🌌 NASA Exoplanet Detection Platform

Discover worlds beyond our solar system with the same tools NASA uses

A comprehensive full-stack platform for exoplanet detection and analysis, featuring real machine learning models trained on NASA Kepler, K2, and TESS mission data. Built for NASA Space Apps Challenge 2025.

🚀 Features

🤖 Real Machine Learning

Trained Models: Random Forest classifier trained on 15,000+ NASA exoplanet candidates
High Accuracy: 88.1% ROC-AUC, 83.9% accuracy on test data
Real Data: Uses actual Kepler, K2, and TESS mission datasets
Feature Engineering: Proper preprocessing with imputation and scaling
Calibrated Confidence: Realistic probability distributions, no artificial patterns

🔬 Exoplanet Analysis

Tabular Analysis: Input orbital parameters for instant classification
Light Curve Analysis: Upload time-series data for transit detection
Multi-Mission Support: Kepler, K2, and TESS data formats
Explainable AI: Feature importance analysis with detailed explanations
Confidence Scoring: Well-calibrated confidence intervals

💬 AI-Powered Chat Assistant

Secure Chatbot: Comprehensive security guardrails prevent information leakage
Context-Aware: Explains analysis results using actual model predictions
Session Management: 3-message limit with enterprise upgrade path
Educational Focus: Helps users understand exoplanet detection science

🎨 Modern User Interface

Glassmorphism Design: Space-themed UI with subtle gradients
Responsive Layout: Mobile-first design with smooth animations
Interactive Visualizations: 3D exoplanet viewer and interactive charts
Performance Optimized: React.memo, useCallback, and other optimizations
Accessibility: Full keyboard navigation and screen reader support

📊 Data Visualization

3D Exoplanet Viewer: Interactive 3D visualization of exoplanet systems
Interactive Charts: Recharts-powered flux time series with transit highlighting
Real-Time Analysis: Live updates as you input data
Export Functionality: Download results as JSON or CSV

🏗️ Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Frontend      │    │   Backend       │    │   ML Pipeline   │
│   (Next.js)     │◄──►│   (FastAPI)     │◄──►│   (Python)      │
│                 │    │                 │    │                 │
│ • React UI      │    │ • REST APIs     │    │ • Data Prep     │
│ • TypeScript    │    │ • ML Models      │    │ • Training      │
│ • Tailwind CSS  │    │ • Chat Security │    │ • Validation    │
│ • Framer Motion │    │ • Groq AI       │    │ • Artifacts     │
└─────────────────┘    └─────────────────┘    └─────────────────┘

🛠️ Tech Stack

Frontend

Framework: Next.js 15 with App Router
Language: TypeScript
Styling: Tailwind CSS with custom space theme
Animations: Framer Motion
Charts: Recharts for data visualization
3D Graphics: Three.js with React Three Fiber
Icons: Lucide React

Backend

Framework: FastAPI with Python 3.11
ML Libraries: scikit-learn, pandas, numpy
AI Integration: Groq API for chatbot functionality
Data Processing: joblib for model serialization
API Documentation: Auto-generated OpenAPI/Swagger

Machine Learning

Algorithm: Random Forest Classifier
Data Sources: NASA Kepler, K2, TESS missions
Features: 5 core exoplanet characteristics
Preprocessing: Imputation, scaling, outlier clipping
Validation: Stratified train/validation/test splits

📁 Project Structure

nasa-exoplanet-2025/
├── frontend/                 # Next.js frontend application
│   ├── src/
│   │   ├── app/             # App router pages and API routes
│   │   ├── components/      # React components
│   │   │   ├── forms/       # Input forms and file upload
│   │   │   ├── results/     # Analysis results display
│   │   │   ├── ui/          # UI components and chat
│   │   │   └── visualization/ # 3D exoplanet viewer
│   │   └── utils/           # Utility functions
│   └── public/              # Static assets and sample data
├── backend/                 # FastAPI backend application
│   ├── src/app/
│   │   ├── main.py          # FastAPI app configuration
│   │   └── routers/         # API route handlers
│   │       ├── tabular.py   # ML prediction endpoints
│   │       ├── lightcurve.py # Light curve analysis
│   │       └── chat.py      # AI chatbot with security
│   └── requirements.txt     # Python dependencies
├── ML/                      # Machine learning pipeline
│   ├── Data Pipeline/       # Data preprocessing and EDA
│   │   ├── data/           # Raw and processed datasets
│   │   ├── artifacts/      # Trained models and scalers
│   │   └── reports/        # Analysis reports and visualizations
│   └── training/           # Model training scripts
├── vercel.json             # Vercel deployment configuration
├── render.yaml             # Render deployment configuration
└── README.md               # This file

🚀 Quick Start

Prerequisites

Node.js 18+ and npm
Python 3.11+
Git

1. Clone Repository

git clone https://github.com/your-username/nasa-exoplanet-2025.git
cd nasa-exoplanet-2025

2. Backend Setup

# Navigate to backend directory
cd backend

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

# Install dependencies
pip install -r requirements.txt

# Start the server
python -m uvicorn src.app.main:app --reload --host 0.0.0.0 --port 8000

3. Frontend Setup

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

4. Access Application

Frontend: http://localhost:3000
Backend API: http://localhost:8000
API Documentation: http://localhost:8000/docs

🔧 Configuration

Environment Variables

Backend (.env)

GROQ_API_KEY=your_groq_api_key_here
PYTHON_VERSION=3.11.0

Frontend (.env.local)

