🌌 Dark Matter Classification System

An intelligent dark matter event classification and anomaly detection system powered by Claude AI.

🚀 Quick Start

Start Both Frontend & Backend (Easy Way)

Windows:

start_dev.bat

Mac/Linux:

chmod +x start_dev.sh
./start_dev.sh

Then open: http://localhost:5173

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Manual Start

Terminal 1 - Backend:

python webapp_backend.py

Terminal 2 - Frontend:

cd webapp
npm install
npm run dev

Then open: http://localhost:5173

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📚 Documentation

Quick Access

• QUICK_START.md - Complete usage guide
• DEPLOYMENT_GUIDE.md - How to deploy publicly

Comprehensive Documentation

• USER_GUIDE.md - Complete user manual with step-by-step instructions
• DEVELOPER_GUIDE.md - Development setup, coding standards, and contribution guide
• ARCHITECTURE.md - System architecture, components, and data flow
• AI_FUNCTIONALITY.md - Claude AI integration, prompt engineering, and workflows
• API_DOCUMENTATION.md - REST API reference with examples

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✨ Features

• 🔬 Event Classification - AI-powered dark matter event analysis
• 📊 Results Dashboard - Real-time visualization and statistics
• ⚠️ Anomaly Detection - Automatic detection of unusual events
• 📈 Data Analytics - Comprehensive dataset insights
• 🤖 Claude AI Integration - Advanced reasoning and analysis
• 📁 Batch Processing - Upload and analyze multiple events

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📖 New! Complete Documentation

We've created comprehensive documentation to help you understand and use the system:

Document	Description	Target Audience
USER_GUIDE.md	Step-by-step instructions for using all features	End Users, Scientists
DEVELOPER_GUIDE.md	Setup, development workflow, and contribution guide	Developers, Contributors
ARCHITECTURE.md	System design, components, and data flow	Technical Leads, Architects
AI_FUNCTIONALITY.md	How Claude AI powers classification and analysis	AI/ML Engineers, Researchers
API_DOCUMENTATION.md	Complete REST API reference with examples	API Users, Integrators

New to the system? Start with USER_GUIDE.md
Want to contribute? Read DEVELOPER_GUIDE.md
Building integrations? See API_DOCUMENTATION.md

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🛠️ Tech Stack

Frontend:

• React + TypeScript
• Vite
• TailwindCSS
• Recharts
• Shadcn/ui

Backend:

• Python 3.11
• Flask
• Anthropic Claude API
• Pandas
• NumPy

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🌐 Deploy Your Own

1. Deploy Backend: Render.com (Free)
2. Deploy Frontend: Vercel.com (Free)

See DEPLOYMENT_GUIDE.md for details.

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📝 Environment Variables

Create a .env file:

ANTHROPIC_API_KEY=your_claude_api_key_here
CLAUDE_API_KEY=your_claude_api_key_here

Get your API key from: https://console.anthropic.com/

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📦 Installation

# Install Python dependencies
pip install -r requirements.txt

# Install frontend dependencies
cd webapp
npm install

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🎯 Project Structure

Technologia_ClaudeSolvathon/
├── webapp/                    # React frontend
│   ├── src/
│   │   ├── pages/            # Dashboard pages
│   │   ├── components/       # Reusable components
│   │   └── lib/             # API integrations
│   └── package.json
├── anomaly_detection_system/  # Anomaly detection
│   └── mainAnomalyDetection.py
├── dataset/                   # Data files
│   └── dark_matter_synthetic_dataset.csv
├── webapp_backend.py          # Flask API server
├── mainClassify.py           # Classification logic
├── requirements.txt           # Python dependencies
├── start_dev.bat             # Windows launcher
├── start_dev.sh              # Mac/Linux launcher
└── .env                      # API keys (gitignored)

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🧪 Testing

Test Backend:

curl http://localhost:5001/api/health

Test Frontend: Open http://localhost:5173 in browser

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📄 License

MIT License - Feel free to use and modify!

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🙏 Acknowledgments

• Anthropic Claude AI
• Dark Matter Research Community
• Open Source Contributors

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📞 Support

• 📖 Read the QUICK_START.md
• 🐛 Report issues on GitHub
• 💬 Check browser console for errors

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Built with ❤️ for Dark Matter Research