AI-Powered Code Review Assistant

A web application that uses DeepSeek AI models to review code, detect bugs, suggest improvements, and provide security analysis. This tool helps developers identify issues in their code and receive actionable feedback with superior performance compared to GPT-3.5 due to specialized code training.

Tech Stack

Frontend

Framework: Next.js 15.3.3 with React 19 and TypeScript 5
UI/Styling: Tailwind CSS 4, Radix UI components, Lucide React icons
Code Editor: Monaco Editor with syntax highlighting
Animations: GSAP with ScrollTrigger
Build Tool: Turbopack

Backend

Framework: FastAPI 0.104.1
Language: Python 3.9+
AI Model: DeepSeek Coder 1.3B (GGUF format)
Model Runtime: llama-cpp-python
Validation: Pydantic 2.5.0
Server: Uvicorn (ASGI)

DevOps & Deployment

Containerization: Docker & Docker Compose
Container Registry: Docker Hub (siddhant004/ai-code-reviewer:latest)
Deployment: Railway / Render / DigitalOcean
CI/CD: GitHub Actions
Code Quality: ESLint, Pylint, Black, isort, pre-commit hooks

Project Structure

ai-code-reviewer/
├── frontend/                     # Next.js app with TypeScript
│   ├── src/                     # Source code
│   │   ├── app/                # Next.js app router
│   │   ├── components/         # React components
│   │   │   ├── ui/            # Reusable UI components (Radix)
│   │   │   ├── CodeEditor.tsx # Monaco code editor
│   │   │   ├── AnalysisResults.tsx
│   │   │   ├── EnhancedResults.tsx
│   │   │   ├── AnalysisConfig.tsx
│   │   │   ├── AnalysisProgress.tsx
│   │   │   ├── EpicLoader.tsx
│   │   │   └── CircuitBoardCity.tsx
│   │   ├── lib/               # Utilities and API client
│   │   └── hooks/             # Custom React hooks
│   ├── package.json           # Frontend dependencies
│   └── next.config.js         # Next.js configuration
├── backend/                    # FastAPI app
│   ├── app/                   # Application code
│   │   ├── main.py           # Entry point
│   │   ├── models/           # Pydantic request/response models
│   │   ├── routers/          # API endpoints
│   │   │   └── analysis.py   # Code analysis routes
│   │   ├── services/         # Business logic
│   │   │   ├── analysis.py   # Static analysis service
│   │   │   └── gguf_service.py # AI model service
│   │   └── utils/            # Utility functions
│   ├── models/               # 🚨 AI model files go here
│   │   └── deepseek-coder-1.3b-instruct.Q4_K_M.gguf
│   ├── requirements.txt      # Python dependencies
│   ├── Dockerfile.prod       # Production container
│   └── .env.example         # Environment variables template
├── .github/                  # GitHub Actions workflows
│   └── workflows/
│       └── lint.yml         # Automated linting
├── docker-compose.yml        # Development environment
├── .pre-commit-config.yaml  # Code quality hooks
├── package.json             # Root scripts for development workflow
├── README.md
└── .gitignore

Quick Start

Prerequisites

Docker & Docker Compose (recommended)
Node.js 18+ and Python 3.9+ (for local development)
Git for cloning the repository

🐳 Docker Deployment (Recommended)

Option 1: Use pre-built Docker image

# Pull and run the production image
docker run -p 8000:8000 \
  -e MODEL_PATH="/app/models/deepseek-coder-1.3b-instruct.Q4_K_M.gguf" \
  -e PORT=8000 \
  siddhant004/ai-code-reviewer:latest

Option 2: Docker Compose

# Clone the repository
git clone https://github.com/Siddhantbg/ai-code-reviewer.git
cd ai-code-reviewer

# Start with Docker Compose
docker-compose up -d

# Access the application
# Frontend: http://localhost:3000
# Backend: http://localhost:8000
# API Docs: http://localhost:8000/docs

📦 Local Development Setup

1. Clone and setup the project

git clone https://github.com/Siddhantbg/ai-code-reviewer.git
cd ai-code-reviewer

2. 🚨 IMPORTANT: Download AI Model

This project requires a DeepSeek Coder model to function properly.

# Create models directory
mkdir -p backend/models

# Download DeepSeek Coder 1.3B model (800MB)
# Download from: https://huggingface.co/microsoft/DeepSeek-Coder-1.3B-Instruct-GGUF
# Place the .gguf file in backend/models/

# Expected file structure:
# backend/models/deepseek-coder-1.3b-instruct.Q4_K_M.gguf

⚠️ Note: This project is specifically configured for DeepSeek models. Using other models requires changes in backend/app/services/gguf_service.py.

