II,126082,"57,504,069" +"https://m.media-amazon.com/images/M/MV5BNzY0ODQ3MTMxN15BMl5BanBnXkFtZTgwMDkwNTg4NjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Misery,1990,R,107 min,"Drama, Thriller",7.8,"After a famous author is rescued from a car crash by a fan of his novels, he comes to realize that the care he is receiving is only the beginning of a nightmare of captivity and abuse.",75,Rob Reiner,James Caan,Kathy Bates,Richard Farnsworth,Frances Sternhagen,184740,"61,276,872" +"https://m.media-amazon.com/images/M/MV5BMjI5NjEzMDYyMl5BMl5BanBnXkFtZTgwNjgwNTg4NjE@._V1_UY98_CR3,0,67,98_AL_.jpg",Awakenings,1990,U,121 min,"Biography, Drama",7.8,"The victims of an encephalitis epidemic many years ago have been catatonic ever since, but now a new drug offers the prospect of reviving them.",74,Penny Marshall,Robert De Niro,Robin Williams,Julie Kavner,Ruth Nelson,125276,"52,096,475" +"https://m.media-amazon.com/images/M/MV5BOTc0ODM1Njk1NF5BMl5BanBnXkFtZTcwMDI5OTEyNw@@._V1_UY98_CR1,0,67,98_AL_.jpg",Majo no takkyûbin,1989,U,103 min,"Animation, Adventure, Drama",7.8,"A young witch, on her mandatory year of independent life, finds fitting into a new community difficult while she supports herself by running an air courier service.",83,Hayao Miyazaki,Kirsten Dunst,Minami Takayama,Rei Sakuma,Kappei Yamaguchi,124193, +"https://m.media-amazon.com/images/M/MV5BODhlNjA5MDEtZDVhNS00ZmM3LTg1YzAtZGRjNjhjNTAzNzVkXkEyXkFqcGdeQXVyNjUwMzI2NzU@._V1_UY98_CR0,0,67,98_AL_.jpg",Glory,1989,R,122 min,"Biography, Drama, History",7.8,"Robert Gould Shaw leads the U.S. Civil War's first all-black volunteer company, fighting prejudices from both his own Union Army, and the Confederates.",78,Edward Zwick,Matthew Broderick,Denzel Washington,Cary Elwes,Morgan Freeman,122779,"26,830,000" +"https://m.media-amazon.com/images/M/MV5BMDQyMDVhZjItMGI0Mi00MDQ1LTk3NmQtZmRjZGQ5ZTQ2ZDU5XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Dip huet seung hung,1989,R,111 min,"Action, Crime, Drama",7.8,A disillusioned assassin accepts one last hit in hopes of using his earnings to restore vision to a singer he accidentally blinded.,82,John Woo,Yun-Fat Chow,Danny Lee,Sally Yeh,Kong Chu,45624, +"https://m.media-amazon.com/images/M/MV5BZTMxMGM5MjItNDJhNy00MWI2LWJlZWMtOWFhMjI5ZTQwMWM3XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Back to the Future Part II,1989,U,108 min,"Adventure, Comedy, Sci-Fi",7.8,"After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985...without interfering with his first trip.",57,Robert Zemeckis,Michael J. Fox,Christopher Lloyd,Lea Thompson,Thomas F. Wilson,481918,"118,500,000" +"https://m.media-amazon.com/images/M/MV5BZTFjNjU4OTktYzljMS00MmFlLWI3NGEtNjNhMTYwYzUyZDgyL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Mississippi Burning,1988,A,128 min,"Crime, Drama, History",7.8,Two F.B.I. Agents with wildly different styles arrive in Mississippi to investigate the disappearance of some civil rights activists.,65,Alan Parker,Gene Hackman,Willem Dafoe,Frances McDormand,Brad Dourif,88214,"34,603,943" +"https://m.media-amazon.com/images/M/MV5BY2QwYmFmZTEtNzY2Mi00ZWMyLWEwY2YtMGIyNGZjMWExOWEyXkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_UX67_CR0,0,67,98_AL_.jpg",Predator,1987,A,107 min,"Action, Adventure, Sci-Fi",7.8,A team of commandos on a mission in a Central American jungle find themselves hunted by an extraterrestrial warrior.,45,John McTiernan,Arnold Schwarzenegger,Carl Weathers,Kevin Peter Hall,Elpidia Carrillo,371387,"59,735,548" +"https://m.media-amazon.com/images/M/MV5BMWY3ODZlOGMtNzJmOS00ZTNjLWI3ZWEtZTJhZTk5NDZjYWRjXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Evil Dead II,1987,A,84 min,"Action, Comedy, Fantasy",7.8,The lone survivor of an onslaught of flesh-possessing spirits holes up in a cabin with a group of strangers while the demons continue their attack.,72,Sam Raimi,Bruce Campbell,Sarah Berry,Dan Hicks,Kassie Wesley DePaiva,148359,"5,923,044" +"https://m.media-amazon.com/images/M/MV5BMDA0NjZhZWUtNmI2NC00MmFjLTgwZDYtYzVjZmNhMDVmOTBkXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Ferris Bueller's Day Off,1986,U,103 min,Comedy,7.8,"A high school wise guy is determined to have a day off from school, despite what the Principal thinks of that.",61,John Hughes,Matthew Broderick,Alan Ruck,Mia Sara,Jeffrey Jones,321382,"70,136,369" +"https://m.media-amazon.com/images/M/MV5BM2ZmNDJiZTUtYjg5Zi00M2I3LTliZjAtNzQ4NTlkYTAzYTAxXkEyXkFqcGdeQXVyNTkyMDc0MjI@._V1_UX67_CR0,0,67,98_AL_.jpg",Down by Law,1986,R,107 min,"Comedy, Crime, Drama",7.8,"Two men are framed and sent to jail, where they meet a murderer who helps them escape and leave the state.",75,Jim Jarmusch,Tom Waits,John Lurie,Roberto Benigni,Nicoletta Braschi,47834,"1,436,000" +"https://m.media-amazon.com/images/M/MV5BODRlMjRkZGEtZWM2Zi00ZjYxLWE0MWUtMmM1YWM2NzZlOTE1XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Goonies,1985,U,114 min,"Adventure, Comedy, Family",7.8,A group of young misfits called The Goonies discover an ancient map and set out on an adventure to find a legendary pirate's long-lost treasure.,62,Richard Donner,Sean Astin,Josh Brolin,Jeff Cohen,Corey Feldman,244430,"61,503,218" +"https://m.media-amazon.com/images/M/MV5BZDRkOWQ5NGUtYTVmOS00ZjNhLWEwODgtOGI2MmUxNTBkMjU0XkEyXkFqcGdeQXVyMjUzOTY1NTc@._V1_UX67_CR0,0,67,98_AL_.jpg",The Color Purple,1985,U,154 min,Drama,7.8,A black Southern woman struggles to find her identity after suffering abuse from her father and others over four decades.,78,Steven Spielberg,Danny Glover,Whoopi Goldberg,Oprah Winfrey,Margaret Avery,78321,"98,467,863" +"https://m.media-amazon.com/images/M/MV5BOTM5N2ZmZTMtNjlmOS00YzlkLTk3YjEtNTU1ZmQ5OTdhODZhXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Breakfast Club,1985,UA,97 min,"Comedy, Drama",7.8,Five high school students meet in Saturday detention and discover how they have a lot more in common than they thought.,66,John Hughes,Emilio Estevez,Judd Nelson,Molly Ringwald,Ally Sheedy,357026,"45,875,171" +"https://m.media-amazon.com/images/M/MV5BMGI0NzI5YjAtNTg0MS00NDA2LWE5ZWItODRmOTAxOTAxYjg2L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",The Killing Fields,1984,UA,141 min,"Biography, Drama, History",7.8,"A journalist is trapped in Cambodia during tyrant Pol Pot's bloody 'Year Zero' cleansing campaign, which claimed the lives of two million 'undesirable' civilians.",76,Roland Joffé,Sam Waterston,Haing S. Ngor,John Malkovich,Julian Sands,51585,"34,700,291" +"https://m.media-amazon.com/images/M/MV5BMTkxMjYyNzgwMl5BMl5BanBnXkFtZTgwMTE3MjYyMTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Ghostbusters,1984,UA,105 min,"Action, Comedy, Fantasy",7.8,Three former parapsychology professors set up shop as a unique ghost removal service.,71,Ivan Reitman,Bill Murray,Dan Aykroyd,Sigourney Weaver,Harold Ramis,355413,"238,632,124" +"https://m.media-amazon.com/images/M/MV5BOTUwMDA3MTYtZjhjMi00ODFmLTg5ZTAtYzgwN2NlODgzMmUwXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Right Stuff,1983,PG,193 min,"Adventure, Biography, Drama",7.8,"The story of the original Mercury 7 astronauts and their macho, seat-of-the-pants approach to the space program.",91,Philip Kaufman,Sam Shepard,Scott Glenn,Ed Harris,Dennis Quaid,56235,"21,500,000" +"https://m.media-amazon.com/images/M/MV5BMTViNjlkYjgtMmE3Zi00ZGVkLTkyMjMtNzc3YzAwNzNiODQ1XkEyXkFqcGdeQXVyMjA0MzYwMDY@._V1_UX67_CR0,0,67,98_AL_.jpg",The King of Comedy,1982,U,109 min,"Comedy, Crime, Drama",7.8,"Rupert Pupkin is a passionate yet unsuccessful comic who craves nothing more than to be in the spotlight and to achieve this, he stalks and kidnaps his idol to take the spotlight for himself.",73,Martin Scorsese,Robert De Niro,Jerry Lewis,Diahnne Abbott,Sandra Bernhard,88511,"2,500,000" +"https://m.media-amazon.com/images/M/MV5BMTQ2ODFlMDAtNzdhOC00ZDYzLWE3YTMtNDU4ZGFmZmJmYTczXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",E.T. the Extra-Terrestrial,1982,U,115 min,"Family, Sci-Fi",7.8,A troubled child summons the courage to help a friendly alien escape Earth and return to his home world.,91,Steven Spielberg,Henry Thomas,Drew Barrymore,Peter Coyote,Dee Wallace,372490,"435,110,554" +"https://m.media-amazon.com/images/M/MV5BNDM3YjNlYmMtOGY3NS00MmRjLWIyY2UtNDA0MWM3OTNlZTY2XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Kramer vs. Kramer,1979,A,105 min,Drama,7.8,"Ted Kramer's wife leaves him, allowing for a lost bond to be rediscovered between Ted and his son, Billy. But a heated custody battle ensues over the divorced couple's son, deepening the wounds left by the separation.",77,Robert Benton,Dustin Hoffman,Meryl Streep,Jane Alexander,Justin Henry,133351,"106,260,000" +"https://m.media-amazon.com/images/M/MV5BZjMyZmU4OGYtNjBiYS00YTIxLWJjMDUtZjczZmQwMTM4YjQxXkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",Days of Heaven,1978,PG,94 min,"Drama, Romance",7.8,A hot-tempered farm laborer convinces the woman he loves to marry their rich but dying boss so that they can have a claim to his fortune.,93,Terrence Malick,Richard Gere,Brooke Adams,Sam Shepard,Linda Manz,52852, +"https://m.media-amazon.com/images/M/MV5BMjIxNDYxMTk2MF5BMl5BanBnXkFtZTgwMjQxNjU3MTE@._V1_UY98_CR0,0,67,98_AL_.jpg",The Outlaw Josey Wales,1976,A,135 min,Western,7.8,Missouri farmer Josey Wales joins a Confederate guerrilla unit and winds up on the run from the Union soldiers who murdered his family.,69,Clint Eastwood,Clint Eastwood,Sondra Locke,Chief Dan George,Bill McKinney,65659,"31,800,000" +"https://m.media-amazon.com/images/M/MV5BZWQzYjBjZmQtZDFiOS00ZDQ1LWI4MDAtMDk1NGE1NDBhYjNhL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Man Who Would Be King,1975,PG,129 min,"Adventure, History, War",7.8,"Two British former soldiers decide to set themselves up as Kings in Kafiristan, a land where no white man has set foot since Alexander the Great.",91,John Huston,Sean Connery,Michael Caine,Christopher Plummer,Saeed Jaffrey,44917, +"https://m.media-amazon.com/images/M/MV5BNzZlMThlYzktMDlmZC00YTI1LThlNzktZWU0MTY4ODc2ZWY4XkEyXkFqcGdeQXVyNTA1NjYyMDk@._V1_UX67_CR0,0,67,98_AL_.jpg",The Conversation,1974,U,113 min,"Drama, Mystery, Thriller",7.8,"A paranoid, secretive surveillance expert has a crisis of conscience when he suspects that the couple he is spying on will be murdered.",85,Francis Ford Coppola,Gene Hackman,John Cazale,Allen Garfield,Frederic Forrest,98611,"4,420,000" +"https://m.media-amazon.com/images/M/MV5BYjhhMDFlZDctYzg1Mi00ZmZiLTgyNTgtM2NkMjRkNzYwZmQ0XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",La planète sauvage,1973,U,72 min,"Animation, Sci-Fi",7.8,"On a faraway planet where blue giants rule, oppressed humanoids rebel against their machine-like leaders.",73,René Laloux,Barry Bostwick,Jennifer Drake,Eric Baugin,Jean Topart,25229,"193,817" +"https://m.media-amazon.com/images/M/MV5BNjZmMWE4NzgtZjc5OS00NTBmLThlY2MtM2MzNTA5NTZiNTFjXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UY98_CR0,0,67,98_AL_.jpg",The Day of the Jackal,1973,A,143 min,"Crime, Drama, Thriller",7.8,"A professional assassin codenamed ""Jackal"" plots to kill Charles de Gaulle, the President of France.",80,Fred Zinnemann,Edward Fox,Terence Alexander,Michel Auclair,Alan Badel,37445,"16,056,255" +"https://m.media-amazon.com/images/M/MV5BMDcxNjhiOTEtMzQ0YS00OTBhLTkxM2QtN2UyZDMzNzIzNWFlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UY98_CR1,0,67,98_AL_.jpg",Badlands,1973,PG,94 min,"Action, Crime, Drama",7.8,An impressionable teenage girl from a dead-end town and her older greaser boyfriend embark on a killing spree in the South Dakota badlands.,93,Terrence Malick,Martin Sheen,Sissy Spacek,Warren Oates,Ramon Bieri,66009, +"https://m.media-amazon.com/images/M/MV5BNTEyMzc0Mjk5MV5BMl5BanBnXkFtZTgwMjI2NDIwMTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Cabaret,1972,A,124 min,"Drama, Music, Musical",7.8,A female girlie club entertainer in Weimar Republic era Berlin romances two men while the Nazi Party rises to power around them.,80,Bob Fosse,Liza Minnelli,Michael York,Helmut Griem,Joel Grey,48334,"42,765,000" +"https://m.media-amazon.com/images/M/MV5BZTllNDU0ZTItYTYxMC00OTI4LThlNDAtZjNiNzdhMWZiYjNmXkEyXkFqcGdeQXVyNzY1NDgwNjQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Willy Wonka & the Chocolate Factory,1971,U,100 min,"Family, Fantasy, Musical",7.8,A poor but hopeful boy seeks one of the five coveted golden tickets that will send him on a tour of Willy Wonka's mysterious chocolate factory.,67,Mel Stuart,Gene Wilder,Jack Albertson,Peter Ostrum,Roy Kinnear,178731,"4,000,000" +"https://m.media-amazon.com/images/M/MV5BNTgwZmIzMmYtZjE3Yy00NzgzLTgxNmUtNjlmZDlkMzlhOTJkXkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_UX67_CR0,0,67,98_AL_.jpg",Midnight Cowboy,1969,A,113 min,Drama,7.8,"A naive hustler travels from Texas to New York City to seek personal fortune, finding a new friend in the process.",79,John Schlesinger,Dustin Hoffman,Jon Voight,Sylvia Miles,John McGiver,101124,"44,785,053" +"https://m.media-amazon.com/images/M/MV5BMTQyNTAzOTI3NF5BMl5BanBnXkFtZTcwNTM0Mjg0Mg@@._V1_UX67_CR0,0,67,98_AL_.jpg",Wait Until Dark,1967,,108 min,Thriller,7.8,A recently blinded woman is terrorized by a trio of thugs while they search for a heroin-stuffed doll they believe is in her apartment.,81,Terence Young,Audrey Hepburn,Alan Arkin,Richard Crenna,Efrem Zimbalist Jr.,27733,"17,550,741" +"https://m.media-amazon.com/images/M/MV5BZTVmMTk2NjUtNjVjNC00OTcwLWE4OWEtNzA4Mjk1ZmIwNDExXkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_UX67_CR0,0,67,98_AL_.jpg",Guess Who's Coming to Dinner,1967,,108 min,"Comedy, Drama",7.8,A couple's attitudes are challenged when their daughter introduces them to her African-American fiancé.,63,Stanley Kramer,Spencer Tracy,Sidney Poitier,Katharine Hepburn,Katharine Houghton,39642,"56,700,000" +"https://m.media-amazon.com/images/M/MV5BOTViZmMwOGEtYzc4Yy00ZGQ1LWFkZDQtMDljNGZlMjAxMjhiXkEyXkFqcGdeQXVyNzM0MTUwNTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Bonnie and Clyde,1967,A,111 min,"Action, Biography, Crime",7.8,"Bored waitress Bonnie Parker falls in love with an ex-con named Clyde Barrow and together they start a violent crime spree through the country, stealing cars and robbing banks.",86,Arthur Penn,Warren Beatty,Faye Dunaway,Michael J. Pollard,Gene Hackman,102415, +"https://m.media-amazon.com/images/M/MV5BNGM0ZTU3NmItZmRmMy00YWNjLWEzMWItYzg3MzcwZmM5NjdiXkEyXkFqcGdeQXVyNDYyMDk5MTU@._V1_UY98_CR2,0,67,98_AL_.jpg",My Fair Lady,1964,U,170 min,"Drama, Family, Musical",7.8,Snobbish phonetics Professor Henry Higgins agrees to a wager that he can make flower girl Eliza Doolittle presentable in high society.,95,George Cukor,Audrey Hepburn,Rex Harrison,Stanley Holloway,Wilfrid Hyde-White,86525,"72,000,000" +"https://m.media-amazon.com/images/M/MV5BNmJkODczNjItNDI5Yy00MGI1LTkyOWItZDNmNjM4ZGI1ZDVlL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Mary Poppins,1964,U,139 min,"Comedy, Family, Fantasy",7.8,"In turn of the century London, a magical nanny employs music and adventure to help two neglected children become closer to their father.",88,Robert Stevenson,Julie Andrews,Dick Van Dyke,David Tomlinson,Glynis Johns,158029,"102,272,727" +"https://m.media-amazon.com/images/M/MV5BZTM1ZjQ2YTktNDM2MS00NGY2LTkzNzItZTU4ODg1ODNkMWYxL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Longest Day,1962,G,178 min,"Action, Drama, History",7.8,"The events of D-Day, told on a grand scale from both the Allied and German points of view.",75,Ken Annakin,Andrew Marton,Gerd Oswald,Bernhard Wicki,Darryl F. Zanuck,52141,"39,100,000" +"https://m.media-amazon.com/images/M/MV5BZTM1MTRiNDctMTFiMC00NGM1LTkyMWQtNTY1M2JjZDczOWQ3XkEyXkFqcGdeQXVyMDI3OTIzOA@@._V1_UY98_CR3,0,67,98_AL_.jpg",Jules et Jim,1962,,105 min,"Drama, Romance",7.8,Decades of a love triangle concerning two friends and an impulsive woman.,97,François Truffaut,Jeanne Moreau,Oskar Werner,Henri Serre,Vanna Urbino,37605, +"https://m.media-amazon.com/images/M/MV5BNGQyNjBjNTUtNTM1OS00YzcyLWFhNTgtNTU0MDg3NzBlMDQzXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UY98_CR0,0,67,98_AL_.jpg",The Innocents,1961,A,100 min,Horror,7.8,A young governess for two children becomes convinced that the house and grounds are haunted.,88,Jack Clayton,Deborah Kerr,Peter Wyngarde,Megs Jenkins,Michael Redgrave,27007,"2,616,000" +"https://m.media-amazon.com/images/M/MV5BNzk5MDk2MjktY2I3NS00ODZkLTk3OTktY2Q3ZDE2MmQ2M2ZmXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UY98_CR2,0,67,98_AL_.jpg",À bout de souffle,1960,U,90 min,"Crime, Drama",7.8,"A small-time thief steals a car and impulsively murders a motorcycle policeman. Wanted by the authorities, he reunites with a hip American journalism student and attempts to persuade her to run away with him to Italy.",,Jean-Luc Godard,Jean-Paul Belmondo,Jean Seberg,Daniel Boulanger,Henri-Jacques Huet,73251,"336,705" +"https://m.media-amazon.com/images/M/MV5BNzNiOGJhMDUtZjNjMC00YmE5LTk3NjQtNGM4ZjAzOGJjZmRlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Red River,1948,Passed,133 min,"Action, Adventure, Drama",7.8,"Dunson leads a cattle drive, the culmination of over 14 years of work, to its destination in Missouri. But his tyrannical behavior along the way causes a mutiny, led by his adopted son.",,Howard Hawks,Arthur Rosson,John Wayne,Montgomery Clift,Joanne Dru,28167, +"https://m.media-amazon.com/images/M/MV5BODI3YzNiZTUtYjEyZS00ODkwLWE2ZDUtNGJmMTNiYTc4ZTM4XkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Key Largo,1948,,100 min,"Action, Crime, Drama",7.8,"A man visits his war buddy's family hotel and finds a gangster running things. As a hurricane approaches, the two end up confronting each other.",,John Huston,Humphrey Bogart,Edward G. Robinson,Lauren Bacall,Lionel Barrymore,36995, +"https://m.media-amazon.com/images/M/MV5BZGU2YmU0MWMtMzg5My00ZmY2LTljMDItMTg2YTI5Y2U2OTE3XkEyXkFqcGdeQXVyMjUxODE0MDY@._V1_UY98_CR0,0,67,98_AL_.jpg",To Have and Have Not,1944,PG,100 min,"Adventure, Comedy, Film-Noir",7.8,"During World War II, American expatriate Harry Morgan helps transport a French Resistance leader and his beautiful wife to Martinique while romancing a sensuous lounge singer.",,Howard Hawks,Humphrey Bogart,Lauren Bacall,Walter Brennan,Dolores Moran,31053, +"https://m.media-amazon.com/images/M/MV5BM2I1YWM4NTYtYjA0Ny00ZDEwLTg3NTgtNzBjMzZhZTk1YTA1XkEyXkFqcGdeQXVyMTY5Nzc4MDY@._V1_UX67_CR0,0,67,98_AL_.jpg",Shadow of a Doubt,1943,PG,108 min,"Film-Noir, Thriller",7.8,"A young girl, overjoyed when her favorite uncle comes to visit the family, slowly begins to suspect that he is in fact the ""Merry Widow"" killer sought by the authorities.",94,Alfred Hitchcock,Teresa Wright,Joseph Cotten,Macdonald Carey,Henry Travers,59556, +"https://m.media-amazon.com/images/M/MV5BOGQ4NDUyNWQtZTEyOC00OTMzLWFhYjAtNDNmYmQ2MWQyMTRmXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Stagecoach,1939,Passed,96 min,"Adventure, Drama, Western",7.8,A group of people traveling on a stagecoach find their journey complicated by the threat of Geronimo and learn something about each other in the process.,93,John Ford,John Wayne,Claire Trevor,Andy Devine,John Carradine,43621, +"https://m.media-amazon.com/images/M/MV5BNjk3YzFjYTktOGY0ZS00Y2EwLTk2NTctYTI1Nzc2OWNiN2I4XkEyXkFqcGdeQXVyNzM0MTUwNTY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Lady Vanishes,1938,,96 min,"Mystery, Thriller",7.8,"While travelling in continental Europe, a rich young playgirl realizes that an elderly lady seems to have disappeared from the train.",98,Alfred Hitchcock,Margaret Lockwood,Michael Redgrave,Paul Lukas,May Whitty,47400, +"https://m.media-amazon.com/images/M/MV5BMmVkOTRiYmItZjE4NS00MWNjLWE0ZmMtYzg5YzFjMjMyY2RkXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Bringing Up Baby,1938,Passed,102 min,"Comedy, Family, Romance",7.8,"While trying to secure a $1 million donation for his museum, a befuddled paleontologist is pursued by a flighty and often irritating heiress and her pet leopard, Baby.",91,Howard Hawks,Katharine Hepburn,Cary Grant,Charles Ruggles,Walter Catlett,55163, +"https://m.media-amazon.com/images/M/MV5BOTUzMzAzMzEzNV5BMl5BanBnXkFtZTgwOTg1NTAwMjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Bride of Frankenstein,1935,,75 min,"Drama, Horror, Sci-Fi",7.8,"Mary Shelley reveals the main characters of her novel survived: Dr. Frankenstein, goaded by an even madder scientist, builds his monster a mate.",95,James Whale,Boris Karloff,Elsa Lanchester,Colin Clive,Valerie Hobson,43542,"4,360,000" +"https://m.media-amazon.com/images/M/MV5BYmYxZGU2NWYtNzQxZS00NmEyLWIzN2YtMDk5MWM0ODc5ZTE4XkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Duck Soup,1933,,69 min,"Comedy, Musical, War",7.8,Rufus T. Firefly is named president/dictator of bankrupt Freedonia and declares war on neighboring Sylvania over the love of wealthy Mrs. Teasdale.,93,Leo McCarey,Groucho Marx,Harpo Marx,Chico Marx,Zeppo Marx,55581, +"https://m.media-amazon.com/images/M/MV5BYmMxZTU2ZDUtM2Y1MS00ZWFmLWJlN2UtNzI0OTJiOTYzMTk3XkEyXkFqcGdeQXVyMjUxODE0MDY@._V1_UX67_CR0,0,67,98_AL_.jpg",Scarface: The Shame of the Nation,1932,PG,93 min,"Action, Crime, Drama",7.8,"An ambitious and nearly insane violent gangster climbs the ladder of success in the mob, but his weaknesses prove to be his downfall.",87,Howard Hawks,Richard Rosson,Paul Muni,Ann Dvorak,Karen Morley,25312, +"https://m.media-amazon.com/images/M/MV5BMTQ0Njc1MjM0OF5BMl5BanBnXkFtZTgwNTY2NTUyMjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Frankenstein,1931,Passed,70 min,"Drama, Horror, Sci-Fi",7.8,Dr. Frankenstein dares to tamper with life and death by creating a human monster out of lifeless body parts.,91,James Whale,Colin Clive,Mae Clarke,Boris Karloff,John Boles,65341, +"https://m.media-amazon.com/images/M/MV5BMTU0OTc3ODk4Ml5BMl5BanBnXkFtZTgwMzM4NzI5NjM@._V1_UX67_CR0,0,67,98_AL_.jpg",Roma,2018,R,135 min,Drama,7.7,A year in the life of a middle-class family's maid in Mexico City in the early 1970s.,96,Alfonso Cuarón,Yalitza Aparicio,Marina de Tavira,Diego Cortina Autrey,Carlos Peralta,140375, +"https://m.media-amazon.com/images/M/MV5BNjRhYzk2NDAtYzA1Mi00MmNmLWE1ZjQtMDBhZmUyMTdjZjBiXkEyXkFqcGdeQXVyNjk1Njg5NTA@._V1_UX67_CR0,0,67,98_AL_.jpg",God's Own Country,2017,,104 min,"Drama, Romance",7.7,"Spring. Yorkshire. Young farmer Johnny Saxby numbs his daily frustrations with binge drinking and casual sex, until the arrival of a Romanian migrant worker for lambing season ignites an intense relationship that sets Johnny on a new path.",85,Francis Lee,Josh O'Connor,Alec Secareanu,Gemma Jones,Ian Hart,25198,"335,609" +"https://m.media-amazon.com/images/M/MV5BNjk1Njk3YjctMmMyYS00Y2I4LThhMzktN2U0MTMyZTFlYWQ5XkEyXkFqcGdeQXVyODM2ODEzMDA@._V1_UY98_CR15,0,67,98_AL_.jpg",Deadpool 2,2018,R,119 min,"Action, Adventure, Comedy",7.7,"Foul-mouthed mutant mercenary Wade Wilson (a.k.a. Deadpool), brings together a team of fellow mutant rogues to protect a young boy with supernatural abilities from the brutal, time-traveling cyborg Cable.",66,David Leitch,Ryan Reynolds,Josh Brolin,Morena Baccarin,Julian Dennison,478586,"324,591,735" +"https://m.media-amazon.com/images/M/MV5BMTUyMjU1OTUwM15BMl5BanBnXkFtZTgwMDg1NDQ2MjI@._V1_UX67_CR0,0,67,98_AL_.jpg",Wind River,2017,R,107 min,"Crime, Drama, Mystery",7.7,A veteran hunter helps an FBI agent investigate the murder of a young woman on a Wyoming Native American reservation.,73,Taylor Sheridan,Kelsey Asbille,Jeremy Renner,Julia Jones,Teo Briones,205444,"33,800,859" +"https://m.media-amazon.com/images/M/MV5BMjUxMDQwNjcyNl5BMl5BanBnXkFtZTgwNzcwMzc0MTI@._V1_UX67_CR0,0,67,98_AL_.jpg",Get Out,2017,R,104 min,"Horror, Mystery, Thriller",7.7,"A young African-American visits his white girlfriend's parents for the weekend, where his simmering uneasiness about their reception of him eventually reaches a boiling point.",85,Jordan Peele,Daniel Kaluuya,Allison Williams,Bradley Whitford,Catherine Keener,492851,"176,040,665" +"https://m.media-amazon.com/images/M/MV5BNjRlZmM0ODktY2RjNS00ZDdjLWJhZGYtNDljNWZkMGM5MTg0XkEyXkFqcGdeQXVyNjAwMjI5MDk@._V1_UX67_CR0,0,67,98_AL_.jpg",Mission: Impossible - Fallout,2018,UA,147 min,"Action, Adventure, Thriller",7.7,"Ethan Hunt and his IMF team, along with some familiar allies, race against time after a mission gone wrong.",86,Christopher McQuarrie,Tom Cruise,Henry Cavill,Ving Rhames,Simon Pegg,291257,"220,159,104" +"https://m.media-amazon.com/images/M/MV5BMjE0NDUyOTc2MV5BMl5BanBnXkFtZTgwODk2NzU3OTE@._V1_UX67_CR0,0,67,98_AL_.jpg",En man som heter Ove,2015,PG-13,116 min,"Comedy, Drama, Romance",7.7,"Ove, an ill-tempered, isolated retiree who spends his days enforcing block association rules and visiting his wife's grave, has finally given up on life just as an unlikely friendship develops with his boisterous new neighbors.",70,Hannes Holm,Rolf Lassgård,Bahar Pars,Filip Berg,Ida Engvoll,47444,"3,358,518" +"https://m.media-amazon.com/images/M/MV5BMjAwNDA5NzEwM15BMl5BanBnXkFtZTgwMTA1MDUyNDE@._V1_UX67_CR0,0,67,98_AL_.jpg",What We Do in the Shadows,2014,R,86 min,"Comedy, Horror",7.7,"Viago, Deacon and Vladislav are vampires who are finding that modern life has them struggling with the mundane - like paying rent, keeping up with the chore wheel, trying to get into nightclubs and overcoming flatmate conflicts.",76,Jemaine Clement,Taika Waititi,Jemaine Clement,Taika Waititi,Cori Gonzalez-Macuer,157498,"3,333,000" +"https://m.media-amazon.com/images/M/MV5BZTlmYTJmMWEtNDRhNy00ODc1LTg2OTMtMjk2ODJhNTA4YTE1XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR0,0,67,98_AL_.jpg",Omoide no Mânî,2014,U,103 min,"Animation, Drama, Family",7.7,"Due to 12 y.o. Anna's asthma, she's sent to stay with relatives of her guardian in the Japanese countryside. She likes to be alone, sketching. She befriends Marnie. Who is the mysterious, blonde Marnie.",72,James Simone,Hiromasa Yonebayashi,Sara Takatsuki,Kasumi Arimura,Nanako Matsushima,32798,"765,127" +"https://m.media-amazon.com/images/M/MV5BMTAwMTU4MDA3NDNeQTJeQWpwZ15BbWU4MDk4NTMxNTIx._V1_UX67_CR0,0,67,98_AL_.jpg",The Theory of Everything,2014,U,123 min,"Biography, Drama, Romance",7.7,A look at the relationship between the famous physicist Stephen Hawking and his wife.,72,James Marsh,Eddie Redmayne,Felicity Jones,Tom Prior,Sophie Perry,404182,"35,893,537" +"https://m.media-amazon.com/images/M/MV5BYTM3ZTllNzItNTNmOS00NzJiLTg1MWMtMjMxNDc0NmJhODU5XkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Kingsman: The Secret Service,2014,A,129 min,"Action, Adventure, Comedy",7.7,"A spy organisation recruits a promising street kid into the agency's training program, while a global threat emerges from a twisted tech genius.",60,Matthew Vaughn,Colin Firth,Taron Egerton,Samuel L. Jackson,Michael Caine,590440,"128,261,724" +"https://m.media-amazon.com/images/M/MV5BNTVkMTFiZWItOTFkOC00YTc3LWFhYzQtZTg3NzAxZjJlNTAyXkEyXkFqcGdeQXVyODE5NzE3OTE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Fault in Our Stars,2014,UA,126 min,"Drama, Romance",7.7,Two teenage cancer patients begin a life-affirming journey to visit a reclusive author in Amsterdam.,69,Josh Boone,Shailene Woodley,Ansel Elgort,Nat Wolff,Laura Dern,344312,"124,872,350" +"https://m.media-amazon.com/images/M/MV5BNTA1NzUzNjY4MV5BMl5BanBnXkFtZTgwNDU0MDI0NTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Me and Earl and the Dying Girl,2015,PG-13,105 min,"Comedy, Drama",7.7,"High schooler Greg, who spends most of his time making parodies of classic movies with his co-worker Earl, finds his outlook forever altered after befriending a classmate who has just been diagnosed with cancer.",74,Alfonso Gomez-Rejon,Thomas Mann,RJ Cyler,Olivia Cooke,Nick Offerman,123210,"6,743,776" +"https://m.media-amazon.com/images/M/MV5BODAzNDMxMzAxOV5BMl5BanBnXkFtZTgwMDMxMjA4MjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Birdman or (The Unexpected Virtue of Ignorance),2014,A,119 min,"Comedy, Drama",7.7,"A washed-up superhero actor attempts to revive his fading career by writing, directing, and starring in a Broadway production.",87,Alejandro G. Iñárritu,Michael Keaton,Zach Galifianakis,Edward Norton,Andrea Riseborough,580291,"42,340,598" +"https://m.media-amazon.com/images/M/MV5BMTQ5NTg5ODk4OV5BMl5BanBnXkFtZTgwODc4MTMzMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",La vie d'Adèle,2013,A,180 min,"Drama, Romance",7.7,"Adèle's life is changed when she meets Emma, a young woman with blue hair, who will allow her to discover desire and to assert herself as a woman and as an adult. In front of others, Adèle grows, seeks herself, loses herself, and ultimately finds herself through love and loss.",89,Abdellatif Kechiche,Léa Seydoux,Adèle Exarchopoulos,Salim Kechiouche,Aurélien Recoing,138741,"2,199,675" +"https://m.media-amazon.com/images/M/MV5BMTgwNTAwMjEzMF5BMl5BanBnXkFtZTcwNzMzODY4OA@@._V1_UY98_CR3,0,67,98_AL_.jpg",Kai po che!,2013,U,130 min,"Drama, Sport",7.7,Three friends growing up in India at the turn of the millennium set out to open a training academy to produce the country's next cricket stars.,40,Abhishek Kapoor,Amit Sadh,Sushant Singh Rajput,Rajkummar Rao,Amrita Puri,32628,"1,122,527" +"https://m.media-amazon.com/images/M/MV5BMTQzMzg2Nzg2MF5BMl5BanBnXkFtZTgwNjUzNzIzMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Broken Circle Breakdown,2012,,111 min,"Drama, Music, Romance",7.7,"Elise and Didier fall in love at first sight, in spite of their differences. He talks, she listens. He's a romantic atheist, she's a religious realist. When their daughter becomes seriously ill, their love is put on trial.",70,Felix van Groeningen,Veerle Baetens,Johan Heldenbergh,Nell Cattrysse,Geert Van Rampelberg,39379,"175,058" +"https://m.media-amazon.com/images/M/MV5BMzA2NDkwODAwM15BMl5BanBnXkFtZTgwODk5MTgzMTE@._V1_UY98_CR0,0,67,98_AL_.jpg",Captain America: The Winter Soldier,2014,UA,136 min,"Action, Adventure, Sci-Fi",7.7,"As Steve Rogers struggles to embrace his role in the modern world, he teams up with a fellow Avenger and S.H.I.E.L.D agent, Black Widow, to battle a new threat from history: an assassin known as the Winter Soldier.",70,Anthony Russo,Joe Russo,Chris Evans,Samuel L. Jackson,Scarlett Johansson,736182,"259,766,572" +"https://m.media-amazon.com/images/M/MV5BOTc3NzAxMjg4M15BMl5BanBnXkFtZTcwMDc2ODQwNw@@._V1_UY98_CR3,0,67,98_AL_.jpg",Rockstar,2011,UA,159 min,"Drama, Music, Musical",7.7,"Janardhan Jakhar chases his dreams of becoming a big Rock star, during which he falls in love with Heer.",,Imtiaz Ali,Ranbir Kapoor,Nargis Fakhri,Shammi Kapoor,Kumud Mishra,39501,"985,912" +"https://m.media-amazon.com/images/M/MV5BOGQzODdlMDktNzU4ZC00N2M3LWFkYTAtYTM1NTE0ZWI5YTg4XkEyXkFqcGdeQXVyMTA1NTM1NDI2._V1_UX67_CR0,0,67,98_AL_.jpg",Nebraska,2013,UA,115 min,"Adventure, Comedy, Drama",7.7,"An aging, booze-addled father makes the trip from Montana to Nebraska with his estranged son in order to claim a million-dollar Mega Sweepstakes Marketing prize.",87,Alexander Payne,Bruce Dern,Will Forte,June Squibb,Bob Odenkirk,112298,"17,654,912" +"https://m.media-amazon.com/images/M/MV5BNzMxNTExOTkyMF5BMl5BanBnXkFtZTcwMzEyNDc0OA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Wreck-It Ralph,2012,U,101 min,"Animation, Adventure, Comedy",7.7,"A video game villain wants to be a hero and sets out to fulfill his dream, but his quest brings havoc to the whole arcade where he lives.",72,Rich Moore,John C. Reilly,Jack McBrayer,Jane Lynch,Sarah Silverman,380195,"189,422,889" +"https://m.media-amazon.com/images/M/MV5BNjg0OTM5OTQyNV5BMl5BanBnXkFtZTgwNDg5NDQ0NTE@._V1_UY98_CR2,0,67,98_AL_.jpg",Le Petit Prince,2015,PG,108 min,"Animation, Adventure, Drama",7.7,"A little girl lives in a very grown-up world with her mother, who tries to prepare her for it. Her neighbor, the Aviator, introduces the