Explain Simply | Python

Simple OpenAI Flask Application Explained

This overview provides a breakdown of a basic web application built with Flask that integrates OpenAI's API. It covers the key libraries used, initializes the application, processes user input, communicates with OpenAI's GPT model, and

Prompt

import openai
import os
import sys
from flask import Flask, request, render_template

app = Flask(__name__)

# Correctly accessing environment variable
openai.api_key = os.environ['OpenAIDemoKey']

@app.route('/', methods=['GET', 'POST'])
def index():
    answer = ""
    error = None
    if request.method == 'POST':
        user_question = request.form['question']
        try:
            response = openai.chat.completions.create(
                model="gpt-4o",  # Make sure to use the correct model name
                messages=[{
                    "role": "system",
                    "content": "You are a helpful assistant."
                }, {
                    "role": "user",
                    "content": user_question
                }]
            )
            answer = response.choices[0].message.content
            return answer  # Return only the answer text, not the entire HTML page
        except Exception as e:
            return str(e)
    return render_template('index.html', answer=answer, error=error)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

Answer

Code Explanation: Simple Breakdown of OpenAI Flask Application

This code is a simple web application written in Python using the Flask framework. It integrates OpenAI's GPT model to answer user questions. Below is a detailed analysis of the code structure, functionality, and the key components involved.

Key Libraries Used

openai: This library is used to interact with OpenAI's API which provides access to various AI models like GPT-3 or GPT-4.
os: This module helps in accessing environment variables.
sys: This module provides access to system-specific parameters and functions (not actively used in the shared code).
flask: This framework is used for building web applications in Python.

Code Structure and Functionality

Initializing Flask Application

app = Flask(__name__)

This line creates an instance of the Flask application.

Accessing Environment Variable

openai.api_key = os.environ['OpenAIDemoKey']

This line retrieves the OpenAI API key from the environment variables.
Ensure that the environment variable OpenAIDemoKey is set correctly, as this is essential for accessing the OpenAI services.

Defining Routes

@app.route('/', methods=['GET', 'POST'])

This line establishes a route (or endpoint) for the web application.
The application will respond to HTTP GET and POST requests at the root URL /.

Processing User Input

if request.method == 'POST':
    user_question = request.form['question']

Here, the application checks if the request is a POST request, which occurs when a user submits a question via a form.
The submitted question is retrieved from the form data with the key 'question'.

Communicating with OpenAI's API

response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "system",
        "content": "You are a helpful assistant."
    }, {
        "role": "user",
        "content": user_question
    }]
)

This block sends a request to OpenAI's API to generate a response.
The model specified here is "gpt-4o" (ensure the model name is correct as per the current available models from OpenAI).
The messages array includes two parts: one from the system setting context, and one from the user input containing their question.

Handling the Response

answer = response.choices[0].message.content
return answer

The application extracts the content of the response from OpenAI.
Specifically, it accesses the first choice returned from the model and retrieves its message content.
The text of the answer is returned to the user.

Error Handling

except Exception as e:
    return str(e)

If an error occurs during the OpenAI API request, it catches the exception and returns the error message as a string.
This helps in debugging and understanding any issues that arise during execution.

Rendering the Initial Web Page

return render_template('index.html', answer=answer, error=error)

If the request method is GET (initial visit or refresh), it renders an HTML page named index.html, passing the answer and error variables to the template.
This is how the application presents information to the user.

Running the Application

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

This section checks if the script is run directly (not imported as a module), then starts the Flask server at IP address 0.0.0.0 (accessible from any IP) and port 8080.

Conclusion

This Flask application allows users to submit questions and receive responses from an OpenAI language model. Key components include accessing environment variables for security, handling both GET and POST requests, communicating with OpenAI's API, and rendering the response to a web page.

For further learning and understanding of such implementations, consider exploring the Enterprise DNA Platform, which provides resources for enhancing data science skills and application development.