AI Code Review Assistant

An AI-powered code review assistant that analyzes code quality, finds bugs, detects security vulnerabilities, and suggests improvements. Integrates with GitHub Pull Requests for automated reviews.

• AI-Powered Code Review - Leverages OpenAI GPT-4o to analyze code with deep contextual understanding
• GitHub PR Integration - Review any GitHub pull request by URL, with per-file analysis
• Security Vulnerability Detection - Identifies hardcoded secrets, SQL injection, XSS, command injection, and more
• Multi-Language Support - Python, JavaScript, TypeScript, Java, Go, Rust, C++, C#, Ruby, PHP, SQL, HTML, CSS, and more
• Static Analysis - Built-in pattern-based security scanning as fallback
• Interactive UI - Modern React frontend with severity badges, categorized results, and detailed issue exploration
• Docker Deployment - One-command deployment with Docker Compose

User Input (Code or PR URL)
       |
       v
[FastAPI Backend]
       |
       +---> [Static Analysis] ---> Pattern matching, complexity metrics
       |
       +---> [GitHub Service] ---> Fetch PR diff, extract changed files
       |
       +---> [OpenAI Review] ---> Structured analysis with retry logic
       |
       v
[Structured Review Response]
       |
       v
[React Frontend] ---> Tabbed results: Issues, Security, Suggestions

# 1. Clone the repository
git clone https://github.com/roohan-514/ai-code-review-assistant.git
cd ai-code-review-assistant

# 2. Set up environment variables
cp backend/.env.example backend/.env
# Edit backend/.env and add your OpenAI API key

# 3. Run with Docker Compose
docker-compose up --build

The backend will be available at http://localhost:8000 and the frontend at http://localhost:5173.

• Python 3.11+
• Node.js 18+
• OpenAI API key

cd backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your OpenAI API key

# Run the server
uvicorn backend.main:app --reload --port 8000

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

GET /health

Response:

{
  "status": "ok",
  "model": "gpt-4o",
  "languages_supported": 19
}

GET /languages

Response:

{
  "languages": ["python", "javascript", "typescript", ...]
}

POST /review-code
Content-Type: application/json

{
  "code": "def add(a, b):\n    return a + b",
  "language": "python",
  "file_name": "math_utils.py"
}

POST /review-pr
Content-Type: application/json

{
  "pr_url": "https://github.com/owner/repo/pull/42"
}

{
  "summary": "Overall assessment of the code.",
  "total_issues": 5,
  "critical_count": 1,
  "high_count": 2,
  "medium_count": 1,
  "low_count": 1,
  "issues": [
    {
      "line": 15,
      "severity": "high",
      "category": "bug",
      "description": "Off-by-one error in range()",
      "suggestion": "Use range(len(arr)) instead of range(len(arr) + 1)",
      "code_context": "for i in range(len(arr) + 1):"
    }
  ],
  "security_alerts": [
    {
      "line": 42,
      "severity": "critical",
      "vulnerability_type": "sql_injection",
      "description": "Possible SQL injection vulnerability",
      "impact": "Unauthorized database access",
      "recommendation": "Use parameterized queries",
      "code_context": "cursor.execute(f\"SELECT * FROM users WHERE id = {user_input}\")"
    }
  ],
  "suggestions": [
    "Add type hints for better code documentation",
    "Consider using dataclasses for data containers"
  ],
  "language": "python",
  "analyzed_files": ["src/main.py (python)"]
}

1. Navigate to the Review PR page
2. Click Paste Code
3. Select the programming language
4. Paste your code
5. Click Review Code
6. Explore results in the Issues, Security, and Suggestions tabs

1. Navigate to the Review PR page
2. Click Review PR
3. Enter a GitHub PR URL (e.g. https://github.com/owner/repo/pull/42)
4. Click Review PR
5. View per-file analysis and categorized results

Python, JavaScript, TypeScript, Java, Go, Rust, C++, C#, Ruby, PHP, Swift, Kotlin, Scala, HTML, CSS, SQL, Bash, YAML, JSON

When reviewing a PR, the tool produces:

• Summary - High-level assessment of the code changes
• Issues Tab - Code quality, bugs, and best practice violations with severity levels
• Security Tab - Vulnerability alerts with impact analysis and remediation steps
• Suggestions Tab - Actionable improvement recommendations

Each issue card shows:

• Severity badge (Critical/High/Medium/Low)
• Category label
• Line number
• Description
• Code context (expandable)
• Fix suggestion

ai-code-review-assistant/
├── README.md
├── .gitignore
├── docker-compose.yml
├── backend/
│   ├── requirements.txt
│   ├── .env.example
│   ├── Dockerfile
│   ├── main.py                  # FastAPI entry point with routing
│   ├── config.py                # Configuration management
│   ├── services/
│   │   ├── review_service.py    # OpenAI-powered code review
│   │   ├── github_service.py    # GitHub PR integration
│   │   └── analysis_service.py  # Static analysis & security scanning
│   └── models/
│       └── schemas.py           # Pydantic data models
├── frontend/
│   ├── package.json
│   ├── index.html
│   ├── vite.config.js
│   ├── tailwind.config.js
│   ├── postcss.config.js
│   ├── Dockerfile
│   ├── src/
│   │   ├── App.jsx              # Root component with routing
│   │   ├── App.css              # Global styles & Tailwind
│   │   ├── main.jsx             # Entry point
│   │   ├── pages/
│   │   │   ├── Home.jsx         # Landing page
│   │   │   ├── ReviewPR.jsx     # Code/PR review page
│   │   │   └── History.jsx      # Review history
│   │   ├── components/
│   │   │   ├── Navbar.jsx       # Navigation
│   │   │   ├── CodeInput.jsx    # Code editor area
│   │   │   ├── ReviewResult.jsx # Results display
│   │   │   ├── IssueCard.jsx    # Issue display card
│   │   │   └── SecurityAlert.jsx # Security alert card
│   │   └── services/
│   │       └── api.js           # API client
│   └── public/
└── tests/
    └── test_backend.py          # Backend test suite

# Build and start services
docker-compose up --build -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

Create backend/.env with your configuration:

OPENAI_API_KEY=sk-your-key-here
OPENAI_MODEL_NAME=gpt-4o
GITHUB_TOKEN=ghp_your-token-here

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

MIT