girl to an extraordinary world where anything is possible, the world of the Little Prince.",70,Mark Osborne,Jeff Bridges,Mackenzie Foy,Rachel McAdams,Marion Cotillard,56720,"1,339,152" +"https://m.media-amazon.com/images/M/MV5BMTM3NzQzMDA5Ml5BMl5BanBnXkFtZTcwODA5NTcyNw@@._V1_UY98_CR0,0,67,98_AL_.jpg",Detachment,2011,,98 min,Drama,7.7,A substitute teacher who drifts from classroom to classroom finds a connection to the students and teachers during his latest assignment.,52,Tony Kaye,Adrien Brody,Christina Hendricks,Marcia Gay Harden,Lucy Liu,77071,"71,177" +"https://m.media-amazon.com/images/M/MV5BMTM4NjY1MDQwMl5BMl5BanBnXkFtZTcwNTI3Njg3NA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Midnight in Paris,2011,PG-13,96 min,"Comedy, Fantasy, Romance",7.7,"While on a trip to Paris with his fiancée's family, a nostalgic screenwriter finds himself mysteriously going back to the 1920s every day at midnight.",81,Woody Allen,Owen Wilson,Rachel McAdams,Kathy Bates,Kurt Fuller,388089,"56,816,662" +"https://m.media-amazon.com/images/M/MV5BMTg4MDk1ODExN15BMl5BanBnXkFtZTgwNzIyNjg3MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Lego Movie,2014,U,100 min,"Animation, Action, Adventure",7.7,"An ordinary LEGO construction worker, thought to be the prophesied as ""special"", is recruited to join a quest to stop an evil tyrant from gluing the LEGO universe into eternal stasis.",83,Christopher Miller,Phil Lord,Chris Pratt,Will Ferrell,Elizabeth Banks,323982,"257,760,692" +"https://m.media-amazon.com/images/M/MV5BNjE5MzYwMzYxMF5BMl5BanBnXkFtZTcwOTk4MTk0OQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Gravity,2013,UA,91 min,"Drama, Sci-Fi, Thriller",7.7,Two astronauts work together to survive after an accident leaves them stranded in space.,96,Alfonso Cuarón,Sandra Bullock,George Clooney,Ed Harris,Orto Ignatiussen,769145,"274,092,705" +"https://m.media-amazon.com/images/M/MV5BMTk2NzczOTgxNF5BMl5BanBnXkFtZTcwODQ5ODczOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Star Trek Into Darkness,2013,UA,132 min,"Action, Adventure, Sci-Fi",7.7,"After the crew of the Enterprise find an unstoppable force of terror from within their own organization, Captain Kirk leads a manhunt to a war-zone world to capture a one-man weapon of mass destruction.",72,J.J. Abrams,Chris Pine,Zachary Quinto,Zoe Saldana,Benedict Cumberbatch,463188,"228,778,661" +"https://m.media-amazon.com/images/M/MV5BMTYwMzMzMDI0NF5BMl5BanBnXkFtZTgwNDQ3NjI3NjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Beasts of No Nation,2015,,137 min,"Drama, War",7.7,"A drama based on the experiences of Agu, a child soldier fighting in the civil war of an unnamed African country.",79,Cary Joji Fukunaga,Abraham Attah,Emmanuel Affadzi,Ricky Adelayitor,Andrew Adote,73964,"83,861" +"https://m.media-amazon.com/images/M/MV5BOGUyZDUxZjEtMmIzMC00MzlmLTg4MGItZWJmMzBhZjE0Mjc1XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",The Social Network,2010,UA,120 min,"Biography, Drama",7.7,"As Harvard student Mark Zuckerberg creates the social networking site that would become known as Facebook, he is sued by the twins who claimed he stole their idea, and by the co-founder who was later squeezed out of the business.",95,David Fincher,Jesse Eisenberg,Andrew Garfield,Justin Timberlake,Rooney Mara,624982,"96,962,694" +"https://m.media-amazon.com/images/M/MV5BMTg5OTMxNzk4Nl5BMl5BanBnXkFtZTcwOTk1MjAwNQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",X: First Class,2011,UA,131 min,"Action, Adventure, Sci-Fi",7.7,"In the 1960s, superpowered humans Charles Xavier and Erik Lensherr work together to find others like them, but Erik's vengeful pursuit of an ambitious mutant who ruined his life causes a schism to divide them.",65,Matthew Vaughn,James McAvoy,Michael Fassbender,Jennifer Lawrence,Kevin Bacon,645512,"146,408,305" +"https://m.media-amazon.com/images/M/MV5BNGQwZjg5YmYtY2VkNC00NzliLTljYTctNzI5NmU3MjE2ODQzXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Hangover,2009,UA,100 min,Comedy,7.7,"Three buddies wake up from a bachelor party in Las Vegas, with no memory of the previous night and the bachelor missing. They make their way around the city in order to find their friend before his wedding.",73,Todd Phillips,Zach Galifianakis,Bradley Cooper,Justin Bartha,Ed Helms,717559,"277,322,503" +"https://m.media-amazon.com/images/M/MV5BMWZiNjE2OWItMTkwNy00ZWQzLWI0NTgtMWE0NjNiYTljN2Q1XkEyXkFqcGdeQXVyNzAwMjYxMzA@._V1_UX67_CR0,0,67,98_AL_.jpg",Skyfall,2012,UA,143 min,"Action, Adventure, Thriller",7.7,"James Bond's loyalty to M is tested when her past comes back to haunt her. When MI6 comes under attack, 007 must track down and destroy the threat, no matter how personal the cost.",81,Sam Mendes,Daniel Craig,Javier Bardem,Naomie Harris,Judi Dench,630614,"304,360,277" +"https://m.media-amazon.com/images/M/MV5BMTM2MTI5NzA3MF5BMl5BanBnXkFtZTcwODExNTc0OA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Silver Linings Playbook,2012,A,122 min,"Comedy, Drama, Romance",7.7,"After a stint in a mental institution, former teacher Pat Solitano moves back in with his parents and tries to reconcile with his ex-wife. Things get more challenging when Pat meets Tiffany, a mysterious girl with problems of her own.",81,David O. Russell,Bradley Cooper,Jennifer Lawrence,Robert De Niro,Jacki Weaver,661871,"132,092,958" +"https://m.media-amazon.com/images/M/MV5BNzljNjY3MDYtYzc0Ni00YjU0LWIyNDUtNTE0ZDRiMGExMjZlXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Argo,2012,A,120 min,"Biography, Drama, Thriller",7.7,"Acting under the cover of a Hollywood producer scouting a location for a science fiction film, a CIA agent launches a dangerous operation to rescue six Americans in Tehran during the U.S. hostage crisis in Iran in 1979.",86,Ben Affleck,Ben Affleck,Bryan Cranston,John Goodman,Alan Arkin,572581,"136,025,503" +"https://m.media-amazon.com/images/M/MV5BMTk5MjM4OTU1OV5BMl5BanBnXkFtZTcwODkzNDIzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",(500) Days of Summer,2009,UA,95 min,"Comedy, Drama, Romance",7.7,"An offbeat romantic comedy about a woman who doesn't believe true love exists, and the young man who falls for her.",76,Marc Webb,Zooey Deschanel,Joseph Gordon-Levitt,Geoffrey Arend,Chloë Grace Moretz,472242,"32,391,374" +"https://m.media-amazon.com/images/M/MV5BMTQ2OTE1Mjk0N15BMl5BanBnXkFtZTcwODE3MDAwNA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Harry Potter and the Deathly Hallows: Part 1,2010,A,146 min,"Adventure, Family, Fantasy",7.7,"As Harry, Ron, and Hermione race against time and evil to destroy the Horcruxes, they uncover the existence of the three most powerful objects in the wizarding world: the Deathly Hallows.",65,David Yates,Daniel Radcliffe,Emma Watson,Rupert Grint,Bill Nighy,479120,"295,983,305" +"https://m.media-amazon.com/images/M/MV5BOTc3YmM3N2QtODZkMC00ZDE5LThjMTQtYTljN2Y1YTYwYWJkXkEyXkFqcGdeQXVyODEzNjM5OTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Gake no ue no Ponyo,2008,U,101 min,"Animation, Adventure, Comedy",7.7,"A five-year-old boy develops a relationship with Ponyo, a young goldfish princess who longs to become a human after falling in love with him.",86,Hayao Miyazaki,Cate Blanchett,Matt Damon,Liam Neeson,Tomoko Yamaguchi,125317,"15,090,400" +"https://m.media-amazon.com/images/M/MV5BOTY4NTU2NTU4NF5BMl5BanBnXkFtZTcwNjE0OTc5MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Frost/Nixon,2008,R,122 min,"Biography, Drama, History",7.7,A dramatic retelling of the post-Watergate television interviews between British talk-show host David Frost and former president Richard Nixon.,80,Ron Howard,Frank Langella,Michael Sheen,Kevin Bacon,Sam Rockwell,103330,"18,593,156" +"https://m.media-amazon.com/images/M/MV5BNDliMTMxOWEtODM3Yi00N2QwLTg4YTAtNTE5YzBlNTA2NjhlXkEyXkFqcGdeQXVyNjE5MjUyOTM@._V1_UX67_CR0,0,67,98_AL_.jpg",Papurika,2006,U,90 min,"Animation, Drama, Fantasy",7.7,"When a machine that allows therapists to enter their patients' dreams is stolen, all Hell breaks loose. Only a young female therapist, Paprika, can stop it.",81,Satoshi Kon,Megumi Hayashibara,Tôru Emori,Katsunosuke Hori,Tôru Furuya,71379,"881,302" +"https://m.media-amazon.com/images/M/MV5BOTA1Mzg3NjIxNV5BMl5BanBnXkFtZTcwNzU2NTc5MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Changeling,2008,R,141 min,"Biography, Crime, Drama",7.7,Grief-stricken mother Christine Collins (Angelina Jolie) takes on the L.A.P.D. to her own detriment when it tries to pass off an obvious impostor as her missing child.,63,Clint Eastwood,Angelina Jolie,Colm Feore,Amy Ryan,Gattlin Griffith,239203,"35,739,802" +"https://m.media-amazon.com/images/M/MV5BMTU2NjQ1Nzc4MF5BMl5BanBnXkFtZTcwNTM0NDk1Mw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Flipped,2010,PG,90 min,"Comedy, Drama, Romance",7.7,Two eighth-graders start to have feelings for each other despite being total opposites.,45,Rob Reiner,Madeline Carroll,Callan McAuliffe,Rebecca De Mornay,Anthony Edwards,81446,"1,752,214" +"https://m.media-amazon.com/images/M/MV5BMzA4ZGM1NjYtMjcxYS00MTdiLWJmNzEtMTUzODY0NDQ0YzUzXkEyXkFqcGdeQXVyMzYwMjQ3OTI@._V1_UY98_CR1,0,67,98_AL_.jpg",Toki o kakeru shôjo,2006,U,98 min,"Animation, Adventure, Comedy",7.7,"A high-school girl named Makoto acquires the power to travel back in time, and decides to use it for her own personal benefits. Little does she know that she is affecting the lives of others just as much as she is her own.",,Mamoru Hosoda,Riisa Naka,Takuya Ishida,Mitsutaka Itakura,Ayami Kakiuchi,60368, +"https://m.media-amazon.com/images/M/MV5BZDNlNjEzMzQtZDM0MS00YzhiLTk0MGUtYTdmNDZiZGVjNTk0L2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",Death Note: Desu nôto,2006,,126 min,"Crime, Drama, Fantasy",7.7,"A battle between the world's two greatest minds begins when Light Yagami finds the Death Note, a notebook with the power to kill, and decides to rid the world of criminals.",,Shûsuke Kaneko,Tatsuya Fujiwara,Ken'ichi Matsuyama,Asaka Seto,Yû Kashii,28630, +"https://m.media-amazon.com/images/M/MV5BMmE3OWZhZDYtOTBjMi00NDIwLTg1NWMtMjg0NjJmZWM4MjliL2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",This Is England,2006,,101 min,"Crime, Drama",7.7,"A young boy becomes friends with a gang of skinheads. Friends soon become like family, and relationships will be pushed to the very limit.",86,Shane Meadows,Thomas Turgoose,Stephen Graham,Jo Hartley,Andrew Shim,115576,"327,919" +"https://m.media-amazon.com/images/M/MV5BMTUxNzc0OTIxMV5BMl5BanBnXkFtZTgwNDI3NzU2NDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Ex Machina,2014,UA,108 min,"Drama, Sci-Fi, Thriller",7.7,A young programmer is selected to participate in a ground-breaking experiment in synthetic intelligence by evaluating the human qualities of a highly advanced humanoid A.I.,78,Alex Garland,Alicia Vikander,Domhnall Gleeson,Oscar Isaac,Sonoya Mizuno,474141,"25,442,958" +"https://m.media-amazon.com/images/M/MV5BMjIxODEyOTQ5Ml5BMl5BanBnXkFtZTcwNjE3NzI5Mw@@._V1_UY98_CR1,0,67,98_AL_.jpg",Efter brylluppet,2006,R,120 min,Drama,7.7,"A manager of an orphanage in India is sent to Copenhagen, Denmark, where he discovers a life-altering family secret.",78,Susanne Bier,Mads Mikkelsen,Sidse Babett Knudsen,Rolf Lassgård,Neeral Mulchandani,32001,"412,544" +"https://m.media-amazon.com/images/M/MV5BMjM1NTkxNjkzMl5BMl5BanBnXkFtZTgwNDgwMDAxMzE@._V1_UY98_CR1,0,67,98_AL_.jpg",The Last King of Scotland,2006,R,123 min,"Biography, Drama, History",7.7,Based on the events of the brutal Ugandan dictator Idi Amin's regime as seen by his personal physician during the 1970s.,74,Kevin Macdonald,James McAvoy,Forest Whitaker,Gillian Anderson,Kerry Washington,175355,"17,605,861" +"https://m.media-amazon.com/images/M/MV5BN2UwNDc5NmEtNjVjZS00OTI5LWE5YjctMWM3ZjBiZGYwMGI2XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Zodiac,2007,UA,157 min,"Crime, Drama, Mystery",7.7,"In the late 1960s/early 1970s, a San Francisco cartoonist becomes an amateur detective obsessed with tracking down the Zodiac Killer, an unidentified individual who terrorizes Northern California with a killing spree.",78,David Fincher,Jake Gyllenhaal,Robert Downey Jr.,Mark Ruffalo,Anthony Edwards,466080,"33,080,084" +"https://m.media-amazon.com/images/M/MV5BZjczMWI1YWMtYTZjOS00ZDc5LWE2MWItMTY3ZGUxNzFkNjJmL2ltYWdlXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Lucky Number Slevin,2006,R,110 min,"Action, Crime, Drama",7.7,"A case of mistaken identity lands Slevin into the middle of a war being plotted by two of the city's most rival crime bosses. Under constant surveillance by Detective Brikowski and assassin Goodkat, he must get them before they get him.",53,Paul McGuigan,Josh Hartnett,Ben Kingsley,Morgan Freeman,Lucy Liu,299524,"22,494,487" +"https://m.media-amazon.com/images/M/MV5BMTQyODczNjU3NF5BMl5BanBnXkFtZTcwNjQ0NDIzMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Joyeux Noël,2005,PG-13,116 min,"Drama, History, Music",7.7,"In December 1914, an unofficial Christmas truce on the Western Front allows soldiers from opposing sides of the First World War to gain insight into each other's way of life.",70,Christian Carion,Diane Kruger,Benno Fürmann,Guillaume Canet,Natalie Dessay,28003,"1,054,361" +"https://m.media-amazon.com/images/M/MV5BNTEzOTYwMTcxN15BMl5BanBnXkFtZTcwNTgyNjI1MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Control,2007,R,122 min,"Biography, Drama, Music",7.7,"A profile of Ian Curtis, the enigmatic singer of Joy Division whose personal, professional, and romantic troubles led him to commit suicide at the age of 23.",78,Anton Corbijn,Sam Riley,Samantha Morton,Craig Parkinson,Alexandra Maria Lara,61609,"871,577" +"https://m.media-amazon.com/images/M/MV5BMTAxNDYxMjg0MjNeQTJeQWpwZ15BbWU3MDcyNTk2OTM@._V1_UX67_CR0,0,67,98_AL_.jpg",Tangled,2010,U,100 min,"Animation, Adventure, Comedy",7.7,"The magically long-haired Rapunzel has spent her entire life in a tower, but now that a runaway thief has stumbled upon her, she is about to discover the world for the first time, and who she really is.",71,Nathan Greno,Byron Howard,Mandy Moore,Zachary Levi,Donna Murphy,405922,"200,821,936" +"https://m.media-amazon.com/images/M/MV5BODFlNTI0ZWQtOTcxNC00OTc0LTkwZDUtMmNkM2I1ZWFlYzZkXkEyXkFqcGdeQXVyNTIzOTk5ODM@._V1_UY98_CR2,0,67,98_AL_.jpg",Zwartboek,2006,R,145 min,"Drama, Thriller, War",7.7,"In the Nazi-occupied Netherlands during World War II, a Jewish singer infiltrates the regional Gestapo headquarters for the Dutch resistance.",71,Paul Verhoeven,Carice van Houten,Sebastian Koch,Thom Hoffman,Halina Reijn,72643,"4,398,392" +"https://m.media-amazon.com/images/M/MV5BMTY5NTAzNTc1NF5BMl5BanBnXkFtZTYwNDY4MDc3._V1_UX67_CR0,0,67,98_AL_.jpg",Brokeback Mountain,2005,A,134 min,"Drama, Romance",7.7,"The story of a forbidden and secretive relationship between two cowboys, and their lives over the years.",87,Ang Lee,Jake Gyllenhaal,Heath Ledger,Michelle Williams,Randy Quaid,323103,"83,043,761" +"https://m.media-amazon.com/images/M/MV5BODE0NTcxNTQzNF5BMl5BanBnXkFtZTcwMzczOTIzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",3:10 to Yuma,2007,A,122 min,"Action, Crime, Drama",7.7,A small-time rancher agrees to hold a captured outlaw who's awaiting a train to go to court in Yuma. A battle of wills ensues as the outlaw tries to psych out the rancher.,76,James Mangold,Russell Crowe,Christian Bale,Ben Foster,Logan Lerman,288797,"53,606,916" +"https://m.media-amazon.com/images/M/MV5BOTk1OTA1MjIyNV5BMl5BanBnXkFtZTcwODQxMTkyMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Crash,2004,UA,112 min,"Crime, Drama, Thriller",7.7,"Los Angeles citizens with vastly separate lives collide in interweaving stories of race, loss and redemption.",66,Paul Haggis,Don Cheadle,Sandra Bullock,Thandie Newton,Karina Arroyave,419483,"54,580,300" +"https://m.media-amazon.com/images/M/MV5BMjZiOTNlMzYtZWYwZS00YWJjLTk5NDgtODkwNjRhMDI0MjhjXkEyXkFqcGdeQXVyMjgyNjk3MzE@._V1_UY98_CR1,0,67,98_AL_.jpg",Kung fu,2004,UA,99 min,"Action, Comedy, Fantasy",7.7,"In Shanghai, China in the 1940s, a wannabe gangster aspires to join the notorious ""Axe Gang"" while residents of a housing complex exhibit extraordinary powers in defending their turf.",78,Stephen Chow,Stephen Chow,Wah Yuen,Qiu Yuen,Siu-Lung Leung,127250,"17,108,591" +"https://m.media-amazon.com/images/M/MV5BYTIyMDFmMmItMWQzYy00MjBiLTg2M2UtM2JiNDRhOWE4NjBhXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Bourne Supremacy,2004,A,108 min,"Action, Mystery, Thriller",7.7,"When Jason Bourne is framed for a CIA operation gone awry, he is forced to resume his former life as a trained assassin to survive.",73,Paul Greengrass,Matt Damon,Franka Potente,Joan Allen,Brian Cox,434841,"176,241,941" +"https://m.media-amazon.com/images/M/MV5BNjk1NzBlY2YtNjJmNi00YTVmLWI2OTgtNDUxNDE5NjUzZmE0XkEyXkFqcGdeQXVyNTc1NTQxODI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Machinist,2004,R,101 min,"Drama, Thriller",7.7,An industrial worker who hasn't slept in a year begins to doubt his own sanity.,61,Brad Anderson,Christian Bale,Jennifer Jason Leigh,Aitana Sánchez-Gijón,John Sharian,358432,"1,082,715" +"https://m.media-amazon.com/images/M/MV5BMTQxNDQwNjQzOV5BMl5BanBnXkFtZTcwNTQxNDYyMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Ray,2004,A,152 min,"Biography, Drama, Music",7.7,"The story of the life and career of the legendary rhythm and blues musician Ray Charles, from his humble beginnings in the South, where he went blind at age seven, to his meteoric rise to stardom during the 1950s and 1960s.",73,Taylor Hackford,Jamie Foxx,Regina King,Kerry Washington,Clifton Powell,138356,"75,331,600" +"https://m.media-amazon.com/images/M/MV5BMTI2NDI5ODk4N15BMl5BanBnXkFtZTYwMTI3NTE3._V1_UX67_CR0,0,67,98_AL_.jpg",Lost in Translation,2003,UA,102 min,"Comedy, Drama",7.7,A faded movie star and a neglected young woman form an unlikely bond after crossing paths in Tokyo.,89,Sofia Coppola,Bill Murray,Scarlett Johansson,Giovanni Ribisi,Anna Faris,415074,"44,585,453" +"https://m.media-amazon.com/images/M/MV5BMTI1NDMyMjExOF5BMl5BanBnXkFtZTcwOTc4MjQzMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Harry Potter and the Goblet of Fire,2005,UA,157 min,"Adventure, Family, Fantasy",7.7,"Harry Potter finds himself competing in a hazardous tournament between rival schools of magic, but he is distracted by recurring nightmares.",81,Mike Newell,Daniel Radcliffe,Emma Watson,Rupert Grint,Eric Sykes,548619,"290,013,036" +"https://m.media-amazon.com/images/M/MV5BODFlMmEwMDgtYjhmZi00ZTE5LTk2NWQtMWE1Y2M0NjkzOGYxXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Man on Fire,2004,UA,146 min,"Action, Crime, Drama",7.7,"In Mexico City, a former CIA operative swears vengeance on those who committed an unspeakable act against the family he was hired to protect.",47,Tony Scott,Denzel Washington,Christopher Walken,Dakota Fanning,Radha Mitchell,329592,"77,911,774" +"https://m.media-amazon.com/images/M/MV5BMzQxNjM5NzkxNV5BMl5BanBnXkFtZTcwMzg5NDMwMg@@._V1_UX67_CR0,0,67,98_AL_.jpg",Coraline,2009,U,100 min,"Animation, Drama, Family",7.7,"An adventurous 11-year-old girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.",80,Henry Selick,Dakota Fanning,Teri Hatcher,John Hodgman,Jennifer Saunders,197761,"75,286,229" +"https://m.media-amazon.com/images/M/MV5BMzkyNzQ1Mzc0NV5BMl5BanBnXkFtZTcwODg3MzUzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Last Samurai,2003,UA,154 min,"Action, Drama",7.7,An American military advisor embraces the Samurai culture he was hired to destroy after he is captured in battle.,55,Edward Zwick,Tom Cruise,Ken Watanabe,Billy Connolly,William Atherton,400049,"111,110,575" +"https://m.media-amazon.com/images/M/MV5BMTI2NzU1NTc1NF5BMl5BanBnXkFtZTcwOTQ1MjAwMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Magdalene Sisters,2002,R,114 min,Drama,7.7,Three young Irish women struggle to maintain their spirits while they endure dehumanizing abuse as inmates of a Magdalene Sisters Asylum.,83,Peter Mullan,Eileen Walsh,Dorothy Duffy,Nora-Jane Noone,Anne-Marie Duff,25938,"4,890,878" +"https://m.media-amazon.com/images/M/MV5BMTI0MTg4NzI3M15BMl5BanBnXkFtZTcwOTE0MTUyMQ@@._V1_UY98_CR0,0,67,98_AL_.jpg",Good Bye Lenin!,2003,R,121 min,"Comedy, Drama, Romance",7.7,"In 1990, to protect his fragile mother from a fatal shock after a long coma, a young man must keep her from learning that her beloved nation of East Germany as she knew it has disappeared.",68,Wolfgang Becker,Daniel Brühl,Katrin Saß,Chulpan Khamatova,Florian Lukas,137981,"4,064,200" +"https://m.media-amazon.com/images/M/MV5BOGY1YmUzN2MtNDQ3NC00Nzc4LWI5M2EtYzUwMGQ4NWM4NjE1XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UY98_CR0,0,67,98_AL_.jpg",In America,2002,PG-13,105 min,Drama,7.7,A family of Irish immigrants adjust to life on the mean streets of Hell's Kitchen while also grieving the death of a child.,76,Jim Sheridan,Paddy Considine,Samantha Morton,Djimon Hounsou,Sarah Bolger,40403,"15,539,266" +"https://m.media-amazon.com/images/M/MV5BYzEyNzc0NjctZjJiZC00MWI1LWJlOTMtYWZkZDAzNzQ0ZDNkXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",I Am Sam,2001,PG-13,132 min,Drama,7.7,A mentally handicapped man fights for custody of his 7-year-old daughter and in the process teaches his cold-hearted lawyer the value of love and family.,28,Jessie Nelson,Sean Penn,Michelle Pfeiffer,Dakota Fanning,Dianne Wiest,142863,"40,311,852" +"https://m.media-amazon.com/images/M/MV5BZjIwZWU0ZDItNzBlNS00MDIwLWFlZjctZTJjODdjZWYxNzczL2ltYWdlXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Adaptation.,2002,R,115 min,"Comedy, Drama",7.7,A lovelorn screenwriter becomes desperate as he tries and fails to adapt 'The Orchid Thief' by Susan Orlean for the screen.,83,Spike Jonze,Nicolas Cage,Meryl Streep,Chris Cooper,Tilda Swinton,178565,"22,245,861" +"https://m.media-amazon.com/images/M/MV5BYWMwMzQxZjQtODM1YS00YmFiLTk1YjQtNzNiYWY1MDE4NTdiXkEyXkFqcGdeQXVyNDYyMDk5MTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Black Hawk Down,2001,A,144 min,"Drama, History, War",7.7,160 elite U.S. soldiers drop into Somalia to capture two top lieutenants of a renegade warlord and find themselves in a desperate battle with a large force of heavily-armed Somalis.,74,Ridley Scott,Josh Hartnett,Ewan McGregor,Tom Sizemore,Eric Bana,364254,"108,638,745" +"https://m.media-amazon.com/images/M/MV5BNjcxMmQ0MmItYTkzYy00MmUyLTlhOTQtMmJmNjE3MDMwYjdlXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Road to Perdition,2002,A,117 min,"Crime, Drama, Thriller",7.7,"A mob enforcer's son witnesses a murder, forcing him and his father to take to the road, and his father down a path of redemption and revenge.",72,Sam Mendes,Tom Hanks,Tyler Hoechlin,Rob Maxey,Liam Aiken,246840,"104,454,762" +"https://m.media-amazon.com/images/M/MV5BNThiMDc1YjUtYmE3Zi00MTM1LTkzM2MtNjdlNzQ4ZDlmYjRmXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UY98_CR1,0,67,98_AL_.jpg",Das Experiment,2001,R,120 min,"Drama, Thriller",7.7,"For two weeks, 20 male participants are hired to play prisoners and guards in a prison. The ""prisoners"" have to follow seemingly mild rules, and the ""guards"" are told to retain order without using physical violence.",60,Oliver Hirschbiegel,Moritz Bleibtreu,Christian Berkel,Oliver Stokowski,Wotan Wilke Möhring,90842,"141,072" +"https://m.media-amazon.com/images/M/MV5BNGY3NWYwNzctNWU5Yi00ZjljLTgyNDgtZjNhZjRlNjc0ZTU1XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Billy Elliot,2000,R,110 min,"Drama, Music",7.7,A talented young boy becomes torn between his unexpected love of dance and the disintegration of his family.,74,Stephen Daldry,Jamie Bell,Julie Walters,Jean Heywood,Jamie Draven,126770,"21,995,263" +"https://m.media-amazon.com/images/M/MV5BZGY5NWUyNDUtZWJhZi00ZjMxLWFmMjMtYmJhZjVkZGZhNWQ4XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Hedwig and the Angry Inch,2001,R,95 min,"Comedy, Drama, Music",7.7,A gender-queer punk-rock singer from East Berlin tours the U.S. with her band as she tells her life story and follows the former lover/band-mate who stole her songs.,85,John Cameron Mitchell,John Cameron Mitchell,Miriam Shor,Stephen Trask,Theodore Liscinski,31957,"3,029,081" +"https://m.media-amazon.com/images/M/MV5BYzVmYzVkMmUtOGRhMi00MTNmLThlMmUtZTljYjlkMjNkMjJkXkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Ocean's Eleven,2001,UA,116 min,"Crime, Thriller",7.7,Danny Ocean and his ten accomplices plan to rob three Las Vegas casinos simultaneously.,74,Steven Soderbergh,George Clooney,Brad Pitt,Julia Roberts,Matt Damon,516372,"183,417,150" +"https://m.media-amazon.com/images/M/MV5BNTIyNThlMjMtMzUyMi00YmEyLTljMmYtMWRhN2Q3ZTllZjA4XkEyXkFqcGdeQXVyMzM4MjM0Nzg@._V1_UY98_CR1,0,67,98_AL_.jpg",Vampire Hunter D: Bloodlust,2000,U,103 min,"Animation, Action, Fantasy",7.7,"When a girl is abducted by a vampire, a legendary bounty hunter is hired to bring her back.",62,Yoshiaki Kawajiri,Andrew Philpot,John Rafter Lee,Pamela Adlon,Wendee Lee,29210,"151,086" +"https://m.media-amazon.com/images/M/MV5BMjZkOTdmMWItOTkyNy00MDdjLTlhNTQtYzU3MzdhZjA0ZDEyXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg","O Brother, Where Art Thou?",2000,U,107 min,"Adventure, Comedy, Crime",7.7,"In the deep south during the 1930s, three escaped convicts search for hidden treasure while a relentless lawman pursues them.",69,Joel Coen,Ethan Coen,George Clooney,John Turturro,Tim Blake Nelson,286742,"45,512,588" +"https://m.media-amazon.com/images/M/MV5BZDYwYzlhOTAtNDAwMC00ZTBhLWI4M2QtMTA1NmJhYTdiNTkxXkEyXkFqcGdeQXVyNTM0NTU5Mg@@._V1_UX67_CR0,0,67,98_AL_.jpg",Interstate 60: Episodes of the Road,2002,R,116 min,"Adventure, Comedy, Drama",7.7,"Neal Oliver, a very confused young man and an artist, takes a journey of a lifetime on a highway I60 that doesn't exist on any of the maps, going to the places he never even heard of, searching for an answer and his dreamgirl.",,Bob Gale,James Marsden,Gary Oldman,Kurt Russell,Matthew Edison,29999, +"https://m.media-amazon.com/images/M/MV5BOGE0ZWI0YzAtY2NkZi00YjkyLWIzYWEtNTJmMzJjODllNjdjXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg","South Park: Bigger, Longer & Uncut",1999,A,81 min,"Animation, Comedy, Fantasy",7.7,"When Stan Marsh and his friends go see an R-rated movie, they start cursing and their parents think that Canada is to blame.",73,Trey Parker,Trey Parker,Matt Stone,Mary Kay Bergman,Isaac Hayes,192112,"52,037,603" +"https://m.media-amazon.com/images/M/MV5BOTA5MzQ3MzI1NV5BMl5BanBnXkFtZTgwNTcxNTYxMTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Office Space,1999,R,89 min,Comedy,7.7,Three company workers who hate their jobs decide to rebel against their greedy boss.,68,Mike Judge,Ron Livingston,Jennifer Aniston,David Herman,Ajay Naidu,241575,"10,824,921" +"https://m.media-amazon.com/images/M/MV5BM2FlNzE0ZmUtMmVkZS00MWQ3LWE4OWQtYjQwZjdhNzRmNWE2XkEyXkFqcGdeQXVyMTAwMzUyOTc@._V1_UX67_CR0,0,67,98_AL_.jpg",Happiness,1998,,134 min,"Comedy, Drama",7.7,"The lives of several individuals intertwine as they go about their lives in their own unique ways, engaging in acts society as a whole might find disturbing in a desperate search for human connection.",81,Todd Solondz,Jane Adams,Jon Lovitz,Philip Seymour Hoffman,Dylan Baker,66408,"2,807,390" +"https://m.media-amazon.com/images/M/MV5BMDZkMTUxYWEtMDY5NS00ZTA5LTg3MTItNTlkZWE1YWRjYjMwL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Training Day,2001,A,122 min,"Crime, Drama, Thriller",7.7,A rookie cop spends his first day as a Los Angeles narcotics officer with a rogue detective who isn't what he appears to be.,69,Antoine Fuqua,Denzel Washington,Ethan Hawke,Scott Glenn,Tom Berenger,390247,"76,631,907" +"https://m.media-amazon.com/images/M/MV5BMjE2OTc3OTk2M15BMl5BanBnXkFtZTgwMjg2NjIyMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Rushmore,1998,UA,93 min,"Comedy, Drama, Romance",7.7,The extracurricular king of Rushmore Preparatory School is put on academic probation.,86,Wes Anderson,Jason Schwartzman,Bill Murray,Olivia Williams,Seymour Cassel,169229,"17,105,219" +"https://m.media-amazon.com/images/M/MV5BYjA2MTA1MjUtYmUyNy00NGZiLTk2NTAtMDk3N2M3YmMwOTc1XkEyXkFqcGdeQXVyMjA0MzYwMDY@._V1_UY98_CR0,0,67,98_AL_.jpg",Abre los ojos,1997,U,119 min,"Drama, Mystery, Sci-Fi",7.7,"A very handsome man finds the love of his life, but he suffers an accident and needs to have his face rebuilt by surgery after it is severely disfigured.",,Alejandro Amenábar,Eduardo Noriega,Penélope Cruz,Chete Lera,Fele Martínez,64082,"368,234" +"https://m.media-amazon.com/images/M/MV5BYmUxY2MyOTQtYjRlMi00ZWEwLTkzODctZDMxNDcyNTFhYjNjXkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UY98_CR1,0,67,98_AL_.jpg",Being John Malkovich,1999,R,113 min,"Comedy, Drama, Fantasy",7.7,A puppeteer discovers a portal that leads literally into the head of movie star John Malkovich.,90,Spike Jonze,John Cusack,Cameron Diaz,Catherine Keener,John Malkovich,312542,"22,858,926" +"https://m.media-amazon.com/images/M/MV5BNWMxZTgzMWEtMTU0Zi00NDc5LWFkZjctMzUxNDIyNzZiMmNjXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",As Good as It Gets,1997,A,139 min,"Comedy, Drama, Romance",7.7,"A single mother and waitress, a misanthropic author, and a gay artist form an unlikely friendship after the artist is assaulted in a robbery.",67,James L. Brooks,Jack Nicholson,Helen Hunt,Greg Kinnear,Cuba Gooding Jr.,275755,"148,478,011" +"https://m.media-amazon.com/images/M/MV5BZWFjYmZmZGQtYzg4YS00ZGE5LTgwYzAtZmQwZjQ2NDliMGVmXkEyXkFqcGdeQXVyNTUyMzE4Mzg@._V1_UY98_CR0,0,67,98_AL_.jpg",The Fifth Element,1997,UA,126 min,"Action, Adventure, Sci-Fi",7.7,"In the colorful future, a cab driver unwittingly becomes the central figure in the search for a legendary cosmic weapon to keep Evil and Mr. Zorg at bay.",52,Luc Besson,Bruce Willis,Milla Jovovich,Gary Oldman,Ian Holm,434125,"63,540,020" +"https://m.media-amazon.com/images/M/MV5BZjFkOWM5NDUtODYwOS00ZDg0LWFkZGUtYzBkYzNjZjU3ODE3XkEyXkFqcGdeQXVyNzQzNzQxNzI@._V1_UX67_CR0,0,67,98_AL_.jpg",Le dîner de cons,1998,PG-13,80 min,Comedy,7.7,"A few friends have a weekly fools' dinner, where each brings a fool along. Pierre finds a champion fool for next dinner. Surprise.",73,Francis Veber,Thierry Lhermitte,Jacques Villeret,Francis Huster,Daniel Prévost,37424,"4,065,116" +"https://m.media-amazon.com/images/M/MV5BYzMzMDZkYWEtODIzNS00YjI3LTkxNTktOWEyZGM3ZWI2MWM4XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Donnie Brasco,1997,A,127 min,"Biography, Crime, Drama",7.7,"An FBI undercover agent infiltrates the mob and finds himself identifying more with the mafia life, at the expense of his regular one.",76,Mike Newell,Al Pacino,Johnny Depp,Michael Madsen,Bruno Kirby,279318,"41,909,762" +"https://m.media-amazon.com/images/M/MV5BMTQzMzcxMzUyMl5BMl5BanBnXkFtZTgwNDI1MjgxMTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Shine,1996,U,105 min,"Biography, Drama, Music",7.7,"Pianist David Helfgott, driven by his father and teachers, has a breakdown. Years later he returns to the piano, to popular if not critical acclaim.",87,Scott Hicks,Geoffrey Rush,Armin Mueller-Stahl,Justin Braine,Sonia Todd,51350,"35,811,509" +"https://m.media-amazon.com/images/M/MV5BZTM2NWI2OGYtYWNhMi00ZTlmLTg2ZTAtMmI5NWRjODA5YTE1XkEyXkFqcGdeQXVyODE2OTYwNTg@._V1_UX67_CR0,0,67,98_AL_.jpg",Primal Fear,1996,A,129 min,"Crime, Drama, Mystery",7.7,"An altar boy is accused of murdering a priest, and the truth is buried several layers deep.",47,Gregory Hoblit,Richard Gere,Laura Linney,Edward Norton,John Mahoney,189716,"56,116,183" +"https://m.media-amazon.com/images/M/MV5BM2U5OWM5NWQtZDYwZS00NmI3LTk4NDktNzcwZjYzNmEzYWU1XkEyXkFqcGdeQXVyNjMwMjk0MTQ@._V1_UY98_CR0,0,67,98_AL_.jpg",Hamlet,1996,PG-13,242 min,Drama,7.7,"Hamlet, Prince of Denmark, returns home to find his father murdered and his mother remarrying the