How to Build a Code Review Assistant Using AI

Hey there! Have you ever been into a situation where you code breaks during the production. Although you have passed the manual review of code but your application still not operating as you have anticipated.

Let’s face it, manual reviews at times not cover all the possible cases. Imagine if we could have an AI based tool that do the heavy lifting—an assistant that reviews our code in seconds and suggests improvements based on industry standards. Sounds awesome, right?

Well, guess what? We’re going to build a code review assistant using the OpenAI’s GPT-4 model. Our assistant will analyze the provided code, highlight specific areas of improvement, and provide suggestions based on coding standards. By the end, we’ll have a functional web-based code-reviewing tool that can assist users in reviewing their code. Let’s dive in!

Setting up the development environment

Before diving into writing our application, We need to setup an environment that is suitable to AI integration and web application development. We’re going to use Flask for our web framework, trust me its super easy to use and Python as a background programming language. We’ll use a simple HTML to build our frontend. Of course, we’ll be using OpenAI’s API to power the brain of our code review assistant.

But wait, What is actually Flask?

Imagine you’re building a resort. Python is your toolbox containing all the fancy tools, but you need a structure to build on top of it, right? That’s where the magic of Flask comes in, its like a structure that helps us to build a simple house, in our case build a small app quickly. Basically Flask is a web framework that supports us when we just want to get things done without a headache of external dependencies. Let’s gather all our required tools by installing the Python and Flask.

Install Python and Flask

Firstly we need to ensure Python is installed on our system. Use the terminal to check if it is already installed by running the following command on our terminal.

python --version

If you see a version number just pops up, congrats, you’re good to go. If it’s not installed, don’t panic, head over to python.org and download it on your system.

Once it is sorted, now open your terminal and create a new project directory as follows:

mkdir code-review-assistant
cd code-review-assistant

Next, we’ll create a virtual environment to manage our project dependencies. Think a virtual environment as a safe place for all the code we’re going to write. It is nice and isolated from the rest of your system. Run the following command to create a virtual environment.

python -m venv venv
source venv/bin/activate  # For Windows: venv\Scripts\activate

Now let’s install Flask and OpenAI, the backbone of our system as follows.

pip install flask openai

OpenAI API keys

Now lets bring the brain of our system—The OpenAI API. To use OpenAI’s API, we’ll need to signup over the OpenAl platform and create an account. After signing in, move towards the API keys section and generate a new key. We’ ll use this key later in our code.

If you face any difficulty in during the signup and API key generation follow this guide to use OpenAI API. Now, we have the API key, lets see how we can utilize it in the next section.

Initialize the OpenAI API

As we have access to our brand new API key, its time to setup our backend so our code review assistant can chat with our GPT model. This initialization process will allows our code reviewing assistant to send requests to the GPT model. We will setup a backend script to handle requests from the front end and interact with the AI model. We’ll also configure API key and use environment variables to ensure API key is not exposed and easy manageable inside our application.

Create the backend script

In our project folder, create a new Python file called app.py. We can create it using the following command:

# For Mac user
touch app.py

# For Windows user
echo. > app.py  # Using command prompt
New-Item -Path app.py -ItemType File # Using PowerShell 

This file will serve as the brain of our operations. Open app.py and add the following code:

import os
import openai
from flask import Flask, request, jsonify

app = Flask(__name__)

# Load OpenAI API key from environment variables
openai.api_key = os.getenv("OPENAI_API_KEY")

@app.route("/review", methods=["POST"])
def review_code():
    # Get the code submitted from the frontend
    code = request.json.get("code")

    if not code:
        return jsonify({"error": "No code provided"}), 400

    # Request GPT-4 to review the provided code
    response = openai.Completion.create(
        model="text-davinci-003",
        prompt=f"Review the following code and suggest improvements:\n\n{code}",
        max_tokens=500,
        temperature=0.7
    )

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

    return jsonify({"feedback": feedback})

if __name__ == "__main__":
    app.run(debug=True)

To keep our application secure, its crucial to manage it properly as we’ll do in coming section.

