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292 lines
8.1 KiB
Plaintext
292 lines
8.1 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Azure DALL·E image generation example\n",
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"\n",
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"This notebook shows how to generate images with the Azure OpenAI service."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"First, we install the necessary dependencies."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install openai\n",
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"# We need requests to retrieve the generated image\n",
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"! pip install requests\n",
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"# We use Pillow to display the generated image\n",
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"! pip install pillow \n",
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"# (Optional) If you want to use Microsoft Active Directory\n",
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"! pip install azure-identity"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import openai"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"Additionally, to properly access the Azure OpenAI Service, we need to create the proper resources at the [Azure Portal](https://portal.azure.com) (you can check a detailed guide on how to do this in the [Microsoft Docs](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal))\n",
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"\n",
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"Once the resource is created, the first thing we need to use is its endpoint. You can get the endpoint by looking at the *\"Keys and Endpoints\"* section under the *\"Resource Management\"* section. Having this, we will set up the SDK using this information:"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"openai.api_base = '' # Add your endpoint here\n",
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"\n",
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"# At the moment DALL·E is only supported by the 2023-06-01-preview API version\n",
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"openai.api_version = '2023-06-01-preview'"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Authentication\n",
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"\n",
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"The Azure OpenAI service supports multiple authentication mechanisms that include API keys and Azure credentials."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"use_azure_active_directory = False"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"#### Authentication using API key\n",
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"\n",
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"To set up the OpenAI SDK to use an *Azure API Key*, we need to set up the `api_type` to `azure` and set `api_key` to a key associated with your endpoint (you can find this key in *\"Keys and Endpoints\"* under *\"Resource Management\"* in the [Azure Portal](https://portal.azure.com))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"if not use_azure_active_directory:\n",
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" openai.api_type = 'azure'\n",
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" openai.api_key = '' # Add your api key here"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Authentication using Microsoft Active Directory\n",
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"Let's now see how we can get a key via Microsoft Active Directory Authentication."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from azure.identity import DefaultAzureCredential\n",
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"\n",
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"if use_azure_active_directory:\n",
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" default_credential = DefaultAzureCredential()\n",
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" token = default_credential.get_token(\"https://cognitiveservices.azure.com/.default\")\n",
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"\n",
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" openai.api_type = 'azure_ad'\n",
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" openai.api_key = token.token"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"A token is valid for a period of time, after which it will expire. To ensure a valid token is sent with every request, you can refresh an expiring token by hooking into requests.auth:"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import typing\n",
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"import time\n",
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"import requests\n",
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"\n",
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"if typing.TYPE_CHECKING:\n",
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" from azure.core.credentials import TokenCredential\n",
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"\n",
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"class TokenRefresh(requests.auth.AuthBase):\n",
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"\n",
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" def __init__(self, credential: \"TokenCredential\", scopes: typing.List[str]) -> None:\n",
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" self.credential = credential\n",
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" self.scopes = scopes\n",
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" self.cached_token: typing.Optional[str] = None\n",
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"\n",
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" def __call__(self, req):\n",
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" if not self.cached_token or self.cached_token.expires_on - time.time() < 300:\n",
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" self.cached_token = self.credential.get_token(*self.scopes)\n",
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" req.headers[\"Authorization\"] = f\"Bearer {self.cached_token.token}\"\n",
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" return req\n",
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"\n",
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"if use_azure_active_directory:\n",
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" session = requests.Session()\n",
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" session.auth = TokenRefresh(default_credential, [\"https://cognitiveservices.azure.com/.default\"])\n",
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"\n",
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" openai.requestssession = session"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generations\n",
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"\n",
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"With setup and authentication complete, you can now generate images on the Azure OpenAI service and retrieve them from the returned URLs."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 1. Generate the images\n",
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"\n",
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"The first step in this process is to actually generate the images:"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"generation_response = openai.Image.create(\n",
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" prompt='A cyberpunk monkey hacker dreaming of a beautiful bunch of bananas, digital art',\n",
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" size='1024x1024',\n",
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" n=2\n",
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")\n",
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"\n",
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"print(generation_response)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Having the response from the `Image.create` call, we download from the URL using `requests`."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import requests\n",
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"\n",
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"# First a little setup\n",
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"image_dir = os.path.join(os.curdir, 'images')\n",
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"# If the directory doesn't exist, create it\n",
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"if not os.path.isdir(image_dir):\n",
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" os.mkdir(image_dir)\n",
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"\n",
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"# With the directory in place, we can initialize the image path (note that filetype should be png)\n",
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"image_path = os.path.join(image_dir, 'generated_image.png')\n",
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"\n",
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"# Now we can retrieve the generated image\n",
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"image_url = generation_response[\"data\"][0][\"url\"] # extract image URL from response\n",
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"generated_image = requests.get(image_url).content # download the image\n",
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"with open(image_path, \"wb\") as image_file:\n",
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" image_file.write(generated_image)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With the image downloaded, we use the [Pillow](https://pypi.org/project/Pillow/) library to open and display it:"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from PIL import Image \n",
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"\n",
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"display(Image.open(image_path))"
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]
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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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"vscode": {
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"interpreter": {
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"hash": "3a5103089ab7e7c666b279eeded403fcec76de49a40685dbdfe9f9c78ad97c17"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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