murderer, his uncle. Meanwhile, war is brewing.",,Kenneth Branagh,Kenneth Branagh,Julie Christie,Derek Jacobi,Kate Winslet,35991,"4,414,535" +"https://m.media-amazon.com/images/M/MV5BZDQzMGE5ODYtZDdiNC00MzZjLTg2NjAtZTk0ODlkYmY4MTQzXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",A Little Princess,1995,U,97 min,"Drama, Family, Fantasy",7.7,A young girl is relegated to servitude at a boarding school when her father goes missing and is presumed dead.,83,Alfonso Cuarón,Liesel Matthews,Eleanor Bron,Liam Cunningham,Rusty Schwimmer,32236,"10,019,307" +"https://m.media-amazon.com/images/M/MV5BZjM4NWRhYTQtYTJlNC00ZmMyLWEzNTAtZDA2MjJjYTQ5ZTVmXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Do lok tin si,1995,UA,99 min,"Comedy, Crime, Drama",7.7,"This Hong Kong-set crime drama follows the lives of a hitman, hoping to get out of the business, and his elusive female partner.",71,Kar-Wai Wong,Leon Lai,Michelle Reis,Takeshi Kaneshiro,Charlie Yeung,26429, +"https://m.media-amazon.com/images/M/MV5BZmVhNWIzOTMtYmVlZC00ZDVmLWIyODEtODEzOTAxYjAwMzVlXkEyXkFqcGdeQXVyMzIwNDY4NDI@._V1_UY98_CR1,0,67,98_AL_.jpg",Il postino,1994,U,108 min,"Biography, Comedy, Drama",7.7,"A simple Italian postman learns to love poetry while delivering mail to a famous poet, and then uses this to woo local beauty Beatrice.",81,Michael Radford,Massimo Troisi,Massimo Troisi,Philippe Noiret,Maria Grazia Cucinotta,33600,"21,848,932" +"https://m.media-amazon.com/images/M/MV5BNzE1Njk0NmItNDhlMC00ZmFlLWI4ZTUtYTY4ZjgzNjkyMTU1XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Clerks,1994,R,92 min,Comedy,7.7,"A day in the lives of two convenience clerks named Dante and Randal as they annoy customers, discuss movies, and play hockey on the store roof.",70,Kevin Smith,Brian O'Halloran,Jeff Anderson,Marilyn Ghigliotti,Lisa Spoonauer,211450,"3,151,130" +"https://m.media-amazon.com/images/M/MV5BZWY0ODc2NDktYmYxNS00MGZiLTk5YjktZjgwZWFhNDQ0MzNhXkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",Short Cuts,1993,R,188 min,"Comedy, Drama",7.7,The day-to-day lives of several suburban Los Angeles residents.,79,Robert Altman,Andie MacDowell,Julianne Moore,Tim Robbins,Bruce Davison,42275,"6,110,979" +"https://m.media-amazon.com/images/M/MV5BNDE0MWE1ZTMtOWFkMS00YjdiLTkwZTItMDljYjY3MjM0NTk5XkEyXkFqcGdeQXVyNDYyMDk5MTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Philadelphia,1993,UA,125 min,Drama,7.7,"When a man with HIV is fired by his law firm because of his condition, he hires a homophobic small time lawyer as the only willing advocate for a wrongful dismissal suit.",66,Jonathan Demme,Tom Hanks,Denzel Washington,Roberta Maxwell,Buzz Kilman,224169,"77,324,422" +"https://m.media-amazon.com/images/M/MV5BN2Y0NWRkNWItZWEwNi00MDNlLWJmZDYtNTkwYzI5Nzg4MjVjXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",The Muppet Christmas Carol,1992,G,85 min,"Comedy, Drama, Family",7.7,The Muppet characters tell their version of the classic tale of an old and bitter miser's redemption on Christmas Eve.,64,Brian Henson,Michael Caine,Kermit the Frog,Dave Goelz,Miss Piggy,50298,"27,281,507" +"https://m.media-amazon.com/images/M/MV5BZDkzOTFmMTUtMmI2OS00MDE4LTg5YTUtODMwNDMzNmI5OGYwL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UY98_CR3,0,67,98_AL_.jpg",Malcolm X,1992,U,202 min,"Biography, Drama, History",7.7,"Biographical epic of the controversial and influential Black Nationalist leader, from his early life and career as a small-time gangster, to his ministry as a member of the Nation of Islam.",73,Spike Lee,Denzel Washington,Angela Bassett,Delroy Lindo,Spike Lee,85819,"48,169,908" +"https://m.media-amazon.com/images/M/MV5BZDNiYmRkNDYtOWU1NC00NmMxLWFkNmUtMGI5NTJjOTJmYTM5XkEyXkFqcGdeQXVyNzQ1ODk3MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",The Last of the Mohicans,1992,UA,112 min,"Action, Adventure, Drama",7.7,Three trappers protect the daughters of a British Colonel in the midst of the French and Indian War.,76,Michael Mann,Daniel Day-Lewis,Madeleine Stowe,Russell Means,Eric Schweig,150409,"75,505,856" +"https://m.media-amazon.com/images/M/MV5BZjVkYmFkZWQtZmNjYy00NmFhLTliMWYtNThlOTUxNjg5ODdhXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR4,0,67,98_AL_.jpg",Kurenai no buta,1992,U,94 min,"Animation, Adventure, Comedy",7.7,"In 1930s Italy, a veteran World War I pilot is cursed to look like an anthropomorphic pig.",83,Hayao Miyazaki,Shûichirô Moriyama,Tokiko Katô,Bunshi Katsura Vi,Tsunehiko Kamijô,77798, +"https://m.media-amazon.com/images/M/MV5BNTYzN2MxODMtMDBhOC00Y2M0LTgzMTItMzQ4NDIyYWIwMDEzL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNTc1NTQxODI@._V1_UX67_CR0,0,67,98_AL_.jpg",Glengarry Glen Ross,1992,R,100 min,"Crime, Drama, Mystery",7.7,An examination of the machinations behind the scenes at a real estate office.,82,James Foley,Al Pacino,Jack Lemmon,Alec Baldwin,Alan Arkin,95826,"10,725,228" +"https://m.media-amazon.com/images/M/MV5BMmRlZDQ1MmUtMzE2Yi00YTkxLTk1MGMtYmIyYWQwODcxYzRlXkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",A Few Good Men,1992,U,138 min,"Drama, Thriller",7.7,Military lawyer Lieutenant Daniel Kaffee defends Marines accused of murder. They contend they were acting under orders.,62,Rob Reiner,Tom Cruise,Jack Nicholson,Demi Moore,Kevin Bacon,235388,"141,340,178" +"https://m.media-amazon.com/images/M/MV5BOWQ1ZWE0MTQtMmEwOS00YjA3LTgyZTAtNjY5ODEyZTJjNDI2XkEyXkFqcGdeQXVyNjE5MjUyOTM@._V1_UX67_CR0,0,67,98_AL_.jpg",Fried Green Tomatoes,1991,PG-13,130 min,Drama,7.7,A housewife who is unhappy with her life befriends an old lady in a nursing home and is enthralled by the tales she tells of people she used to know.,64,Jon Avnet,Kathy Bates,Jessica Tandy,Mary Stuart Masterson,Mary-Louise Parker,66941,"82,418,501" +"https://m.media-amazon.com/images/M/MV5BMTgxMDMxMTctNDY0Zi00ZmNlLWFlYmQtODA2YjY4MDk4MjU1XkEyXkFqcGdeQXVyNTc1NTQxODI@._V1_UX67_CR0,0,67,98_AL_.jpg",Barton Fink,1991,U,116 min,"Comedy, Drama, Thriller",7.7,A renowned New York playwright is enticed to California to write for the movies and discovers the hellish truth of Hollywood.,69,Joel Coen,Ethan Coen,John Turturro,John Goodman,Judy Davis,113240,"6,153,939" +"https://m.media-amazon.com/images/M/MV5BMTY2Njk3MTAzM15BMl5BanBnXkFtZTgwMTY5Mzk4NjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Miller's Crossing,1990,R,115 min,"Crime, Drama, Thriller",7.7,"Tom Reagan, an advisor to a Prohibition-era crime boss, tries to keep the peace between warring mobs but gets caught in divided loyalties.",66,Joel Coen,Ethan Coen,Gabriel Byrne,Albert Finney,John Turturro,125822,"5,080,409" +"https://m.media-amazon.com/images/M/MV5BMDhiOTM2OTctODk3Ny00NWI4LThhZDgtNGQ4NjRiYjFkZGQzXkEyXkFqcGdeQXVyMTA0MjU0Ng@@._V1_UX67_CR0,0,67,98_AL_.jpg",Who Framed Roger Rabbit,1988,U,104 min,"Animation, Adventure, Comedy",7.7,A toon-hating detective is a cartoon rabbit's only hope to prove his innocence when he is accused of murder.,83,Robert Zemeckis,Bob Hoskins,Christopher Lloyd,Joanna Cassidy,Charles Fleischer,182009,"156,452,370" +"https://m.media-amazon.com/images/M/MV5BNDcwMTYzMjctN2M2Yy00ZDcxLWJhNTEtMGNhYzEwYzc2NDE4XkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UY98_CR0,0,67,98_AL_.jpg",Spoorloos,1988,,107 min,"Mystery, Thriller",7.7,"Rex and Saskia, a young couple in love, are on vacation. They stop at a busy service station and Saskia is abducted. After three years and no sign of Saskia, Rex begins receiving letters from the abductor.",,George Sluizer,Bernard-Pierre Donnadieu,Gene Bervoets,Johanna ter Steege,Gwen Eckhaus,33982, +"https://m.media-amazon.com/images/M/MV5BYjE3ODY5OWEtZmE0Mi00MjUxLTg5MmUtZmFkMzM1N2VjMmU5XkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",Withnail & I,1987,R,107 min,"Comedy, Drama",7.7,"In 1969, two substance-abusing, unemployed actors retreat to the countryside for a holiday that proves disastrous.",84,Bruce Robinson,Richard E. Grant,Paul McGann,Richard Griffiths,Ralph Brown,40396,"1,544,889" +"https://m.media-amazon.com/images/M/MV5BZTk0NDU4YmItOTk0ZS00ODc2LTkwNGItNWI5MDJkNTJiYWMxXkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_UX67_CR0,0,67,98_AL_.jpg",The Last Emperor,1987,U,163 min,"Biography, Drama, History",7.7,The story of the final Emperor of China.,76,Bernardo Bertolucci,John Lone,Joan Chen,Peter O'Toole,Ruocheng Ying,94326,"43,984,230" +"https://m.media-amazon.com/images/M/MV5BMmQwNzczZDItNmI0OS00MjRmLTliYWItZWIyMjk1MTU4ZTQ4L2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Empire of the Sun,1987,U,153 min,"Action, Drama, History",7.7,A young English boy struggles to survive under Japanese occupation during World War II.,62,Steven Spielberg,Christian Bale,John Malkovich,Miranda Richardson,Nigel Havers,115677,"22,238,696" +"https://m.media-amazon.com/images/M/MV5BZjEyZTdhNDMtMWFkMS00ZmRjLWEyNmEtZDU3MWFkNDEzMDYwXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Der Name der Rose,1986,R,130 min,"Crime, Drama, Mystery",7.7,An intellectually nonconformist friar investigates a series of mysterious deaths in an isolated abbey.,54,Jean-Jacques Annaud,Sean Connery,Christian Slater,Helmut Qualtinger,Elya Baskin,102031,"7,153,487" +"https://m.media-amazon.com/images/M/MV5BMzExOTczNTgtN2Q1Yy00MmI1LWE0NjgtNmIwMzdmZGNlODU1XkEyXkFqcGdeQXVyNDkzNTM2ODg@._V1_UX67_CR0,0,67,98_AL_.jpg",Blue Velvet,1986,A,120 min,"Drama, Mystery, Thriller",7.7,"The discovery of a severed human ear found in a field leads a young man on an investigation related to a beautiful, mysterious nightclub singer and a group of psychopathic criminals who have kidnapped her child.",76,David Lynch,Isabella Rossellini,Kyle MacLachlan,Dennis Hopper,Laura Dern,181285,"8,551,228" +"https://m.media-amazon.com/images/M/MV5BY2E1YWRlNzAtYzAwYy00MDg5LTlmYTUtYjdlZDI0NzFkNjNlL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjQ2MjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",The Purple Rose of Cairo,1985,U,82 min,"Comedy, Fantasy, Romance",7.7,"In New Jersey in 1935, a movie character walks off the screen and into the real world.",75,Woody Allen,Mia Farrow,Jeff Daniels,Danny Aiello,Irving Metzman,47102,"10,631,333" +"https://m.media-amazon.com/images/M/MV5BMTUxMjEzMzI2MV5BMl5BanBnXkFtZTgwNTU3ODAxMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",After Hours,1985,UA,97 min,"Comedy, Crime, Drama",7.7,An ordinary word processor has the worst night of his life after he agrees to visit a girl in Soho who he met that evening at a coffee shop.,90,Martin Scorsese,Griffin Dunne,Rosanna Arquette,Verna Bloom,Tommy Chong,59635,"10,600,000" +"https://m.media-amazon.com/images/M/MV5BMGUwMjM0MTEtOGY2NS00MjJmLWEyMDAtYmNkMWJjOWJlNGM0XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Zelig,1983,PG,79 min,Comedy,7.7,"""Documentary"" about a man who can look and act like whoever he's around, and meets various famous people.",,Woody Allen,Woody Allen,Mia Farrow,Patrick Horgan,John Buckwalter,39881,"11,798,616" +"https://m.media-amazon.com/images/M/MV5BMTU5MzMwMzAzM15BMl5BanBnXkFtZTcwNjYyMjA0Mg@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Verdict,1982,U,129 min,Drama,7.7,A lawyer sees the chance to salvage his career and self-respect by taking a medical malpractice case to trial rather than settling.,77,Sidney Lumet,Paul Newman,Charlotte Rampling,Jack Warden,James Mason,36096,"54,000,000" +"https://m.media-amazon.com/images/M/MV5BMzcyYWE5YmQtNDE1Yi00ZjlmLWFlZTAtMzRjODBiYjM3OTA3XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Star Trek II: The Wrath of Khan,1982,U,113 min,"Action, Adventure, Sci-Fi",7.7,"With the assistance of the Enterprise crew, Admiral Kirk must stop an old nemesis, Khan Noonien Singh, from using the life-generating Genesis Device as the ultimate weapon.",67,Nicholas Meyer,William Shatner,Leonard Nimoy,DeForest Kelley,James Doohan,112704,"78,912,963" +"https://m.media-amazon.com/images/M/MV5BODBmOWU2YWMtZGUzZi00YzRhLWJjNDAtYTUwNWVkNDcyZmU5XkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",First Blood,1982,A,93 min,"Action, Adventure",7.7,A veteran Green Beret is forced by a cruel Sheriff and his deputies to flee into the mountains and wage an escalating one-man war against his pursuers.,61,Ted Kotcheff,Sylvester Stallone,Brian Dennehy,Richard Crenna,Bill McKinney,226541,"47,212,904" +"https://m.media-amazon.com/images/M/MV5BNWU3MDFkYWQtMWQ5YS00YTcwLThmNDItODY4OWE2ZTdhZmIwXkEyXkFqcGdeQXVyMjUzOTY1NTc@._V1_UX67_CR0,0,67,98_AL_.jpg",Ordinary People,1980,U,124 min,Drama,7.7,"The accidental death of the older son of an affluent family deeply strains the relationships among the bitter mother, the good-natured father, and the guilt-ridden younger son.",86,Robert Redford,Donald Sutherland,Mary Tyler Moore,Judd Hirsch,Timothy Hutton,47099,"54,800,000" +"https://m.media-amazon.com/images/M/MV5BZjA3YjdhMWEtYjc2Ni00YzVlLWI0MTUtMGZmNTJjNmU0Yzk2XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Airplane!,1980,U,88 min,Comedy,7.7,A man afraid to fly must ensure that a plane lands safely after the pilots become sick.,78,Jim Abrahams,David Zucker,Jerry Zucker,Robert Hays,Julie Hagerty,214882,"83,400,000" +"https://m.media-amazon.com/images/M/MV5BYzYyNjg3OTctNzA2ZS00NjkzLWE4MmYtZDAzZWQ0NzkyMTJhXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Rupan sansei: Kariosutoro no shiro,1979,U,100 min,"Animation, Adventure, Family",7.7,"A dashing thief, his gang of desperadoes and an intrepid policeman struggle to free a princess from an evil count's clutches, and learn the hidden secret to a fabulous treasure that she holds part of a key to.",71,Hayao Miyazaki,Yasuo Yamada,Eiko Masuyama,Kiyoshi Kobayashi,Makio Inoue,27014, +"https://m.media-amazon.com/images/M/MV5BNzk1OGU2NmMtNTdhZC00NjdlLWE5YTMtZTQ0MGExZTQzOGQyXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Halloween,1978,A,91 min,"Horror, Thriller",7.7,"Fifteen years after murdering his sister on Halloween night 1963, Michael Myers escapes from a mental hospital and returns to the small town of Haddonfield, Illinois to kill again.",87,John Carpenter,Donald Pleasence,Jamie Lee Curtis,Tony Moran,Nancy Kyes,233106,"47,000,000" +"https://m.media-amazon.com/images/M/MV5BYmVhMDQ1YWUtYjgxOS00NzYyLWI0ZGItNTg3ZjM0MmQ4NmIwXkEyXkFqcGdeQXVyMjQzMzQzODY@._V1_UY98_CR3,0,67,98_AL_.jpg",Le locataire,1976,R,126 min,"Drama, Thriller",7.7,A bureaucrat rents a Paris apartment where he finds himself drawn into a rabbit hole of dangerous paranoia.,71,Roman Polanski,Roman Polanski,Isabelle Adjani,Melvyn Douglas,Jo Van Fleet,39889,"1,924,733" +"https://m.media-amazon.com/images/M/MV5BMTYxMDk1NTA5NF5BMl5BanBnXkFtZTcwNDkzNzA2NA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Love and Death,1975,PG,85 min,"Comedy, War",7.7,"In czarist Russia, a neurotic soldier and his distant cousin formulate a plot to assassinate Napoleon.",89,Woody Allen,Woody Allen,Diane Keaton,Georges Adet,Frank Adu,36037, +"https://m.media-amazon.com/images/M/MV5BMjE1NDY0NDk3Ml5BMl5BanBnXkFtZTcwMTAzMTM3NA@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Taking of Pelham One Two Three,1974,U,104 min,"Action, Crime, Thriller",7.7,"In New York, armed men hijack a subway car and demand a ransom for the passengers. Even if it's paid, how could they get away?",68,Joseph Sargent,Walter Matthau,Robert Shaw,Martin Balsam,Hector Elizondo,26729, +"https://m.media-amazon.com/images/M/MV5BZGZmMWE1MDYtNzAyNC00MDMzLTgzZjQtNTQ5NjYzN2E4MzkzXkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Blazing Saddles,1974,A,93 min,"Comedy, Western",7.7,"In order to ruin a western town, a corrupt politician appoints a black Sheriff, who promptly becomes his most formidable adversary.",73,Mel Brooks,Cleavon Little,Gene Wilder,Slim Pickens,Harvey Korman,125993,"119,500,000" +"https://m.media-amazon.com/images/M/MV5BYTU4ZTI0NzAtYzMwNi00YmMxLThmZWItNTY5NzgyMDAwYWVhXkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_UX67_CR0,0,67,98_AL_.jpg",Serpico,1973,A,130 min,"Biography, Crime, Drama",7.7,An honest New York cop named Frank Serpico blows the whistle on rampant corruption in the force only to have his comrades turn against him.,87,Sidney Lumet,Al Pacino,John Randolph,Jack Kehoe,Biff McGuire,109941,"29,800,000" +"https://m.media-amazon.com/images/M/MV5BNGZiMTkyNzQtMDdmZi00ZDNkLWE4YTAtZGNlNTIzYzQyMGM2XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Enter the Dragon,1973,A,102 min,"Action, Crime, Drama",7.7,A secret agent comes to an opium lord's island fortress with other fighters for a martial-arts tournament.,83,Robert Clouse,Bruce Lee,John Saxon,Jim Kelly,Ahna Capri,96561,"25,000,000" +"https://m.media-amazon.com/images/M/MV5BZjBhYzU3NWItOWZjMy00NjI5LWFmYmItZmIyOWFlMDIxMWNiXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Deliverance,1972,U,109 min,"Adventure, Drama, Thriller",7.7,"Intent on seeing the Cahulawassee River before it's dammed and turned into a lake, outdoor fanatic Lewis Medlock takes his friends on a canoeing trip they'll never forget into the dangerous American back-country.",80,John Boorman,Jon Voight,Burt Reynolds,Ned Beatty,Ronny Cox,98740,"7,056,013" +"https://m.media-amazon.com/images/M/MV5BOTZhY2E3NmItMGIwNi00OTA2LThkYmEtODFiZTM0NGI0ZWU5XkEyXkFqcGdeQXVyNTc1NTQxODI@._V1_UY98_CR1,0,67,98_AL_.jpg",The French Connection,1971,A,104 min,"Action, Crime, Drama",7.7,A pair of NYC cops in the Narcotics Bureau stumble onto a drug smuggling job with a French connection.,94,William Friedkin,Gene Hackman,Roy Scheider,Fernando Rey,Tony Lo Bianco,110075,"15,630,710" +"https://m.media-amazon.com/images/M/MV5BMzdhMTM2YTItOWU2YS00MTM0LTgyNDYtMDM1OWM3NzkzNTM2XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Dirty Harry,1971,A,102 min,"Action, Crime, Thriller",7.7,"When a madman calling himself ""the Scorpio Killer"" menaces the city, tough-as-nails San Francisco Police Inspector ""Dirty"" Harry Callahan is assigned to track down and ferret out the crazed psychopath.",90,Don Siegel,Clint Eastwood,Andrew Robinson,Harry Guardino,Reni Santoni,143292,"35,900,000" +"https://m.media-amazon.com/images/M/MV5BNGE3ZWZiNzktMDIyOC00ZmVhLThjZTktZjQ5NjI4NGVhMDBlXkEyXkFqcGdeQXVyMjI4MjA5MzA@._V1_UX67_CR0,0,67,98_AL_.jpg",Where Eagles Dare,1968,U,158 min,"Action, Adventure, War",7.7,"Allied agents stage a daring raid on a castle where the Nazis are holding American brigadier general George Carnaby prisoner, but that's not all that's really going on.",63,Brian G. Hutton,Richard Burton,Clint Eastwood,Mary Ure,Patrick Wymark,51913, +"https://m.media-amazon.com/images/M/MV5BZDVhNzQxZDEtMzcyZC00ZDg1LWFkZDctOWYxZTY0ZmYzYjc2XkEyXkFqcGdeQXVyMjA0MDQ0Mjc@._V1_UX67_CR0,0,67,98_AL_.jpg",The Odd Couple,1968,G,105 min,Comedy,7.7,"Two friends try sharing an apartment, but their ideas of housekeeping and lifestyles are as different as night and day.",86,Gene Saks,Jack Lemmon,Walter Matthau,John Fiedler,Herb Edelman,31572,"44,527,234" +"https://m.media-amazon.com/images/M/MV5BM2Y1ZTI0NzktYzU3MS00YmE1LThkY2EtMDc0NGYxNTNlZDA5XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Dirty Dozen,1967,,150 min,"Action, Adventure, War",7.7,"During World War II, a rebellious U.S. Army Major is assigned a dozen convicted murderers to train and lead them into a mass assassination mission of German officers.",73,Robert Aldrich,Lee Marvin,Ernest Borgnine,Charles Bronson,John Cassavetes,67183,"45,300,000" +"https://m.media-amazon.com/images/M/MV5BZjNkNGJjYWEtM2IyNi00ZjM5LWFlYjYtYjQ4NTU5MGFlMTI2XkEyXkFqcGdeQXVyMTMxMTY0OTQ@._V1_UY98_CR3,0,67,98_AL_.jpg",Belle de jour,1967,A,100 min,"Drama, Romance",7.7,A frigid young housewife decides to spend her midweek afternoons as a prostitute.,,Luis Buñuel,Catherine Deneuve,Jean Sorel,Michel Piccoli,Geneviève Page,40274,"26,331" +"https://m.media-amazon.com/images/M/MV5BMTRjOTA1NzctNzFmMy00ZjcwLWExYjgtYWQyZDM5ZWY1Y2JlXkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",A Man for All Seasons,1966,U,120 min,"Biography, Drama, History",7.7,"The story of Sir Thomas More, who stood up to King Henry VIII when the King rejected the Roman Catholic Church to obtain a divorce and remarry.",72,Fred Zinnemann,Paul Scofield,Wendy Hiller,Robert Shaw,Leo McKern,31222,"28,350,000" +"https://m.media-amazon.com/images/M/MV5BZTU5ZThjNzAtNjc4NC00OTViLWIxYTYtODFmMTk5Y2NjZjZiL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Repulsion,1965,,105 min,"Drama, Horror, Thriller",7.7,A sex-repulsed woman who disapproves of her sister's boyfriend sinks into depression and has horrific visions of rape and violence.,91,Roman Polanski,Catherine Deneuve,Ian Hendry,John Fraser,Yvonne Furneaux,48883, +"https://m.media-amazon.com/images/M/MV5BYzdlYmQ3MWMtMDY3My00MzVmLTg0YmMtYjRlZDUzNjBlMmE0L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Zulu,1964,U,138 min,"Drama, History, War",7.7,Outnumbered British soldiers do battle with Zulu warriors at Rorke's Drift.,77,Cy Endfield,Stanley Baker,Jack Hawkins,Ulla Jacobsson,James Booth,35999, +"https://m.media-amazon.com/images/M/MV5BMTQ2MzE0OTU3NV5BMl5BanBnXkFtZTcwNjQxNTgzNA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Goldfinger,1964,A,110 min,"Action, Adventure, Thriller",7.7,"While investigating a gold magnate's smuggling, James Bond uncovers a plot to contaminate the Fort Knox gold reserve.",87,Guy Hamilton,Sean Connery,Gert Fröbe,Honor Blackman,Shirley Eaton,174119,"51,081,062" +"https://m.media-amazon.com/images/M/MV5BMTAxNDA1ODc5MDleQTJeQWpwZ15BbWU4MDg2MDA4OTEx._V1_UX67_CR0,0,67,98_AL_.jpg",The Birds,1963,A,119 min,"Drama, Horror, Mystery",7.7,A wealthy San Francisco socialite pursues a potential boyfriend to a small Northern California town that slowly takes a turn for the bizarre when birds of all kinds suddenly begin to attack people.,90,Alfred Hitchcock,Rod Taylor,Tippi Hedren,Jessica Tandy,Suzanne Pleshette,171739,"11,403,529" +"https://m.media-amazon.com/images/M/MV5BOWNlMTJmMWUtYjk0MC00M2U4LWI1ODItZDgxNDZiODFmNjc5XkEyXkFqcGdeQXVyMTAwMzUyOTc@._V1_UX67_CR0,0,67,98_AL_.jpg",Cape Fear,1962,Passed,106 min,"Drama, Thriller",7.7,A lawyer's family is stalked by a man he once helped put in jail.,76,J. Lee Thompson,Gregory Peck,Robert Mitchum,Polly Bergen,Lori Martin,26457, +"https://m.media-amazon.com/images/M/MV5BZjM3ZTAzZDYtZmFjZS00YmQ1LWJlOWEtN2I4MDRmYzY5YmRlL2ltYWdlXkEyXkFqcGdeQXVyMjgyNjk3MzE@._V1_UX67_CR0,0,67,98_AL_.jpg",Peeping Tom,1960,,101 min,"Drama, Horror, Thriller",7.7,"A young man murders women, using a movie camera to film their dying expressions of terror.",,Michael Powell,Karlheinz Böhm,Anna Massey,Moira Shearer,Maxine Audley,31354,"83,957" +"https://m.media-amazon.com/images/M/MV5BMzYyNzU0MTM1OF5BMl5BanBnXkFtZTcwMzE1ODE1NA@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Magnificent Seven,1960,Approved,128 min,"Action, Adventure, Western",7.7,Seven gunfighters are hired by Mexican peasants to liberate their village from oppressive bandits.,74,John Sturges,Yul Brynner,Steve McQueen,Charles Bronson,Eli Wallach,87719,"4,905,000" +"https://m.media-amazon.com/images/M/MV5BNzBiMWRhNzQtMjZhZS00NzFmLWE5YWMtOWY4NzIxMjYzZTEyXkEyXkFqcGdeQXVyMzg2MzE2OTE@._V1_UY98_CR3,0,67,98_AL_.jpg",Les yeux sans visage,1960,,90 min,"Drama, Horror",7.7,"A surgeon causes an accident which leaves his daughter disfigured, and goes to extremes to give her a new face.",90,Georges Franju,Pierre Brasseur,Alida Valli,Juliette Mayniel,Alexandre Rignault,27620,"52,709" +"https://m.media-amazon.com/images/M/MV5BYTExYjM3MDYtMzg4MC00MjU4LTljZjAtYzdlMTFmYTJmYTE4XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Invasion of the Body Snatchers,1956,Approved,80 min,"Drama, Horror, Sci-Fi",7.7,A small-town doctor learns that the population of his community is being replaced by emotionless alien duplicates.,92,Don Siegel,Kevin McCarthy,Dana Wynter,Larry Gates,King Donovan,44839, +"https://m.media-amazon.com/images/M/MV5BMTg2ODcxOTU1OV5BMl5BanBnXkFtZTgwNzA3ODI1MDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Rebel Without a Cause,1955,PG-13,111 min,Drama,7.7,"A rebellious young man with a troubled past comes to a new town, finding friends and enemies.",89,Nicholas Ray,James Dean,Natalie Wood,Sal Mineo,Jim Backus,83363, +"https://m.media-amazon.com/images/M/MV5BYTVlM2JmOGQtNWEwYy00NDQzLWIyZmEtOGZhMzgxZGRjZDA0XkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Ladykillers,1955,,91 min,"Comedy, Crime",7.7,Five oddball criminals planning a bank robbery rent rooms on a cul-de-sac from an octogenarian widow under the pretext that they are classical musicians.,91,Alexander Mackendrick,Alec Guinness,Peter Sellers,Cecil Parker,Herbert Lom,26464, +"https://m.media-amazon.com/images/M/MV5BYmFlNTA1NWItODQxNC00YjFmLWE3ZWYtMzg3YTkwYmMxMjY2XkEyXkFqcGdeQXVyMTMxMTY0OTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Sabrina,1954,Passed,113 min,"Comedy, Drama, Romance",7.7,"A playboy becomes interested in the daughter of his family's chauffeur, but it's his more serious brother who would be the better man for her.",72,Billy Wilder,Humphrey Bogart,Audrey Hepburn,William Holden,Walter Hampden,59415, +"https://m.media-amazon.com/images/M/MV5BMWM1ZDhlM2MtNDNmMi00MDk4LTg5MjgtODE4ODk1MjYxOTIwXkEyXkFqcGdeQXVyNjc0MzMzNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",The Quiet Man,1952,Passed,129 min,"Comedy, Drama, Romance",7.7,"A retired American boxer returns to the village of his birth in Ireland, where he falls for a spirited redhead whose brother is contemptuous of their union.",,John Ford,John Wayne,Maureen O'Hara,Barry Fitzgerald,Ward Bond,34677,"10,550,000" +"https://m.media-amazon.com/images/M/MV5BMTU5NTBmYTAtOTgyYi00NGM0LWE0ODctZjNiYWM5MmIxYzE4XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Day the Earth Stood Still,1951,U,92 min,"Drama, Sci-Fi",7.7,An alien lands and tells the people of Earth that they must live peacefully or be destroyed as a danger to other planets.,,Robert Wise,Michael Rennie,Patricia Neal,Hugh Marlowe,Sam Jaffe,76315, +"https://m.media-amazon.com/images/M/MV5BYzM3YjE2NGMtODY3Zi00NTY0LWE4Y2EtMTE5YzNmM2U1NTg2XkEyXkFqcGdeQXVyMTY5Nzc4MDY@._V1_UX67_CR0,0,67,98_AL_.jpg",The African Queen,1951,PG,105 min,"Adventure, Drama, Romance",7.7,"In WWI Africa, a gin-swilling riverboat captain is persuaded by a strait-laced missionary to use his boat to attack an enemy warship.",91,John Huston,Humphrey Bogart,Katharine Hepburn,Robert Morley,Peter Bull,71481,"536,118" +"https://m.media-amazon.com/images/M/MV5BYWUxMzViZTUtNTYxNy00YjY4LWJmMjYtMzNlOThjNjhiZmZkXkEyXkFqcGdeQXVyMDI2NDg0NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Gilda,1946,Approved,110 min,"Drama, Film-Noir, Romance",7.7,A small-time gambler hired to work in a Buenos Aires casino discovers his employer's new wife is his former lover.,,Charles Vidor,Rita Hayworth,Glenn Ford,George Macready,Joseph Calleia,27991, +"https://m.media-amazon.com/images/M/MV5BMjAxMTI1Njk3OF5BMl5BanBnXkFtZTgwNjkzODk4NTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Fantasia,1940,G,125 min,"Animation, Family, Fantasy",7.7,A collection of animated interpretations of great works of Western classical music.,96,James Algar,Samuel Armstrong,Ford Beebe Jr.,Norman Ferguson,David Hand,88662,"76,408,097" +"https://m.media-amazon.com/images/M/MV5BYjllMmE0Y2YtYWIwZi00OWY1LWJhNWItYzM2MmNiYmFiZmRmXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Invisible Man,1933,TV-PG,71 min,"Horror, Sci-Fi",7.7,"A scientist finds a way of becoming invisible, but in doing so, he becomes murderously insane.",87,James Whale,Claude Rains,Gloria Stuart,William Harrigan,Henry Travers,30683, +"https://m.media-amazon.com/images/M/MV5BODQ0M2Y5M2QtZGIwMC00MzJjLThlMzYtNmE3ZTMzZTYzOGEwXkEyXkFqcGdeQXVyMTkxNjUyNQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Dark Waters,2019,PG-13,126 min,"Biography, Drama, History",7.6,A corporate defense attorney takes on an environmental lawsuit against a chemical company that exposes a lengthy history of pollution.,73,Todd Haynes,Mark Ruffalo,Anne Hathaway,Tim Robbins,Bill Pullman,60408, +"https://m.media-amazon.com/images/M/MV5BMjIwOTA3NDI3MF5BMl5BanBnXkFtZTgwNzIzMzA5NTM@._V1_UX67_CR0,0,67,98_AL_.jpg",Searching,2018,U/A,102 min,"Drama, Mystery, Thriller",7.6,"After his teenage daughter goes missing, a desperate father tries to find clues on her laptop.",71,Aneesh Chaganty,John Cho,Debra Messing,Joseph Lee,Michelle La,140840,"26,020,957" +"https://m.media-amazon.com/images/M/MV5BOTg4ZTNkZmUtMzNlZi00YmFjLTk1MmUtNWQwNTM0YjcyNTNkXkEyXkFqcGdeQXVyNjg2NjQwMDQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Once Upon a Time... in Hollywood,2019,A,161 min,"Comedy, Drama",7.6,A faded television actor and his stunt double strive to achieve fame and success in the final years of Hollywood's Golden Age in 1969 Los Angeles.,83,Quentin Tarantino,Leonardo DiCaprio,Brad Pitt,Margot Robbie,Emile Hirsch,551309,"142,502,728" +"https://m.media-amazon.com/images/M/MV5BNzk2NmU3NmEtMTVhNS00NzJhLWE1M2ItMThjZjI5NWM3YmFmXkEyXkFqcGdeQXVyMjA1MzUyODk@._V1_UY98_CR1,0,67,98_AL_.jpg",Nelyubov,2017,R,127 min,Drama,7.6,A couple going through a divorce must team up to find their son who has disappeared during one of their bitter arguments.,86,Andrey Zvyagintsev,Maryana Spivak,Aleksey Rozin,Matvey Novikov,Marina Vasileva,29765,"566,356" +"https://m.media-amazon.com/images/M/MV5BMjg4ZmY1MmItMjFjOS00ZTg2LWJjNDYtNDM2YmM2NzhiNmZhXkEyXkFqcGdeQXVyNTAzMTY4MDA@._V1_UX67_CR0,0,67,98_AL_.jpg",The Florida Project,2017,A,111 min,Drama,7.6,"Set over one summer, the film follows precocious six-year-old Moonee as she courts mischief and adventure with her ragtag playmates and bonds with her rebellious but caring mother, all while living in the shadows of Walt Disney World.",92,Sean Baker,Brooklynn Prince,Bria Vinaite,Willem Dafoe,Christopher Rivera,95181,"5,904,366" +"https://m.media-amazon.com/images/M/MV5BYmM4YzA5NjUtZGEyOS00YzllLWJmM2UtZjhhNmJhM2E1NjUxXkEyXkFqcGdeQXVyMTkxNjUyNQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Just Mercy,2019,A,137 min,"Biography, Crime, Drama",7.6,World-renowned civil rights defense attorney Bryan Stevenson works to free a wrongly condemned death row prisoner.,68,Destin Daniel Cretton,Michael B. Jordan,Jamie Foxx,Brie Larson,Charlie Pye Jr.,46739, +"https://m.media-amazon.com/images/M/MV5BMjQ2NDU3NDE0M15BMl5BanBnXkFtZTgwMjA3OTg0MDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Gifted,2017,PG-13,101 min,Drama,7.6,"Frank, a single man raising his child prodigy niece Mary, is drawn into a custody battle with his mother.",60,Marc Webb,Chris Evans,Mckenna Grace,Lindsay Duncan,Octavia Spencer,99643,"24,801,212" +"https://m.media-amazon.com/images/M/MV5BOWVmZGQ0MGYtMDI1Yy00MDkxLWJiYjQtMmZjZmQ0NDFmMDRhXkEyXkFqcGdeQXVyNjg3MDMxNzU@._V1_UX67_CR0,0,67,98_AL_.jpg",The Peanut Butter Falcon,2019,PG-13,97 min,"Adventure, Comedy, Drama",7.6,Zak runs away from his care home to make his dream of becoming a wrestler come true.,70,Tyler Nilson,Michael Schwartz,Zack Gottsagen,Ann Owens,Dakota Johnson,66346,"13,122,642" +"https://m.media-amazon.com/images/M/MV5BMTc5NzQzNjk2NF5BMl5BanBnXkFtZTgwODU0MjI5NjE@._V1_UY98_CR0,0,67,98_AL_.jpg",Victoria,2015,,138 min,"Crime, Drama, Romance",7.6,A young Spanish woman who has recently moved to Berlin finds her flirtation with a local guy turn potentially deadly as their night out with his friends reveals a dangerous secret.,77,Sebastian Schipper,Laia Costa,Frederick Lau,Franz Rogowski,Burak Yigit,52903, +"https://m.media-amazon.com/images/M/MV5BMTkwODUzODA0OV5BMl5BanBnXkFtZTgwMTA3ODkxNzE@._V1_UY98_CR0,0,67,98_AL_.jpg",Mustang,2015,PG-13,97 min,Drama,7.6,"When five orphan girls are seen innocently playing with boys on a beach, their scandalized conservative guardians confine them while forced marriages are arranged.",83,Deniz Gamze Ergüven,Günes Sensoy,Doga Zeynep Doguslu,Tugba Sunguroglu,Elit