mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
b485c3048b
Causing problems indexing docs and notebook images don't render after markdown conversion anyways
242 lines
8.0 KiB
Plaintext
242 lines
8.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c613812f",
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"metadata": {},
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"source": [
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"# Gradio\n",
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"\n",
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"There are many 1000s of `Gradio` apps on `Hugging Face Spaces`. This library puts them at the tips of your LLM's fingers 🦾\n",
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"\n",
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"Specifically, `gradio-tools` is a Python library for converting `Gradio` apps into tools that can be leveraged by a large language model (LLM)-based agent to complete its task. For example, an LLM could use a `Gradio` tool to transcribe a voice recording it finds online and then summarize it for you. Or it could use a different `Gradio` tool to apply OCR to a document on your Google Drive and then answer questions about it.\n",
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"\n",
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"It's very easy to create you own tool if you want to use a space that's not one of the pre-built tools. Please see this section of the gradio-tools documentation for information on how to do that. All contributions are welcome!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "231b46c2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# !pip install gradio_tools"
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]
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},
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{
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"cell_type": "markdown",
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"id": "17608431",
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"metadata": {},
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"source": [
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"## Using a tool"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "423f9dad",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gradio_tools.tools import StableDiffusionTool"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "30b8f077",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ✔\n",
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"\n",
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"Job Status: Status.STARTING eta: None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'/Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/b61c1dd9-47e2-46f1-a47c-20d27640993d/tmp4ap48vnm.jpg'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"local_file_path = StableDiffusionTool().langchain.run(\n",
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" \"Please create a photo of a dog riding a skateboard\"\n",
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")\n",
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"local_file_path"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "b7bdfd26",
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"metadata": {},
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"outputs": [],
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"source": [
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"from PIL import Image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "98e09784",
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"metadata": {},
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"outputs": [],
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"source": [
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"im = Image.open(local_file_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "98e1e602",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"display(im)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3aeeeeb5",
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"metadata": {},
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"source": [
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"## Using within an agent"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "4a9d45b7",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ✔\n",
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"Loaded as API: https://taesiri-blip-2.hf.space ✔\n",
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"Loaded as API: https://microsoft-promptist.hf.space ✔\n",
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"Loaded as API: https://damo-vilab-modelscope-text-to-video-synthesis.hf.space ✔\n",
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m\n",
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"Thought: Do I need to use a tool? Yes\n",
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"Action: StableDiffusionPromptGenerator\n",
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"Action Input: A dog riding a skateboard\u001b[0m\n",
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"Job Status: Status.STARTING eta: None\n",
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"\n",
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"Observation: \u001b[38;5;200m\u001b[1;3mA dog riding a skateboard, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? Yes\n",
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"Action: StableDiffusion\n",
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"Action Input: A dog riding a skateboard, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha\u001b[0m\n",
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"Job Status: Status.STARTING eta: None\n",
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"\n",
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"Job Status: Status.PROCESSING eta: None\n",
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"\n",
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"Observation: \u001b[36;1m\u001b[1;3m/Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? Yes\n",
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"Action: ImageCaptioner\n",
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"Action Input: /Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/integrations/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg\u001b[0m\n",
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"Job Status: Status.STARTING eta: None\n",
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"\n",
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"Observation: \u001b[33;1m\u001b[1;3ma painting of a dog sitting on a skateboard\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? Yes\n",
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"Action: TextToVideo\n",
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"Action Input: a painting of a dog sitting on a skateboard\u001b[0m\n",
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"Job Status: Status.STARTING eta: None\n",
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"Due to heavy traffic on this app, the prediction will take approximately 73 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis)\n",
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"\n",
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"Job Status: Status.IN_QUEUE eta: 73.89824726581574\n",
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"Due to heavy traffic on this app, the prediction will take approximately 42 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis)\n",
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"\n",
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"Job Status: Status.IN_QUEUE eta: 42.49370198879602\n",
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"\n",
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"Job Status: Status.IN_QUEUE eta: 21.314297944849187\n",
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"\n",
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"Observation: \u001b[31;1m\u001b[1;3m/var/folders/bm/ylzhm36n075cslb9fvvbgq640000gn/T/tmp5snj_nmzf20_cb3m.mp4\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
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"AI: Here is a video of a painting of a dog sitting on a skateboard.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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}
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],
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"source": [
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"from langchain.agents import initialize_agent\n",
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"from langchain.llms import OpenAI\n",
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"from gradio_tools.tools import (\n",
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" StableDiffusionTool,\n",
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" ImageCaptioningTool,\n",
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" StableDiffusionPromptGeneratorTool,\n",
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" TextToVideoTool,\n",
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")\n",
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"\n",
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"from langchain.memory import ConversationBufferMemory\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
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"tools = [\n",
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" StableDiffusionTool().langchain,\n",
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" ImageCaptioningTool().langchain,\n",
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" StableDiffusionPromptGeneratorTool().langchain,\n",
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" TextToVideoTool().langchain,\n",
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"]\n",
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"\n",
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"\n",
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"agent = initialize_agent(\n",
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" tools, llm, memory=memory, agent=\"conversational-react-description\", verbose=True\n",
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")\n",
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"output = agent.run(\n",
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" input=(\n",
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" \"Please create a photo of a dog riding a skateboard \"\n",
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" \"but improve my prompt prior to using an image generator.\"\n",
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" \"Please caption the generated image and create a video for it using the improved prompt.\"\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "67642c82",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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