How to Integrate ChatGPT Into Your Python App

How to Integrate ChatGPT Into Your Python App

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ChatGPT, powered by OpenAI, is an advanced large language model that can generate human-like responses to user queries. By integrating OpenAI into your application or platform, you can enhance user interactions and provide intelligent conversational capabilities.

In this article, we will explore how to build a ChatGPT plugin using Python and Flask, a popular web framework. We’ll cover the prerequisites, project setup, implementation details, testing process, and further customization options. Let’s get started!


Before diving into the implementation, make sure you have the following prerequisites:

  • Python: Install Python on your system. You can download and install Python from the official Python website. Ensure you choose a version compatible with the code you’ll be using (Python 3.x is recommended).
  • Flask: Flask is a lightweight web framework for Python. You can install Flask using pip, the Python package manager, by executing the following command in your terminal or command prompt:
pip install flask
  • OpenAI Python library: The OpenAI Python library allows us to interact with the OpenAI API seamlessly. We can make API requests and retrieve responses using this library. Install it via pip by running the following command:
pip install openai
  • OpenAI API key: Sign up for an account on the OpenAI platform and obtain an API key. The API key is necessary to authenticate your requests to the OpenAI API. Keep your API key handy, as we will use it later in the implementation.

Setting Up the Project

Create a new directory for your project and navigate to it in your terminal or command prompt. This directory will serve as the root directory for our ChatGPT plugin.

To maintain a clean and isolated environment, create a virtual environment specifically for this project. Run the following command to initialize a virtual environment named chatgpt-venv:

python -m venv chatgpt-venv

Then, activate the virtual environment. This can be done with the following command in Windows:


For macOS and Linux:

source chatgpt-venv/bin/activate

Next, install Flask and the OpenAI library within the virtual environment:

pip install flask openai

Implementing the ChatGPT Plugin

Create a new Python file named and open it in your favorite code editor. Add the following code to the file:

from flask import Flask, request, jsonify
import openai

app = Flask(__name__)
openai.api_key = 'YOUR_OPENAI_API_KEY'

@app.route('/chat', methods=['POST'])
def chat():
    data = request.json
    user_message = data['message']

    response = openai.Completion.create(

    assistant_reply = response.choices[0].text.strip()

    return jsonify({'message': assistant_reply})

if __name__ == '__main__':

In this code snippet, we import the necessary modules (Flask, request, jsonify, and openai) and create a Flask application instance. Replace YOUR_API_KEY with your actual OpenAI API key obtained from the OpenAI platform.

We define a route /chat that handles POST requests. Inside the chat() function, we extract the user’s message from the request JSON payload. We then make a request to the OpenAI ChatGPT API using the openai.Completion.create() method, passing the user’s message as the prompt and specifying other parameters like the model (text-davinci-003), maximum tokens, number of response alternatives, and temperature. Finally, we extract the assistant’s reply from the API response and return it as a JSON response.

Testing the ChatGPT Plugin

In the terminal or command prompt, ensure that your virtual environment is active. Then, run the Flask server by executing the following command:


The server will start running on http://localhost:5000.

Now, you can test the ChatGPT plugin by sending a POST request to http://localhost:5000/chat with a JSON payload containing the user’s message. You can use a tool like cURL, Postman, or write a simple Python script. Here’s an example using the requests library:

import requests

url = 'http://localhost:5000/chat'
data = {'message': 'Hello, how are you?'}
response =, json=data)


This code sends a POST request to the /chat endpoint with the user’s message as JSON data. It then prints the assistant’s reply received from the ChatGPT plugin.

Congratulations! You have successfully built and tested a ChatGPT plugin using Python and Flask. This plugin acts as an interface between your application and the OpenAI ChatGPT API, allowing you to leverage advanced language generation capabilities.

Extending the Plugin Functionality

The basic ChatGPT plugin we’ve built provides a solid foundation for integrating ChatGPT into your application. However, you can extend its functionality to enhance user interactions and customize the behavior based on your specific requirements. Let’s look into a few ways you can extend the plugin.

Conversation Context

By maintaining a conversation context, you can have more interactive and coherent conversations with the ChatGPT model. The OpenAI API allows you to include an array of messages, where each message has a role (“system”, “user”, or “assistant”) and content (the text of the message).

To implement conversation context, you can either store previous messages in a data structure (such as a list or database) as the conversation progresses. Or you can also include the previous messages as part of the messages parameter when making an API request and pass the entire conversation history to provide context to the model. This way, the model can refer to previous messages and generate responses that are consistent with the conversation flow.

Integration with Additional Platforms

The ChatGPT plugin can be integrated into various platforms to provide conversational capabilities across different channels. Some examples include:

  • Integrating the plugin into a web chat widget on your website.
  • Building a chatbot for popular messaging platforms like Facebook Messenger, Slack, or WhatsApp.
  • Integrating the plugin into voice assistants or chat-based applications.

By extending the plugin’s functionality to different platforms, you can reach a wider audience and provide a seamless conversational experience across various channels. Remember to consider each platform’s specific requirements and limitations and adapt the plugin accordingly.

Final Words

Remember to handle errors, implement conversation context if desired, and secure your API key properly when deploying the plugin to a production environment. Feel free to experiment with different models, adjust parameters, and extend the functionality based on your specific use case.