Iscan,35785,"845,464" +"https://m.media-amazon.com/images/M/MV5BNjM0NTc0NzItM2FlYS00YzEwLWE0YmUtNTA2ZWIzODc2OTgxXkEyXkFqcGdeQXVyNTgwNzIyNzg@._V1_UX67_CR0,0,67,98_AL_.jpg",Guardians of the Galaxy Vol. 2,2017,UA,136 min,"Action, Adventure, Comedy",7.6,"The Guardians struggle to keep together as a team while dealing with their personal family issues, notably Star-Lord's encounter with his father the ambitious celestial being Ego.",67,James Gunn,Chris Pratt,Zoe Saldana,Dave Bautista,Vin Diesel,569974,"389,813,101" +"https://m.media-amazon.com/images/M/MV5BMjM3MjQ1MzkxNl5BMl5BanBnXkFtZTgwODk1ODgyMjI@._V1_UX67_CR0,0,67,98_AL_.jpg",Baby Driver,2017,UA,113 min,"Action, Crime, Drama",7.6,"After being coerced into working for a crime boss, a young getaway driver finds himself taking part in a heist doomed to fail.",86,Edgar Wright,Ansel Elgort,Jon Bernthal,Jon Hamm,Eiza González,439406,"107,825,862" +"https://m.media-amazon.com/images/M/MV5BYWFlOWI3YTMtYTk3NS00YWQ2LTlmYTMtZjk0ZDk4Y2NjODI0XkEyXkFqcGdeQXVyNTQxNTQ4Mg@@._V1_UX67_CR0,0,67,98_AL_.jpg",Only the Brave,2017,UA,134 min,"Action, Biography, Drama",7.6,"Based on the true story of the Granite Mountain Hotshots, a group of elite firefighters who risk everything to protect a town from a historic wildfire.",72,Joseph Kosinski,Josh Brolin,Miles Teller,Jeff Bridges,Jennifer Connelly,58371,"18,340,051" +"https://m.media-amazon.com/images/M/MV5BMjIxOTI0MjU5NV5BMl5BanBnXkFtZTgwNzM4OTk4NTE@._V1_UX67_CR0,0,67,98_AL_.jpg",Bridge of Spies,2015,UA,142 min,"Drama, History, Thriller",7.6,"During the Cold War, an American lawyer is recruited to defend an arrested Soviet spy in court, and then help the CIA facilitate an exchange of the spy for the Soviet captured American U2 spy plane pilot, Francis Gary Powers.",81,Steven Spielberg,Tom Hanks,Mark Rylance,Alan Alda,Amy Ryan,287659,"72,313,754" +"https://m.media-amazon.com/images/M/MV5BMTEzNzY0OTg0NTdeQTJeQWpwZ15BbWU4MDU3OTg3MjUz._V1_UX67_CR0,0,67,98_AL_.jpg",Incredibles 2,2018,UA,118 min,"Animation, Action, Adventure",7.6,The Incredibles family takes on a new mission which involves a change in family roles: Bob Parr (Mr. Incredible) must manage the house while his wife Helen (Elastigirl) goes out to save the world.,80,Brad Bird,Craig T. Nelson,Holly Hunter,Sarah Vowell,Huck Milner,250057,"608,581,744" +"https://m.media-amazon.com/images/M/MV5BMjI4MzU5NTExNF5BMl5BanBnXkFtZTgwNzY1MTEwMDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Moana,2016,U,107 min,"Animation, Adventure, Comedy",7.6,"In Ancient Polynesia, when a terrible curse incurred by the Demigod Maui reaches Moana's island, she answers the Ocean's call to seek out the Demigod to set things right.",81,Ron Clements,John Musker,Don Hall,Chris Williams,Auli'i Cravalho,272784,"248,757,044" +"https://m.media-amazon.com/images/M/MV5BMjA5NjM3NTk1M15BMl5BanBnXkFtZTgwMzg1MzU2NjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Sicario,2015,A,121 min,"Action, Crime, Drama",7.6,An idealistic FBI agent is enlisted by a government task force to aid in the escalating war against drugs at the border area between the U.S. and Mexico.,82,Denis Villeneuve,Emily Blunt,Josh Brolin,Benicio Del Toro,Jon Bernthal,371291,"46,889,293" +"https://m.media-amazon.com/images/M/MV5BNmZkYjQzY2QtNjdkNC00YjkzLTk5NjUtY2MyNDNiYTBhN2M2XkEyXkFqcGdeQXVyMjMwNDgzNjc@._V1_UX67_CR0,0,67,98_AL_.jpg",Creed,2015,A,133 min,"Drama, Sport",7.6,"The former World Heavyweight Champion Rocky Balboa serves as a trainer and mentor to Adonis Johnson, the son of his late friend and former rival Apollo Creed.",82,Ryan Coogler,Michael B. Jordan,Sylvester Stallone,Tessa Thompson,Phylicia Rashad,247666,"109,767,581" +"https://m.media-amazon.com/images/M/MV5BYTYxZjQ2YTktNmVkMC00ZTY4LThkZmItMDc4MTJiYjVhZjM0L2ltYWdlXkEyXkFqcGdeQXVyMjgyNjk3MzE@._V1_UY98_CR1,0,67,98_AL_.jpg",Leviafan,2014,R,140 min,"Crime, Drama",7.6,"In a Russian coastal town, Kolya is forced to fight the corrupt mayor when he is told that his house will be demolished. He recruits a lawyer friend to help, but the man's arrival brings further misfortune for Kolya and his family.",92,Andrey Zvyagintsev,Aleksey Serebryakov,Elena Lyadova,Roman Madyanov,Vladimir Vdovichenkov,49397,"1,092,800" +"https://m.media-amazon.com/images/M/MV5BMTg4NDA1OTA5NF5BMl5BanBnXkFtZTgwMDQ2MDM5ODE@._V1_UX67_CR0,0,67,98_AL_.jpg",Hell or High Water,2016,R,102 min,"Action, Crime, Drama",7.6,A divorced father and his ex-con older brother resort to a desperate scheme in order to save their family's ranch in West Texas.,88,David Mackenzie,Chris Pine,Ben Foster,Jeff Bridges,Gil Birmingham,204175,"26,862,450" +"https://m.media-amazon.com/images/M/MV5BMjA5ODgyNzcxMV5BMl5BanBnXkFtZTgwMzkzOTYzMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Philomena,2013,PG-13,98 min,"Biography, Comedy, Drama",7.6,"A world-weary political journalist picks up the story of a woman's search for her son, who was taken away from her decades ago after she became pregnant and was forced to live in a convent.",77,Stephen Frears,Judi Dench,Steve Coogan,Sophie Kennedy Clark,Mare Winningham,94212,"37,707,719" +"https://m.media-amazon.com/images/M/MV5BMTgwODk3NDc1N15BMl5BanBnXkFtZTgwNTc1NjQwMjE@._V1_UX67_CR0,0,67,98_AL_.jpg",Dawn of the Planet of the Apes,2014,UA,130 min,"Action, Adventure, Drama",7.6,A growing nation of genetically evolved apes led by Caesar is threatened by a band of human survivors of the devastating virus unleashed a decade earlier.,79,Matt Reeves,Gary Oldman,Keri Russell,Andy Serkis,Kodi Smit-McPhee,411599,"208,545,589" +"https://m.media-amazon.com/images/M/MV5BNGMxZjFkN2EtMDRiMS00ZTBjLWI0M2MtZWUyYjFhZGViZDJlXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",El cuerpo,2012,,112 min,"Mystery, Thriller",7.6,A detective searches for the body of a femme fatale which has gone missing from a morgue.,,Oriol Paulo,Jose Coronado,Hugo Silva,Belén Rueda,Aura Garrido,57549, +"https://m.media-amazon.com/images/M/MV5BZGIxODNjM2YtZjA5Mi00MjA5LTk2YjItODE0OWI5NThjNTBmXkEyXkFqcGdeQXVyNzQ1ODk3MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Serbuan maut,2011,A,101 min,"Action, Thriller",7.6,A S.W.A.T. team becomes trapped in a tenement run by a ruthless mobster and his army of killers and thugs.,73,Gareth Evans,Iko Uwais,Ananda George,Ray Sahetapy,Donny Alamsyah,190531,"4,105,123" +"https://m.media-amazon.com/images/M/MV5BMjMxNjU0ODU5Ml5BMl5BanBnXkFtZTcwNjI4MzAyOA@@._V1_UX67_CR0,0,67,98_AL_.jpg",End of Watch,2012,A,109 min,"Action, Crime, Drama",7.6,"Shot documentary-style, this film follows the daily grind of two young police officers in LA who are partners and friends, and what happens when they meet criminal forces greater than themselves.",68,David Ayer,Jake Gyllenhaal,Michael Peña,Anna Kendrick,America Ferrera,228132,"41,003,371" +"https://m.media-amazon.com/images/M/MV5BZDY3ZGI0ZDAtMThlNy00MzAxLTg4YjAtNjkwYTkxNmQ4MjdlXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Kari-gurashi no Arietti,2010,U,94 min,"Animation, Adventure, Family",7.6,"The Clock family are four-inch-tall people who live anonymously in another family's residence, borrowing simple items to make their home. Life changes for the Clocks when their teenage daughter, Arrietty, is discovered.",80,Hiromasa Yonebayashi,Amy Poehler,Mirai Shida,Ryûnosuke Kamiki,Tatsuya Fujiwara,80939,"19,202,743" +"https://m.media-amazon.com/images/M/MV5BNmE5ZmE3OGItNTdlNC00YmMxLWEzNjctYzAwOGQ5ODg0OTI0XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",A Star Is Born,2018,UA,136 min,"Drama, Music, Romance",7.6,A musician helps a young singer find fame as age and alcoholism send his own career into a downward spiral.,88,Bradley Cooper,Lady Gaga,Bradley Cooper,Sam Elliott,Greg Grunberg,334312,"215,288,866" +"https://m.media-amazon.com/images/M/MV5BODhkZDIzNjgtOTA5ZS00MmMzLWFkNjYtM2Y2MzFjN2FkNjAzL2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",True Grit,2010,PG-13,110 min,"Drama, Western",7.6,A stubborn teenager enlists the help of a tough U.S. Marshal to track down her father's murderer.,80,Ethan Coen,Joel Coen,Jeff Bridges,Matt Damon,Hailee Steinfeld,311822,"171,243,005" +"https://m.media-amazon.com/images/M/MV5BNDY2OTE5MzE0Nl5BMl5BanBnXkFtZTcwNDAyOTc2NA@@._V1_UX67_CR0,0,67,98_AL_.jpg",Hævnen,2010,R,118 min,"Drama, Romance",7.6,"The lives of two Danish families cross each other, and an extraordinary but risky friendship comes into bud. But loneliness, frailty and sorrow lie in wait.",65,Susanne Bier,Mikael Persbrandt,Trine Dyrholm,Markus Rygaard,Wil Johnson,38491,"1,008,098" +"https://m.media-amazon.com/images/M/MV5BMTY3NjY0MTQ0Nl5BMl5BanBnXkFtZTcwMzQ2MTc0Mw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Despicable Me,2010,U,95 min,"Animation, Comedy, Crime",7.6,"When a criminal mastermind uses a trio of orphan girls as pawns for a grand scheme, he finds their love is profoundly changing him for the better.",72,Pierre Coffin,Chris Renaud,Steve Carell,Jason Segel,Russell Brand,500851,"251,513,985" +"https://m.media-amazon.com/images/M/MV5BNjg3ODQyNTIyN15BMl5BanBnXkFtZTcwMjUzNzM5NQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",50/50,2011,R,100 min,"Comedy, Drama, Romance",7.6,"Inspired by a true story, a comedy centered on a 27-year-old guy who learns of his cancer diagnosis and his subsequent struggle to beat the disease.",72,Jonathan Levine,Joseph Gordon-Levitt,Seth Rogen,Anna Kendrick,Bryce Dallas Howard,315426,"35,014,192" +"https://m.media-amazon.com/images/M/MV5BMTMzNzEzMDYxM15BMl5BanBnXkFtZTcwMTc0NTMxMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Kick-Ass,2010,UA,117 min,"Action, Comedy, Crime",7.6,"Dave Lizewski is an unnoticed high school student and comic book fan who one day decides to become a superhero, even though he has no powers, training or meaningful reason to do so.",66,Matthew Vaughn,Aaron Taylor-Johnson,Nicolas Cage,Chloë Grace Moretz,Garrett M. Brown,524081,"48,071,303" +"https://m.media-amazon.com/images/M/MV5BMjI2ODE4ODAtMDA3MS00ODNkLTg4N2EtOGU0YjZmNGY4NjZlXkEyXkFqcGdeQXVyMTY5MDE5NA@@._V1_UY98_CR0,0,67,98_AL_.jpg",Celda 211,2009,,113 min,"Action, Adventure, Crime",7.6,"The story of two men on different sides of a prison riot -- the inmate leading the rebellion and the young guard trapped in the revolt, who poses as a prisoner in a desperate attempt to survive the ordeal.",,Daniel Monzón,Luis Tosar,Alberto Ammann,Antonio Resines,Manuel Morón,63882, +"https://m.media-amazon.com/images/M/MV5BMjAxOTU3Mzc1M15BMl5BanBnXkFtZTcwMzk1ODUzNg@@._V1_UX67_CR0,0,67,98_AL_.jpg",Moneyball,2011,PG-13,133 min,"Biography, Drama, Sport",7.6,Oakland A's general manager Billy Beane's successful attempt to assemble a baseball team on a lean budget by employing computer-generated analysis to acquire new players.,87,Bennett Miller,Brad Pitt,Robin Wright,Jonah Hill,Philip Seymour Hoffman,369529,"75,605,492" +"https://m.media-amazon.com/images/M/MV5BYmFmNjY5NDYtZjlhNi00YjQ5LTgzNzctNWRiNWUzNmIyNjc4XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UY98_CR0,0,67,98_AL_.jpg",La piel que habito,2011,R,120 min,"Drama, Horror, Thriller",7.6,"A brilliant plastic surgeon, haunted by past tragedies, creates a type of synthetic skin that withstands any kind of damage. His guinea pig: a mysterious and volatile woman who holds the key to his obsession.",70,Pedro Almodóvar,Antonio Banderas,Elena Anaya,Jan Cornet,Marisa Paredes,138959,"3,185,812" +"https://m.media-amazon.com/images/M/MV5BMTU5MDg0NTQ1N15BMl5BanBnXkFtZTcwMjA4Mjg3Mg@@._V1_UY98_CR1,0,67,98_AL_.jpg",Zombieland,2009,A,88 min,"Adventure, Comedy, Fantasy",7.6,"A shy student trying to reach his family in Ohio, a gun-toting tough guy trying to find the last Twinkie, and a pair of sisters trying to get to an amusement park join forces to travel across a zombie-filled America.",73,Ruben Fleischer,Jesse Eisenberg,Emma Stone,Woody Harrelson,Abigail Breslin,520041,"75,590,286" +"https://m.media-amazon.com/images/M/MV5BMzc0ZmUyZjAtZThkMi00ZDY5LTg5YjctYmUwM2FiYjMyMDI5XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Die Welle,2008,,107 min,"Drama, Thriller",7.6,A high school teacher's experiment to demonstrate to his students what life is like under a dictatorship spins horribly out of control when he forms a social unit with a life of its own.,,Dennis Gansel,Jürgen Vogel,Frederick Lau,Max Riemelt,Jennifer Ulrich,102742, +"https://m.media-amazon.com/images/M/MV5BMTg0NjEwNjUxM15BMl5BanBnXkFtZTcwMzk0MjQ5Mg@@._V1_UX67_CR0,0,67,98_AL_.jpg",Sherlock Holmes,2009,PG-13,128 min,"Action, Adventure, Mystery",7.6,Detective Sherlock Holmes and his stalwart partner Watson engage in a battle of wits and brawn with a nemesis whose plot is a threat to all of England.,57,Guy Ritchie,Robert Downey Jr.,Jude Law,Rachel McAdams,Mark Strong,583158,"209,028,679" +"https://m.media-amazon.com/images/M/MV5BMjEzOTE3ODM3OF5BMl5BanBnXkFtZTcwMzYyODI4Mg@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Blind Side,2009,UA,129 min,"Biography, Drama, Sport",7.6,"The story of Michael Oher, a homeless and traumatized boy who became an All-American football player and first-round NFL draft pick with the help of a caring woman and her family.",53,John Lee Hancock,Quinton Aaron,Sandra Bullock,Tim McGraw,Jae Head,293266,"255,959,475" +"https://m.media-amazon.com/images/M/MV5BMTIzNTg3NzkzNV5BMl5BanBnXkFtZTcwNzMwMjU2MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Visitor,2007,PG-13,104 min,Drama,7.6,A college professor travels to New York City to attend a conference and finds a young couple living in his apartment.,79,Tom McCarthy,Richard Jenkins,Haaz Sleiman,Danai Gurira,Hiam Abbass,41544,"9,422,422" +"https://m.media-amazon.com/images/M/MV5BMTU0NzY0MTY5OF5BMl5BanBnXkFtZTcwODY3MDEwMg@@._V1_UY98_CR3,0,67,98_AL_.jpg",Seven Pounds,2008,UA,123 min,Drama,7.6,A man with a fateful secret embarks on an extraordinary journey of redemption by forever changing the lives of seven strangers.,36,Gabriele Muccino,Will Smith,Rosario Dawson,Woody Harrelson,Michael Ealy,286770,"69,951,824" +"https://m.media-amazon.com/images/M/MV5BMTcwMzU0OTY3NF5BMl5BanBnXkFtZTYwNzkwNjg2._V1_UX67_CR0,0,67,98_AL_.jpg",Eastern Promises,2007,R,100 min,"Action, Crime, Drama",7.6,A teenager who dies during childbirth leaves clues in her journal that could tie her child to a rape involving a violent Russian mob family.,82,David Cronenberg,Naomi Watts,Viggo Mortensen,Armin Mueller-Stahl,Josef Altin,227760,"17,114,882" +"https://m.media-amazon.com/images/M/MV5BMjkyMTE1OTYwNF5BMl5BanBnXkFtZTcwMDIxODYzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",Stardust,2007,U,127 min,"Adventure, Family, Fantasy",7.6,"In a countryside town bordering on a magical land, a young man makes a promise to his beloved that he'll retrieve a fallen star by venturing into the magical realm.",66,Matthew Vaughn,Charlie Cox,Claire Danes,Sienna Miller,Ian McKellen,255036,"38,634,938" +"https://m.media-amazon.com/images/M/MV5BMjEzMjEzNTIzOF5BMl5BanBnXkFtZTcwMTg2MjAyMw@@._V1_UY98_CR0,0,67,98_AL_.jpg",The Secret of Kells,2009,,71 min,"Animation, Adventure, Family",7.6,"A young boy in a remote medieval outpost under siege from barbarian raids is beckoned to adventure when a celebrated master illuminator arrives with an ancient book, brimming with secret wisdom and powers.",81,Tomm Moore,Nora Twomey,Evan McGuire,Brendan Gleeson,Mick Lally,31779,"686,383" +"https://m.media-amazon.com/images/M/MV5BYjc4MjA2ZDgtOGY3YS00NDYzLTlmNTEtYWMxMzcwZjgzYWNjXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Inside Man,2006,R,129 min,"Crime, Drama, Mystery",7.6,"A police detective, a bank robber, and a high-power broker enter high-stakes negotiations after the criminal's brilliant heist spirals into a hostage situation.",76,Spike Lee,Denzel Washington,Clive Owen,Jodie Foster,Christopher Plummer,339757,"88,513,495" +"https://m.media-amazon.com/images/M/MV5BYmM2NDNiNGItMTRhMi00ZDA2LTgzOWMtZTE2ZjFhMDQ2M2U5XkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Gone Baby Gone,2007,R,114 min,"Crime, Drama, Mystery",7.6,"Two Boston area detectives investigate a little girl's kidnapping, which ultimately turns into a crisis both professionally and personally.",72,Ben Affleck,Morgan Freeman,Ed Harris,Casey Affleck,Michelle Monaghan,250590,"20,300,218" +"https://m.media-amazon.com/images/M/MV5BOTBmZDZkNWYtODIzYi00N2Y4LWFjMmMtNmM1OGYyNGVhYzUzXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",La Vie En Rose,2007,PG-13,140 min,"Biography, Drama, Music",7.6,"Biopic of the iconic French singer Édith Piaf. Raised by her grandmother in a brothel, she was discovered while singing on a street corner at the age of 19. Despite her success, Piaf's life was filled with tragedy.",66,Olivier Dahan,Marion Cotillard,Sylvie Testud,Pascal Greggory,Emmanuelle Seigner,82781,"10,301,706" +"https://m.media-amazon.com/images/M/MV5BMTI5MjA2Mzk2M15BMl5BanBnXkFtZTcwODY1MDUzMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Huo Yuan Jia,2006,PG-13,104 min,"Action, Biography, Drama",7.6,"A biography of Chinese Martial Arts Master Huo Yuanjia, who is the founder and spiritual guru of the Jin Wu Sports Federation.",70,Ronny Yu,Jet Li,Li Sun,Yong Dong,Yun Qu,72863,"24,633,730" +"https://m.media-amazon.com/images/M/MV5BY2VkMzZlZDAtNTkzNS00MDIzLWFmOTctMWQwZjQ1OWJiYzQ1XkEyXkFqcGdeQXVyNTIzOTk5ODM@._V1_UY98_CR1,0,67,98_AL_.jpg",The Illusionist,2006,U,110 min,"Drama, Fantasy, Mystery",7.6,"In turn-of-the-century Vienna, a magician uses his abilities to secure the love of a woman far above his social standing.",68,Neil Burger,Edward Norton,Jessica Biel,Paul Giamatti,Rufus Sewell,354728,"39,868,642" +"https://m.media-amazon.com/images/M/MV5BMTI5Mzk1MDc2M15BMl5BanBnXkFtZTcwMjIzMDA0MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Dead Man's Shoes,2004,,90 min,"Crime, Drama, Thriller",7.6,A disaffected soldier returns to his hometown to get even with the thugs who brutalized his mentally-challenged brother years ago.,52,Shane Meadows,Paddy Considine,Gary Stretch,Toby Kebbell,Stuart Wolfenden,49728,"6,013" +"https://m.media-amazon.com/images/M/MV5BNzU3NDg4NTAyNV5BMl5BanBnXkFtZTcwOTg2ODg1Mg@@._V1_UX67_CR0,0,67,98_AL_.jpg",Harry Potter and the Half-Blood Prince,2009,UA,153 min,"Action, Adventure, Family",7.6,"As Harry Potter begins his sixth year at Hogwarts, he discovers an old book marked as ""the property of the Half-Blood Prince"" and begins to learn more about Lord Voldemort's dark past.",78,David Yates,Daniel Radcliffe,Emma Watson,Rupert Grint,Michael Gambon,474827,"301,959,197" +"https://m.media-amazon.com/images/M/MV5BNWMxYTZlOTUtZDExMi00YzZmLTkwYTMtZmM2MmRjZmQ3OGY4XkEyXkFqcGdeQXVyMTAwMzUyMzUy._V1_UX67_CR0,0,67,98_AL_.jpg",300,2006,A,117 min,"Action, Drama",7.6,King Leonidas of Sparta and a force of 300 men fight the Persians at Thermopylae in 480 B.C.,52,Zack Snyder,Gerard Butler,Lena Headey,David Wenham,Dominic West,732876,"210,614,939" +"https://m.media-amazon.com/images/M/MV5BMjRjOTMwMDEtNTY4NS00OWRjLWI4ZWItZDgwYmZhMzlkYzgxXkEyXkFqcGdeQXVyODIxOTg5MTc@._V1_UY98_CR1,0,67,98_AL_.jpg",Match Point,2005,R,124 min,"Drama, Romance, Thriller",7.6,"At a turning point in his life, a former tennis pro falls for an actress who happens to be dating his friend and soon-to-be brother-in-law.",72,Woody Allen,Scarlett Johansson,Jonathan Rhys Meyers,Emily Mortimer,Matthew Goode,206294,"23,089,926" +"https://m.media-amazon.com/images/M/MV5BY2IzNGNiODgtOWYzOS00OTI0LTgxZTUtOTA5OTQ5YmI3NGUzXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Watchmen,2009,A,162 min,"Action, Drama, Mystery",7.6,"In 1985 where former superheroes exist, the murder of a colleague sends active vigilante Rorschach into his own sprawling investigation, uncovering something that could completely change the course of history as we know it.",56,Zack Snyder,Jackie Earle Haley,Patrick Wilson,Carla Gugino,Malin Akerman,500799,"107,509,799" +"https://m.media-amazon.com/images/M/MV5BMTYzZWE3MDAtZjZkMi00MzhlLTlhZDUtNmI2Zjg3OWVlZWI0XkEyXkFqcGdeQXVyNDk3NzU2MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",Lord of War,2005,R,122 min,"Action, Crime, Drama",7.6,An arms dealer confronts the morality of his work as he is being chased by an INTERPOL Agent.,62,Andrew Niccol,Nicolas Cage,Ethan Hawke,Jared Leto,Bridget Moynahan,294140,"24,149,632" +"https://m.media-amazon.com/images/M/MV5BMzQ2ZTBhNmEtZDBmYi00ODU0LTgzZmQtNmMxM2M4NzM1ZjE4XkEyXkFqcGdeQXVyNjE5MjUyOTM@._V1_UX67_CR0,0,67,98_AL_.jpg",Saw,2004,UA,103 min,"Horror, Mystery, Thriller",7.6,"Two strangers awaken in a room with no recollection of how they got there, and soon discover they're pawns in a deadly game perpetrated by a notorious serial killer.",46,James Wan,Cary Elwes,Leigh Whannell,Danny Glover,Ken Leung,379020,"56,000,369" +"https://m.media-amazon.com/images/M/MV5BMjA0MjIyOTI3MF5BMl5BanBnXkFtZTcwODM5NTY5MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg","Synecdoche, New York",2008,R,124 min,Drama,7.6,"A theatre director struggles with his work, and the women in his life, as he creates a life-size replica of New York City inside a warehouse as part of his new play.",67,Charlie Kaufman,Philip Seymour Hoffman,Samantha Morton,Michelle Williams,Catherine Keener,83158,"3,081,925" +"https://m.media-amazon.com/images/M/MV5BMTgxMjQ4NzE5OF5BMl5BanBnXkFtZTcwNzkwOTkyMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Mysterious Skin,2004,R,105 min,Drama,7.6,"A teenage hustler and a young man obsessed with alien abductions cross paths, together discovering a horrible, liberating truth.",73,Gregg Araki,Brady Corbet,Joseph Gordon-Levitt,Elisabeth Shue,Chase Ellison,65939,"697,181" +"https://m.media-amazon.com/images/M/MV5BNjIwOGJhY2QtMTA5Yi00MDhlLWE5OTgtYmIzZDNlM2UwZjMyXkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_UX67_CR0,0,67,98_AL_.jpg",Jeux d'enfants,2003,R,93 min,"Comedy, Drama, Romance",7.6,"As adults, best friends Julien and Sophie continue the odd game they started as children -- a fearless competition to outdo one another with daring and outrageous stunts. While they often act out to relieve one another's pain, their game might be a way to avoid the fact that they are truly meant for one another.",45,Yann Samuell,Guillaume Canet,Marion Cotillard,Thibault Verhaeghe,Joséphine Lebas-Joly,67360,"548,707" +"https://m.media-amazon.com/images/M/MV5BZWI4ZTgwMzktNjk3Yy00OTlhLTg3YTAtMTA1MWVlMWJiOTRiXkEyXkFqcGdeQXVyMTAwMzUyOTc@._V1_UX67_CR0,0,67,98_AL_.jpg",Un long dimanche de fiançailles,2004,U,133 min,"Drama, Mystery, Romance",7.6,"Tells the story of a young woman's relentless search for her fiancé, who has disappeared from the trenches of the Somme during World War One.",76,Jean-Pierre Jeunet,Audrey Tautou,Gaspard Ulliel,Jodie Foster,Dominique Pinon,70925,"6,167,817" +"https://m.media-amazon.com/images/M/MV5BMTUzNDgyMzg3Ml5BMl5BanBnXkFtZTcwMzIxNTAwMQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Station Agent,2003,R,89 min,"Comedy, Drama",7.6,"When his only friend dies, a man born with dwarfism moves to rural New Jersey to live a life of solitude, only to meet a chatty hot dog vendor and a woman dealing with her own personal loss.",81,Tom McCarthy,Peter Dinklage,Patricia Clarkson,Bobby Cannavale,Paul Benjamin,67370,"5,739,376" +"https://m.media-amazon.com/images/M/MV5BMjA4MjI2OTM5N15BMl5BanBnXkFtZTcwNDA1NjUzMw@@._V1_UX67_CR0,0,67,98_AL_.jpg",21 Grams,2003,UA,124 min,"Crime, Drama, Thriller",7.6,"A freak accident brings together a critically ill mathematician, a grieving mother, and a born-again ex-con.",70,Alejandro G. Iñárritu,Sean Penn,Benicio Del Toro,Naomi Watts,Danny Huston,224545,"16,290,476" +"https://m.media-amazon.com/images/M/MV5BYmNlNDVjMWUtZDZjNS00YTBmLWE3NGUtNDcxMzE0YTQ2ODMxXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Boksuneun naui geot,2002,R,129 min,"Crime, Drama, Thriller",7.6,"A recently laid off factory worker kidnaps his former boss' friend's daughter, hoping to use the ransom money to pay for his sister's kidney transplant.",56,Chan-wook Park,Kang-ho Song,Shin Ha-kyun,Bae Doona,Ji-Eun Lim,62659,"45,289" +"https://m.media-amazon.com/images/M/MV5BMTMxNzYzNzUzMV5BMl5BanBnXkFtZTYwNjcwMjE3._V1_UX67_CR0,0,67,98_AL_.jpg",Finding Neverland,2004,U,106 min,"Biography, Drama, Family",7.6,The story of Sir J.M. Barrie's friendship with a family who inspired him to create Peter Pan.,67,Marc Forster,Johnny Depp,Kate Winslet,Julie Christie,Radha Mitchell,198677,"51,680,613" +"https://m.media-amazon.com/images/M/MV5BNmE0YjdlYTktMTU4Ni00Mjk2LWI3NWMtM2RjNmFiOTk4YjYxL2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR1,0,67,98_AL_.jpg",25th Hour,2002,R,135 min,Drama,7.6,"Cornered by the DEA, convicted New York drug dealer Montgomery Brogan reevaluates his life in the 24 remaining hours before facing a seven-year jail term.",68,Spike Lee,Edward Norton,Barry Pepper,Philip Seymour Hoffman,Rosario Dawson,169708,"13,060,843" +"https://m.media-amazon.com/images/M/MV5BODNiZmY2MWUtMjFhMy00ZmM2LTg2MjYtNWY1OTY5NGU2MjdjL2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR0,0,67,98_AL_.jpg",The Butterfly Effect,2004,U,113 min,"Drama, Sci-Fi, Thriller",7.6,"Evan Treborn suffers blackouts during significant events of his life. As he grows up, he finds a way to remember these lost memories and a supernatural way to alter his life by reading his journal.",30,Eric Bress,J. Mackye Gruber,Ashton Kutcher,Amy Smart,Melora Walters,451479,"57,938,693" +"https://m.media-amazon.com/images/M/MV5BYTFkM2ViMmQtZmI5NS00MjQ2LWEyN2EtMTI1ZmNlZDU3MTZjXkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",28 Days Later...,2002,A,113 min,"Drama, Horror, Sci-Fi",7.6,"Four weeks after a mysterious, incurable virus spreads throughout the UK, a handful of survivors try to find sanctuary.",73,Danny Boyle,Cillian Murphy,Naomie Harris,Christopher Eccleston,Alex Palmer,376853,"45,064,915" +"https://m.media-amazon.com/images/M/MV5BMDc2MGYwYzAtNzE2Yi00YmU3LTkxMDUtODk2YjhiNDM5NDIyXkEyXkFqcGdeQXVyMTEwNDU1MzEy._V1_UX67_CR0,0,67,98_AL_.jpg",Batoru rowaiaru,2000,,114 min,"Action, Adventure, Drama",7.6,"In the future, the Japanese government captures a class of ninth-grade students and forces them to kill each other under the revolutionary ""Battle Royale"" act.",81,Kinji Fukasaku,Tatsuya Fujiwara,Aki Maeda,Tarô Yamamoto,Takeshi Kitano,169091, +"https://m.media-amazon.com/images/M/MV5BYmUzODQ5MGItZTZlNy00MDBhLWIxMmItMjg4Y2QyNDFlMWQ2XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",The Royal Tenenbaums,2001,A,110 min,"Comedy, Drama",7.6,The eccentric members of a dysfunctional family reluctantly gather under the same roof for various reasons.,76,Wes Anderson,Gene Hackman,Gwyneth Paltrow,Anjelica Huston,Ben Stiller,266842,"52,364,010" +"https://m.media-amazon.com/images/M/MV5BNDhjMzc3ZTgtY2Y4MC00Y2U3LWFiMDctZGM3MmM4N2YzNDQ5XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Y tu mamá también,2001,A,106 min,Drama,7.6,"In Mexico, two teenage boys and an attractive older woman embark on a road trip and learn a thing or two about life, friendship, sex, and each other.",88,Alfonso Cuarón,Maribel Verdú,Gael García Bernal,Daniel Giménez Cacho,Ana López Mercado,115827,"13,622,333" +"https://m.media-amazon.com/images/M/MV5BNjQ3NWNlNmQtMTE5ZS00MDdmLTlkZjUtZTBlM2UxMGFiMTU3XkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_UX67_CR0,0,67,98_AL_.jpg",Harry Potter and the Sorcerer's Stone,2001,U,152 min,"Adventure, Family, Fantasy",7.6,"An orphaned boy enrolls in a school of wizardry, where he learns the truth about himself, his family and the terrible evil that haunts the magical world.",64,Chris Columbus,Daniel Radcliffe,Rupert Grint,Richard Harris,Maggie Smith,658185,"317,575,550" +"https://m.media-amazon.com/images/M/MV5BMTAxMDE4Mzc3ODNeQTJeQWpwZ15BbWU4MDY2Mjg4MDcx._V1_UX67_CR0,0,67,98_AL_.jpg",The Others,2001,PG-13,101 min,"Horror, Mystery, Thriller",7.6,A woman who lives in her darkened old family house with her two photosensitive children becomes convinced that the home is haunted.,74,Alejandro Amenábar,Nicole Kidman,Christopher Eccleston,Fionnula Flanagan,Alakina Mann,337651,"96,522,687" +"https://m.media-amazon.com/images/M/MV5BYjg5ZDkzZWEtZDQ2ZC00Y2ViLThhMzYtMmIxZDYzYTY2Y2Y2XkEyXkFqcGdeQXVyODAwMTU1MTE@._V1_UY98_CR1,0,67,98_AL_.jpg",Blow,2001,R,124 min,"Biography, Crime, Drama",7.6,"The story of how George Jung, along with the Medellín Cartel headed by Pablo Escobar, established the American cocaine market in the 1970s in the United States.",52,Ted Demme,Johnny Depp,Penélope Cruz,Franka Potente,Rachel Griffiths,240714,"52,990,775" +"https://m.media-amazon.com/images/M/MV5BYWFlY2E3ODQtZWNiNi00ZGU4LTkzNWEtZTQ2ZTViMWRhYjIzL2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Enemy at the Gates,2001,A,131 min,"Drama, History, War",7.6,A Russian and a German sniper play a game of cat-and-mouse during the Battle of Stalingrad.,53,Jean-Jacques Annaud,Jude Law,Ed Harris,Joseph Fiennes,Rachel Weisz,243729,"51,401,758" +"https://m.media-amazon.com/images/M/MV5BZTI3YzZjZjEtMDdjOC00OWVjLTk0YmYtYzI2MGMwZjFiMzBlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Minority Report,2002,A,145 min,"Action, Crime, Mystery",7.6,"In a future where a special police unit is able to arrest murderers before they commit their crimes, an officer from that unit is himself accused of a future murder.",80,Steven Spielberg,Tom Cruise,Colin Farrell,Samantha Morton,Max von Sydow,508417,"132,072,926" +"https://m.media-amazon.com/images/M/MV5BMTA3OTYxMzg0MDFeQTJeQWpwZ15BbWU4MDY1MjY0MTEx._V1_UX67_CR0,0,67,98_AL_.jpg",The Hurricane,1999,R,146 min,"Biography, Drama, Sport",7.6,"The story of Rubin 'Hurricane' Carter, a boxer wrongly imprisoned for murder, and the people who aided in his fight to prove his innocence.",74,Norman Jewison,Denzel Washington,Vicellous Shannon,Deborah Kara Unger,Liev Schreiber,91557,"50,668,906" +"https://m.media-amazon.com/images/M/MV5BZTM2ZGJmNjQtN2UyOS00NjcxLWFjMDktMDE2NzMyNTZlZTBiXkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",American Psycho,2000,A,101 min,"Comedy, Crime, Drama",7.6,"A wealthy New York City investment banking executive, Patrick Bateman, hides his alternate psychopathic ego from his co-workers and friends as he delves deeper into his violent, hedonistic fantasies.",64,Mary Harron,Christian Bale,Justin Theroux,Josh Lucas,Bill Sage,490062,"15,070,285" +"https://m.media-amazon.com/images/M/MV5BMmU5ZjFmYjQtYmNjZC00Yjk4LWI1ZTQtZDJiMjM0YjQyNDU0L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Lola rennt,1998,UA,81 min,"Crime, Drama, Thriller",7.6,"After a botched money delivery, Lola has 20 minutes to come up with 100,000 Deutschmarks.",77,Tom Tykwer,Franka Potente,Moritz Bleibtreu,Herbert Knaup,Nina Petri,188317,"7,267,585" +"https://m.media-amazon.com/images/M/MV5BYjEzMTM2NjAtNWFmZC00MTVlLTgyMmQtMGQyNTFjZDk5N2NmXkEyXkFqcGdeQXVyNzQ1ODk3MTQ@._V1_UX67_CR0,0,67,98_AL_.jpg",The Thin Red Line,1998,A,170 min,"Drama, War",7.6,"Adaptation of James Jones' autobiographical 1962 novel, focusing on the conflict at Guadalcanal during the second World War.",78,Terrence Malick,Jim Caviezel,Sean Penn,Nick Nolte,Kirk Acevedo,172710,"36,400,491" +"https://m.media-amazon.com/images/M/MV5BODkxNGQ1NWYtNzg0Ny00Yjg3LThmZTItMjE2YjhmZTQ0ODY5XkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Mulan,1998,U,88 min,"Animation, Adventure, Family",7.6,"To save her father from death in the army, a young maiden secretly goes in his place and becomes one of China's greatest heroines in the process.",71,Tony Bancroft,Barry Cook,Ming-Na Wen,Eddie Murphy,BD Wong,256906,"120,620,254" +"https://m.media-amazon.com/images/M/MV5BNjA2ZDY3ZjYtZmNiMC00MDU5LTgxMWEtNzk1YmI3NzdkMTU0XkEyXkFqcGdeQXVyNjQyMjcwNDM@._V1_UX67_CR0,0,67,98_AL_.jpg",Fear and Loathing in Las Vegas,1998,R,118 min,"Adventure, Comedy, Drama",7.6,An oddball journalist and his psychopathic lawyer travel to Las Vegas for a series of psychedelic