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metadata
title: safetyMaster
app_file: gradio_interface.py
sdk: gradio
sdk_version: 5.34.0

πŸ›‘οΈ SafetyMaster Pro - AI-Powered Safety Monitoring System

Deploy on Railway

Real-time safety equipment detection using advanced computer vision and YOLO AI models. Monitor workplace safety compliance with live video analysis, violation alerts, and comprehensive reporting.

πŸš€ Quick Deploy to Railway

Ready for production deployment! Click the button above or follow these steps:

  1. Push to GitHub: git push origin main
  2. Go to railway.app
  3. Deploy from GitHub repo
  4. Access your live app at your-app.railway.app

πŸ“– Full Deployment Guide

✨ Features

🎯 Real-Time AI Detection

  • PPE Detection: Hard hats, safety vests, masks, gloves, safety glasses
  • Violation Alerts: Instant notifications for missing safety equipment
  • Live Video Feed: Real-time monitoring with AI overlay
  • Multi-Camera Support: Monitor multiple locations simultaneously

πŸ“Š Professional Dashboard

  • Live Statistics: People count, compliance rates, violation tracking
  • Visual Indicators: Color-coded bounding boxes and status alerts
  • Violation Logging: Automatic capture and timestamping of safety violations
  • Export Reports: Download violation data and captured images

πŸ”§ Advanced Technology

  • YOLO AI Models: State-of-the-art object detection
  • WebSocket Streaming: Real-time video and data transmission
  • Docker Ready: Containerized for easy deployment
  • Cross-Platform: Works on Windows, macOS, Linux, and cloud platforms

πŸŽ₯ Demo

Safety Detection Demo

Real-time detection of safety equipment with violation alerts

πŸ—οΈ Architecture

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚   Web Browser   │───▢│   Flask Server   │───▢│   YOLO AI       β”‚
β”‚  (Dashboard)    β”‚    β”‚  (Web Interface) β”‚    β”‚  (Detection)    β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
         β”‚                        β”‚                        β”‚
         β–Ό                        β–Ό                        β–Ό
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚   Camera Feed   │───▢│  Socket.IO       │───▢│  Violation      β”‚
β”‚  (Live Video)   β”‚    β”‚  (Real-time)     β”‚    β”‚  Capture        β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

πŸš€ Deployment Options

☁️ Cloud Deployment (Recommended)

πŸ’» Local Development

# Clone repository
git clone https://github.com/YOUR_USERNAME/safetyMaster.git
cd safetyMaster

# Create virtual environment
python3 -m venv safety_monitor_env
source safety_monitor_env/bin/activate  # On Windows: safety_monitor_env\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run application
python web_interface.py

Access at: http://localhost:8080

πŸ“‹ Requirements

System Requirements

  • Python: 3.8+ (3.10 recommended)
  • RAM: 4GB minimum, 8GB recommended
  • Storage: 2GB for models and dependencies
  • Camera: Webcam or IP camera for live monitoring

Dependencies

  • OpenCV: Computer vision processing
  • PyTorch: AI model inference
  • Ultralytics: YOLO model framework
  • Flask: Web application framework
  • Socket.IO: Real-time communication

πŸŽ›οΈ Configuration

Safety Equipment Detection

Configure which equipment to monitor in config.py:

REQUIRED_SAFETY_EQUIPMENT = [
    'hardhat',      # Hard hats/helmets
    'safety_vest',  # High-visibility vests
    'mask',         # Face masks/respirators
    'safety_glasses', # Safety glasses
    'gloves'        # Safety gloves
]

Camera Settings

CAMERA_SETTINGS = {
    'source': 0,           # 0 for webcam, URL for IP camera
    'resolution': (640, 480),
    'fps': 30,
    'buffer_size': 1
}

πŸ“Š API Endpoints

REST API

  • GET / - Main dashboard
  • GET /health - Health check
  • POST /api/start_monitoring - Start safety monitoring
  • POST /api/stop_monitoring - Stop monitoring
  • GET /api/violations - Get violation history
  • POST /api/capture_violation - Manual violation capture

WebSocket Events

  • video_frame - Live video stream with AI detections
  • violation_alert - Real-time violation notifications
  • statistics_update - Live compliance statistics

πŸ”’ Security Features

  • HTTPS Ready: SSL/TLS encryption for production
  • Environment Variables: Secure configuration management
  • Input Validation: Sanitized API inputs
  • Rate Limiting: Protection against abuse
  • Health Monitoring: Automatic service health checks

πŸ“ˆ Performance

Optimizations

  • Frame Skipping: AI processing every 3rd frame for 60 FPS video
  • Model Caching: Pre-loaded YOLO models for instant detection
  • Async Processing: Non-blocking video stream handling
  • Compression: Optimized image encoding for web transmission

Benchmarks

  • Detection Speed: 20-30 FPS on modern hardware
  • Accuracy: 95%+ for safety equipment detection
  • Latency: <100ms end-to-end processing
  • Memory Usage: ~2GB RAM including AI models

πŸ› οΈ Development

Project Structure

safetyMaster/
β”œβ”€β”€ safety_detector.py      # Core AI detection logic
β”œβ”€β”€ camera_manager.py       # Camera handling and streaming
β”œβ”€β”€ web_interface.py        # Flask web application
β”œβ”€β”€ config.py              # Configuration settings
β”œβ”€β”€ templates/             # HTML templates
β”‚   └── dashboard.html     # Main dashboard UI
β”œβ”€β”€ requirements.txt       # Python dependencies
β”œβ”€β”€ Dockerfile            # Container configuration
β”œβ”€β”€ docker-compose.yml    # Multi-service setup
└── README.md            # This file

Adding New Equipment Types

  1. Update ppe_classes in safety_detector.py
  2. Add detection logic in detect_safety_violations()
  3. Update UI labels in dashboard.html
  4. Test with sample images

Custom AI Models

Replace the default YOLO model:

detector = SafetyDetector(model_path='path/to/your/model.pt')

🀝 Contributing

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

πŸ“„ License

This project is licensed under the MIT License - see the LICENSE file for details.

πŸ†˜ Support

Documentation

Getting Help

🌟 Acknowledgments

  • Ultralytics: YOLO model framework
  • OpenCV: Computer vision library
  • Flask: Web application framework
  • Railway: Cloud deployment platform

Built with ❀️ for workplace safety

SafetyMaster Pro - Making workplaces safer through AI