Leverage ChatGPT, Whisper, and DALL-E APIs to build 10 innovative AI projects, 2nd Edition

GitHub

https://github.com/PacktPublishing/Building-AI-Applications-with-ChatGPT-APIs

2. Building a ChatGPT Clone

This chapter guides readers through building a ChatGPT clone—a chatbot leveraging OpenAI’s GPT-3.5 language model—using the Flask Python web framework. The application features a simple, elegant frontend for user interaction and a backend that communicates with the ChatGPT API to generate human-like responses.

Key points covered include:

  • Setting up the Flask backend: Creating a Flask app with routes to serve the frontend and handle API requests. The backend securely manages the OpenAI API key via environment variables or a separate config file to avoid exposing sensitive information.

  • Frontend development: Building an HTML interface using Bootstrap for styling, featuring a chat window, input field, and submit button. The frontend resides in a Flask-recognized templates folder.

  • Connecting frontend and backend: Using jQuery to capture user input, send it asynchronously via AJAX GET requests to the Flask backend, and dynamically update the chat window with both user messages and AI responses without page reloads.

  • Enhancing UI design: Applying Bootstrap CSS and custom styles to create a dark-themed, user-friendly chat interface with scrollable chat history and styled input controls.

  • Intercepting ChatGPT API endpoints: Defining Flask routes that receive user messages, forward them to the OpenAI ChatGPT API, and return generated responses to the frontend.

  • Implementing conversation retention: Maintaining a conversation history list that stores alternating user and assistant messages with role identifiers. This history is sent with each API call to provide context, enabling the chatbot to generate coherent, context-aware replies across multiple interactions.

  • Testing and validation: Demonstrating the chatbot’s ability to remember user-provided information (e.g., user name, preferences) and respond accordingly, confirming effective context retention.

Technical requirements include Python 3.7+, Flask, an OpenAI API key, and a code editor like VSCode. The chapter emphasizes best practices such as using virtual environments, securing API keys, and organizing code for maintainability.

The chapter concludes by preparing readers for the next topic: building and deploying an AI-powered code bug-fixing SaaS application with Flask and ChatGPT, including deployment to Azure and WordPress integration.

Overall, this chapter provides a comprehensive, step-by-step tutorial on creating a functional, customizable ChatGPT clone with Flask, covering backend setup, frontend design, API integration, real-time interaction, and context-aware conversation management.

  1. How does the ChatGPT clone implemented in this chapter manage conversation context retention using the conversation_history list and role identifiers, and what is the exact sequence of appending user and assistant messages to maintain coherent dialogue?

  2. What are the detailed steps and code structure recommended for securely storing and accessing the OpenAI API key in the Flask ChatGPT clone project to prevent accidental exposure, including how to configure .gitignore and use a separate config.py file?

  3. How does the jQuery-based frontend in the ChatGPT clone handle real-time user input submission and response display without page refresh, specifically detailing the AJAX GET request to the /get Flask route and the DOM manipulation to append chat messages dynamically?

3. Creating and Deploying a Code Bug-Fixing Application Using Flask

This chapter guides you through building and deploying an AI-powered code bug-fixing SaaS application called Code Bug Fixer using the Flask web framework and OpenAI’s GPT-3.5 model. The app helps developers by explaining code errors and providing fixed code snippets, streamlining debugging across any programming language.

Key Components and Workflow:

  • Multiple ChatGPT API Requests: The app sends two simultaneous requests to ChatGPT—one to explain the error in plain English and another to fix the buggy code.

  • Flask Backend: Handles GET and POST requests, processes user input (buggy code and error message), calls the OpenAI API, and renders results.

  • Frontend UI: Built with HTML and CSS, featuring two input text areas for code and error, a "Code Fix" button, and two read-only text areas displaying the explanation and fixed code.

  • Styling: Uses CSS for a clean, modern, two-column layout with a blue-and-white color scheme and interactive button styling.

Development Setup:

  • Create a VSCode project named CodeBugFixer with files: app.py, config.py, and a templates/index.html.

  • Install Python 3.7+, Flask, and OpenAI Python libraries in a virtual environment.

  • Store the OpenAI API key securely in config.py.

  • Implement Flask routes and logic in app.py to handle form submissions and interact with the ChatGPT API.

Testing:

  • Demonstrated fixing Python and Java code examples with typical errors.

  • The app successfully explains errors and provides corrected code, verified by running the fixed code in appropriate IDEs.

  • Shows versatility in handling multiple programming languages.

Deployment to Azure Cloud:

  • Instructions to create a free Azure account and install the Azure CLI on macOS, Windows, or Linux.

  • Prepare the project for deployment by creating a requirements.txt listing dependencies (Flask==3.0.2, openai==1.13.3).

  • Deploy the app using the Azure CLI command az webapp up --name <app_name>.

  • After deployment, the app is accessible globally via the provided Azure URL.

  • Optionally embed the deployed app into a WordPress site using an iframe.

  • Manage the deployed app through the Azure portal, with options to update or delete the deployment.

Summary:

By following this chapter, you learn to build a scalable Flask web app that leverages OpenAI’s GPT-3.5 for code debugging assistance, create a user-friendly interface, test with real code examples, and deploy the app to Azure cloud for worldwide access. This equips you with practical skills to develop and host AI-powered SaaS applications.

  1. How does the Code Bug Fixer application architecturally handle simultaneous requests to OpenAI’s GPT-3.5 API to both explain and fix code errors, and what are the specific prompt structures used for each request?

  2. What is the detailed step-by-step process, including file structure and Flask route handling, for setting up the Code Bug Fixer project in VSCode to securely integrate the OpenAI API key and manage GET vs POST requests for user-submitted code and errors?

  3. What are the exact commands and configuration steps required to deploy the Flask-based Code Bug Fixer app to Microsoft Azure using the Azure CLI, including preparation of the requirements.txt file and naming conventions critical for successful deployment?