escapades.,41,Terry Gilliam,Johnny Depp,Benicio Del Toro,Tobey Maguire,Michael Lee Gogin,259753,"10,680,275" +"https://m.media-amazon.com/images/M/MV5BMTkyNTAzZDYtNWUzYi00ODVjLTliZjYtNjc2YzJmODZhNTg3XkEyXkFqcGdeQXVyNjUxMDQ0MTg@._V1_UY98_CR6,0,67,98_AL_.jpg",Funny Games,1997,A,108 min,"Crime, Drama, Thriller",7.6,"Two violent young men take a mother, father, and son hostage in their vacation cabin and force them to play sadistic ""games"" with one another for their own amusement.",69,Michael Haneke,Susanne Lothar,Ulrich Mühe,Arno Frisch,Frank Giering,65058, +"https://m.media-amazon.com/images/M/MV5BMGExOGExM2UtNWM5ZS00OWEzLTllNzYtM2NlMTJlYjBlZTJkXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Dark City,1998,A,100 min,"Mystery, Sci-Fi, Thriller",7.6,"A man struggles with memories of his past, which include a wife he cannot remember and a nightmarish world no one else ever seems to wake up from.",66,Alex Proyas,Rufus Sewell,Kiefer Sutherland,Jennifer Connelly,William Hurt,187927,"14,378,331" +"https://m.media-amazon.com/images/M/MV5BMzk1MmI4NzAtOGRiNS00YjY1LTllNmEtZDhiZDM4MjU2NTMxXkEyXkFqcGdeQXVyNjc3MjQzNTI@._V1_UY98_CR1,0,67,98_AL_.jpg",Sleepers,1996,UA,147 min,"Crime, Drama, Thriller",7.6,"After a prank goes disastrously wrong, a group of boys are sent to a detention center where they are brutalized. Thirteen years later, an unexpected random encounter with a former guard gives them a chance for revenge.",49,Barry Levinson,Robert De Niro,Kevin Bacon,Brad Pitt,Jason Patric,186734,"49,100,000" +"https://m.media-amazon.com/images/M/MV5BYWUxOWY4NDctMDFmMS00ZTQwLWExMGEtODg0ZWNhOTE5NzZmXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UY98_CR0,0,67,98_AL_.jpg",Lost Highway,1997,A,134 min,"Mystery, Thriller",7.6,"Anonymous videotapes presage a musician's murder conviction, and a gangster's girlfriend leads a mechanic astray.",52,David Lynch,Bill Pullman,Patricia Arquette,John Roselius,Louis Eppolito,131101,"3,796,699" +"https://m.media-amazon.com/images/M/MV5BNzk1MjU3MDQyMl5BMl5BanBnXkFtZTcwNjc1OTM2MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Sense and Sensibility,1995,U,136 min,"Drama, Romance",7.6,"Rich Mr. Dashwood dies, leaving his second wife and her three daughters poor by the rules of inheritance. The two eldest daughters are the title opposites.",84,Ang Lee,Emma Thompson,Kate Winslet,James Fleet,Tom Wilkinson,102598,"43,182,776" +"https://m.media-amazon.com/images/M/MV5BZjI0ZWFiMmQtMjRlZi00ZmFhLWI4NmYtMjQ5YmY0MzIyMzRiXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Die Hard: With a Vengeance,1995,A,128 min,"Action, Adventure, Thriller",7.6,"John McClane and a Harlem store owner are targeted by German terrorist Simon in New York City, where he plans to rob the Federal Reserve Building.",58,John McTiernan,Bruce Willis,Jeremy Irons,Samuel L. Jackson,Graham Greene,364420,"100,012,499" +"https://m.media-amazon.com/images/M/MV5BYTJlZmQ1OTAtODQzZi00NGIzLWI1MmEtZGE4NjFlOWRhODIyXkEyXkFqcGdeQXVyNTc1NTQxODI@._V1_UY98_CR0,0,67,98_AL_.jpg",Dead Man,1995,R,121 min,"Adventure, Drama, Fantasy",7.6,"On the run after murdering a man, accountant William Blake encounters a strange aboriginal American man named Nobody who prepares him for his journey into the spiritual world.",62,Jim Jarmusch,Johnny Depp,Gary Farmer,Crispin Glover,Lance Henriksen,90442,"1,037,847" +"https://m.media-amazon.com/images/M/MV5BNmRiZDZkN2EtNWI5ZS00ZDg3LTgyNDItMWI5NjVlNmE5ODJiXkEyXkFqcGdeQXVyMjQwMjk0NjI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Bridges of Madison County,1995,A,135 min,"Drama, Romance",7.6,Photographer Robert Kincaid wanders into the life of housewife Francesca Johnson for four days in the 1960s.,69,Clint Eastwood,Clint Eastwood,Meryl Streep,Annie Corley,Victor Slezak,73172,"71,516,617" +"https://m.media-amazon.com/images/M/MV5BNjEzYjJmNzgtNDkwNy00MTQ4LTlmMWMtNzA4YjE2NjI0ZDg4XkEyXkFqcGdeQXVyNjU0OTQ0OTY@._V1_UX67_CR0,0,67,98_AL_.jpg",Apollo 13,PG,U,140 min,"Adventure, Drama, History",7.6,NASA must devise a strategy to return Apollo 13 to Earth safely after the spacecraft undergoes massive internal damage putting the lives of the three astronauts on board in jeopardy.,77,Ron Howard,Tom Hanks,Bill Paxton,Kevin Bacon,Gary Sinise,269197,"173,837,933" +"https://m.media-amazon.com/images/M/MV5BNTliYTI1YTctMTE0Mi00NDM0LThjZDgtYmY3NGNiODBjZjAwXkEyXkFqcGdeQXVyMTAwMzUyOTc@._V1_UX67_CR0,0,67,98_AL_.jpg",Trois couleurs: Blanc,1994,U,92 min,"Comedy, Drama, Romance",7.6,"After his wife divorces him, a Polish immigrant plots to get even with her.",88,Krzysztof Kieslowski,Zbigniew Zamachowski,Julie Delpy,Janusz Gajos,Jerzy Stuhr,64390,"1,464,625" +"https://m.media-amazon.com/images/M/MV5BYjcxMzM3OWMtNmM3Yy00YzBkLTkxMmQtMDk4MmM3Y2Y4MDliL2ltYWdlXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",Falling Down,1993,R,113 min,"Action, Crime, Drama",7.6,An ordinary man frustrated with the various flaws he sees in society begins to psychotically and violently lash out against them.,56,Joel Schumacher,Michael Douglas,Robert Duvall,Barbara Hershey,Rachel Ticotin,171640,"40,903,593" +"https://m.media-amazon.com/images/M/MV5BMTM5MDY5MDQyOV5BMl5BanBnXkFtZTgwMzM3NzMxMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Dazed and Confused,1993,U,102 min,Comedy,7.6,The adventures of high school and junior high students on the last day of school in May 1976.,78,Richard Linklater,Jason London,Wiley Wiggins,Matthew McConaughey,Rory Cochrane,165465,"7,993,039" +"https://m.media-amazon.com/images/M/MV5BMTQxNDYzMTg1M15BMl5BanBnXkFtZTgwNzk4MDgxMTE@._V1_UX67_CR0,0,67,98_AL_.jpg",My Cousin Vinny,1992,UA,120 min,"Comedy, Crime",7.6,"Two New Yorkers accused of murder in rural Alabama while on their way back to college call in the help of one of their cousins, a loudmouth lawyer with no trial experience.",68,Jonathan Lynn,Joe Pesci,Marisa Tomei,Ralph Macchio,Mitchell Whitfield,107325,"52,929,168" +"https://m.media-amazon.com/images/M/MV5BMTY5NjI2MjQxMl5BMl5BanBnXkFtZTgwMDA2MzM2NzE@._V1_UY98_CR0,0,67,98_AL_.jpg",Omohide poro poro,1991,U,118 min,"Animation, Drama, Romance",7.6,A twenty-seven-year-old office worker travels to the countryside while reminiscing about her childhood in Tokyo.,90,Isao Takahata,Miki Imai,Toshirô Yanagiba,Yoko Honna,Mayumi Izuka,27071,"453,243" +"https://m.media-amazon.com/images/M/MV5BNjg5ZDM0MTEtYTZmNC00NDJiLWI5MTktYzk4N2QxY2IxZTc2L2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UY98_CR3,0,67,98_AL_.jpg",Delicatessen,1991,R,99 min,"Comedy, Crime",7.6,Post-apocalyptic surrealist black comedy about the landlord of an apartment building who occasionally prepares a delicacy for his odd tenants.,66,Marc Caro,Jean-Pierre Jeunet,Marie-Laure Dougnac,Dominique Pinon,Pascal Benezech,80487,"1,794,187" +"https://m.media-amazon.com/images/M/MV5BMzFkM2YwOTQtYzk2Mi00N2VlLWE3NTItN2YwNDg1YmY0ZDNmXkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_UX67_CR0,0,67,98_AL_.jpg",Home Alone,1990,U,103 min,"Comedy, Family",7.6,An eight-year-old troublemaker must protect his house from a pair of burglars when he is accidentally left home alone by his family during Christmas vacation.,63,Chris Columbus,Macaulay Culkin,Joe Pesci,Daniel Stern,John Heard,488817,"285,761,243" +"https://m.media-amazon.com/images/M/MV5BNWFlYWY2YjYtNjdhNi00MzVlLTg2MTMtMWExNzg4NmM5NmEzXkEyXkFqcGdeQXVyMDk5Mzc5MQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",The Godfather: Part III,1990,A,162 min,"Crime, Drama",7.6,"Follows Michael Corleone, now in his 60s, as he seeks to free his family from crime and find a suitable successor to his empire.",60,Francis Ford Coppola,Al Pacino,Diane Keaton,Andy Garcia,Talia Shire,359809,"66,666,062" +"https://m.media-amazon.com/images/M/MV5BMjE0ODEwNjM2NF5BMl5BanBnXkFtZTcwMjU2Mzg3NA@@._V1_UX67_CR0,0,67,98_AL_.jpg",When Harry Met Sally...,1989,UA,95 min,"Comedy, Drama, Romance",7.6,"Harry and Sally have known each other for years, and are very good friends, but they fear sex would ruin the friendship.",76,Rob Reiner,Billy Crystal,Meg Ryan,Carrie Fisher,Bruno Kirby,195663,"92,823,600" +"https://m.media-amazon.com/images/M/MV5BN2JlZTBhYTEtZDE3OC00NTA3LTk5NTQtNjg5M2RjODllM2M0XkEyXkFqcGdeQXVyNjk1Njg5NTA@._V1_UX67_CR0,0,67,98_AL_.jpg",The Little Mermaid,1989,U,83 min,"Animation, Family, Fantasy",7.6,A mermaid princess makes a Faustian bargain in an attempt to become human and win a prince's love.,88,Ron Clements,John Musker,Jodi Benson,Samuel E. Wright,Rene Auberjonois,237696,"111,543,479" +"https://m.media-amazon.com/images/M/MV5BODk1ZWM4ZjItMjFhZi00MDMxLTgxNmYtODFhNWZlZTkwM2UwXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_UX67_CR0,0,67,98_AL_.jpg",The Naked Gun: From the Files of Police Squad!,1988,U,85 min,"Comedy, Crime",7.6,Incompetent police Detective Frank Drebin must foil an attempt to assassinate Queen Elizabeth II.,76,David Zucker,Leslie Nielsen,Priscilla Presley,O.J. Simpson,Ricardo Montalban,152871,"78,756,177" +"https://m.media-amazon.com/images/M/MV5BM2I1ZWNkYjEtYWY3ZS00MmMwLWI5OTEtNWNkZjNiYjIwNzY0XkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UX67_CR0,0,67,98_AL_.jpg","Planes, Trains & Automobiles",1987,U,93 min,"Comedy, Drama",7.6,A man must struggle to travel home for Thanksgiving with a lovable oaf of a shower curtain ring salesman as his only companion.,72,John Hughes,Steve Martin,John Candy,Laila Robins,Michael McKean,124773,"49,530,280" +"https://m.media-amazon.com/images/M/MV5BZTllNWNlZjctMWQwMS00ZDc3LTg5ZjMtNzhmNzhjMmVhYTFlXkEyXkFqcGdeQXVyNTc1NTQxODI@._V1_UX67_CR0,0,67,98_AL_.jpg",Lethal Weapon,1987,A,109 min,"Action, Crime, Thriller",7.6,Two newly paired cops who are complete opposites must put aside their differences in order to catch a gang of drug smugglers.,68,Richard Donner,Mel Gibson,Danny Glover,Gary Busey,Mitchell Ryan,236894,"65,207,127" +"https://m.media-amazon.com/images/M/MV5BZmI5YzM1MjItMzFmNy00NGFkLThlMDUtZjZmYTZkM2QxMjU3XkEyXkFqcGdeQXVyNzkwMjQ5NzM@._V1_UX67_CR0,0,67,98_AL_.jpg",Blood Simple,1984,A,99 min,"Crime, Drama, Thriller",7.6,"The owner of a seedy small-town Texas bar discovers that one of his employees is having an affair with his wife. A chaotic chain of misunderstandings, lies and mischief ensues after he devises a plot to have them murdered.",82,Joel Coen,Ethan Coen,John Getz,Frances McDormand,Dan Hedaya,87745,"2,150,000" +"https://m.media-amazon.com/images/M/MV5BNWQ4MGZlZmYtZjY0MS00N2JhLWE0NmMtOTMwMTk4NDQ4NjE2XkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_UX67_CR0,0,67,98_AL_.jpg",On Golden Pond,1981,UA,109 min,Drama,7.6,"Norman is a curmudgeon with an estranged relationship with his daughter Chelsea. At Golden Pond, he and his wife nevertheless agree to care for Billy, the son of Chelsea's new boyfriend, and a most unexpected relationship blooms.",68,Mark Rydell,Katharine Hepburn,Henry Fonda,Jane Fonda,Doug McKeon,27650,"119,285,432" +"https://m.media-amazon.com/images/M/MV5BN2VlNjNhZWQtMTY2OC00Y2E1LWJkNGUtMDU4M2ViNzliMGYwXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Mad Max 2,1981,A,96 min,"Action, Adventure, Sci-Fi",7.6,"In the post-apocalyptic Australian wasteland, a cynical drifter agrees to help a small, gasoline-rich community escape a horde of bandits.",77,George Miller,Mel Gibson,Bruce Spence,Michael Preston,Max Phipps,166588,"12,465,371" +"https://m.media-amazon.com/images/M/MV5BYTU2MWRiMTMtYzAzZi00NGYzLTlkMDEtNWQ3MzZlNTJlNzZkL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Warriors,1979,UA,92 min,"Action, Crime, Thriller",7.6,"In the near future, a charismatic leader summons the street gangs of New York City in a bid to take it over. When he is killed, The Warriors are falsely blamed and now must fight their way home while every other gang is hunting them down.",65,Walter Hill,Michael Beck,James Remar,Dorsey Wright,Brian Tyler,93878,"22,490,039" +"https://m.media-amazon.com/images/M/MV5BMGQ0OGM5YjItYzYyMi00NmVmLWI3ODMtMTY2NGRkZmI5MWU2XkEyXkFqcGdeQXVyMzI0NDc4ODY@._V1_UX67_CR0,0,67,98_AL_.jpg",The Muppet Movie,1979,U,95 min,"Adventure, Comedy, Family",7.6,"Kermit and his newfound friends trek across America to find success in Hollywood, but a frog legs merchant is after Kermit.",74,James Frawley,Jim Henson,Frank Oz,Jerry Nelson,Richard Hunt,32802,"76,657,000" +"https://m.media-amazon.com/images/M/MV5BNDQ3MzNjMDItZjE0ZS00ZTYxLTgxNTAtM2I4YjZjNWFjYjJlL2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Escape from Alcatraz,1979,A,112 min,"Action, Biography, Crime",7.6,"Alcatraz is the most secure prison of its time. It is believed that no one can ever escape from it, until three daring men make a possible successful attempt at escaping from one of the most infamous prisons in the world.",76,Don Siegel,Clint Eastwood,Patrick McGoohan,Roberts Blossom,Jack Thibeau,121731,"43,000,000" +"https://m.media-amazon.com/images/M/MV5BMzZiODUwNzktNzBiZi00MDc4LThkMGMtZmE3MTE0M2E1MTM3L2ltYWdlXkEyXkFqcGdeQXVyNTAyODkwOQ@@._V1_UX67_CR0,0,67,98_AL_.jpg",Watership Down,1978,U,91 min,"Animation, Adventure, Drama",7.6,"Hoping to escape destruction by human developers and save their community, a colony of rabbits, led by Hazel and Fiver, seek out a safe place to set up a new warren.",64,Martin Rosen,John Hubley,John Hurt,Richard Briers,Ralph Richardson,33656, +"https://m.media-amazon.com/images/M/MV5BNDU1MjQ0YWMtMWQ2MS00NTdmLTg1MGItNDA5NTNkNTRhOTIyXkEyXkFqcGdeQXVyNTIzOTk5ODM@._V1_UX67_CR0,0,67,98_AL_.jpg",Midnight Express,1978,A,121 min,"Biography, Crime, Drama",7.6,"Billy Hayes, an American college student, is caught smuggling drugs out of Turkey and thrown into prison.",59,Alan Parker,Brad Davis,Irene Miracle,Bo Hopkins,Paolo Bonacelli,73662,"35,000,000" +"https://m.media-amazon.com/images/M/MV5BMjM1NjE5NjQxN15BMl5BanBnXkFtZTgwMjYzMzQxMDE@._V1_UX67_CR0,0,67,98_AL_.jpg",Close Encounters of the Third Kind,1977,U,138 min,"Drama, Sci-Fi",7.6,"Roy Neary, an electric lineman, watches how his quiet and ordinary daily life turns upside down after a close encounter with a UFO.",90,Steven Spielberg,Richard Dreyfuss,François Truffaut,Teri Garr,Melinda Dillon,184966,"132,088,635" +"https://m.media-amazon.com/images/M/MV5BYzZhODNiOWYtMmNkNS00OTFhLTkzYzktYTQ4ZmNmZWMyN2ZiL2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",The Long Goodbye,1973,A,112 min,"Comedy, Crime, Drama",7.6,"Private investigator Philip Marlowe helps a friend out of a jam, but in doing so gets implicated in his wife's murder.",87,Robert Altman,Elliott Gould,Nina van Pallandt,Sterling Hayden,Mark Rydell,26337,"959,000" +"https://m.media-amazon.com/images/M/MV5BYjRmY2VjN2ItMzBmYy00YTRjLWFiMTgtNGZhNWJjMjk3YjZjXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Giù la testa,1971,PG,157 min,"Drama, War, Western",7.6,A low-life bandit and an I.R.A. explosives expert rebel against the government and become heroes of the Mexican Revolution.,77,Sergio Leone,Rod Steiger,James Coburn,Romolo Valli,Maria Monti,30144,"696,690" +"https://m.media-amazon.com/images/M/MV5BMzAyNDUwYzUtN2NlMC00ODliLWExMjgtMGMzNmYzZmUwYTg1XkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Kelly's Heroes,1970,GP,144 min,"Adventure, Comedy, War",7.6,A group of U.S. soldiers sneaks across enemy lines to get their hands on a secret stash of Nazi treasure.,50,Brian G. Hutton,Clint Eastwood,Telly Savalas,Don Rickles,Carroll O'Connor,45338,"1,378,435" +"https://m.media-amazon.com/images/M/MV5BMjAwMTExODExNl5BMl5BanBnXkFtZTgwMjM2MDgyMTE@._V1_UX67_CR0,0,67,98_AL_.jpg",The Jungle Book,1967,U,78 min,"Animation, Adventure, Family",7.6,Bagheera the Panther and Baloo the Bear have a difficult time trying to convince a boy to leave the jungle for human civilization.,65,Wolfgang Reitherman,Phil Harris,Sebastian Cabot,Louis Prima,Bruce Reitherman,166409,"141,843,612" +"https://m.media-amazon.com/images/M/MV5BYTE4YWU0NjAtMjNiYi00MTNiLTgwYzctZjk0YjY5NGVhNWQwXkEyXkFqcGdeQXVyMTY5Nzc4MDY@._V1_UY98_CR0,0,67,98_AL_.jpg",Blowup,1966,A,111 min,"Drama, Mystery, Thriller",7.6,A fashion photographer unknowingly captures a death on film after following two lovers in a park.,82,Michelangelo Antonioni,David Hemmings,Vanessa Redgrave,Sarah Miles,John Castle,56513, +"https://m.media-amazon.com/images/M/MV5BZjQyMGUwNzAtNTc2MC00Y2FjLThlM2ItZGRjNzM0OWVmZGYyXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",A Hard Day's Night,1964,U,87 min,"Comedy, Music, Musical",7.6,"Over two ""typical"" days in the life of The Beatles, the boys struggle to keep themselves and Sir Paul McCartney's mischievous grandfather in check while preparing for a live television performance.",96,Richard Lester,John Lennon,Paul McCartney,George Harrison,Ringo Starr,40351,"13,780,024" +"https://m.media-amazon.com/images/M/MV5BNGEwMTRmZTQtMDY4Ni00MTliLTk5ZmMtOWMxYWMyMTllMDg0L2ltYWdlL2ltYWdlXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Breakfast at Tiffany's,1961,A,115 min,"Comedy, Drama, Romance",7.6,"A young New York socialite becomes interested in a young man who has moved into her apartment building, but her past threatens to get in the way.",76,Blake Edwards,Audrey Hepburn,George Peppard,Patricia Neal,Buddy Ebsen,166544, +"https://m.media-amazon.com/images/M/MV5BODk3YjdjZTItOGVhYi00Mjc2LTgzMDAtMThmYTVkNTBlMWVkXkEyXkFqcGdeQXVyNDY2MTk1ODk@._V1_UX67_CR0,0,67,98_AL_.jpg",Giant,1956,G,201 min,"Drama, Western",7.6,Sprawling epic covering the life of a Texas cattle rancher and his family and associates.,84,George Stevens,Elizabeth Taylor,Rock Hudson,James Dean,Carroll Baker,34075, +"https://m.media-amazon.com/images/M/MV5BM2U3YzkxNGMtYWE0YS00ODk0LTk1ZGEtNjk3ZTE0MTk4MzJjXkEyXkFqcGdeQXVyNDk0MDg4NDk@._V1_UX67_CR0,0,67,98_AL_.jpg",From Here to Eternity,1953,Passed,118 min,"Drama, Romance, War",7.6,"In Hawaii in 1941, a private is cruelly punished for not boxing on his unit's team, while his captain's wife and second-in-command are falling in love.",85,Fred Zinnemann,Burt Lancaster,Montgomery Clift,Deborah Kerr,Donna Reed,43374,"30,500,000" +"https://m.media-amazon.com/images/M/MV5BZTBmMjUyMjItYTM4ZS00MjAwLWEyOGYtYjMyZTUxN2I3OTMxXkEyXkFqcGdeQXVyNjc1NTYyMjg@._V1_UX67_CR0,0,67,98_AL_.jpg",Lifeboat,1944,,97 min,"Drama, War",7.6,Several survivors of a torpedoed merchant ship in World War II find themselves in the same lifeboat with one of the crew members of the U-boat that sank their ship.,78,Alfred Hitchcock,Tallulah Bankhead,John Hodiak,Walter Slezak,William Bendix,26471, +"https://m.media-amazon.com/images/M/MV5BMTY5ODAzMTcwOF5BMl5BanBnXkFtZTcwMzYxNDYyNA@@._V1_UX67_CR0,0,67,98_AL_.jpg",The 39 Steps,1935,,86 min,"Crime, Mystery, Thriller",7.6,"A man in London tries to help a counter-espionage Agent. But when the Agent is killed, and the man stands accused, he must go on the run to save himself and stop a spy ring which is trying to steal top secret information.",93,Alfred Hitchcock,Robert Donat,Madeleine Carroll,Lucie Mannheim,Godfrey Tearle,51853, diff --git a/examples/evaluation/Getting_Started_with_OpenAI_Evals.ipynb b/examples/evaluation/Getting_Started_with_OpenAI_Evals.ipynb new file mode 100644 index 0000000..b610647 --- /dev/null +++ b/examples/evaluation/Getting_Started_with_OpenAI_Evals.ipynb @@ -0,0 +1,1257 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Getting Started with OpenAI Evals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "The [OpenAI Evals](https://github.com/openai/evals/tree/main) framework consists of\n", + "1. A framework to evaluate an LLM (large language model) or a system built on top of an LLM.\n", + "2. An open-source registry of challenging evals\n", + "\n", + "This notebook will cover:\n", + "* Introduction to Evaluation and the [OpenAI Evals](https://github.com/openai/evals/tree/main) library\n", + "* Building an Eval\n", + "* Running an Eval\n", + "\n", + "#### What are evaluations/ `evals`?\n", + "\n", + "Evaluation is the process of validating and testing the outputs that your LLM applications are producing. Having strong evaluations (\"evals\") will mean a more stable, reliable application that is resilient to code and model changes. An eval is a task used to measure the quality of the output of an LLM or LLM system. Given an input prompt, an output is generated. We evaluate this output with a set of ideal answers and find the quality of the LLM system.\n", + "\n", + "#### Importance of Evaluations\n", + "\n", + "If you are building with foundational models like `GPT-4`, creating high quality evals is one of the most impactful things you can do. Developing AI solutions involves an iterative design process. [Without evals, it can be very difficult and time intensive to understand](https://youtu.be/XGJNo8TpuVA?feature=shared&t=1089) how different model versions and prompts might affect your use case.\n", + "\n", + "With OpenAI’s [continuous model upgrades](https://platform.openai.com/docs/models/continuous-model-upgrades), evals allow you to efficiently test model performance for your use cases in a standardized way. Developing a suite of evals customized to your objectives will help you quickly and effectively understand how new models may perform for your use cases. You can also make evals a part of your CI/CD pipeline to make sure you achieve the desired accuracy before deploying.\n", + "\n", + "#### Types of evals\n", + "\n", + "There are two main ways we can evaluate/grade completions: writing some validation logic in code\n", + "or using the model itself to inspect the answer. We’ll introduce each with some examples.\n", + "\n", + "**Writing logic for answer checking**\n", + "\n", + "The simplest and most common type of eval has an input and an ideal response or answer. For example,\n", + "we can have an eval sample where the input is \"What year was Obama elected president for the first\n", + "time?\" and the ideal answer is \"2008\". We feed the input to a model and get the completion. If the model\n", + "says \"2008\", it is then graded as correct. We can write a string match to check if the completion includes the phrase \"2008\". If it does, we consider it correct.\n", + "\n", + "Consider another eval where the input is to generate valid JSON: We can write some code that\n", + "attempts to parse the completion as JSON and then considers the completion correct if it is\n", + "parsable.\n", + "\n", + "**Model grading: A two stage process where the model first answers the question, then we ask a\n", + "model to look at the response to check if it’s correct.**\n", + "\n", + "Consider an input that asks the model to write a funny joke. The model then generates a\n", + "completion. We then create a new input to the model to answer the question: \"Is this following\n", + "joke funny? First reason step by step, then answer yes or no\" that includes the completion.\" We\n", + "finally consider the original completion correct if the new model completion ends with \"yes\".\n", + "\n", + "Model grading works best with the latest, most powerful models like `GPT-4` and if we give them the ability\n", + "to reason before making a judgment. Model grading will have an error rate, so it is important to validate\n", + "the performance with human evaluation before running the evals at scale. For best results, it makes\n", + "sense to use a different model to do grading from the one that did the completion, like using `GPT-4` to\n", + "grade `GPT-3.5` answers.\n", + "\n", + "#### OpenAI Eval Templates\n", + "\n", + "In using evals, we have discovered several \"templates\" that accommodate many different benchmarks. We have implemented these templates in the OpenAI Evals library to simplify the development of new evals. For example, we have defined 2 types of eval templates that can be used out of the box:\n", + "\n", + "* **Basic Eval Templates**: These contain deterministic functions to compare the output to the ideal_answers. In cases where the desired model response has very little variation, such as answering multiple choice questions or simple questions with a straightforward answer, we have found this following templates to be useful.\n", + "\n", + "* **Model-Graded Templates**: These contain functions where an LLM compares the output to the ideal_answers and attempts to judge the accuracy. In cases where the desired model response can contain significant variation, such as answering an open-ended question, we have found that using the model to grade itself is a viable strategy for automated evaluation.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting Setup\n", + "\n", + "First, go to [github.com/openai/evals](https://github.com/openai/evals), clone the repository with `git clone git@github.com:openai/evals.git` and go through the [setup instructions](https://github.com/openai/evals). \n", + "\n", + "To run evals later in this notebook, you will need to set up and specify your OpenAI API key. After you obtain an API key, specify it using the `OPENAI_API_KEY` environment variable. \n", + "\n", + "Please be aware of the costs associated with using the API when running evals." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-27T02:24:59.629Z", + "start_time": "2024-03-27T02:24:50.505893Z" + } + }, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "import pandas as pd\n", + "\n", + "client = OpenAI()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Building an evaluation for OpenAI Evals framework\n", + "\n", + "At its core, an eval is a dataset and an eval class that is defined in a YAML file. To start creating an eval, we need\n", + "\n", + "1. The test dataset in the `jsonl` format.\n", + "2. The eval template to be used\n", + "\n", + "### Creating the eval dataset\n", + "Lets create a dataset for a use case where we are evaluating the model's ability to generate syntactically correct SQL. In this use case, we have a series of tables that are related to car manufacturing\n", + "\n", + "First we will need to create a system prompt that we would like to evaluate. We will pass in instructions for the model as well as an overview of the table structure:\n", + "```\n", + "\"TASK: Answer the following question with syntactically correct SQLite SQL. The SQL should be correct and be in context of the previous question-answer pairs.\\nTable car_makers, columns = [*,Id,Maker,FullName,Country]\\nTable car_names, columns = [*,MakeId,Model,Make]\\nTable cars_data, columns = [*,Id,MPG,Cylinders,Edispl,Horsepower,Weight,Accelerate,Year]\\nTable continents, columns = [*,ContId,Continent]\\nTable countries, columns = [*,CountryId,CountryName,Continent]\\nTable model_list, columns = [*,ModelId,Maker,Model]\\nForeign_keys = [countries.Continent = continents.ContId,car_makers.Country = countries.CountryId,model_list.Maker = car_makers.Id,car_names.Model = model_list.Model,cars_data.Id = car_names.MakeId]\"\n", + "```\n", + "\n", + "For this prompt, we can ask a specific question:\n", + "```\n", + "\"Q: how many car makers are their in germany?\"\n", + "```\n", + "\n", + "And we have an expected answer:\n", + "```\n", + "\"A: SELECT count ( * ) FROM CAR_MAKERS AS T1 JOIN COUNTRIES AS T2 ON T1.Country = T2.CountryId WHERE T2.CountryName = 'germany'\"\n", + "```\n", + "\n", + "The dataset needs to be in the following format:\n", + "```\n", + "\"input\": [{\"role\": \"system\", \"content\": \"\"}, {\"role\": \"user\", \"content\": }, \"ideal\": \"correct answer\"]\n", + "```\n", + "\n", + "Putting it all together, we get:\n", + "```\n", + "{\"input\": [{\"role\": \"system\", \"content\": \"TASK: Answer the following question with syntactically correct SQLite SQL. The SQL should be correct and be in context of the previous question-answer pairs.\\nTable car_makers, columns = [*,Id,Maker,FullName,Country]\\nTable car_names, columns = [*,MakeId,Model,Make]\\nTable cars_data, columns = [*,Id,MPG,Cylinders,Edispl,Horsepower,Weight,Accelerate,Year]\\nTable continents, columns = [*,ContId,Continent]\\nTable countries, columns = [*,CountryId,CountryName,Continent]\\nTable model_list, columns = [*,ModelId,Maker,Model]\\nForeign_keys = [countries.Continent = continents.ContId,car_makers.Country = countries.CountryId,model_list.Maker = car_makers.Id,car_names.Model = model_list.Model,cars_data.Id = car_names.MakeId]\\n\"}, {\"role\": \"system\", \"content\": \"Q: how many car makers are their in germany\"}, \"ideal\": [\"A: SELECT count ( * ) FROM CAR_MAKERS AS T1 JOIN COUNTRIES AS T2 ON T1.Country = T2.CountryId WHERE T2.CountryName = 'germany'\"]}\n", + "```\n", + "\n", + "\n", + "One way to speed up the process of building eval datasets, is to use `GPT-4` to generate synthetic data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-18T07:23:04.862331Z", + "start_time": "2024-03-18T07:23:04.717601Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Q: What is the average horsepower for cars made in Europe?\n", + "A: SELECT AVG(cars_data.Horsepower) FROM cars_data JOIN car_names ON cars_data.Id = car_names.MakeId JOIN model_list ON car_names.Model = model_list.Model JOIN car_makers ON model_list.Maker = car_makers.Id JOIN countries ON car_makers.Country = countries.CountryId JOIN continents ON countries.Continent = continents.ContId WHERE continents.Continent = 'Europe'\n", + "\n", + "Q: What is the average horsepower for cars made in the USA?\n", + "A: SELECT AVG(cars_data.Horsepower) FROM cars_data JOIN car_names ON cars_data.Id = car_names.MakeId JOIN car_makers ON car_names.MakeId = car_makers.Id JOIN countries ON car_makers.Country = countries.CountryId WHERE countries.CountryName = 'USA'\n", + "\n", + "Q: What is the average horsepower for cars produced in countries from the continent with the id '3'?\n", + "A: SELECT AVG(cars_data.Horsepower) FROM cars_data JOIN car_names ON cars_data.Id = car_names.MakeId JOIN model_list ON car_names.Model = model_list.Model JOIN car_makers ON model_list.Maker = car_makers.Id JOIN countries ON car_makers.Country = countries.CountryId JOIN continents ON countries.Continent = continents.ContId WHERE continents.ContId = '3'\n", + "\n", + "Q: What is the average horsepower for cars made by makers from Europe?\n", + "A: SELECT AVG(cars_data.Horsepower) FROM cars_data JOIN car_names ON cars_data.Id = car_names.MakeId JOIN model_list ON car_names.Model = model_list.Model JOIN car_makers ON model_list.Maker = car_makers.Id JOIN countries ON car_makers.Country = countries.CountryId JOIN continents ON countries.Continent = continents.ContId WHERE continents.Continent = 'Europe'\n", + "\n", + "Q: What is the average horsepower for cars made in the USA?\n", + "\n", + "A: SELECT AVG(cars_data.Horsepower) FROM cars_data JOIN car_names ON cars_data.Id = car_names.MakeId JOIN car_makers ON car_names.MakeId = car_makers.Id JOIN countries ON car_makers.Country = countries.CountryId WHERE countries.CountryName = 'USA'\n", + "\n" + ] + } + ], + "source": [ + "## Use GPT-4 to generate synthetic data\n", + "# Define the system prompt and user input (these should be filled as per the specific use case)\n", + "system_prompt = \"\"\"You are a helpful assistant that can ask questions about a database table and write SQL queries to answer the question.\n", + " A user will pass in a table schema and your job is to return a question answer pairing. The question should relevant to the schema of the table,\n", + " and you can speculate on its contents. You will then have to generate a SQL query to answer the question. Below are some examples of what this should look like.\n", + "\n", + " Example 1\n", + " ```````````\n", + " User input: Table museum, columns = [*,Museum_ID,Name,Num_of_Staff,Open_Year]\\nTable visit, columns = [*,Museum_ID,visitor_ID,Num_of_Ticket,Total_spent]\\nTable visitor, columns = [*,ID,Name,Level_of_membership,Age]\\nForeign_keys = [visit.visitor_ID = visitor.ID,visit.Museum_ID = museum.Museum_ID]\\n\n", + " Assistant Response:\n", + " Q: How many visitors have visited the museum with the most staff?