Smart Trash AI

An AI-powered educational platform for waste sorting and environmental education, developed as part of the SMART TRASH research project at Magdeburg-Stendal University of Applied Sciences.

🌐 Live Demo: https://smart-trash-ai.lovable.app/

🎯 Project Overview

This educational platform is being developed under Pillar 2: Robot-Assisted Citizen-Oriented Education of the SMART TRASH research initiative. The project aims to create innovative robot-human interaction solutions for environmental education, with plans for integration into educational robots and expansion across German cities.

🏛️ Academic Context

Institution: Magdeburg-Stendal University of Applied Sciences Project: Smart-TRASH (Smart Technologies for Recycling and Sustainable Handling) Focus Area: Robot-Assisted Citizen-Oriented Education Research Goal: Sustainable waste stream quality improvement through innovative educational technology

🚀 Innovation Potential & Transfer Opportunities

• High innovation in robot-human interaction for environmental education
• Technology transfer opportunities through spin-offs and cooperations
• Lighthouse project potential for Saxony-Anhalt and Germany
• Measurable impact on waste sorting quality through citizen engagement
• Scalable deployment from Magdeburg to other cities across Germany

🎯 Development Goals

• Leverage recent robotic advances for waste separation education
• Create touchscreen interfaces with multi-language support
• Develop AI-powered waste detection and educational feedback
• Sustainably enhance waste stream quality through citizen engagement
• Enable minimal-effort organizational integration (installation, charging, relocation)

🔬 Implementation & Integration

• Educational robot integration for physical learning environments
• Urban education workflow integration with minimal organizational adjustments
• Waste quality measurement and improvement tracking
• Demonstration of successful project integration and measurable impact

🌟 Features

🎮 Interactive Game Experience

• Drag-and-drop waste sorting game with real-time feedback
• Progressive difficulty levels with diverse waste items
• Gamification elements including scoring, streaks, and achievements
• Multi-language support for international accessibility
• Responsive design optimized for touchscreen interfaces

🤖 AI-Powered Education

• Graph RAG (Retrieval-Augmented Generation) using Neo4j knowledge graphs
• Multiple LLM models via OpenRouter API for diverse educational responses
• Real-time waste classification with computer vision integration
• Personalized learning paths based on user performance
• Intelligent feedback system with structured educational responses

📊 Analytics & Research

• Comprehensive user interaction tracking for research purposes
• Performance analytics to measure educational effectiveness
• Feedback systems for continuous improvement
• Data export capabilities for academic research
• Waste quality improvement metrics

🎨 Modern User Interface

• Responsive design optimized for desktop, mobile, and robot touchscreens
• Accessibility features following WCAG guidelines
• Dark/light theme support for user preference
• Intuitive navigation suitable for all age groups
• Multi-language interface for diverse user groups

🛠️ Technology Stack

• Frontend: React 18.x with TypeScript
• Styling: Tailwind CSS with shadcn/ui components
• Database: Neo4j AuraDB for knowledge graph storage
• AI/ML: OpenRouter API with multiple LLM models (Mistral, Llama, Qwen)
• Build Tool: Vite for fast development and building
• Deployment: Lovable platform with custom domain support
• Version Control: Git with GitHub integration

🚀 Getting Started

Prerequisites

• Node.js 18+ and npm
• Neo4j AuraDB instance
• OpenRouter API key

Installation

1. Clone the repository

   git clone https://github.com/KaziNafiulHassan/Smart-Trash-AI.git
   cd Smart-Trash-AI

2. Install dependencies

   npm install

3. Set up environment variables

   # Copy example files
   cp .env.example .env
   cp neo4j.env.example neo4j.env
   
   # Edit with your actual credentials (see Environment Setup section)

4. Start development server

   npm run dev

5. Open in browser Navigate to http://localhost:5173

Build for Production

npm run build
npm run preview

🔧 Environment Setup

This project requires several environment variables to function properly. For security reasons, these are not included in the repository.

Required Environment Files

1. .env - Main application environment variables
2. neo4j.env - Neo4j database credentials

Configuration Details

.env requires:

• VITE_NEO4J_URI - Your Neo4j AuraDB connection URI
• VITE_NEO4J_USERNAME - Your Neo4j username
• VITE_NEO4J_PASSWORD - Your Neo4j password
• VITE_OPENROUTER_API_KEY - Your OpenRouter API key for LLM services
• VITE_SITE_URL - Your deployment URL
• VITE_SITE_NAME - Application name

neo4j.env requires:

• NEO4J_URI - Your Neo4j AuraDB connection URI
• NEO4J_USERNAME - Your Neo4j username
• NEO4J_PASSWORD - Your Neo4j password
• NEO4J_DATABASE - Database name (usually 'neo4j')

Security Notes

• Never commit .env or neo4j.env files to version control
• These files are automatically ignored by Git
• Use the .example files as templates for new deployments

🤝 Contributing

We welcome contributions from the academic community and industry partners! This project is part of ongoing research at Magdeburg-Stendal University of Applied Sciences.

Development Workflow

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

Research Collaboration

For academic collaborations, research partnerships, or technology transfer opportunities, please contact the research team at Magdeburg-Stendal University of Applied Sciences.

📊 Research & Analytics

This platform includes comprehensive analytics for research purposes:

• User interaction patterns
• Learning effectiveness metrics
• Waste sorting accuracy improvements
• Educational content engagement
• Multi-language usage statistics

Data collected supports ongoing research into robot-assisted environmental education and citizen engagement strategies.

🔮 Future Development

Robot Integration Roadmap

• Physical robot integration with touchscreen interfaces
• Computer vision modules for real-time waste detection
• Multi-language voice interaction capabilities
• Adaptive learning algorithms based on user performance
• IoT connectivity for smart city integration

Expansion Plans

• Pilot deployment in Magdeburg educational institutions
• Scaling to other Saxony-Anhalt cities based on measured success
• National expansion as a German lighthouse project
• International collaboration opportunities

📄 License

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

🏫 Academic Citation

If you use this software in your research, please cite:

@software{smart_trash_ai_2024,
  title={Smart Trash AI: EcoSort Adventures},
  author={SMART TRASH Pillar Research Team},
  institution={Magdeburg-Stendal University of Applied Sciences},
  year={2024},
  url={https://github.com/KaziNafiulHassan/Smart-Trash-AI}
}

📞 Contact & Support

Research Team: SMART TRASH Pillar Institution: Magdeburg-Stendal University of Applied Sciences Project Focus: Robot-Assisted Citizen-Oriented Education

For technical support, research collaboration, or technology transfer inquiries, please open an issue in this repository or contact the research team directly.

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This project is part of the SMART TRASH Pillar research initiative, focusing on innovative robot-human interaction for environmental education and sustainable waste management.