AI Vision Waste Classification System

Overview

This is an AI-powered waste classification application that uses computer vision to detect and categorize different types of waste materials. The system combines a React frontend with a Node.js/Express backend and a Python FastAPI service for AI processing, utilizing YOLO models for real-time object detection.

System Architecture

Frontend Architecture

• Framework: React 18 with TypeScript
• Build Tool: Vite for fast development and optimized builds
• UI Framework: Shadcn/ui components built on Radix UI primitives
• Styling: Tailwind CSS with CSS variables for theming
• State Management: TanStack Query for server state management
• Routing: Wouter for lightweight client-side routing

Backend Architecture

• Primary Backend: Node.js with Express server
• AI Processing: Separate Python FastAPI service for YOLO model inference
• Communication: WebSocket connections for real-time data streaming
• Session Management: In-memory storage with plans for database integration

Database Architecture

• ORM: Drizzle with PostgreSQL support
• Schema: Comprehensive tables for users, detection sessions, results, and model configurations
• Migration Strategy: Drizzle Kit for schema management

Key Components

Frontend Components

1. CameraFeed: Handles webcam access and real-time video streaming
2. ControlPanel: Configuration interface for detection parameters and model settings
3. PerformanceMetrics: Real-time system performance monitoring
4. UI Components: Complete Shadcn/ui component library for consistent interface

Backend Services

1. Express Server: Main application server handling HTTP requests and WebSocket connections
2. Python Backend: FastAPI service with YOLO model integration
3. WebSocket Manager: Real-time communication between frontend and AI processing
4. Performance Monitor: System resource monitoring (CPU, memory, GPU usage)
5. File Handler: Manages model uploads and data exports

AI/ML Components

1. YOLO Detector: Ultralytics YOLO implementation for waste classification
2. Model Configuration: Configurable confidence thresholds and class filtering
3. Real-time Processing: Frame-by-frame analysis with performance optimization

Data Flow

1. Camera Feed: User webcam captures video frames
2. Frame Processing: Frames sent via WebSocket to Python backend
3. AI Inference: YOLO model processes frames and detects waste objects
4. Results Streaming: Detection results sent back to frontend via WebSocket
5. Data Storage: Results optionally stored in PostgreSQL database
6. Performance Monitoring: System metrics continuously monitored and displayed

Waste Classification Categories

• Plastic (Plastik)
• Glass (Kaca)
• Cans (Kaleng)

External Dependencies

Frontend Dependencies

• React Ecosystem: React, React DOM, React Router (Wouter)
• UI Framework: Radix UI primitives, Lucide icons
• State Management: TanStack Query
• Styling: Tailwind CSS, class-variance-authority
• Build Tools: Vite, TypeScript

Backend Dependencies

• Server Framework: Express.js, WebSocket support
• Database: Drizzle ORM, PostgreSQL driver (@neondatabase/serverless)
• Validation: Zod for schema validation
• Session Management: Connect-pg-simple for PostgreSQL sessions

Python AI Backend

• Web Framework: FastAPI with uvicorn
• Computer Vision: OpenCV, Ultralytics YOLO
• Performance: NumPy, psutil for system monitoring
• File Handling: aiofiles for async file operations

Deployment Strategy

Development Environment

• Frontend: Vite dev server with HMR
• Backend: tsx for TypeScript execution with hot reload
• Database: Neon PostgreSQL or local PostgreSQL instance
• AI Service: FastAPI with uvicorn in development mode

Production Build

• Frontend: Static build output served by Express
• Backend: Compiled TypeScript bundle with ESBuild
• Database: Production PostgreSQL instance
• AI Service: Production FastAPI deployment
• Environment: NODE_ENV-based configuration

Database Migration

• Command: npm run db:push for schema deployment
• Drizzle Kit handles PostgreSQL schema synchronization
• Migration files stored in ./migrations directory

Recent Changes

• July 07, 2025 - Theme and Camera Selection Added
  • Added dark/light mode toggle with theme provider
  • Implemented camera selection dropdown for multiple cameras
  • Updated all components to use theme-aware styling (surface/muted colors)
  • Enhanced camera hook to support device enumeration and switching
  • Improved responsive design for better light/dark mode contrast

Changelog

• July 07, 2025. Initial setup with AI waste classification system
• July 07, 2025. Added theme toggle and camera selection features

User Preferences

Preferred communication style: Simple, everyday language. UI Preferences: Dark/light theme toggle, camera selection support, theme-aware styling