Syntax Sentry API

A FastAPI-based service for analyzing code patterns and detecting suspicious coding behaviors across multiple programming languages.

Features

• Multi-language Support: Analyzes Python, Java, C++, and JavaScript code
• Behavioral Analysis: Detects copy, paste, keystroke, and tab patterns
• MongoDB Integration: Stores analysis results and responses
• Comprehensive Logging: Detailed logging for debugging and monitoring
• RESTful API: Clean API endpoints for integration

API Endpoints

POST /execute

Execute code analysis scripts for different programming languages and behaviors.

Request Body:

{
  "script_name": "cpp.py",
  "object_id": "67f559c9dfb01510b8393ffd"
}

Supported Scripts:

• paste.py - Paste behavior analysis
• copymain.py - Copy behavior analysis
• keymain.py - Keystroke analysis
• tab.py - Tab usage analysis
• cpp.py - C++ code analysis
• py.py - Python code analysis
• java.py - Java code analysis
• javascript.py - JavaScript code analysis

Response:

{
  "suspiciousness_percentage": 33.5,
  "reasons": [
    "Inconsistent spacing around operators found in ~100.0% of relevant lines.",
    "Lack of TODO/FIXME markers, often present in human development cycles."
  ],
  "factor_scores": {
    "comment_density": {
      "score": 0.8,
      "details": "0.00 (0/1)"
    }
  }
}

Quick Start

Local Development

1. Clone the repository

   git clone <repository-url>
   cd py-api

2. Install dependencies

   pip install -r requirements.txt

3. Set up environment variables

   cp env.example .env
   # Edit .env with your configuration

4. Run the application

   uvicorn main:app --reload --host 0.0.0.0 --port 8000

5. Access the API

   • API: http://localhost:8000
   • Interactive docs: http://localhost:8000/docs

Docker Deployment

1. Build and run with Docker Compose

   docker-compose up --build

2. Or build and run manually

   docker build -t syntax-sentry-api .
   docker run -p 8000:8000 syntax-sentry-api

Deployment Options

1. Render (Recommended)

1. Connect your GitHub repository to Render

2. Create a new Web Service

3. Configure the service:

   • Build Command: pip install -r requirements.txt
   • Start Command: uvicorn main:app --host 0.0.0.0 --port $PORT
   • Environment: Python 3
   • Plan: Free or Starter

4. Set environment variables in Render dashboard:

   • MONGODB_URL: Your MongoDB connection string
   • MONGODB_DATABASE: Database name
   • LOG_LEVEL: INFO
   • ENVIRONMENT: production

5. Deploy: Render will automatically deploy from your render.yaml configuration

2. Heroku

1. Install Heroku CLI

2. Login and create app

   heroku login
   heroku create your-app-name

3. Set environment variables

   heroku config:set MONGODB_URL="your-mongodb-url"
   heroku config:set MONGODB_DATABASE="test"
   heroku config:set LOG_LEVEL="INFO"
   heroku config:set ENVIRONMENT="production"

4. Deploy

   git push heroku main

3. Vercel

1. Install Vercel CLI

   npm i -g vercel

2. Deploy

   vercel --prod

3. Set environment variables in Vercel dashboard

4. Railway

1. Connect GitHub repository to Railway
2. Configure environment variables
3. Deploy automatically

5. DigitalOcean App Platform

1. Create new app from GitHub
2. Configure build settings:
   • Build Command: pip install -r requirements.txt
   • Run Command: uvicorn main:app --host 0.0.0.0 --port $PORT
3. Set environment variables
4. Deploy

Environment Variables

Variable	Description	Default	Required
MONGODB_URL	MongoDB connection string	-	Yes
MONGODB_DATABASE	Database name	test	No
API_HOST	API host	0.0.0.0	No
API_PORT	API port	8000	No
LOG_LEVEL	Logging level	INFO	No
LOG_DIRECTORY	Log directory	logs	No
SECRET_KEY	Secret key for security	-	Yes (production)
ENVIRONMENT	Environment type	development	No

Project Structure

py-api/
├── main.py                 # FastAPI application
├── requirements.txt        # Python dependencies
├── checkcodetype.py       # Language detection utilities
├── *.py                   # Analysis scripts for different languages
├── schema/                # JSON schema definitions
├── Dockerfile             # Docker configuration
├── docker-compose.yml     # Docker Compose setup
├── render.yaml           # Render deployment config
├── vercel.json           # Vercel deployment config
├── Procfile              # Heroku deployment config
├── env.example           # Environment variables template
└── README.md             # This file

Dependencies

• FastAPI: Modern web framework for building APIs
• Uvicorn: ASGI server for running FastAPI
• PyMongo: MongoDB driver for Python
• Radon: Code complexity analysis
• PyCodeStyle: Python style checker

Monitoring and Logging

• Logs are stored in the logs/ directory
• Daily log rotation with timestamp-based filenames
• Console and file logging enabled
• Health check endpoint available at /health

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License.

Support

For support and questions, please open an issue in the repository.