3. Install dependencies

# Install all dependencies at once
npm run install:all

This will install:

Root dependencies
Frontend dependencies
Backend dependencies

4. Environment setup

# Backend environment
cd backend
cp .env.example .env

# Edit .env file with your configuration:
# MODEL_PATH=./models/deepseek-coder-1.3b-instruct.Q4_K_M.gguf
# PORT=8000

5. Start development servers

# Start both frontend and backend
npm run dev

Or start them individually:

# Start only frontend
npm run dev:frontend

# Start only backend  
npm run dev:backend

6. Alternative: Docker development

npm run docker:up

Access the Application

Frontend: http://localhost:3000
Backend API: http://localhost:8000
API Documentation: http://localhost:8000/docs

Features

Core Features ✅

Interactive code editor with Monaco Editor
- Syntax highlighting for multiple languages
- File upload functionality
- Example code templates
- Drag and drop support
Multi-language support
- Python, JavaScript, TypeScript, Java, C++, and more
- Automatic language detection from file extension
AI-powered code analysis using DeepSeek models
- Detection of bugs, security issues, and style problems
- Code metrics (complexity, maintainability)
- Severity-based issue categorization
- Suggestions and explanations for each issue
Modern, responsive UI with Tailwind CSS
- Analysis results dashboard
- Issue breakdown by severity
- Export functionality for analysis reports
RESTful API with FastAPI
- Health check endpoint
- Code analysis endpoint
- Supported languages endpoint
- AI model status endpoint

Advanced Features ⚡

Real-time WebSocket connections for live analysis
Analysis progress tracking with cancellation support
Multiple analysis methods: Quick, Comprehensive, Custom
Configurable analysis rules and severity levels
Analysis history and project management
Export reports in JSON, CSV, PDF formats
Keyboard shortcuts for power users
Dark/Light theme support

API Documentation

Core Endpoints

# Health check
GET /health

# Code analysis with static tools
POST /api/analyze
{
  "code": "print('hello world')",
  "language": "python",
  "analysis_type": "comprehensive"
}

# AI-powered analysis  
POST /api/analyze-with-ai
{
  "code": "def hello():\n    print('world')",
  "language": "python"
}

# Get supported languages
GET /api/languages

# Check AI model status
GET /api/model/status

Example Response

{
  "analysis_id": "analysis-123",
  "language": "python", 
  "summary": {
    "overall_score": 8.5,
    "total_issues": 2,
    "critical_issues": 0,
    "suggestions_count": 3
  },
  "issues": [
    {
      "id": "issue-1",
      "type": "style",
      "severity": "low", 
      "line_number": 1,
      "description": "Missing docstring",
      "suggestion": "Add a docstring to describe the function"
    }
  ]
}

Development

Project Scripts

# Development
npm run dev              # Start both frontend and backend
npm run dev:frontend     # Start only frontend
npm run dev:backend      # Start only backend

# Installation
npm run install:all      # Install all dependencies

# Docker
npm run docker:up        # Start Docker containers
npm run docker:down      # Stop Docker containers

# Testing
npm run test:frontend    # Run frontend tests
npm run test:backend     # Run backend tests

# Linting and Formatting
npm run lint:frontend    # Lint frontend code
npm run lint:backend     # Lint backend code
npm run format:backend   # Format backend code

Running Tests

# Backend tests
cd backend
pytest tests/ -v

# Frontend tests
cd frontend  
npm test

# End-to-end tests
npm run test:e2e

Code Quality Tools

ESLint 9 for TypeScript/React code
Pylint 3.0.3 for Python code quality
Black 23.11.0 for Python code formatting
Pre-commit hooks for automated checks
GitHub Actions for CI/CD

Environment Variables

Backend (.env)

# AI Model Configuration
MODEL_PATH=./models/deepseek-coder-1.3b-instruct.Q4_K_M.gguf
MODEL_TYPE=gguf
MAX_TOKENS=2048
TEMPERATURE=0.1

# Server Configuration  
PORT=8000
UVICORN_HOST=0.0.0.0
WORKERS=1
TIMEOUT=300

# CORS Settings
CORS_ORIGINS=["http://localhost:3000"]

# Logging
LOG_LEVEL=INFO
PYTHONUNBUFFERED=1

Frontend (.env.local)

# API Configuration
NEXT_PUBLIC_API_URL=http://localhost:8000
NEXT_PUBLIC_WS_URL=ws://localhost:8000

# App Configuration
NEXT_PUBLIC_APP_NAME="AI Code Reviewer"
NEXT_PUBLIC_APP_VERSION="1.0.0"

Deployment

Docker Hub

Pre-built image available at: siddhant004/ai-code-reviewer:latest

Railway Deployment

# Deploy to Railway
railway login
railway init
railway up --image siddhant004/ai-code-reviewer:latest

# Set environment variables
railway variables set PORT=8000
railway variables set MODEL_PATH="/app/models/deepseek-coder-1.3b-instruct.Q4_K_M.gguf"

Render/DigitalOcean

Deploy using the Docker image with the required environment variables.

Contributing

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Install pre-commit hooks: pre-commit install
Make your changes and commit: git commit -m 'Add amazing feature'
Push to the branch: git push origin feature/amazing-feature
Open a Pull Request

Development Workflow

Follow the existing code style and conventions
Add tests for new features
Update documentation as needed
Ensure all checks pass before submitting PR

License

MIT License - see the LICENSE file for details.

Acknowledgments

DeepSeek AI for the excellent code models
Hugging Face for model hosting
FastAPI for the Python web framework
Next.js for the React framework

Made with ❤️ by Siddhant Bhagat

Name		Name	Last commit message	Last commit date
Latest commit History 52 Commits
Documents		Documents
Test Files		Test Files
assets		assets
backend		backend
frontend		frontend
.gitattributes		.gitattributes
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
README.md		README.md
docker-compose.prod.yml		docker-compose.prod.yml
docker-compose.yml		docker-compose.yml
lint.yml		lint.yml
package.json		package.json
railway.json		railway.json

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