AI Code Review & Documentation Assistant

This is a full-stack web application that leverages the power of Large Language Models (LLMs) to provide instant code analysis, improvement suggestions, and documentation. Users can paste a code snippet, select the programming language, and receive an AI-generated review. All analyses are saved in a history log for future reference.

✨ Features

• AI-Powered Analysis: Get an in-depth explanation of your code, a list of actionable improvement suggestions, and a summary of potential bugs.

• Multi-Language Support: Supports popular languages like Python, JavaScript, TypeScript, Java, and C++.

• Persistent History: Every analysis is automatically saved to a PostgreSQL database.

• Modern Tech Stack: Built with a professional-grade stack including Next.js, FastAPI, and Docker.

• Responsive UI: Clean, user-friendly interface built with Tailwind CSS that works on all devices.

🛠️ Tech Stack

• Frontend: Next.js, React, TypeScript, Tailwind CSS

• Backend: Python, FastAPI

• Database: PostgreSQL

• AI Integration: Google Gemini API

• Containerization: Docker & Docker Compose

🚀 Local Setup and Installation

Follow these steps to get the entire application running on your local machine.

Prerequisites

Make sure you have the following installed:

• Node.js (v18 or later)

• Python (v3.8 or later)

• Docker Desktop

1. Clone the Repository

git clone [https://github.com/your-github-username/ai-code-reviewer.git](https://www.google.com/search?q=https://github.com/your-github-username/ai-code-reviewer.git)
cd ai-code-reviewer

2. Set Up the Database

The PostgreSQL database runs in a Docker container.

• Make sure Docker Desktop is running on your machine.

• In the root directory of the project, run the following command to start the database in the background:

docker-compose up -d

This will start a PostgreSQL server on localhost:5432.

3. Configure the Backend

• Navigate to the backend directory:

cd backend

• Create and activate a Python virtual environment:

For macOS/Linux

python3 -m venv venv
source venv/bin/activate

For Windows

python -m venv venv
.\\venv\\Scripts\\activate

• Install the required Python libraries:

pip install -r requirements.txt

• Create a .env file for your environment variables by copying the example file:

cp .env.example .env

• Edit the .env file:

• Get your API key from Google AI Studio.

• Open the .env file and add your key. It should look like this:

  # .env
  GEMINI_API_KEY=YOUR_API_KEY_HERE
  DATABASE_URL="postgresql://myuser:mypassword@localhost/ai_reviewer"

4. Configure the Frontend

• In a new terminal window, navigate to the frontend directory:

cd frontend

• Install the required Node.js packages:

npm install

5. Run the Application

You need to have two terminals running simultaneously: one for the backend and one for the frontend.

• Terminal 1: Start the Backend

• Make sure you are in the backend directory with the (venv) activated.

• Run the FastAPI server:

  uvicorn main:app --reload

• The backend will be running at http://127.0.0.1:8000.

• Terminal 2: Start the Frontend

• Make sure you are in the frontend directory.

• Run the Next.js development server:

  npm run dev

• The frontend will be running at http://localhost:3000.

You can now open your browser and navigate to http://localhost:3000 to use the application!

📂 Project Structure

ai-code-reviewer/
├── backend/
│   ├── .env                \# Environment variables (API key, DB URL)
│   ├── database.py         \# Database connection setup
│   ├── main.py             \# FastAPI application and API endpoints
│   ├── models.py           \# SQLAlchemy database models
│   └── requirements.txt    \# Python dependencies
│
├── frontend/
│   ├── src/app/
│   │   ├── page.tsx        \# Main React component for the UI
│   │   └── globals.css     \# Global styles
│   └── package.json        \# Node.js dependencies
│
├── .gitignore
├── docker-compose.yml      \# Docker configuration for the database
└── README.md               \# This file

📄 API Endpoints

• POST /api/analyze: Receives code and language, returns AI analysis, and saves to history.

• GET /api/history: Returns a list of all past analyses from the database.