\n", + " A: SELECT count ( * ) FROM VISIT AS T1 JOIN MUSEUM AS T2 ON T1.Museum_ID = T2.Museum_ID WHERE T2.Num_of_Staff = ( SELECT max ( Num_of_Staff ) FROM MUSEUM ) \n", + " ```````````\n", + "\n", + " Example 2\n", + " ```````````\n", + " User input: Table museum, columns = [*,Museum_ID,Name,Num_of_Staff,Open_Year]\\nTable visit, columns = [*,Museum_ID,visitor_ID,Num_of_Ticket,Total_spent]\\nTable visitor, columns = [*,ID,Name,Level_of_membership,Age]\\nForeign_keys = [visit.visitor_ID = visitor.ID,visit.Museum_ID = museum.Museum_ID]\\n\n", + " Assistant Response:\n", + " Q: What are the names who have a membership level higher than 4?\n", + " A: SELECT Name FROM VISITOR AS T1 WHERE T1.Level_of_membership > 4 \n", + " ```````````\n", + "\n", + " Example 3\n", + " ```````````\n", + " User input: Table museum, columns = [*,Museum_ID,Name,Num_of_Staff,Open_Year]\\nTable visit, columns = [*,Museum_ID,visitor_ID,Num_of_Ticket,Total_spent]\\nTable visitor, columns = [*,ID,Name,Level_of_membership,Age]\\nForeign_keys = [visit.visitor_ID = visitor.ID,visit.Museum_ID = museum.Museum_ID]\\n\n", + " Assistant Response:\n", + " Q: How many tickets of customer id 5?\n", + " A: SELECT count ( * ) FROM VISIT AS T1 JOIN VISITOR AS T2 ON T1.visitor_ID = T2.ID WHERE T2.ID = 5 \n", + " ```````````\n", + " \"\"\"\n", + "\n", + "user_input = \"Table car_makers, columns = [*,Id,Maker,FullName,Country]\\nTable car_names, columns = [*,MakeId,Model,Make]\\nTable cars_data, columns = [*,Id,MPG,Cylinders,Edispl,Horsepower,Weight,Accelerate,Year]\\nTable continents, columns = [*,ContId,Continent]\\nTable countries, columns = [*,CountryId,CountryName,Continent]\\nTable model_list, columns = [*,ModelId,Maker,Model]\\nForeign_keys = [countries.Continent = continents.ContId,car_makers.Country = countries.CountryId,model_list.Maker = car_makers.Id,car_names.Model = model_list.Model,cars_data.Id = car_names.MakeId]\"\n", + "\n", + "messages = [{\n", + " \"role\": \"system\",\n", + " \"content\": system_prompt\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": user_input\n", + " }\n", + "]\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"gpt-4-turbo-preview\",\n", + " messages=messages,\n", + " temperature=0.7,\n", + " n=5\n", + ")\n", + "\n", + "for choice in completion.choices:\n", + " print(choice.message.content + \"\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have the synthetic data, we need to convert it to match the format of the eval dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'input': [{'role': 'system', 'content': 'TASK: Answer the following question with syntactically correct SQLite SQL. The SQL should be correct and be in context of the previous question-answer pairs.\\nTable car_makers, columns = [*,Id,Maker,FullName,Country]\\nTable car_names, columns = [*,MakeId,Model,Make]\\nTable cars_data, columns = [*,Id,MPG,Cylinders,Edispl,Horsepower,Weight,Accelerate,Year]\\nTable continents, columns = [*,ContId,Continent]\\nTable countries, columns = [*,CountryId,CountryName,Continent]\\nTable model_list, columns = [*,ModelId,Maker,Model]\\nForeign_keys = [countries.Continent = continents.ContId,car_makers.Country = countries.CountryId,model_list.Maker = car_makers.Id,car_names.Model = model_list.Model,cars_data.Id = car_names.MakeId]'}, {'role': 'user', 'content': 'What is the average horsepower for cars made in Europe?'}], 'ideal': \"SELECT AVG(cars_data.Horsepower) FROM cars_data JOIN car_names ON cars_data.Id = car_names.MakeId JOIN model_list ON car_names.Model = model_list.Model JOIN car_makers ON model_list.Maker = car_makers.Id JOIN countries ON car_makers.Country = countries.CountryId JOIN continents ON countries.Continent = continents.ContId WHERE continents.Continent = 'Europe'\"}\n", + "{'input': [{'role': 'system', 'content': 'TASK: Answer the following question with syntactically correct SQLite SQL. The SQL should be correct and be in context of the previous question-answer pairs.\\nTable car_makers, columns = [*,Id,Maker,FullName,Country]\\nTable car_names, columns = [*,MakeId,Model,Make]\\nTable cars_data, columns = [*,Id,MPG,Cylinders,Edispl,Horsepower,Weight,Accelerate,Year]\\nTable continents, columns = [*,ContId,Continent]\\nTable countries, columns = [*,CountryId,CountryName,Continent]\\nTable model_list, columns = [*,ModelId,Maker,Model]\\nForeign_keys = [countries.Continent = continents.ContId,car_makers.Country = countries.CountryId,model_list.Maker = car_makers.Id,car_names.Model = model_list.Model,cars_data.Id = car_names.MakeId]'}, {'role': 'user', 'content': 'What is the average horsepower for cars made in the USA?'}], 'ideal': \"SELECT AVG(cars_data.Horsepower) FROM cars_data JOIN car_names ON cars_data.Id = car_names.MakeId JOIN car_makers ON car_names.MakeId = car_makers.Id JOIN countries ON car_makers.Country = countries.CountryId WHERE countries.CountryName = 'USA'\"}\n", + "{'input': [{'role': 'system', 'content': 'TASK: Answer the following question with syntactically correct SQLite SQL. The SQL should be correct and be in context of the previous question-answer pairs.\\nTable car_makers, columns = [*,Id,Maker,FullName,Country]\\nTable car_names, columns = [*,MakeId,Model,Make]\\nTable cars_data, columns = [*,Id,MPG,Cylinders,Edispl,Horsepower,Weight,Accelerate,Year]\\nTable continents, columns = [*,ContId,Continent]\\nTable countries, columns = [*,CountryId,CountryName,Continent]\\nTable model_list, columns = [*,ModelId,Maker,Model]\\nForeign_keys = [countries.Continent = continents.ContId,car_makers.Country = countries.CountryId,model_list.Maker = car_makers.Id,car_names.Model = model_list.Model,cars_data.Id = car_names.MakeId]'}, {'role': 'user', 'content': \"What is the average horsepower for cars produced in countries from the continent with the id '3'?\"}], 'ideal': \"SELECT AVG(cars_data.Horsepower) FROM cars_data JOIN car_names ON cars_data.Id = car_names.MakeId JOIN model_list ON car_names.Model = model_list.Model JOIN car_makers ON model_list.Maker = car_makers.Id JOIN countries ON car_makers.Country = countries.CountryId JOIN continents ON countries.Continent = continents.ContId WHERE continents.ContId = '3'\"}\n", + "{'input': [{'role': 'system', 'content': 'TASK: Answer the following question with syntactically correct SQLite SQL. The SQL should be correct and be in context of the previous question-answer pairs.\\nTable car_makers, columns = [*,Id,Maker,FullName,Country]\\nTable car_names, columns = [*,MakeId,Model,Make]\\nTable cars_data, columns = [*,Id,MPG,Cylinders,Edispl,Horsepower,Weight,Accelerate,Year]\\nTable continents, columns = [*,ContId,Continent]\\nTable countries, columns = [*,CountryId,CountryName,Continent]\\nTable model_list, columns = [*,ModelId,Maker,Model]\\nForeign_keys = [countries.Continent = continents.ContId,car_makers.Country = countries.CountryId,model_list.Maker = car_makers.Id,car_names.Model = model_list.Model,cars_data.Id = car_names.MakeId]'}, {'role': 'user', 'content': 'What is the average horsepower for cars made by makers from Europe?'}], 'ideal': \"SELECT AVG(cars_data.Horsepower) FROM cars_data JOIN car_names ON cars_data.Id = car_names.MakeId JOIN model_list ON car_names.Model = model_list.Model JOIN car_makers ON model_list.Maker = car_makers.Id JOIN countries ON car_makers.Country = countries.CountryId JOIN continents ON countries.Continent = continents.ContId WHERE continents.Continent = 'Europe'\"}\n", + "{'input': [{'role': 'system', 'content': 'TASK: Answer the following question with syntactically correct SQLite SQL. The SQL should be correct and be in context of the previous question-answer pairs.\\nTable car_makers, columns = [*,Id,Maker,FullName,Country]\\nTable car_names, columns = [*,MakeId,Model,Make]\\nTable cars_data, columns = [*,Id,MPG,Cylinders,Edispl,Horsepower,Weight,Accelerate,Year]\\nTable continents, columns = [*,ContId,Continent]\\nTable countries, columns = [*,CountryId,CountryName,Continent]\\nTable model_list, columns = [*,ModelId,Maker,Model]\\nForeign_keys = [countries.Continent = continents.ContId,car_makers.Country = countries.CountryId,model_list.Maker = car_makers.Id,car_names.Model = model_list.Model,cars_data.Id = car_names.MakeId]'}, {'role': 'user', 'content': 'What is the average horsepower for cars made in the USA?'}], 'ideal': \"SELECT AVG(cars_data.Horsepower) FROM cars_data JOIN car_names ON cars_data.Id = car_names.MakeId JOIN car_makers ON car_names.MakeId = car_makers.Id JOIN countries ON car_makers.Country = countries.CountryId WHERE countries.CountryName = 'USA'\"}\n" + ] + } + ], + "source": [ + "eval_data = []\n", + "input_prompt = \"TASK: Answer the following question with syntactically correct SQLite SQL. The SQL should be correct and be in context of the previous question-answer pairs.\\nTable car_makers, columns = [*,Id,Maker,FullName,Country]\\nTable car_names, columns = [*,MakeId,Model,Make]\\nTable cars_data, columns = [*,Id,MPG,Cylinders,Edispl,Horsepower,Weight,Accelerate,Year]\\nTable continents, columns = [*,ContId,Continent]\\nTable countries, columns = [*,CountryId,CountryName,Continent]\\nTable model_list, columns = [*,ModelId,Maker,Model]\\nForeign_keys = [countries.Continent = continents.ContId,car_makers.Country = countries.CountryId,model_list.Maker = car_makers.Id,car_names.Model = model_list.Model,cars_data.Id = car_names.MakeId]\"\n", + "\n", + "for choice in completion.choices:\n", + " question = choice.message.content.split(\"Q: \")[1].split(\"\\n\")[0] # Extracting the question\n", + " answer = choice.message.content.split(\"\\nA: \")[1].split(\"\\n\")[0] # Extracting the answer\n", + " eval_data.append({\n", + " \"input\": [\n", + " {\"role\": \"system\", \"content\": input_prompt},\n", + " {\"role\": \"user\", \"content\": question},\n", + " ],\n", + " \"ideal\": answer\n", + " })\n", + "\n", + "for item in eval_data:\n", + " print(item)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we need to create the eval registry to run it in the framework.\n", + "\n", + "The evals framework requires a `.yaml` file structured with the following properties:\n", + "* `id` - An identifier for your eval\n", + "* `description` - A short description of your eval\n", + "* `disclaimer` - An additional notes about your eval\n", + "* `metrics` - There are three types of eval metrics we can choose from: match, includes, fuzzyMatch\n", + "\n", + "For our eval, we will configure the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-18T07:23:04.716044Z", + "start_time": "2024-03-18T07:23:04.708437Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nspider-sql:\\n id: spider-sql.dev.v0\\n metrics: [accuracy]\\n description: Eval that scores SQL code from 194 examples in the Spider Text-to-SQL test dataset. The problems are selected by taking the first 10 problems for each database that appears in the test set.\\n Yu, Tao, et al. \"Spider; A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task.\" Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018, https://doi.org/10.18653/v1/d18-1425.\\n disclaimer: Problems are solved zero-shot with no prompting other than the schema; performance may improve with training examples, fine tuning, or a different schema format. Evaluation is currently done through model-grading, where SQL code is not actually executed; the model may judge correct SQL to be incorrect, or vice-versa.\\n\\n '" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "spider-sql:\n", + " id: spider-sql.dev.v0\n", + " metrics: [accuracy]\n", + " description: Eval that scores SQL code from 194 examples in the Spider Text-to-SQL test dataset. The problems are selected by taking the first 10 problems for each database that appears in the test set.\n", + " Yu, Tao, et al. \\\"Spider; A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task.\\\" Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018, https://doi.org/10.18653/v1/d18-1425.\n", + " disclaimer: Problems are solved zero-shot with no prompting other than the schema; performance may improve with training examples, fine tuning, or a different schema format. Evaluation is currently done through model-grading, where SQL code is not actually executed; the model may judge correct SQL to be incorrect, or vice-versa.\n", + "\n", + " \"\"\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Running an evaluation\n", + "\n", + "We can run this eval using the `oaieval` CLI. To get setup, install the library: `pip install .` (if you are running the [OpenAI Evals library](github.com/openai/evals) locally) or `pip install oaieval` if you are running an existing eval.\n", + "\n", + "Then, run the eval using the CLI: `oaieval gpt-3.5-turbo spider-sql`\n", + "\n", + "This command expects a model name and an eval set name. Note that we provide two command line interfaces (CLIs): `oaieval` for running a single eval and `oaievalset` for running a set of evals. The valid eval names are specified in the YAML files under `evals/registry/evals` and their corresponding implementations can be found in `evals/elsuite`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-18T07:29:03.774758Z", + "start_time": "2024-03-18T07:26:29.321664Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "!pip install evals --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `oaieval` CLI can accept various flags to modify the default behavior. You can run `oaieval --help` to see a full list of CLI options. \n", + "\n", + "After running that command, you’ll see the final report of accuracy printed to the console, as well as a file path to a temporary file that contains the full report." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These CLIs can accept various flags to modify their default behavior. You can run `oaieval --help` to see a full list of CLI options. \n", + "\n", + "After running that command, you’ll see the final report of accuracy printed to the console, as well as a file path to a temporary file that contains the full report." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-18T07:31:42.602736Z", + "start_time": "2024-03-18T07:29:03.776339Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-03-26 19:44:39,836] [registry.py:257] Loading registry from /Users/shyamal/.virtualenvs/openai/lib/python3.11/site-packages/evals/registry/evals\n", + "[2024-03-26 19:44:43,623] [registry.py:257] Loading registry from /Users/shyamal/.evals/evals\n", + "[2024-03-26 19:44:43,635] [oaieval.py:189] \u001b[1;35mRun started: 240327024443FACXGMKA\u001b[0m\n", + "[2024-03-26 19:44:43,663] [registry.py:257] Loading registry from /Users/shyamal/.virtualenvs/openai/lib/python3.11/site-packages/evals/registry/modelgraded\n", + "[2024-03-26 19:44:43,851] [registry.py:257] Loading registry from /Users/shyamal/.evals/modelgraded\n", + "[2024-03-26 19:44:43,853] [data.py:90] Fetching /Users/shyamal/.virtualenvs/openai/lib/python3.11/site-packages/evals/registry/data/sql/spider_sql.jsonl\n", + "[2024-03-26 19:44:43,878] [eval.py:36] Evaluating 25 samples\n", + "[2024-03-26 19:44:43,952] [eval.py:144] Running in threaded mode with 10 threads!\n", + " 0%| | 0/25 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
specfinal_reportrun_idevent_idsample_idtypedatacreated_bycreated_at
0{'completion_fns': ['gpt-3.5-turbo'], 'eval_name': 'spider-sql.dev.v0', 'base_eval': 'spider-sql', 'split': 'dev', 'run_config': {'completion_fns': ['gpt-3.5-turbo'], 'eval_spec': {'cls': 'evals.elsuite.modelgraded.classify:ModelBasedClassify', 'registry_path': '/Users/shyamal/.virtualenvs/openai/lib/python3.11/site-packages/evals/registry', 'args': {'samples_jsonl': 'sql/spider_sql.jsonl', 'eval_type': 'cot_classify', 'modelgraded_spec': 'sql'}, 'key': 'spider-sql.dev.v0', 'group': 'sql'}, 'seed': 20220722, 'max_samples': 25, 'command': '/Users/shyamal/.virtualenvs/openai/bin/oaieval gpt-3.5-turbo spider-sql --max_samples 25', 'initial_settings': {'visible': False}}, 'created_by': '', 'run_id': '240327024443FACXGMKA', 'created_at': '2024-03-27 02:44:43.626043'}NaNNaNNaNNaNNaNNaNNaNNaT
1NaN{'counts/Correct': 20, 'counts/Incorrect': 5, 'score': 0.8}NaNNaNNaNNaNNaNNaNNaT
2NaNNaN240327024443FACXGMKA0.0spider-sql.dev.88sampling{'prompt': [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\n", + "Use only the following tables and columns:\n", + "Table: players. Columns: player_id (number), first_name (text), last_name (text), hand (text), birth_date (time), country_code (text)\n", + "Table: matches. Columns: best_of (number), draw_size (number), loser_age (number), loser_entry (text), loser_hand (text), loser_ht (number), loser_id (number), loser_ioc (text), loser_name (text), loser_rank (number), loser_rank_points (number), loser_seed (number), match_num (number), minutes (number), round (text), score (text), surface (text), tourney_date (time), tourney_id (text), tourney_level (text), tourney_name (text), winner_age (number), winner_entry (text), winner_hand (text), winner_ht (number), winner_id (number), winner_ioc (text), winner_name (text), winner_rank (number), winner_rank_points (number), winner_seed (number), year (number)\n", + "Table: rankings. Columns: ranking_date (time), ranking (number), player_id (number), ranking_points (number), tours (number)\n", + "\n", + "Question: Find the average rank of winners in all matches.\n", + "', 'role': 'system'}], 'sampled': ['SELECT AVG(winner_rank) AS average_rank_of_winners\n", + "FROM matches;']}2024-03-27 02:44:44.821110+00:00
3NaNNaN240327024443FACXGMKA1.0spider-sql.dev.82sampling{'prompt': [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\n", + "Use only the following tables and columns:\n", + "Table: players. Columns: player_id (number), first_name (text), last_name (text), hand (text), birth_date (time), country_code (text)\n", + "Table: matches. Columns: best_of (number), draw_size (number), loser_age (number), loser_entry (text), loser_hand (text), loser_ht (number), loser_id (number), loser_ioc (text), loser_name (text), loser_rank (number), loser_rank_points (number), loser_seed (number), match_num (number), minutes (number), round (text), score (text), surface (text), tourney_date (time), tourney_id (text), tourney_level (text), tourney_name (text), winner_age (number), winner_entry (text), winner_hand (text), winner_ht (number), winner_id (number), winner_ioc (text), winner_name (text), winner_rank (number), winner_rank_points (number), winner_seed (number), year (number)\n", + "Table: rankings. Columns: ranking_date (time), ranking (number), player_id (number), ranking_points (number), tours (number)\n", + "\n", + "Question: Find the total number of matches.\n", + "', 'role': 'system'}], 'sampled': ['SELECT COUNT(*) AS total_matches\n", + "FROM matches;']}2024-03-27 02:44:44.831848+00:00
4NaNNaN240327024443FACXGMKA2.0spider-sql.dev.25sampling{'prompt': [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\n", + "Use only the following tables and columns:\n", + "Table: continents. Columns: ContId (number), Continent (text)\n", + "Table: countries. Columns: CountryId (number), CountryName (text), Continent (number)\n", + "Table: car_makers. Columns: Id (number), Maker (text), FullName (text), Country (text)\n", + "Table: model_list. Columns: ModelId (number), Maker (number), Model (text)\n", + "Table: car_names. Columns: MakeId (number), Model (text), Make (text)\n", + "Table: cars_data. Columns: Id (number), MPG (text), Cylinders (number), Edispl (number), Horsepower (text), Weight (number), Accelerate (number), Year (number)\n", + "\n", + "Question: How many countries exist?\n", + "', 'role': 'system'}], 'sampled': ['SELECT COUNT(*) AS TotalCountries\n", + "FROM countries;']}2024-03-27 02:44:44.996647+00:00
\n", + "" + ], + "text/plain": [ + " spec \\\n", + "0 {'completion_fns': ['gpt-3.5-turbo'], 'eval_name': 'spider-sql.dev.v0', 'base_eval': 'spider-sql', 'split': 'dev', 'run_config': {'completion_fns': ['gpt-3.5-turbo'], 'eval_spec': {'cls': 'evals.elsuite.modelgraded.classify:ModelBasedClassify', 'registry_path': '/Users/shyamal/.virtualenvs/openai/lib/python3.11/site-packages/evals/registry', 'args': {'samples_jsonl': 'sql/spider_sql.jsonl', 'eval_type': 'cot_classify', 'modelgraded_spec': 'sql'}, 'key': 'spider-sql.dev.v0', 'group': 'sql'}, 'seed': 20220722, 'max_samples': 25, 'command': '/Users/shyamal/.virtualenvs/openai/bin/oaieval gpt-3.5-turbo spider-sql --max_samples 25', 'initial_settings': {'visible': False}}, 'created_by': '', 'run_id': '240327024443FACXGMKA', 'created_at': '2024-03-27 02:44:43.626043'} \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " final_report \\\n", + "0 NaN \n", + "1 {'counts/Correct': 20, 'counts/Incorrect': 5, 'score': 0.8} \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " run_id event_id sample_id type \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 240327024443FACXGMKA 0.0 spider-sql.dev.88 sampling \n", + "3 240327024443FACXGMKA 1.0 spider-sql.dev.82 sampling \n", + "4 240327024443FACXGMKA 2.0 spider-sql.dev.25 sampling \n", + "\n", + " data \\\n", + "0 NaN \n", + "1 NaN \n", + "2 {'prompt': [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\n", + "Use only the following tables and columns:\n", + "Table: players. Columns: player_id (number), first_name (text), last_name (text), hand (text), birth_date (time), country_code (text)\n", + "Table: matches. Columns: best_of (number), draw_size (number), loser_age (number), loser_entry (text), loser_hand (text), loser_ht (number), loser_id (number), loser_ioc (text), loser_name (text), loser_rank (number), loser_rank_points (number), loser_seed (number), match_num (number), minutes (number), round (text), score (text), surface (text), tourney_date (time), tourney_id (text), tourney_level (text), tourney_name (text), winner_age (number), winner_entry (text), winner_hand (text), winner_ht (number), winner_id (number), winner_ioc (text), winner_name (text), winner_rank (number), winner_rank_points (number), winner_seed (number), year (number)\n", + "Table: rankings. Columns: ranking_date (time), ranking (number), player_id (number), ranking_points (number), tours (number)\n", + "\n", + "Question: Find the average rank of winners in all matches.\n", + "', 'role': 'system'}], 'sampled': ['SELECT AVG(winner_rank) AS average_rank_of_winners\n", + "FROM matches;']} \n", + "3 {'prompt': [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\n", + "Use only the following tables and columns:\n", + "Table: players. Columns: player_id (number), first_name (text), last_name (text), hand (text), birth_date (time), country_code (text)\n", + "Table: matches. Columns: best_of (number), draw_size (number), loser_age (number), loser_entry (text), loser_hand (text), loser_ht (number), loser_id (number), loser_ioc (text), loser_name (text), loser_rank (number), loser_rank_points (number), loser_seed (number), match_num (number), minutes (number), round (text), score (text), surface (text), tourney_date (time), tourney_id (text), tourney_level (text), tourney_name (text), winner_age (number), winner_entry (text), winner_hand (text), winner_ht (number), winner_id (number), winner_ioc (text), winner_name (text), winner_rank (number), winner_rank_points (number), winner_seed (number), year (number)\n", + "Table: rankings. Columns: ranking_date (time), ranking (number), player_id (number), ranking_points (number), tours (number)\n", + "\n", + "Question: Find the total number of matches.\n", + "', 'role': 'system'}], 'sampled': ['SELECT COUNT(*) AS total_matches\n", + "FROM matches;']} \n", + "4 {'prompt': [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\n", + "Use only the following tables and columns:\n", + "Table: continents. Columns: ContId (number), Continent (text)\n", + "Table: countries. Columns: CountryId (number), CountryName (text), Continent (number)\n", + "Table: car_makers. Columns: Id (number), Maker (text), FullName (text), Country (text)\n", + "Table: model_list. Columns: ModelId (number), Maker (number), Model (text)\n", + "Table: car_names. Columns: MakeId (number), Model (text), Make (text)\n", + "Table: cars_data. Columns: Id (number), MPG (text), Cylinders (number), Edispl (number), Horsepower (text), Weight (number), Accelerate (number), Year (number)\n", + "\n", + "Question: How many countries exist?\n", + "', 'role': 'system'}], 'sampled': ['SELECT COUNT(*) AS TotalCountries\n", + "FROM countries;']} \n", + "\n", + " created_by created_at \n", + "0 NaN NaT \n", + "1 NaN NaT \n", + "2 2024-03-27 02:44:44.821110+00:00 \n", + "3 2024-03-27 02:44:44.831848+00:00 \n", + "4 2024-03-27 02:44:44.996647+00:00 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "log_name = '240327024443FACXGMKA_gpt-3.5-turbo_spider-sql.jsonl' # \"EDIT THIS\" - copy from above\n", + "events = f\"/tmp/evallogs/{log_name}\"\n", + "display(pd.read_json(events, lines=True).head(5))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# processing the log events generated by oaieval\n", + "\n", + "with open(events, \"r\") as f:\n", + " events_df = pd.read_json(f, lines=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This file will contain structured logs of the evaluation. The first entry provides a detailed specification of the evaluation, including the completion functions, evaluation name, run configuration, creator’s name, run ID, and creation timestamp." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'completion_fns': ['gpt-3.5-turbo'],\n", + " 'eval_name': 'spider-sql.dev.v0',\n", + " 'base_eval': 'spider-sql',\n", + " 'split': 'dev',\n", + " 'run_config': {'completion_fns': ['gpt-3.5-turbo'],\n", + " 'eval_spec': {'cls': 'evals.elsuite.modelgraded.classify:ModelBasedClassify',\n", + " 'registry_path': '/Users/shyamal/.virtualenvs/openai/lib/python3.11/site-packages/evals/registry',\n", + " 'args': {'samples_jsonl': 'sql/spider_sql.jsonl',\n", + " 'eval_type': 'cot_classify',\n", + " 'modelgraded_spec': 'sql'},\n", + " 'key': 'spider-sql.dev.v0',\n", + " 'group': 'sql'},\n", + " 'seed': 20220722,\n", + " 'max_samples': 25,\n", + " 'command': '/Users/shyamal/.virtualenvs/openai/bin/oaieval gpt-3.5-turbo spider-sql --max_samples 25',\n", + " 'initial_settings': {'visible': False}},\n", + " 'created_by': '',\n", + " 'run_id': '240327024443FACXGMKA',\n", + " 'created_at': '2024-03-27 02:44:43.626043'}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(events_df.iloc[0].spec)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also look at the entry which provides the final report of the evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'counts/Correct': 20, 'counts/Incorrect': 5, 'score': 0.8}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(events_df.dropna(subset=['final_report']).iloc[0]['final_report'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also review individual evaluation events that provide specific samples (`sample_id`), results, event types, and metadata." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "run_id 240327024443FACXGMKA\n", + "event_id 0.0\n", + "sample_id spider-sql.dev.88\n", + "type sampling\n", + "data {'prompt': [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\n", + "Use only the following tables and columns:\n", + "Table: players. Columns: player_id (number), first_name (text), last_name (text), hand (text), birth_date (time), country_code (text)\n", + "Table: matches. Columns: best_of (number), draw_size (number), loser_age (number), loser_entry (text), loser_hand (text), loser_ht (number), loser_id (number), loser_ioc (text), loser_name (text), loser_rank (number), loser_rank_points (number), loser_seed (number), match_num (number), minutes (number), round (text), score (text), surface (text), tourney_date (time), tourney_id (text), tourney_level (text), tourney_name (text), winner_age (number), winner_entry (text), winner_hand (text), winner_ht (number), winner_id (number), winner_ioc (text), winner_name (text), winner_rank (number), winner_rank_points (number), winner_seed (number), year (number)\n", + "Table: rankings. Columns: ranking_date (time), ranking (number), player_id (number), ranking_points (number), tours (number)\n", + "\n", + "Question: Find the average rank of winners in all matches.\n", + "', 'role': 'system'}], 'sampled': ['SELECT AVG(winner_rank) AS average_rank_of_winners\n", + "FROM matches;']}\n", + "created_at 2024-03-27 02:44:44.821110+00:00\n", + "Name: 2, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.set_option('display.max_colwidth', None) # None means no truncation\n", + "display(events_df.iloc[2][['run_id', 'event_id', 'sample_id', 'type', 'data', 'created_at']])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prompt: [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\\nUse only the following tables and columns:\\nTable: players. Columns: player_id (number), first_name (text), last_name (text), hand (text), birth_date (time), country_code (text)\\nTable: matches. Columns: best_of (number), draw_size (number), loser_age (number), loser_entry (text), loser_hand (text), loser_ht (number), loser_id (number), loser_ioc (text), loser_name (text), loser_rank (number), loser_rank_points (number), loser_seed (number), match_num (number), minutes (number), round (text), score (text), surface (text), tourney_date (time), tourney_id (text), tourney_level (text), tourney_name (text), winner_age (number), winner_entry (text), winner_hand (text), winner_ht (number), winner_id (number), winner_ioc (text), winner_name (text), winner_rank (number), winner_rank_points (number), winner_seed (number), year (number)\\nTable: rankings. Columns: ranking_date (time), ranking (number), player_id (number), ranking_points (number), tours (number)\\n\\nQuestion: Find the average rank of winners in all matches.\\n', 'role': 'system'}]\n", + "Sampled: ['SELECT AVG(winner_rank) AS average_rank_of_winners\\nFROM matches;']\n", + "----------\n", + "Prompt: [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\\nUse only the following tables and columns:\\nTable: players. Columns: player_id (number), first_name (text), last_name (text), hand (text), birth_date (time), country_code (text)\\nTable: matches. Columns: best_of (number), draw_size (number), loser_age (number), loser_entry (text), loser_hand (text), loser_ht (number), loser_id (number), loser_ioc (text), loser_name (text), loser_rank (number), loser_rank_points (number), loser_seed (number), match_num (number), minutes (number), round (text), score (text), surface (text), tourney_date (time), tourney_id (text), tourney_level (text), tourney_name (text), winner_age (number), winner_entry (text), winner_hand (text), winner_ht (number), winner_id (number), winner_ioc (text), winner_name (text), winner_rank (number), winner_rank_points (number), winner_seed (number), year (number)\\nTable: rankings. Columns: ranking_date (time), ranking (number), player_id (number), ranking_points (number), tours (number)\\n\\nQuestion: Find the total number of matches.\\n', 'role': 'system'}]\n", + "Sampled: ['SELECT COUNT(*) AS total_matches\\nFROM matches;']\n", + "----------\n", + "Prompt: [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\\nUse only the following tables and columns:\\nTable: continents. Columns: ContId (number), Continent (text)\\nTable: countries. Columns: CountryId (number), CountryName (text), Continent (number)\\nTable: car_makers. Columns: Id (number), Maker (text), FullName (text), Country (text)\\nTable: model_list. Columns: ModelId (number), Maker (number), Model (text)\\nTable: car_names. Columns: MakeId (number), Model (text), Make (text)\\nTable: cars_data. Columns: Id (number), MPG (text), Cylinders (number), Edispl (number), Horsepower (text), Weight (number), Accelerate (number), Year (number)\\n\\nQuestion: How many countries exist?\\n', 'role': 'system'}]\n", + "Sampled: ['SELECT COUNT(*) AS TotalCountries\\nFROM countries;']\n", + "----------\n", + "Prompt: [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\\nUse only the following tables and columns:\\nTable: TV_Channel. Columns: id (text), series_name (text), Country (text), Language (text), Content (text), Pixel_aspect_ratio_PAR (text), Hight_definition_TV (text), Pay_per_view_PPV (text), Package_Option (text)\\nTable: TV_series. Columns: id (number), Episode (text), Air_Date (text), Rating (text), Share (number), 18_49_Rating_Share (text), Viewers_m (text), Weekly_Rank (number), Channel (text)\\nTable: Cartoon. Columns: id (number), Title (text), Directed_by (text), Written_by (text), Original_air_date (text), Production_code (number), Channel (text)\\n\\nQuestion: What is the name and directors of all the cartoons that are ordered by air date?\\n', 'role': 'system'}]\n", + "Sampled: ['SELECT Title, Directed_by\\nFROM Cartoon\\nORDER BY Original_air_date;']\n", + "----------\n", + "Prompt: [{'content': 'Answer the following question with syntactically correct SQLite SQL. Be creative but the SQL must be correct.\\nUse only the following tables and columns:\\nTable: stadium. Columns: Stadium_ID (number), Location (text), Name (text), Capacity (number), Highest (number), Lowest (number), Average (number)\\nTable: singer. Columns: Singer_ID (number), Name (text), Country (text), Song_Name (text), Song_release_year (text), Age (number), Is_male (others)\\nTable: concert. Columns: concert_ID (number), concert_Name (text), Theme (text), Stadium_ID (text), Year (text)\\nTable: singer_in_concert. Columns: concert_ID (number), Singer_ID (text)\\n\\nQuestion: Show the name and the release year of the song by the youngest singer.\\n', 'role': 'system'}]\n", + "Sampled: ['```sql\\nSELECT s.Name, s.Song_release_year\\nFROM singer s\\nWHERE s.Age = (SELECT MIN(Age) FROM singer)\\n```']\n", + "----------\n" + ] + } + ], + "source": [ + "# Inspect samples\n", + "for i, row in events_df[events_df['type'] == 'sampling'].head(5).iterrows():\n", + " data = pd.json_normalize(row['data'])\n", + " print(f\"Prompt: {data['prompt'].iloc[0]}\")\n", + " print(f\"Sampled: {data['sampled'].iloc[0]}\")\n", + " print(\"-\" * 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's review our failures to understand which tests did not succeed." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def pretty_print_text(prompt):\n", + " # Define markers for the sections\n", + " markers = {\n", + " \"question\": \"[Question]:\",\n", + " \"expert\": \"[Expert]:\",\n", + " \"submission\": \"[Submission]:\",\n", + " \"end\": \"[END DATA]\"\n", + " }\n", + " \n", + " # Function to extract text between markers\n", + " def extract_text(start_marker, end_marker):\n", + " start = prompt.find(start_marker) + len(start_marker)\n", + " end = prompt.find(end_marker)\n", + " text = prompt[start:end].strip()\n", + " if start_marker == markers[\"question\"]:\n", + " text = text.split(\"\\n\\nQuestion:\")[-1].strip() if \"\\n\\nQuestion:\" in text else text\n", + " elif start_marker == markers[\"submission\"]:\n", + " text = text.replace(\"```sql\", \"\").replace(\"```\", \"\").strip()\n", + " return text\n", + " \n", + " # Extracting text for each section\n", + " question_text = extract_text(markers[\"question\"], markers[\"expert\"])\n", + " expert_text = extract_text(markers[\"expert\"], markers[\"submission\"])\n", + " submission_text = extract_text(markers[\"submission\"], markers[\"end\"])\n", + " \n", + " # HTML color codes and formatting\n", + " colors = {\n", + " \"question\": 'QUESTION:
