Building a ChatGPT Clone

The first application in this chapter and in this book is going to be a ChatGPT clone that utilizes OpenAI’s powerful language model to generate human-like responses to user input. The application will be built using Flask, a lightweight web framework for Python, and will feature a simple and elegant frontend interface for users to interact with. With just a few lines of code, users will be able to input their text and receive a response generated by the OpenAI language model. Specifically, the application will be able to do the following:

• Render a simple browser chat interface to interact with a chatbot.
• The chatbot will accept user input and send it to the ChatGPT API.
• Display the response from the ChatGPT API to the user.
• Show chat history.

The clone app will be able to take in a user’s input, send it to OpenAI’s API, and receive a response, which will be displayed to the user in real time. The app will be...

Technical Requirements

In order to fully benefit from this chapter, you will require some fundamental tools. In-depth explanations of all installations not covered in the preceding chapter will be provided.

You will require the following:

• Python 3.7 or later installed on your computer.
• An OpenAI API key, obtained from your OpenAI account
• A code editor, such as PyCharm (recommended)
• The Flask framework installed in your Python virtual environment

The code examples from this chapter can be found on GitHub at https://github.com/PacktPublishing/Building-AI-Applications-with-ChatGPT-APIs/tree/main/Chapter02%20ChatGPTClone.

In the next section, we will start building the ChatGPT clone with Flask by setting up a backend that communicates with the ChatGPT API to generate responses to user input. You will create a simple Flask application and gradually enhance it by incorporating the ChatGPT API. We will also structure the code and store the ChatGPT API key...

Creating a ChatGPT Clone with Flask

To create a ChatGPT clone with Flask, you will need to set up a backend that communicates with the ChatGPT API to generate responses to user input. Flask is a popular Python web framework that can be used to create web applications, including ChatGPT clones. We will begin our project by creating a simple Flask application and gradually enhance it by incorporating the ChatGPT API, additional web pages, and the frontend.

To get started, initiate a new PyCharm project and give it the name ChatGPT_Clone. Create a new file named app.py to serve as the backend of your application, where you’ll define your Flask application and interact with the ChatGPT API. Once you have created the project, you will need to install the Flask package using pip. You can do this by running the following command in your project’s terminal:

$ pip install flask

After installing Flask, you can start building your Flask application. To do this, you need...

Frontend HTML Generation

Let’s start creating the HTML and CSS necessary to generate the frontend of our chat application. We will be using HTML, CSS, and Bootstrap to create the user interface and jQuery to handle the communication between the frontend and the backend. The HTML and CSS will be responsible for creating the structure and style of the user interface.

When creating a Flask web application, it is recommended to keep your HTML files in a separate folder called templates. This is because Flask uses the Jinja2 template engine, which allows you to write reusable HTML code in a more modular way by separating it into smaller, reusable pieces called templates.

To create the templates folder, simply create a new directory in your project directory and name it templates. Inside the templates folder, you can create your HTML file, called index.html in this case. To do that, right-click on the templates folder and select New | HTML file. Here is what the project directory...

Enhancing the ChatGPT Clone Design

In order to enhance the design and aesthetics of the ChatGPT Clone, we will be applying some CSS code. By applying CSS modifications, we can improve the overall visual appeal of the chat application and create a more user-friendly interface.

Let’s first add an external style sheet provided by the Bootstrap framework. To do that, under the page title, you can add the following:

<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css">

When added to the HTML code, it allows the use of the various classes provided by Bootstrap to style the web page. This link points to version 3.3.7 of the Bootstrap framework, and it includes various CSS rules for styling common HTML elements such as headings, paragraphs, forms, and buttons, among others. Using Bootstrap can help to ensure consistency in the design of the web page while also simplifying the task of styling and making...

Intercepting ChatGPT API Endpoints

Our ChatGPT clone needs to use JavaScript to handle user interactions with the chat application in real time. To do that, we can use a jQuery script that listens for a click on the submit button, gets the user input from the input field, sends a GET request to the server with the user input as a query parameter, and then receives a response from ChatGPT. The use of JavaScript in this way will enable the chat application to update and display new chat messages without the need for a page refresh, making for a more seamless and user-friendly experience.

We can write the JavaScript code just under the input-group class, as shown here:

        <div class="input-group">
            <input type="text" id="userInput" class="form-control">
          ...

Summary

In this chapter of the book, you were introduced to the process of building a ChatGPT clone, which is a chatbot that utilizes OpenAI’s language model to generate human-like responses to user input. The application was built using Flask, a lightweight web framework for Python, and was customizable to allow for the use of different OpenAI models and other options such as the length of the generated text.

We also covered topics such as creating and generating the frontend HTML for the ChatGPT clone, intercepting ChatGPT API endpoints, passing user input from the frontend to the backend using AJAX, and displaying the generated text in the frontend.

In Chapter 3, you will learn how to create and deploy an AI-powered code bug fixing SaaS application using Flask and the ChatGPT language model. You will become proficient in using the ChatGPT API. You will learn how to create a web form that accepts user inputs, deploy the application to the Azure cloud platform, and integrate...