PlagiaScan

AI-Powered Plagiarism Detection Platform

A complete, production-ready plagiarism detection system with semantic and lexical analysis, built with FastAPI, React, and modern ML techniques.

Features

• 🚀 Multi-format Support: PDF, DOCX, TXT, HTML with OCR fallback
• 🧠 Hybrid Detection: Semantic (embeddings) + Lexical (MinHash) matching
• 📊 Visual Reports: Detailed match highlighting and scoring
• 🔐 Secure: JWT authentication and protected routes
• ⚡ Scalable: Async processing with Celery workers
• 🎨 Modern UI: React + Tailwind CSS responsive design

Quick Start (Local Mode)

The application is configured to run entirely locally without Docker, using SQLite and a local Vector Database.

Prerequisites

• Node.js 18+ (for frontend)
• Python 3.10+ (for backend)

1. Backend Setup

Open a terminal in the backend folder:

cd backend

# 1. Install dependencies
pip install -r requirements.txt

# 2. Initialize Database
python init_db.py

# 3. Start Backend Server
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

2. Frontend Setup

Open a new terminal in the frontend folder:

cd frontend

# 1. Install dependencies
npm install

# 2. Start Frontend Server
npm run dev

3. Access the App

• Frontend: http://localhost:5173
• Backend API: http://localhost:8000
• API Docs: http://localhost:8000/docs

Architecture (Local Mode)

┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│   React     │────▶│   FastAPI    │────▶│   SQLite    │
│  Frontend   │     │   Backend    │     │ (Local File)│
└─────────────┘     └──────────────┘     └─────────────┘
                           │
                           ├────▶ Qdrant (Local Storage)
                           └────▶ Background Tasks

Tech Stack

Backend

• FastAPI: High-performance async API
• Celery: Distributed task queue
• PostgreSQL: Relational database
• Qdrant: Vector database for semantic search
• Sentence-Transformers: ML embeddings
• MinHash: Lexical fingerprinting

Frontend

• React: UI framework
• Vite: Build tool
• Tailwind CSS: Styling
• Axios: HTTP client
• React Router: Navigation

API Endpoints

Authentication

• POST /api/v1/auth/register - Register new user
• POST /api/v1/auth/login - Login

Documents

• POST /api/v1/documents/ - Upload document
• GET /api/v1/documents/ - List documents
• GET /api/v1/documents/{id} - Get document

Scans

• POST /api/v1/scans/ - Initiate scan
• GET /api/v1/scans/{id} - Get scan results

Testing

# Backend tests
cd backend
pytest

# Run specific test
pytest tests/test_ml.py

Project Structure

plag/
├── backend/
│   ├── app/
│   │   ├── api/          # API routes
│   │   ├── core/         # Business logic
│   │   ├── db/           # Database
│   │   ├── models/       # SQLAlchemy models
│   │   └── worker.py     # Celery tasks
│   └── tests/
├── frontend/
│   └── src/
│       ├── pages/        # React pages
│       └── api.js        # API client
├── docs/
│   ├── architecture.md
│   ├── openapi.yaml
│   └── schema.sql
└── docker-compose.yml

Environment Variables

Create .env in project root:

DATABASE_URL=postgresql://plagiascan:plagiascan_dev@localhost:5432/plagiascan
REDIS_URL=redis://localhost:6379/0
QDRANT_URL=http://localhost:6333
SECRET_KEY=your-secret-key-here

Development

Add New Endpoint

1. Create route in backend/app/api/v1/endpoints/
2. Register in backend/app/main.py
3. Add tests in backend/tests/

Add New Page

1. Create component in frontend/src/pages/
2. Add route in frontend/src/App.jsx

Production Deployment

Recommended Setup

• Backend: Deploy to cloud (AWS, GCP, Azure)
• Database: Managed PostgreSQL
• Vector DB: Qdrant Cloud or self-hosted
• Frontend: Vercel, Netlify, or CDN
• Workers: Separate worker instances

Security Checklist

• Change SECRET_KEY in production
• Enable HTTPS/TLS
• Configure CORS properly
• Add rate limiting
• Set up monitoring
• Enable database backups

License

MIT

Contributing

Contributions welcome! Please read CONTRIBUTING.md first.

Support

For issues and questions, please open a GitHub issue.