Creating and Deploying an AI Code Bug Fixing SaaS Application Using Flask

In this chapter, we will dive into the creation and deployment of an AI-powered code bug-fixing SaaS application using Flask. This application will leverage OpenAI’s GPT-3 language model to provide code error explanations and fixes to users. With the rapid growth of software development, the need for effective and efficient error-debugging solutions has become more important than ever. By building this SaaS application, we aim to provide developers with a powerful tool to speed up their debugging process and improve the quality of their code.

To develop this SaaS application, we will be utilizing a tool that you may already be well acquainted with, namely, the Flask framework for building web applications in Python. Our aim is to become proficient in utilizing this framework to build an effective and scalable web application that can deliver efficient code error solutions to its users. Furthermore,...

Technical Requirements

The technical requirements for this project are as follows:

• Python 3.7 or later installed on your machine.
• A code editor, such as PyCharm (recommended).
• A Python virtual environment.
• The Flask web framework installed in the virtual environment
• An OpenAI API key.
• Access to Microsoft Azure.

The code examples from this chapter can be found on GitHub at https://github.com/PacktPublishing/Building-AI-Applications-with-ChatGPT-APIs/tree/main/Chapter03%20CodeBugFixer.

Performing Multiple ChatGPT API Requests

Let’s first dive into the ChatGPT API requests and learn how to build your project and the app.py file. In this project, we will explore how to make multiple ChatGPT API requests to get the explanations and fixed code for our input queries. We will go through each step of the process and provide clear examples, so you can follow along with ease.

To fix users’ code, ChatGPT needs two key components: some buggy code and the error provided by the system. The idea behind the Code Bug-Fixing application is that you provide ChatGPT with two separate requests simultaneously (see Figure 3.1):

• Request 1: ChatGPT uses the buggy code and the error to fix the code.
• Request 2: ChatGPT uses the buggy code and the error to explain the error to the user in plain English.

Figure 3.1: Code Bug Fixer Request/Response Map

As shown in Figure 3.2, the design of the code bug-fixing app is a web application...

Setting Up the Code Bug Fixer Project

Creating a PyCharm project is an essential first step in any Python development process. By following a few simple steps, you can create a new PyCharm project in just a few minutes. Once you’ve set up your project, you’ll be ready to start building and testing your code:

1. Open PyCharm: Double-click on the PyCharm icon on your desktop or search for it in your applications folder to open it.
2. On the PyCharm welcome screen, click on Create New Project or go to File | New Project.
3. Choose the directory where you want to save your project. You can either create a new directory or select an existing one.
4. Select the Python interpreter: Choose the version of Python you want to use for your project.
5. Configure project settings: Give your project the name CodeBugFixer, and choose a project location.
6. Once you’ve configured all the settings, click Create to create your new PyCharm project.

After creating...

Implementing the Code Bug Fixer Backend

The backend of the Code Bug-Fixing application should ensure that we properly send our buggy code to ChatGPT and, on the other side, receive the correct response. For this reason, you need to make sure that the index() function is able to clearly distinguish between GET and POST requests. You can add the following modification to the index() function to achieve that:

@app.route("/", methods=["GET", "POST"])
def index():
    if request.method == "POST":
        # Code Errr
        code = request.form["code"]
        error = request.form["error"]
        prompt = (f"Explain the error in this code without fixing it:"
              ...

Using Text Areas and Containers

In this section, you will learn how to create a simple user interface for your CodeBugFixer application using HTML and CSS. We will walk through the process of designing a simple form for users to input their code and error message and display the results of the ChatGPT API call. By the end of this section, you will have a fully functional web application that can help users fix errors in their code.

To modify the index.html file, first, you need to open the file located in the templates folder of your project. You can open this file in any text editor, or you can use the built-in HTML editor in PyCharm. Once you have the file open, you add the basic HTML structure:

<!DOCTYPE html>
<html>
<head>
    <meta charset="UTF-8">
    <title>Code Bug Fixer</title>
</head>
<body>
<div class="header">
    <h1>Code Bug...

Testing the Code Bug Fixer App

Now, we will learn how to test our Code Bug Fixer application. We will write some test cases to ensure that the application is functioning correctly and handling different scenarios appropriately. Testing is a critical part of software development, as it ensures that the application works as intended and meets the user’s requirements.

The Code Bug Fixer app can fix any programming language, making it a versatile tool. Therefore, we will demonstrate its effectiveness by using examples written in two different programming languages: Java and Python.

The Python example has the following properties:

• Code:

  def factorial(n):
      if n == 0:
          return 1
      else:
          return n * factorial(n-1)
  print("The factorial of 5 is: " + factorial(5))

• Error: Type Error:

This code is returning an error because...

Deploying the ChatGPT App to the Azure Cloud

Now let’s upload our Code Bug Fixer application to the Azure cloud platform. Azure is a cloud computing service provided by Microsoft that allows developers to host, manage, and scale their applications in the cloud. By hosting our application on Azure, we can make our application accessible to a wider audience. We will go through the steps required to create an Azure account, set up our application for deployment, and deploy it to Azure using the Azure CLI.

You can create your Azure account by following these steps:

1. Go to the Azure website at https://azure.microsoft.com/free/ and click on Start free.
2. Sign in with your Microsoft account or create one if you don’t have one.
3. Enter your personal information and payment details. You won’t be charged unless you upgrade to a paid plan.
4. Choose your subscription type and agree to the terms and conditions.
5. Verify your account by entering a phone number...

Summary

This chapter described the creation and deployment of an AI-powered SaaS application, Code Bug Fixer, which uses OpenAI’s GPT-3 language model to provide code error explanations and fixes to users. It covered building the application using Flask, creating a web form that accepts user input for code and error messages, and designing a web interface for displaying the generated explanations and solutions. The chapter also provided instructions on how to test and deploy the application to the Azure cloud platform, offering security and scalability features to the application.

Furthermore, you learned how to create a user interface for Code Bug Fixer using HTML and CSS, adding a basic HTML structure, a header, an input form, and two columns containing text areas for the user to enter their code and error message. The testing process involved running test cases for the application in two different programming languages, Python and Java. By following the given steps, users...