', \n", + " \"expert\": 'EXPECTED:
', \n", + " \"submission\": 'SUBMISSION:
' \n", + " }\n", + " color_end = ''\n", + " \n", + " # Display each section with color\n", + " from IPython.display import display, HTML\n", + " display(HTML(f\"{colors['question']}{question_text}{color_end}\"))\n", + " display(HTML(f\"{colors['expert']}{expert_text}{color_end}\"))\n", + " display(HTML(f\"{colors['submission']}{submission_text}{color_end}\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "QUESTION:
How many countries have a republic as their form of government?\n", + "\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "EXPECTED:
SELECT count(*) FROM country WHERE GovernmentForm = \"Republic\"\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "SUBMISSION:
SELECT COUNT(*) \n", + "FROM country \n", + "WHERE GovernmentForm LIKE '%Republic%'\n", + "\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n" + ] + }, + { + "data": { + "text/html": [ + "QUESTION:
Return the document id, template id, and description for the document with the name Robbin CV.\n", + "\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "EXPECTED:
SELECT document_id , template_id , Document_Description FROM Documents WHERE document_name = \"Robbin CV\"\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "SUBMISSION:
SELECT Documents.Document_ID, Documents.Template_ID, Documents.Document_Description\n", + "FROM Documents\n", + "JOIN Templates ON Documents.Template_ID = Templates.Template_ID\n", + "WHERE Documents.Document_Name = 'Robbin CV';\n", + "\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n" + ] + }, + { + "data": { + "text/html": [ + "QUESTION:
Which professionals live in the state of Indiana or have done treatment on more than 2 treatments? List his or her id, last name and cell phone.\n", + "\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "EXPECTED:
SELECT professional_id , last_name , cell_number FROM Professionals WHERE state = 'Indiana' UNION SELECT T1.professional_id , T1.last_name , T1.cell_number FROM Professionals AS T1 JOIN Treatments AS T2 ON T1.professional_id = T2.professional_id GROUP BY T1.professional_id HAVING count(*) > 2\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "SUBMISSION:
SELECT professional_id, last_name, cell_number\n", + "FROM Professionals\n", + "WHERE state = 'Indiana'\n", + "OR professional_id IN (\n", + " SELECT professional_id\n", + " FROM Treatments\n", + " GROUP BY professional_id\n", + " HAVING COUNT(*) > 2\n", + ");\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n" + ] + }, + { + "data": { + "text/html": [ + "QUESTION:
What is the continent name which Anguilla belongs to?\n", + "\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "EXPECTED:
SELECT Continent FROM country WHERE Name = \"Anguilla\"\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "SUBMISSION:
SELECT c.Continent\n", + "FROM country c\n", + "WHERE c.Code = 'AIA';\n", + "\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n" + ] + }, + { + "data": { + "text/html": [ + "QUESTION:
How many airlines do we have?\n", + "\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "EXPECTED:
SELECT count(*) FROM AIRLINES\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "SUBMISSION:
SELECT COUNT(DISTINCT Airline) AS TotalAirlines\n", + "FROM airlines;\n", + "************" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n" + ] + } + ], + "source": [ + "# Inspect metrics where choice is made and print only the prompt, result, and expected result if the choice is incorrect\n", + "for i, row in events_df[events_df['type'] == 'metrics'].iterrows():\n", + " if row['data']['choice'] == 'Incorrect':\n", + " # Get the previous row's data, which contains the prompt and the expected result\n", + " prev_row = events_df.iloc[i-1]\n", + " prompt = prev_row['data']['prompt'][0]['content'] if 'prompt' in prev_row['data'] and len(prev_row['data']['prompt']) > 0 else \"Prompt not available\"\n", + " expected_result = prev_row['data'].get('ideal', 'Expected result not provided')\n", + " \n", + " # Current row's data will be the actual result\n", + " result = row['data'].get('result', 'Actual result not provided')\n", + " \n", + " pretty_print_text(prompt)\n", + " print(\"-\" * 40)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reviewing some of the failures we see the following:\n", + "* The second incorrect answer had an unnecessary join with the 'Templates' table. Our eval was able to accurately identify this and flag this as incorrect. \n", + "* Few other answers have minor syntax differences that caused the answers to get flagged.\n", + " * In situations like this, it would be worthwhile exploring whether we should continue iterating on the prompt to ensure certain stylistic choices, or if we should modify the evaluation suite to capture this variation.\n", + " * This type of failure hints at the potential need for model-graded evals as a way to ensure accuracy in grading the results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Building out effective evals is a core part of the development cycle of LLM-based applications. The OpenAI Evals framework provides the core structure of building evals out of the box, and allows you to quickly spin up new tests for your various use cases. In this guide, we demonstrated step-by-step how to create an eval, run it, and analyze the results.\n", + "\n", + "The example shown in this guide represent a straightfoward use case for evals. As you continue to explore this framework, we recommend you explore creating more complex model-graded evals for actual production use cases. Happy evaluating!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/gpt4o/data/keynote_recap.mp4 b/examples/gpt4o/data/keynote_recap.mp4 new file mode 100644 index 0000000..818b90e Binary files /dev/null and b/examples/gpt4o/data/keynote_recap.mp4 differ diff --git a/examples/gpt4o/data/triangle.png b/examples/gpt4o/data/triangle.png new file mode 100644 index 0000000..cb522cb Binary files /dev/null and b/examples/gpt4o/data/triangle.png differ diff --git a/examples/gpt4o/introduction_to_gpt4o.ipynb b/examples/gpt4o/introduction_to_gpt4o.ipynb new file mode 100644 index 0000000..c5dae8f --- /dev/null +++ b/examples/gpt4o/introduction_to_gpt4o.ipynb @@ -0,0 +1,809 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to GPT-4o\n", + "GPT-4o (\"o\" for \"omni\") is designed to handle a combination of text, audio, and video inputs, and can generate outputs in text, audio, and image formats.\n", + "\n", + "### Background\n", + "Before GPT-4o, users could interact with ChatGPT using Voice Mode, which operated with three separate models. GPT-4o will integrate these capabilities into a single model that's trained across text, vision, and audio. This unified approach ensures that all inputs—whether text, visual, or auditory—are processed cohesively by the same neural network.\n", + "\n", + "### Current API Capabilities\n", + "Currently, the API supports `{text, image}` inputs only, with `{text}` outputs, the same modalities as `gpt-4-turbo`. Additional modalities, including audio, will be introduced soon. This guide will help you get started with using GPT-4o for text, image, and video understanding.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting Started" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install OpenAI SDK for Python\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade openai --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure the OpenAI client and submit a test request\n", + "To setup the client for our use, we need to create an API key to use with our request. Skip these steps if you already have an API key for usage. \n", + "\n", + "You can get an API key by following these steps:\n", + "1. [Create a new project](https://help.openai.com/en/articles/9186755-managing-your-work-in-the-api-platform-with-projects)\n", + "2. [Generate an API key in your project](https://platform.openai.com/api-keys)\n", + "3. (RECOMMENDED, BUT NOT REQUIRED) [Setup your API key for all projects as an env var](https://platform.openai.com/docs/quickstart/step-2-set-up-your-api-key)\n", + "\n", + "Once we have this setup, let's start with a simple {text} input to the model for our first request. We'll use both `system` and `user` messages for our first request, and we'll receive a response from the `assistant` role." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI \n", + "import os\n", + "\n", + "## Set the API key and model name\n", + "MODEL=\"gpt-4o\"\n", + "client = OpenAI(api_key=os.environ.get(\"OPENAI_API_KEY\", \"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Assistant: Of course! \n", + "\n", + "\\[ 2 + 2 = 4 \\]\n", + "\n", + "If you have any other questions, feel free to ask!\n" + ] + } + ], + "source": [ + "completion = client.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant. Help me with my math homework!\"}, # <-- This is the system message that provides context to the model\n", + " {\"role\": \"user\", \"content\": \"Hello! Could you solve 2+2?\"} # <-- This is the user message for which the model will generate a response\n", + " ]\n", + ")\n", + "\n", + "print(\"Assistant: \" + completion.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Image Processing\n", + "GPT-4o can directly process images and take intelligent actions based on the image. We can provide images in two formats:\n", + "1. Base64 Encoded\n", + "2. URL\n", + "\n", + "Let's first view the image we'll use, then try sending this image as both Base64 and as a URL link to the API" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image, display, Audio, Markdown\n", + "import base64\n", + "\n", + "IMAGE_PATH = \"data/triangle.png\"\n", + "\n", + "# Preview image for context\n", + "display(Image(IMAGE_PATH))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Base64 Image Processing" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To find the area of the triangle, we can use Heron's formula. First, we need to find the semi-perimeter of the triangle.\n", + "\n", + "The sides of the triangle are 6, 5, and 9.\n", + "\n", + "1. Calculate the semi-perimeter \\( s \\):\n", + "\\[ s = \\frac{a + b + c}{2} = \\frac{6 + 5 + 9}{2} = 10 \\]\n", + "\n", + "2. Use Heron's formula to find the area \\( A \\):\n", + "\\[ A = \\sqrt{s(s-a)(s-b)(s-c)} \\]\n", + "\n", + "Substitute the values:\n", + "\\[ A = \\sqrt{10(10-6)(10-5)(10-9)} \\]\n", + "\\[ A = \\sqrt{10 \\cdot 4 \\cdot 5 \\cdot 1} \\]\n", + "\\[ A = \\sqrt{200} \\]\n", + "\\[ A = 10\\sqrt{2} \\]\n", + "\n", + "So, the area of the triangle is \\( 10\\sqrt{2} \\) square units.\n" + ] + } + ], + "source": [ + "# Open the image file and encode it as a base64 string\n", + "def encode_image(image_path):\n", + " with open(image_path, \"rb\") as image_file:\n", + " return base64.b64encode(image_file.read()).decode(\"utf-8\")\n", + "\n", + "base64_image = encode_image(IMAGE_PATH)\n", + "\n", + "response = client.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant that responds in Markdown. Help me with my math homework!\"},\n", + " {\"role\": \"user\", \"content\": [\n", + " {\"type\": \"text\", \"text\": \"What's the area of the triangle?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\n", + " \"url\": f\"data:image/png;base64,{base64_image}\"}\n", + " }\n", + " ]}\n", + " ],\n", + " temperature=0.0,\n", + ")\n", + "\n", + "print(response.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### URL Image Processing" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To find the area of the triangle, we can use Heron's formula. Heron's formula states that the area of a triangle with sides of length \\(a\\), \\(b\\), and \\(c\\) is:\n", + "\n", + "\\[ \\text{Area} = \\sqrt{s(s-a)(s-b)(s-c)} \\]\n", + "\n", + "where \\(s\\) is the semi-perimeter of the triangle:\n", + "\n", + "\\[ s = \\frac{a + b + c}{2} \\]\n", + "\n", + "For the given triangle, the side lengths are \\(a = 5\\), \\(b = 6\\), and \\(c = 9\\).\n", + "\n", + "First, calculate the semi-perimeter \\(s\\):\n", + "\n", + "\\[ s = \\frac{5 + 6 + 9}{2} = \\frac{20}{2} = 10 \\]\n", + "\n", + "Now, apply Heron's formula:\n", + "\n", + "\\[ \\text{Area} = \\sqrt{10(10-5)(10-6)(10-9)} \\]\n", + "\\[ \\text{Area} = \\sqrt{10 \\cdot 5 \\cdot 4 \\cdot 1} \\]\n", + "\\[ \\text{Area} = \\sqrt{200} \\]\n", + "\\[ \\text{Area} = 10\\sqrt{2} \\]\n", + "\n", + "So, the area of the triangle is \\(10\\sqrt{2}\\) square units.\n" + ] + } + ], + "source": [ + "response = client.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant that responds in Markdown. Help me with my math homework!\"},\n", + " {\"role\": \"user\", \"content\": [\n", + " {\"type\": \"text\", \"text\": \"What's the area of the triangle?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\n", + " \"url\": \"https://upload.wikimedia.org/wikipedia/commons/e/e2/The_Algebra_of_Mohammed_Ben_Musa_-_page_82b.png\"}\n", + " }\n", + " ]}\n", + " ],\n", + " temperature=0.0,\n", + ")\n", + "\n", + "print(response.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Video Processing\n", + "While it's not possible to directly send a video to the API, GPT-4o can understand videos if you sample frames and then provide them as images. It performs better at this task than GPT-4 Turbo.\n", + "\n", + "Since GPT-4o in the API does not yet support audio-in (as of May 2024), we'll use a combination of GPT-4o and Whisper to process both the audio and visual for a provided video, and showcase two usecases:\n", + "1. Summarization\n", + "2. Question and Answering\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup for Video Processing\n", + "We'll use two python packages for video processing - opencv-python and moviepy. \n", + "\n", + "These require [ffmpeg](https://ffmpeg.org/about.html), so make sure to install this beforehand. Depending on your OS, you may need to run `brew install ffmpeg` or `sudo apt install ffmpeg`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install opencv-python --quiet\n", + "%pip install moviepy --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Process the video into two components: frames and audio" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "from moviepy.editor import VideoFileClip\n", + "import time\n", + "import base64\n", + "\n", + "# We'll be using the OpenAI DevDay Keynote Recap video. You can review the video here: https://www.youtube.com/watch?v=h02ti0Bl6zk\n", + "VIDEO_PATH = \"data/keynote_recap.mp4\"" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MoviePy - Writing audio in data/keynote_recap.mp3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MoviePy - Done.\n", + "Extracted 218 frames\n", + "Extracted audio to data/keynote_recap.mp3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + } + ], + "source": [ + "def process_video(video_path, seconds_per_frame=2):\n", + " base64Frames = []\n", + " base_video_path, _ = os.path.splitext(video_path)\n", + "\n", + " video = cv2.VideoCapture(video_path)\n", + " total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))\n", + " fps = video.get(cv2.CAP_PROP_FPS)\n", + " frames_to_skip = int(fps * seconds_per_frame)\n", + " curr_frame=0\n", + "\n", + " # Loop through the video and extract frames at specified sampling rate\n", + " while curr_frame < total_frames - 1:\n", + " video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame)\n", + " success, frame = video.read()\n", + " if not success:\n", + " break\n", + " _, buffer = cv2.imencode(\".jpg\", frame)\n", + " base64Frames.append(base64.b64encode(buffer).decode(\"utf-8\"))\n", + " curr_frame += frames_to_skip\n", + " video.release()\n", + "\n", + " # Extract audio from video\n", + " audio_path = f\"{base_video_path}.mp3\"\n", + " clip = VideoFileClip(video_path)\n", + " clip.audio.write_audiofile(audio_path, bitrate=\"32k\")\n", + " clip.audio.close()\n", + " clip.close()\n", + "\n", + " print(f\"Extracted {len(base64Frames)} frames\")\n", + " print(f\"Extracted audio to {audio_path}\")\n", + " return base64Frames, audio_path\n", + "\n", + "# Extract 1 frame per second. You can adjust the `seconds_per_frame` parameter to change the sampling rate\n", + "base64Frames, audio_path = process_video(VIDEO_PATH, seconds_per_frame=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/jpeg": { + "width": 600 + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Display the frames and audio for context\n", + "display_handle = display(None, display_id=True)\n", + "for img in base64Frames:\n", + " display_handle.update(Image(data=base64.b64decode(img.encode(\"utf-8\")), width=600))\n", + " time.sleep(0.025)\n", + "\n", + "Audio(audio_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 1: Summarization\n", + "Now that we have both the video frames and the audio, let's run a few different tests to generate a video summary to compare the results of using the models with different modalities. We should expect to see that the summary generated with context from both visual and audio inputs will be the most accurate, as the model is able to use the entire context from the video.\n", + "\n", + "1. Visual Summary\n", + "2. Audio Summary\n", + "3. Visual + Audio Summary\n", + "\n", + "#### Visual Summary\n", + "The visual summary is generated by sending the model only the frames from the video. With just the frames, the model is likely to capture the visual aspects, but will miss any details discussed by the speaker." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "## Video Summary: OpenAI DevDay Keynote Recap\n", + "\n", + "The video appears to be a keynote recap from OpenAI's DevDay event. Here are the key points covered in the video:\n", + "\n", + "1. **Introduction and Event Overview**:\n", + " - The video starts with the title \"OpenAI DevDay\" and transitions to \"Keynote Recap.\"\n", + " - The event venue is shown, with attendees gathering and the stage set up.\n", + "\n", + "2. **Keynote Presentation**:\n", + " - A speaker, presumably from OpenAI, takes the stage to present.\n", + " - The presentation covers various topics related to OpenAI's latest developments and announcements.\n", + "\n", + "3. **Announcements**:\n", + " - **GPT-4 Turbo**: Introduction of GPT-4 Turbo, highlighting its enhanced capabilities and performance.\n", + " - **JSON Mode**: A new feature that allows for structured data output in JSON format.\n", + " - **Function Calling**: Demonstration of improved function calling capabilities, making interactions more efficient.\n", + " - **Context Length and Control**: Enhancements in context length and user control over the model's responses.\n", + " - **Better Knowledge Integration**: Improvements in the model's knowledge base and retrieval capabilities.\n", + "\n", + "4. **Product Demonstrations**:\n", + " - **DALL-E 3**: Introduction of DALL-E 3 for advanced image generation.\n", + " - **Custom Models**: Announcement of custom models, allowing users to tailor models to specific needs.\n", + " - **API Enhancements**: Updates to the API, including threading, retrieval, and code interpreter functionalities.\n", + "\n", + "5. **Pricing and Token Efficiency**:\n", + " - Discussion on GPT-4 Turbo pricing, emphasizing cost efficiency with reduced input and output tokens.\n", + "\n", + "6. **New Features and Tools**:\n", + " - Introduction of new tools and features for developers, including a variety of GPT-powered applications.\n", + " - Emphasis on building with natural language and the ease of creating custom applications.\n", + "\n", + "7. **Closing Remarks**:\n", + " - The speaker concludes the presentation, thanking the audience and highlighting the future of OpenAI's developments.\n", + "\n", + "The video ends with the OpenAI logo and the event title \"OpenAI DevDay.\"\n" + ] + } + ], + "source": [ + "response = client.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are generating a video summary. Please provide a summary of the video. Respond in Markdown.\"},\n", + " {\"role\": \"user\", \"content\": [\n", + " \"These are the frames from the video.\",\n", + " *map(lambda x: {\"type\": \"image_url\", \n", + " \"image_url\": {\"url\": f'data:image/jpg;base64,{x}', \"detail\": \"low\"}}, base64Frames)\n", + " ],\n", + " }\n", + " ],\n", + " temperature=0,\n", + ")\n", + "print(response.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results are as expected - the model is able to capture the high level aspects of the video visuals, but misses the details provided in the speech.\n", + "\n", + "#### Audio Summary\n", + "The audio summary is generated by sending the model the audio transcript. With just the audio, the model is likely to bias towards the audio content, and will miss the context provided by the presentations and visuals.\n", + "\n", + "`{audio}` input for GPT-4o isn't currently available but will be coming soon! For now, we use our existing `whisper-1` model to process the audio" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Summary\n", + "\n", + "Welcome to OpenAI's first-ever Dev Day. Key announcements include:\n", + "\n", + "- **GPT-4 Turbo**: A new model supporting up to 128,000 tokens of context, featuring JSON mode for valid JSON responses, improved instruction following, and better knowledge retrieval from external documents or databases. It is also significantly cheaper than GPT-4.\n", + "- **New Features**: \n", + " - **Dolly 3**, **GPT-4 Turbo with Vision**, and a new **Text-to-Speech model** are now available in the API.\n", + " - **Custom Models**: A program where OpenAI researchers help companies create custom models tailored to their specific use cases.\n", + " - **Increased Rate Limits**: Doubling tokens per minute for established GPT-4 customers and allowing requests for further rate limit changes.\n", + "- **GPTs**: Tailored versions of ChatGPT for specific purposes, programmable through conversation, with options for private or public sharing, and a forthcoming GPT Store.\n", + "- **Assistance API**: Includes persistent threads, built-in retrieval, a code interpreter, and improved function calling.\n", + "\n", + "OpenAI is excited about the future of AI integration and looks forward to seeing what users will create with these new tools. The event concludes with an invitation to return next year for more advancements.\n" + ] + } + ], + "source": [ + "# Transcribe the audio\n", + "transcription = client.audio.transcriptions.create(\n", + " model=\"whisper-1\",\n", + " file=open(audio_path, \"rb\"),\n", + ")\n", + "## OPTIONAL: Uncomment the line below to print the transcription\n", + "#print(\"Transcript: \", transcription.text + \"\\n\\n\")\n", + "\n", + "response = client.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\":\"\"\"You are generating a transcript summary. Create a summary of the provided transcription. Respond in Markdown.\"\"\"},\n", + " {\"role\": \"user\", \"content\": [\n", + " {\"type\": \"text\", \"text\": f\"The audio transcription is: {transcription.text}\"}\n", + " ],\n", + " }\n", + " ],\n", + " temperature=0,\n", + ")\n", + "print(response.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The audio summary is biased towards the content discussed during the speech, but comes out with much less structure than the video summary.\n", + "\n", + "#### Audio + Visual Summary\n", + "The Audio + Visual summary is generated by sending the model both the visual and the audio from the video at once. When sending both of these, the model is expected to better summarize since it can perceive the entire video at once." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "## Video Summary: OpenAI Dev Day\n", + "\n", + "### Introduction\n", + "- The video begins with the title \"OpenAI Dev Day\" and transitions to a keynote recap.\n", + "\n", + "### Event Overview\n", + "- The event is held at a venue with a sign reading \"OpenAI Dev Day.\"\n", + "- Attendees are seen entering and gathering in a large hall.\n", + "\n", + "### Keynote Presentation\n", + "- The keynote speaker introduces the event and announces the launch of GPT-4 Turbo.\n", + "- **GPT-4 Turbo**:\n", + " - Supports up to 128,000 tokens of context.\n", + " - Introduces a new feature called JSON mode for valid JSON responses.\n", + " - Improved function calling capabilities.\n", + " - Enhanced instruction-following and knowledge retrieval from external documents or databases.\n", + " - Knowledge updated up to April 2023.\n", + " - Available in the API along with DALL-E 3, GPT-4 Turbo with Vision, and a new Text-to-Speech model.\n", + "\n", + "### Custom Models\n", + "- Launch of a new program called Custom Models.\n", + " - Researchers will collaborate with companies to create custom models tailored to specific use cases.\n", + " - Higher rate limits and the ability to request changes to rate limits and quotas directly in API settings.\n", + "\n", + "### Pricing and Performance\n", + "- **GPT-4 Turbo**:\n", + " - 3x cheaper for prompt tokens and 2x cheaper for completion tokens compared to GPT-4.\n", + " - Doubling the tokens per minute for established GPT-4 customers.\n", + "\n", + "### Introduction of GPTs\n", + "- **GPTs**:\n", + " - Tailored versions of ChatGPT for specific purposes.\n", + " - Combine instructions, expanded knowledge, and actions for better performance and control.\n", + " - Can be created without coding, through conversation.\n", + " - Options to make GPTs private, share publicly, or create for company use in ChatGPT Enterprise.\n", + " - Announcement of the upcoming GPT Store.\n", + "\n", + "### Assistance API\n", + "- **Assistance API**:\n", + " - Includes persistent threads for handling long conversation history.\n", + " - Built-in retrieval and code interpreter with a working Python interpreter in a sandbox environment.\n", + " - Improved function calling.\n", + "\n", + "### Conclusion\n", + "- The speaker emphasizes the potential of integrating intelligence everywhere, providing \"superpowers on demand.\"\n", + "- Encourages attendees to return next year, hinting at even more advanced developments.\n", + "- The event concludes with thanks to the attendees.\n", + "\n", + "### Closing\n", + "- The video ends with the OpenAI logo and a final thank you message.\n" + ] + } + ], + "source": [ + "## Generate a summary with visual and audio\n", + "response = client.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\":\"\"\"You are generating a video summary. Create a summary of the provided video and its transcript. Respond in Markdown\"\"\"},\n", + " {\"role\": \"user\", \"content\": [\n", + " \"These are the frames from the video.\",\n", + " *map(lambda x: {\"type\": \"image_url\", \n", + " \"image_url\": {\"url\": f'data:image/jpg;base64,{x}', \"detail\": \"low\"}}, base64Frames),\n", + " {\"type\": \"text\", \"text\": f\"The audio transcription is: {transcription.text}\"}\n", + " ],\n", + " }\n", + "],\n", + " temperature=0,\n", + ")\n", + "print(response.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After combining both the video and audio, we're able to get a much more detailed and comprehensive summary for the event which uses information from both the visual and audio elements from the video.\n", + "\n", + "### Example 2: Question and Answering\n", + "For the Q&A, we'll use the same concept as before to ask questions of our processed video while running the same 3 tests to demonstrate the benefit of combining input modalities:\n", + "1. Visual Q&A\n", + "2. Audio Q&A\n", + "3. Visual + Audio Q&A " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "QUESTION = \"Question: Why did Sam Altman have an example about raising windows and turning the radio on?