README.md

metadata

title: Plate Detector
emoji: 🏃
colorFrom: gray
colorTo: gray
sdk: gradio
sdk_version: 6.14.0
python_version: '3.10'
app_file: app.py
pinned: false
license: mit

🚗 Vehicle Intelligence System

Advanced license plate detection, OCR, and natural language database querying system with security hardening.

🎯 Features

Detection Pipeline

• 🎥 YOLO Plate Detection - Real-time license plate localization
• 🔤 PaddleOCR - High-accuracy OCR for plate text extraction
• 🚗 Vehicle Classification - Classify vehicle types (car, truck, bike, etc.)
• 🛡️ Confidence Scoring - All detections include confidence metrics

Database & Analytics

• 📊 Supabase PostgreSQL - Robust cloud database with automatic backups
• 🔍 NLP-to-SQL - Ask questions in natural language, AI converts to SQL
• 🛡️ SQL Validation - Prevents injection attacks and dangerous queries
• ⚡ Optimized Indexes - Fast queries with proper database indexing
• 📈 Real-time Analytics - Dashboard with state distribution, hourly traffic, suspicious vehicles

Security Features

• ✅ SQL Injection Prevention - Whitelist/blacklist keyword validation
• 🔒 Read-Only Queries - Only SELECT queries allowed
• 📝 Query History - Track all database queries for audit
• 🔐 State Extraction - Automatically extract state codes from license plates

📂 Modular Architecture

plate-detector/
├── app.py              # Main Gradio UI with 3 tabs
├── detector.py         # YOLO, OCR, vehicle classification
├── database.py         # SQL validation, LLM, database operations
├── requirements.txt    # Python dependencies
└── .env               # Environment variables (Supabase, HF token)

Files Explained

app.py - Simplified UI layer

• Detection tab: Upload images, detect plates
• Database Query tab: NLP-to-SQL search with example queries
• Analytics tab: Real-time dashboard with metrics

detector.py - Computer Vision Module

• License plate detection using YOLO
• OCR using PaddleOCR
• Vehicle classification (10 types)
• State code extraction (TN, KA, KL, etc.)
• Image preprocessing and augmentation

database.py - Data & Security Layer

• SQL validation (blocks DELETE, UPDATE, INSERT, DROP)
• SQLCoder LLM integration for NLP-to-SQL
• Database initialization with improved schema
• Automatic index creation for performance
• Query history tracking
• Analytics functions (suspicious vehicles, hourly traffic, etc.)

🚀 Deployment

Environment Variables

Create .env file with:

DATABASE_URL=postgresql://user:password@host:5432/postgres
HF_TOKEN=hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxx

Run Locally

pip install -r requirements.txt
python app.py

Visit: http://localhost:7860

Deploy on Hugging Face Spaces

1. Push code to HF Spaces repository
2. Add secrets in Settings:
   • DATABASE_URL - Supabase connection string
   • HF_TOKEN - Hugging Face API token

🛡️ Security Measures

SQL Validation

ALLOWED_KEYWORDS = ["SELECT", "FROM", "WHERE", "COUNT", ...]
BLOCKED_KEYWORDS = ["DROP", "DELETE", "UPDATE", "INSERT", ...]

# Only SELECT queries allowed
# Blocks all dangerous operations

Database Schema

CREATE TABLE vehicle_logs (
    id BIGSERIAL PRIMARY KEY,
    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    plate TEXT,
    state TEXT,
    vehicle_type TEXT,
    vehicle_conf FLOAT,
    camera_id TEXT,
    location TEXT,
    image_url TEXT,
    date DATE,
    hour INTEGER,
    day TEXT
);

Indexes for Performance

CREATE INDEX idx_plate ON vehicle_logs(plate);
CREATE INDEX idx_state ON vehicle_logs(state);
CREATE INDEX idx_vehicle_type ON vehicle_logs(vehicle_type);
CREATE INDEX idx_timestamp ON vehicle_logs(timestamp);
CREATE INDEX idx_date ON vehicle_logs(date);

🎯 Example Queries

• "How many cars today?"
• "Show all trucks"
• "Count bikes"
• "Show TN vehicles"
• "Show vehicles after 6pm"
• "List latest 10 detections"
• "Count vehicles by type"
• "Show high confidence detections"
• "List all unique plates"
• "Show suspicious vehicles"

📊 Analytics Dashboard

• Vehicles by State - Distribution of detections across states
• Traffic by Hour - Hourly traffic patterns
• Top Plates - Most frequently detected license plates
• Suspicious Vehicles - High-frequency detections (>20 times)

🔮 Future Improvements

• Vector search for similar plates
• Async database queries for better performance
• Image storage and retrieval
• Camera tracking and geolocation
• Result caching with Redis
• FastAPI backend separation
• React dashboard frontend

📦 Dependencies

• ultralytics - YOLO detection
• paddleocr - OCR
• transformers - Vehicle classification
• sqlalchemy - Database ORM
• huggingface-hub - LLM inference
• gradio - Web UI
• opencv - Image processing

📄 License

MIT License - See LICENSE file

🤝 Contributing

Contributions welcome! Please submit pull requests or issues.

---

Live Demo: https://huggingface.co/spaces/BARATH0070/plate-detector