code/09.UpdateSamples/Aug/ghmodel_phi35_instruct_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from azure.ai.inference import ChatCompletionsClient\n",
    "from azure.ai.inference.models import SystemMessage, UserMessage\n",
    "from azure.core.credentials import AzureKeyCredential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "endpoint = \"https://models.inference.ai.azure.com\"\n",
    "model_name = \"Phi-3.5-mini-instruct\"\n",
    "token = \"Your GitHub Models Token\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = ChatCompletionsClient(\n",
    "    endpoint=endpoint,\n",
    "    credential=AzureKeyCredential(token),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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    "response = client.complete(\n",
    "    messages=[\n",
    "        SystemMessage(content=\"You are my Python programming assistant，helping me generate Python code\"),\n",
    "        UserMessage(content=\"create a flask restful api project\"),\n",
    "    ],\n",
    "    model=model_name,\n",
    "    temperature=1.0,\n",
    "    max_tokens=3096,\n",
    "    top_p=1.0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' To create a Flask RESTful API project, follow the steps below:\\n\\n1. **Set up development environment**\\n\\nYou first need to have Python installed on your machine. The latest version of Python can be downloaded from the official website: https://www.python.org/downloads/\\n\\n2. **Install Flask**\\n\\nFlask is a popular lightweight web framework for Python. You can install it using pip, the Python package installer. Run the following command in your terminal:\\n\\n```\\npip install Flask\\n```\\n\\n3. **Install Flask-RESTful**\\n\\nFlask-RESTful is an extension for Flask that adds support for quickly building REST APIs. Install it using pip:\\n\\n```\\npip install flask-restful\\n```\\n\\n4. **Create a new Python file**\\n\\nLet\\'s name the file `app.py`. Open this file and write the following code:\\n\\n```python\\nfrom flask import Flask\\nfrom flask_restful import Resource, Api\\n\\napp = Flask(__name__)\\napi = Api(app)\\n\\nclass HelloWorld(Resource):\\n    def get(self):\\n        return {\\'hello\\': \\'world\\'}\\n\\napi.add_resource(HelloWorld, \\'/\\')\\n\\nif __name__ == \\'__main__\\':\\n    app.run(debug=True)\\n```\\n\\nThis code sets up a basic Flask application with one basic resource, the `HelloWorld` resource, which returns a JSON response containing `{\\'hello\\': \\'world\\'}` when accessed via a GET request to the root URL (`/`).\\n\\n5. **Run the application**\\n\\nOpen your terminal, navigate to the directory containing the `app.py` file, and run the application:\\n\\n```\\npython app.py\\n```\\n\\nYou should see output similar to the following:\\n\\n```\\n * Serving Flask app \"app.py\"\\n * Environment: production\\n * Debug mode: on\\n * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\\n * Debugger is active!\\n * > Here are the URLs for individual routes:\\n * > * For information on the API, see http://flask.pocoo.org/docs/1.0/api/\\n * > * The debugger catches CPython exceptions and provides feedback on JavaScript errors\\n * > * The profiler helps you analyze the performance of your app\\n * > * The Werkzeug debugger helps you interrupt execution and inspect your code\\n * Serving Flask app \"app.py\"\\n * Environment: production\\n * Debug mode: on\\n * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\\n```\\n\\nNow you have a running Flask RESTful API! You can access it at `http://127.0.0.1:5000/` on your web browser or, more commonly, programmatically using tools like curl or Postman.\\n\\n6. **Expand your API (optional)**\\n\\nTo expand your RESTful API, you can define additional resources by creating new subclasses of the `Resource` class and registering their URLs in the `api.add_resource()` method.\\n\\nHere is an example of extending our `HelloWorld` API:\\n\\n```python\\nclass HelloUser(Resource):\\n    def get(self, name):\\n        return {\\'message\\': f\\'Hello, {name}!\\'}\\n\\napi.add_resource(HelloUser, \\'/hello/<string:name>\\')\\n```\\n\\nThis will add a new endpoint `/hello/<name>` which will accept `GET` requests to return a message saying hello to the provided name.\\n\\n7. **Test your API**\\n\\nYou can test your API using HTTP tools like curl, Postman, or by directly accessing your browser. Here\\'s an example of making a GET request using curl:\\n\\n```\\ncurl http://127.0.0.1:5000/\\ncurl http://127.0.0.1:5000/hello/John\\n```\\n\\nYou\\'ve now created a simple Flask RESTful API project. Remember to keep learning more about RESTful APIs and practice coding to get better at it. Good luck!'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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