Configure API key and environment variables

We need to ensure the API key remains secured throughout the process. To securely use our API, we can set it as an environment variable. We need to create a .env file in our project directory by using the following command:

echo “OPENAI_API_KEY=your-openai-api-key” > .env

To ensure our application can read this API key. We can use python-dotenv to manage this process as follows:

pip install python-dotenv

Next, modify the app.py to load our environment variables from the .env file:

from dotenv import load_dotenv

# Load environment variables
load_dotenv()

This activity will ensure our API will remain secure while allowing the application to interact with OpenAI’s API.

As the backend part of our application is complete, let’s deep dive towards creating the frontend part of our application.

Creation of web interface

Let’s create a simple interface so any user can submit its code for review and receive an AI powered feedback.

Creation of HTML file

Inside our project folder, create a new folder named templates. Inside this new folder, create an HTML file named index.html.

mkdir templates
touch templates/index.html

In index.html, add the following code to create a simple user interface.

 <title>Code Review Assistant</title>
    
        body {
            font-family: Arial, sans-serif;
            padding: 20px;
            background-color: #f4f4f9;
        }
        .container {
            max-width: 800px;
            margin: 0 auto;
        }
        textarea {
            width: 100%;
            height: 200px;
            margin-bottom: 20px;
            padding: 10px;
            border: 1px solid #ccc;
        }
        button {
            padding: 10px 20px;
            background-color: #007bff;
            color: white;
            border: none;
            cursor: pointer;
        }
        .feedback {
            margin-top: 20px;
            background-color: #f8f9fa;
            padding: 20px;
            border: 1px solid #ccc;
        }
    


    <div class="container">
        <h1>AI Code Review Assistant</h1>
        <textarea id="code-input"></textarea>
        <button>Submit for Review</button>
        <div id="feedback" class="feedback"></div>
    </div>

    
        function submitCode() {
            const code = document.getElementById('code-input').value;
            fetch('/review', {
                method: 'POST',
                headers: {
                    'Content-Type': 'application/json',
                },
                body: JSON.stringify({ code }),
            })
            .then(response => response.json())
            .then(data => {
                document.getElementById('feedback').innerText = data.feedback;
            })
            .catch((error) => {
                console.error('Error:', error);
            });
        }
    

Link frontend to the Backend

To connect the dots, add the following code to the app.py file to serve this HTML page when the root URL is accessed.

@app.route("/")
def index():
    return render_template("index.html")

It will render the index.html as the homepage of our application, allowing users to interact with AI code reviewers.

With that everything is set up, now let’s test our application.

Running the application

Now it’s time to run our application and utilize our AI code review assistant in action.

Start our Flask development server by running the following command:

python app.py

It will start the server on http://127.0.0.1:5000/. We can open this link to observe our web interface. We’ll see a text area where we can paste our code. Once we click the “Submit for Review” button, the AI will analyze the code and provide feedback under the button.

Congratulations! you’ve built a simple but powerful AI-driven assistant. We have used the OpenAI’s GPT-4 model for accomplishing this project. It allows developers to input their code, receive feedback, and make instant improvements based on the provided suggestions.

While it is a basic version, but it provides a solid foundation to build our next unicorn on top of it. We can extend the features by introducing version control systems like Github or provide support to completing the missing logic inside the code.

With AI as our review partner, we can streamline the development process and ensure code quality with minimal effort. Keep coding!

Explore more on Metaschool

If you found building an AI-powered code review assistant helpful, you might be interested in exploring more hands-on projects to expand your skills. Check out our other courses on Metaschool:

• Build a YeBot with OpenAI API: Learn how to create a sophisticated chatbot using OpenAI’s powerful API, perfect for enhancing your understanding of AI-driven applications.
• Build a Code Translator Using NextJS and OpenAI API: Dive into building a code translation tool using NextJS and OpenAI, a great way to apply your knowledge in a practical and innovative way.

Explore these courses and more to continue your journey in AI on the Metaschool platform!

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