\"" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Visual QA: \n", + "Sam Altman used the example about raising windows and turning the radio on to demonstrate the function calling capability of GPT-4 Turbo. The example illustrated how the model can interpret and execute multiple commands in a more structured and efficient manner. The \"before\" and \"after\" comparison showed how the model can now directly call functions like `raise_windows()` and `radio_on()` based on natural language instructions, showcasing improved control and functionality.\n" + ] + } + ], + "source": [ + "qa_visual_response = client.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"Use the video to answer the provided question. Respond in Markdown.\"},\n", + " {\"role\": \"user\", \"content\": [\n", + " \"These are the frames from the video.\",\n", + " *map(lambda x: {\"type\": \"image_url\", \"image_url\": {\"url\": f'data:image/jpg;base64,{x}', \"detail\": \"low\"}}, base64Frames),\n", + " QUESTION\n", + " ],\n", + " }\n", + " ],\n", + " temperature=0,\n", + ")\n", + "print(\"Visual QA:\\n\" + qa_visual_response.choices[0].message.content)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Audio QA:\n", + "The provided transcription does not include any mention of Sam Altman or an example about raising windows and turning the radio on. Therefore, I cannot provide an answer based on the given transcription.\n" + ] + } + ], + "source": [ + "qa_audio_response = client.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\":\"\"\"Use the transcription to answer the provided question. Respond in Markdown.\"\"\"},\n", + " {\"role\": \"user\", \"content\": f\"The audio transcription is: {transcription.text}. \\n\\n {QUESTION}\"},\n", + " ],\n", + " temperature=0,\n", + ")\n", + "print(\"Audio QA:\\n\" + qa_audio_response.choices[0].message.content)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Both QA:\n", + "Sam Altman used the example of raising windows and turning the radio on to demonstrate the improved function calling capabilities of GPT-4 Turbo. The example illustrated how the model can now handle multiple function calls more effectively and follow instructions better. In the \"before\" scenario, the model had to be prompted separately for each action, whereas in the \"after\" scenario, the model could handle both actions in a single prompt, showcasing its enhanced ability to manage and execute multiple tasks simultaneously.\n" + ] + } + ], + "source": [ + "qa_both_response = client.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\":\"\"\"Use the video and transcription to answer the provided question.\"\"\"},\n", + " {\"role\": \"user\", \"content\": [\n", + " \"These are the frames from the video.\",\n", + " *map(lambda x: {\"type\": \"image_url\", \n", + " \"image_url\": {\"url\": f'data:image/jpg;base64,{x}', \"detail\": \"low\"}}, base64Frames),\n", + " {\"type\": \"text\", \"text\": f\"The audio transcription is: {transcription.text}\"},\n", + " QUESTION\n", + " ],\n", + " }\n", + " ],\n", + " temperature=0,\n", + ")\n", + "print(\"Both QA:\\n\" + qa_both_response.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparing the three answers, the most accurate answer is generated by using both the audio and visual from the video. Sam Altman did not discuss the raising windows or radio on during the Keynote, but referenced an improved capability for the model to execute multiple functions in a single request while the examples were shown behind him.\n", + "\n", + "## Conclusion\n", + "Integrating many input modalities such as audio, visual, and textual, significantly enhances the performance of the model on a diverse range of tasks. This multimodal approach allows for more comprehensive understanding and interaction, mirroring more closely how humans perceive and process information. \n", + "\n", + "Currently, GPT-4o in the API supports text and image inputs, with audio capabilities coming soon." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/multimodal/Using_GPT4_Vision_With_Function_Calling.ipynb b/examples/multimodal/Using_GPT4_Vision_With_Function_Calling.ipynb new file mode 100644 index 0000000..5ca2869 --- /dev/null +++ b/examples/multimodal/Using_GPT4_Vision_With_Function_Calling.ipynb @@ -0,0 +1,574 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# How to use GPT-4 Vision with Function Calling \n", + "\n", + "The new GPT-4 Turbo model, available as gpt-4-turbo-2024-04-09 as of April 2024, now enables function calling with vision capabilities, better reasoning and a knowledge cutoff date of Dec 2023. Using images with function calling will unlock multimodal use cases and the ability to use reasoning, allowing you to go beyond OCR and image descriptions.\n", + "\n", + "We will go through two examples to demonstrate the use of function calling with GPT-4 Turbo with Vision:\n", + "\n", + "1. Simulating a customer service assistant for delivery exception support\n", + "2. Analyzing an organizational chart to extract employee information" + ], + "metadata": { + "collapsed": false + }, + "id": "7b3d17451440ca82" + }, + { + "cell_type": "markdown", + "source": [ + "### Installation and Setup" + ], + "metadata": { + "collapsed": false + }, + "id": "feffa794bc28be22" + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "!pip install pymupdf --quiet\n", + "!pip install openai --quiet\n", + "!pip install matplotlib --quiet\n", + "# instructor makes it easy to work with function calling\n", + "!pip install instructor --quiet" + ], + "metadata": { + "collapsed": false + }, + "id": "6ce24dfd4e4bbc47", + "execution_count": null + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2024-04-10T03:50:37.564145Z", + "start_time": "2024-04-10T03:50:37.560040Z" + } + }, + "outputs": [], + "source": [ + "import base64\n", + "import os\n", + "from enum import Enum\n", + "from io import BytesIO\n", + "from typing import Iterable\n", + "from typing import List\n", + "from typing import Literal, Optional\n", + "\n", + "import fitz\n", + "# Instructor is powered by Pydantic, which is powered by type hints. Schema validation, prompting is controlled by type annotations\n", + "import instructor\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from IPython.display import display\n", + "from PIL import Image\n", + "from openai import OpenAI\n", + "from pydantic import BaseModel, Field" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 1. Simulating a customer service assistant for delivery exception support\n", + "We will simulate a customer service assistant for a delivery service that is equipped to analyze images of packages. The assistant will perform the following actions based on the image analysis:\n", + "- If a package appears damaged in the image, automatically process a refund according to policy.\n", + "- If the package looks wet, initiate a replacement.\n", + "- If the package appears normal and not damaged, escalate to an agent." + ], + "metadata": { + "collapsed": false + }, + "id": "14ab8f931cc759d" + }, + { + "cell_type": "markdown", + "source": [ + "Let's look at the sample images of packages that the customer service assistant will analyze to determine the appropriate action. We will encode the images as base64 strings for processing by the model." + ], + "metadata": { + "collapsed": false + }, + "id": "ca35604f27f93e77" + }, + { + "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Encoded image: wet_package.jpg\n", + "Encoded image: damaged_package.jpg\n", + "Encoded image: normal_package.jpg\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to encode the image as base64\n", + "def encode_image(image_path: str):\n", + " # check if the image exists\n", + " if not os.path.exists(image_path):\n", + " raise FileNotFoundError(f\"Image file not found: {image_path}\")\n", + " with open(image_path, \"rb\") as image_file:\n", + " return base64.b64encode(image_file.read()).decode('utf-8')\n", + "\n", + "\n", + "# Sample images for testing\n", + "image_dir = \"images\"\n", + "\n", + "# encode all images within the directory\n", + "image_files = os.listdir(image_dir)\n", + "image_data = {}\n", + "for image_file in image_files:\n", + " image_path = os.path.join(image_dir, image_file)\n", + " # encode the image with key as the image file name\n", + " image_data[image_file.split('.')[0]] = encode_image(image_path)\n", + " print(f\"Encoded image: {image_file}\")\n", + "\n", + "\n", + "def display_images(image_data: dict):\n", + " fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", + " for i, (key, value) in enumerate(image_data.items()):\n", + " img = Image.open(BytesIO(base64.b64decode(value)))\n", + " ax = axs[i]\n", + " ax.imshow(img)\n", + " ax.axis(\"off\")\n", + " ax.set_title(key)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "display_images(image_data)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T03:50:38.120670Z", + "start_time": "2024-04-10T03:50:37.565555Z" + } + }, + "id": "2f2066d40dbbfbe8", + "execution_count": 45 + }, + { + "cell_type": "markdown", + "source": [ + "We have successfully encoded the sample images as base64 strings and displayed them. The customer service assistant will analyze these images to determine the appropriate action based on the package condition.\n", + "\n", + "Let's now define the functions/tools for order processing, such as escalating an order to an agent, refunding an order, and replacing an order. We will create placeholder functions to simulate the processing of these actions based on the identified tools. We will be using Pydantic models to define the structure of the data for order actions.\n" + ], + "metadata": { + "collapsed": false + }, + "id": "f4b16c5219dff6db" + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "MODEL = \"gpt-4-turbo-2024-04-09\"\n", + "\n", + "class Order(BaseModel):\n", + " \"\"\"Represents an order with details such as order ID, customer name, product name, price, status, and delivery date.\"\"\"\n", + " order_id: str = Field(..., description=\"The unique identifier of the order\")\n", + " product_name: str = Field(..., description=\"The name of the product\")\n", + " price: float = Field(..., description=\"The price of the product\")\n", + " status: str = Field(..., description=\"The status of the order\")\n", + " delivery_date: str = Field(..., description=\"The delivery date of the order\")\n", + "# Placeholder functions for order processing\n", + "\n", + "def get_order_details(order_id):\n", + " # Placeholder function to retrieve order details based on the order ID\n", + " return Order(\n", + " order_id=order_id,\n", + " product_name=\"Product X\",\n", + " price=100.0,\n", + " status=\"Delivered\",\n", + " delivery_date=\"2024-04-10\",\n", + " )\n", + "\n", + "def escalate_to_agent(order: Order, message: str):\n", + " # Placeholder function to escalate the order to a human agent\n", + " return f\"Order {order.order_id} has been escalated to an agent with message: `{message}`\"\n", + "\n", + "def refund_order(order: Order):\n", + " # Placeholder function to process a refund for the order\n", + " return f\"Order {order.order_id} has been refunded successfully.\"\n", + "\n", + "def replace_order(order: Order):\n", + " # Placeholder function to replace the order with a new one\n", + " return f\"Order {order.order_id} has been replaced with a new order.\"\n", + "\n", + "class FunctionCallBase(BaseModel):\n", + " rationale: Optional[str] = Field(..., description=\"The reason for the action.\")\n", + " image_description: Optional[str] = Field(\n", + " ..., description=\"The detailed description of the package image.\"\n", + " )\n", + " action: Literal[\"escalate_to_agent\", \"replace_order\", \"refund_order\"]\n", + " message: Optional[str] = Field(\n", + " ...,\n", + " description=\"The message to be escalated to the agent if action is escalate_to_agent\",\n", + " )\n", + " # Placeholder functions to process the action based on the order ID\n", + " def __call__(self, order_id):\n", + " order: Order = get_order_details(order_id=order_id)\n", + " if self.action == \"escalate_to_agent\":\n", + " return escalate_to_agent(order, self.message)\n", + " if self.action == \"replace_order\":\n", + " return replace_order(order)\n", + " if self.action == \"refund_order\":\n", + " return refund_order(order)\n", + "\n", + "class EscalateToAgent(FunctionCallBase):\n", + " \"\"\"Escalate to an agent for further assistance.\"\"\"\n", + " pass\n", + "\n", + "class OrderActionBase(FunctionCallBase):\n", + " pass\n", + "\n", + "class ReplaceOrder(OrderActionBase):\n", + " \"\"\"Tool call to replace an order.\"\"\"\n", + " pass\n", + "\n", + "class RefundOrder(OrderActionBase):\n", + " \"\"\"Tool call to refund an order.\"\"\"\n", + " pass" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T03:50:38.135826Z", + "start_time": "2024-04-10T03:50:38.122420Z" + } + }, + "id": "115b5a0bf208b054", + "execution_count": 46 + }, + { + "cell_type": "markdown", + "source": [ + "### Simulating user messages and processing the package images\n", + "\n", + "We will simulate user messages containing the package images and process the images using the GPT-4 Turbo with Vision model. The model will identify the appropriate tool call based on the image analysis and the predefined actions for damaged, wet, or normal packages. We will then process the identified action based on the order ID and display the results." + ], + "metadata": { + "collapsed": false + }, + "id": "5716c84cb57e3fb3" + }, + { + "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing delivery exception support for different package images...\n", + "\n", + "===================== Simulating user message 1 =====================\n", + "- Tool call: refund_order for provided img: damaged_package\n", + "- Parameters: rationale='The package is visibly damaged with significant tears and crushing, indicating potential harm to the contents.' image_description='The package in the image shows extensive damage, including deep creases and tears in the cardboard. The package is also wrapped with extra tape, suggesting prior attempts to secure it after damage.' action='refund_order' message=None\n", + ">> Action result: Order 12345 has been refunded successfully.\n", + "\n", + "===================== Simulating user message 2 =====================\n", + "- Tool call: escalate_to_agent for provided img: normal_package\n", + "- Parameters: rationale='The package appears normal and not damaged, requiring further assistance for any potential issues not visible in the image.' image_description='A cardboard box on a wooden floor, appearing intact and undamaged, with no visible signs of wear, tear, or wetness.' action='escalate_to_agent' message='Please review this package for any issues not visible in the image. The package appears normal and undamaged.'\n", + ">> Action result: Order 12345 has been escalated to an agent with message: `Please review this package for any issues not visible in the image. The package appears normal and undamaged.`\n", + "\n", + "===================== Simulating user message 3 =====================\n", + "- Tool call: replace_order for provided img: wet_package\n", + "- Parameters: rationale='The package appears wet, which may have compromised the contents, especially since it is labeled as fragile.' image_description=\"The package in the image shows significant wetness on the top surface, indicating potential water damage. The box is labeled 'FRAGILE', which suggests that the contents are delicate and may be more susceptible to damage from moisture.\" action='replace_order' message=None\n", + ">> Action result: Order 12345 has been replaced with a new order.\n" + ] + } + ], + "source": [ + "# extract the tool call from the response\n", + "ORDER_ID = \"12345\" # Placeholder order ID for testing\n", + "INSTRUCTION_PROMPT = \"You are a customer service assistant for a delivery service, equipped to analyze images of packages. If a package appears damaged in the image, automatically process a refund according to policy. If the package looks wet, initiate a replacement. If the package appears normal and not damaged, escalate to agent. For any other issues or unclear images, escalate to agent. You must always use tools!\"\n", + "\n", + "def delivery_exception_support_handler(test_image: str):\n", + " payload = {\n", + " \"model\": MODEL,\n", + " \"response_model\": Iterable[RefundOrder | ReplaceOrder | EscalateToAgent],\n", + " \"tool_choice\": \"auto\", # automatically select the tool based on the context\n", + " \"temperature\": 0.0, # for less diversity in responses\n", + " \"seed\": 123, # Set a seed for reproducibility\n", + " }\n", + " payload[\"messages\"] = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": INSTRUCTION_PROMPT,\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": f\"data:image/jpeg;base64,{image_data[test_image]}\"\n", + " }\n", + " },\n", + " ],\n", + " }\n", + " ]\n", + " function_calls = instructor.from_openai(\n", + " OpenAI(), mode=instructor.Mode.PARALLEL_TOOLS\n", + " ).chat.completions.create(**payload)\n", + " for tool in function_calls:\n", + " print(f\"- Tool call: {tool.action} for provided img: {test_image}\")\n", + " print(f\"- Parameters: {tool}\")\n", + " print(f\">> Action result: {tool(ORDER_ID)}\")\n", + " return tool\n", + "\n", + "\n", + "print(\"Processing delivery exception support for different package images...\")\n", + "\n", + "print(\"\\n===================== Simulating user message 1 =====================\")\n", + "assert delivery_exception_support_handler(\"damaged_package\").action == \"refund_order\"\n", + "\n", + "print(\"\\n===================== Simulating user message 2 =====================\")\n", + "assert delivery_exception_support_handler(\"normal_package\").action == \"escalate_to_agent\"\n", + "\n", + "print(\"\\n===================== Simulating user message 3 =====================\")\n", + "assert delivery_exception_support_handler(\"wet_package\").action == \"replace_order\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T03:50:53.927476Z", + "start_time": "2024-04-10T03:50:38.136857Z" + } + }, + "id": "dacd602d9e2728d9", + "execution_count": 47 + }, + { + "cell_type": "markdown", + "source": [ + "## 2. Analyzing an organizational chart to extract employee information\n", + "\n", + "For the second example, we will analyze an organizational chart image to extract employee information, such as employee names, roles, managers, and manager roles. We will use GPT-4 Turbo with Vision to process the organizational chart image and extract structured data about the employees in the organization. Indeed, function calling lets us go beyond OCR to actually deduce and translate hierarchical relationships within the chart.\n", + "\n", + "We will start with a sample organizational chart in PDF format that we want to analyze and convert the first page of the PDF to a JPEG image for analysis." + ], + "metadata": { + "collapsed": false + }, + "id": "78604bb7ff61b472" + }, + { + "cell_type": "code", + "outputs": [ + { + "data": { + "text/plain": "", + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to convert a single page PDF page to a JPEG image\n", + "def convert_pdf_page_to_jpg(pdf_path: str, output_path: str, page_number=0):\n", + " if not os.path.exists(pdf_path):\n", + " raise FileNotFoundError(f\"PDF file not found: {pdf_path}\")\n", + " doc = fitz.open(pdf_path)\n", + " page = doc.load_page(page_number) # 0 is the first page\n", + " pix = page.get_pixmap()\n", + " # Save the pixmap as a JPEG\n", + " pix.save(output_path)\n", + "\n", + "\n", + "def display_img_local(image_path: str):\n", + " img = Image.open(image_path)\n", + " display(img)\n", + "\n", + "\n", + "pdf_path = 'data/org-chart-sample.pdf'\n", + "output_path = 'org-chart-sample.jpg'\n", + "\n", + "convert_pdf_page_to_jpg(pdf_path, output_path)\n", + "display_img_local(output_path)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T03:50:54.033536Z", + "start_time": "2024-04-10T03:50:53.929732Z" + } + }, + "id": "c3fc6f64b2a2f8e6", + "execution_count": 48 + }, + { + "cell_type": "markdown", + "source": [ + "The organizational chart image has been successfully extracted from the PDF file and displayed. Let's now define a function to analyze the organizational chart image using the new GPT4 Turbo with Vision. The function will extract information about the employees, their roles, and their managers from the image. We will use function/tool calling to specify the input parameters for the organizational structure, such as the employee name, role, and manager's name and role. We will use Pydantic models to define the structure of the data.\n" + ], + "metadata": { + "collapsed": false + }, + "id": "5f6b4299c99721bc" + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "base64_img = encode_image(output_path)\n", + "\n", + "class RoleEnum(str, Enum):\n", + " \"\"\"Defines possible roles within an organization.\"\"\"\n", + " CEO = \"CEO\"\n", + " CTO = \"CTO\"\n", + " CFO = \"CFO\"\n", + " COO = \"COO\"\n", + " EMPLOYEE = \"Employee\"\n", + " MANAGER = \"Manager\"\n", + " INTERN = \"Intern\"\n", + " OTHER = \"Other\"\n", + "\n", + "class Employee(BaseModel):\n", + " \"\"\"Represents an employee, including their name, role, and optional manager information.\"\"\"\n", + " employee_name: str = Field(..., description=\"The name of the employee\")\n", + " role: RoleEnum = Field(..., description=\"The role of the employee\")\n", + " manager_name: Optional[str] = Field(None, description=\"The manager's name, if applicable\")\n", + " manager_role: Optional[RoleEnum] = Field(None, description=\"The manager's role, if applicable\")\n", + "\n", + "\n", + "class EmployeeList(BaseModel):\n", + " \"\"\"A list of employees within the organizational structure.\"\"\"\n", + " employees: List[Employee] = Field(..., description=\"A list of employees\")\n", + "\n", + "def parse_orgchart(base64_img: str) -> EmployeeList:\n", + " response = instructor.from_openai(OpenAI()).chat.completions.create(\n", + " model='gpt-4-turbo',\n", + " response_model=EmployeeList,\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": 'Analyze the given organizational chart and very carefully extract the information.',\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": f\"data:image/jpeg;base64,{base64_img}\"\n", + " }\n", + " },\n", + " ],\n", + " }\n", + " ],\n", + " )\n", + " return response" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T03:50:54.043273Z", + "start_time": "2024-04-10T03:50:54.034475Z" + } + }, + "id": "b11469486fc95c23", + "execution_count": 49 + }, + { + "cell_type": "markdown", + "source": [ + "Now, we will define a function to parse the response from GPT-4 Turbo with Vision and extract the employee data. We will tabulate the extracted data for easy visualization. Please note that the accuracy of the extracted data may vary based on the complexity and clarity of the input image." + ], + "metadata": { + "collapsed": false + }, + "id": "73619ee88ed5cd2" + }, + { + "cell_type": "code", + "outputs": [ + { + "data": { + "text/plain": " employee_name role manager_name manager_role\n0 Juliana Silva CEO None None\n1 Kim Chun Hei CFO Juliana Silva CEO\n2 Chad Gibbons CTO Juliana Silva CEO\n3 Chiaki Sato COO Juliana Silva CEO\n4 Cahaya Dewi Manager Kim Chun Hei CFO\n5 Shawn Garcia Manager Chad Gibbons CTO\n6 Aaron Loeb Manager Chiaki Sato COO\n7 Drew Feig Employee Cahaya Dewi Manager\n8 Richard Sanchez Employee Cahaya Dewi Manager\n9 Sacha Dubois Intern Cahaya Dewi Manager\n10 Olivia Wilson Employee Shawn Garcia Manager\n11 Matt Zhang Intern Shawn Garcia Manager\n12 Avery Davis Employee Aaron Loeb Manager\n13 Harper Russo Employee Aaron Loeb Manager\n14 Taylor Alonso Intern Aaron Loeb Manager", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
employee_namerolemanager_namemanager_role
0Juliana SilvaCEONoneNone
1Kim Chun HeiCFOJuliana SilvaCEO
2Chad GibbonsCTOJuliana SilvaCEO
3Chiaki SatoCOOJuliana SilvaCEO
4Cahaya DewiManagerKim Chun HeiCFO
5Shawn GarciaManagerChad GibbonsCTO
6Aaron LoebManagerChiaki SatoCOO
7Drew FeigEmployeeCahaya DewiManager
8Richard SanchezEmployeeCahaya DewiManager
9Sacha DuboisInternCahaya DewiManager
10Olivia WilsonEmployeeShawn GarciaManager
11Matt ZhangInternShawn GarciaManager
12Avery DavisEmployeeAaron LoebManager
13Harper RussoEmployeeAaron LoebManager
14Taylor AlonsoInternAaron LoebManager
\n
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# call the functions to analyze the organizational chart and parse the response\n", + "result = parse_orgchart(base64_img)\n", + "\n", + "# tabulate the extracted data\n", + "df = pd.DataFrame([{\n", + " 'employee_name': employee.employee_name,\n", + " 'role': employee.role.value,\n", + " 'manager_name': employee.manager_name,\n", + " 'manager_role': employee.manager_role.value if employee.manager_role else None\n", + "} for employee in result.employees])\n", + "\n", + "display(df)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T03:51:03.255172Z", + "start_time": "2024-04-10T03:50:54.044659Z" + } + }, + "id": "962c5ea24b8a9dc9", + "execution_count": 50 + }, + { + "cell_type": "markdown", + "source": [ + "The extracted data from the organizational chart has been successfully parsed and displayed in a DataFrame. This approach allows us to leverage GPT-4 Turbo with Vision capabilities to extract structured information from images, such as organizational charts and diagrams, and process the data for further analysis. By using function calling, we can extend the functionality of multimodal models to perform specific tasks or call external functions." + ], + "metadata": { + "collapsed": false + }, + "id": "c55b5fbf97ae7ba8" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/multimodal/data/org-chart-sample.pdf b/examples/multimodal/data/org-chart-sample.pdf new file mode 100644 index 0000000..4f34935 Binary files /dev/null and b/examples/multimodal/data/org-chart-sample.pdf differ diff --git a/examples/multimodal/images/damaged_package.jpg b/examples/multimodal/images/damaged_package.jpg new file mode 100644 index 0000000..b6321c5 Binary files /dev/null and b/examples/multimodal/images/damaged_package.jpg differ diff --git a/examples/multimodal/images/normal_package.jpg b/examples/multimodal/images/normal_package.jpg new file mode 100644 index 0000000..bd90527 Binary files /dev/null and b/examples/multimodal/images/normal_package.jpg differ diff --git a/examples/multimodal/images/wet_package.jpg b/examples/multimodal/images/wet_package.jpg new file mode 100644 index 0000000..214a103 Binary files /dev/null and b/examples/multimodal/images/wet_package.jpg differ diff --git a/examples/vector_databases/README.md b/examples/vector_databases/README.md index 322ae84..ebbb8fe 100644 --- a/examples/vector_databases/README.md +++ b/examples/vector_databases/README.md @@ -10,6 +10,7 @@ Each provider has their own named directory, with a standard notebook to introdu - [AnalyticDB](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/get-started-with-analyticdb-for-postgresql) - [Cassandra/Astra DB](https://docs.datastax.com/en/astra-serverless/docs/vector-search/qandasimsearch-quickstart.html) - [Azure AI Search](https://learn.microsoft.com/azure/search/search-get-started-vector) +- [Azure SQL Database](https://learn.microsoft.com/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql) - [Chroma](https://docs.trychroma.com/getting-started) - [Elasticsearch](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html) - [Hologres](https://www.alibabacloud.com/help/en/hologres/latest/procedure-to-use-hologres) @@ -24,7 +25,7 @@ Each provider has their own named directory, with a standard notebook to introdu - [Redis](https://github.com/RedisVentures/simple-vecsim-intro) - [SingleStoreDB](https://www.singlestore.com/blog/how-to-get-started-with-singlestore/) - [Supabase](https://supabase.com/docs/guides/ai) -- [Tembo](https://tembo.io/docs/tembo-stacks/vector-db) +- [Tembo](https://tembo.io/docs/product/stacks/ai/vectordb) - [Typesense](https://typesense.org/docs/guide/) - [Vespa AI](https://vespa.ai/) - [Weaviate](https://weaviate.io/developers/weaviate/quickstart) diff --git a/images/elbow_chart.png b/images/elbow_chart.png new file mode 100644 index 0000000..1d8645b Binary files /dev/null and b/images/elbow_chart.png differ diff --git a/images/train1.jpeg b/images/train1.jpeg new file mode 100644 index 0000000..a1511b9 Binary files /dev/null and b/images/train1.jpeg differ diff --git a/images/train17.jpeg b/images/train17.jpeg new file mode 100644 index 0000000..bc071d8 Binary files /dev/null and b/images/train17.jpeg differ diff --git a/images/train2.jpeg b/images/train2.jpeg new file mode 100644 index 0000000..eecf2bf Binary files /dev/null and b/images/train2.jpeg differ diff --git a/registry.yaml b/registry.yaml index 4a166b6..4a79c40 100644 --- a/registry.yaml +++ b/registry.yaml @@ -56,7 +56,7 @@ path: examples/Clustering_for_transaction_classification.ipynb date: 2022-10-20 authors: - - colin-jarvis + - colin-openai - ted-at-openai tags: - embeddings @@ -207,13 +207,12 @@ - ted-at-openai tags: - completions - - tiktoken - title: Multiclass Classification for Transactions path: examples/Multiclass_classification_for_transactions.ipynb date: 2022-10-20 authors: - - colin-jarvis + - colin-openai tags: - embeddings - completions @@ -484,6 +483,15 @@ - embeddings - completions +- title: Getting Started with OpenAI Evals + path: examples/evaluation/Getting_Started_with_OpenAI_Evals.ipynb + date: 2024-03-21 + authors: + - royziv11 + - shyamal-anadkat + tags: + - completions + - title: Fine-Tuned Q&A - collect data path: examples/fine-tuned_qa/olympics-1-collect-data.ipynb date: 2022-03-10 @@ -1201,12 +1209,12 @@ path: examples/How_to_use_guardrails.ipynb date: 2023-12-19 authors: - - colin-jarvis + - colin-openai tags: - guardrails - title: How to combine GPT4 with Vision with RAG to create a clothing matchmaker app - path: examples/How_to_combine_GPT4v_with_RAG_Outfit_Assistant.ipynb + path: examples/How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb date: 2024-02-16 authors: - teomusatoiu @@ -1242,3 +1250,67 @@ - teomusatoiu tags: - moderation + + +- title: Summarizing Long Documents + path: examples/Summarizing_long_documents.ipynb + date: 2024-04-19 + authors: + - joe-at-openai + tags: + - chat + +- title: Using GPT4 Vision with Function Calling + path: examples/multimodal/Using_GPT4_Vision_With_Function_Calling.ipynb + date: 2024-04-09 + authors: + - shyamal-anadkat + tags: + - chat + - vision + +- title: Synthetic data generation (Part 1) + path: examples/SDG1.ipynb + date: 2024-04-10 + authors: + - dylanra-openai + tags: + - completions + + +- title: CLIP embeddings to improve multimodal RAG with GPT-4 Vision + path: examples/custom_image_embedding_search.ipynb + date: 2024-04-10 + authors: + - dylanra-openai + tags: + - vision + - embeddings + +- title: Batch processing with the Batch API + path: examples/batch_processing.ipynb + date: 2024-04-24 + authors: + - katiagg + tags: + - batch + - completions + +- title: Using tool required for customer service + path: examples/Using_tool_required_for_customer_service.ipynb + date: 2024-05-01 + authors: + - colin-openai + tags: + - completions + - functions + +- title: Introduction to gpt-4o + path: examples/gpt4o/introduction_to_gpt4o.ipynb + date: 2024-05-13 + authors: + - justonf + tags: + - completions + - vision + - whisper \ No newline at end of file