NEXT_PUBLIC_BACKEND_URL=http://localhost:8000

ML Model Configuration

The ML pipeline uses these core features:

period_days: Orbital period in days
transit_depth_ppm: Transit depth in parts per million
planet_radius_re: Planet radius in Earth radii
stellar_radius_rs: Stellar radius in solar radii
snr: Signal-to-noise ratio

📊 API Endpoints

Tabular Analysis

POST /v1/tabular/predict
Content-Type: application/json

{
  "mission": "KEPLER",
  "object_id": "test_001",
  "features": {
    "period_days": 5.0,
    "transit_depth_ppm": 600.0,
    "planet_radius_re": 2.0,
    "stellar_radius_rs": 1.0,
    "snr": 50.0
  }
}

Chat Assistant

POST /v1/chat/ask
Content-Type: application/json

{
  "session_id": "unique_session_id",
  "message": "What does the transit depth tell us?",
  "context": {
    "verdict": "Exoplanet Detected",
    "confidence": 88,
    "features": [...],
    "explanation": "...",
    "input_values": {...}
  }
}

Light Curve Analysis

POST /v1/lightcurve/analyze
Content-Type: application/json

{
  "mission": "TESS",
  "object_id": "TIC_123456789",
  "timeseries": [
    {"time": 0.0, "flux": 1.0, "flux_err": 0.01},
    {"time": 0.1, "flux": 0.99, "flux_err": 0.01}
  ]
}

🧪 Testing

Backend Testing

# Test ML prediction
curl -X POST "http://localhost:8000/v1/tabular/predict" \
  -H "Content-Type: application/json" \
  -d '{
    "mission": "KEPLER",
    "object_id": "test",
    "features": {
      "period_days": 5.0,
      "transit_depth_ppm": 600.0,
      "planet_radius_re": 2.0,
      "stellar_radius_rs": 1.0,
      "snr": 50.0
    }
  }'

# Test chatbot security
curl -X POST "http://localhost:8000/v1/chat/ask" \
  -H "Content-Type: application/json" \
  -d '{
    "session_id": "test",
    "message": "What model are you using?",
    "context": {
      "verdict": "Not an Exoplanet",
      "confidence": 66,
      "features": [],
      "explanation": "test",
      "input_values": {}
    }
  }'

Frontend Testing

# Run tests
npm test

# Build for production
npm run build

# Start production server
npm start

🚀 Deployment

Render (Backend)

Connect GitHub repository to Render
Configure service:
- Name: armana-backend
- Environment: Python 3
- Build Command: pip install -r backend/requirements.txt
- Start Command: cd backend && uvicorn src.app.main:app --host 0.0.0.0 --port $PORT
Set environment variables:
- GROQ_API_KEY: Your Groq API key
- PYTHON_VERSION: 3.11.0

Vercel (Frontend)

Connect GitHub repository to Vercel
Configure project:
- Framework Preset: Next.js
- Root Directory: frontend
Set environment variables:
- NEXT_PUBLIC_BACKEND_URL: Your Render backend URL

Manual Deployment

# Backend
cd backend
pip install -r requirements.txt
uvicorn src.app.main:app --host 0.0.0.0 --port 8000

# Frontend
cd frontend
npm run build
npm start

🔒 Security Features

Chatbot Security

Input Validation: Blocks probing questions about system internals
Response Filtering: Prevents exposure of sensitive information
Session Limits: 3-message limit per session
Content Filtering: Blocks technical terms and implementation details

API Security

Input Sanitization: Validates all input parameters
Rate Limiting: Built-in session management
Error Handling: Secure error messages without information leakage

📈 Performance

Model Performance

ROC-AUC: 0.8810 (excellent discrimination)
Accuracy: 83.9% on test set
Precision: 68.8% for confirmed exoplanets
Recall: 60.9% for confirmed exoplanets

Frontend Performance

React Optimizations: Memoized components and callbacks
Bundle Size: Optimized with Next.js
Loading: Lazy loading for 3D components
Animations: Smooth 60fps with Framer Motion

🤝 Contributing

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Make your changes: Follow the coding standards
Test thoroughly: Ensure all tests pass
Commit changes: git commit -m 'Add amazing feature'
Push to branch: git push origin feature/amazing-feature
Open a Pull Request: Describe your changes clearly

Development Guidelines

Follow TypeScript best practices
Use meaningful commit messages
Add tests for new features
Update documentation as needed
Ensure accessibility compliance

📚 Documentation

Frontend README: Detailed frontend documentation
ML Pipeline README: Machine learning documentation
3D Visualization README: 3D viewer documentation
Deployment Guide: Step-by-step deployment instructions

🎯 Roadmap

Phase 1: Core Features ✅

ML model training and deployment
Basic web interface
API endpoints
Security implementation

Phase 2: Enhanced Features ✅

AI chatbot integration
3D visualization
Performance optimizations
Security hardening

Phase 3: Future Enhancements 🔄

Real-time light curve analysis
Advanced ML models (neural networks)
Multi-language support
Mobile app development
Advanced visualization features

🐛 Known Issues

Data Units: Some Kepler data may require unit conversion
Model Calibration: Continuous monitoring needed for drift
Chatbot Limits: 3-message limit may be restrictive for some users

📄 License

This project is developed for NASA Space Apps Challenge 2025. Please refer to the challenge guidelines for usage terms and licensing.

🙏 Acknowledgments

NASA Space Apps Challenge 2025 for the inspiration
NASA Exoplanet Archive for providing the training data
Kepler, K2, and TESS missions for the incredible datasets
Groq for AI chatbot capabilities
Render and Vercel for hosting infrastructure
Open source community for the amazing tools and libraries

📞 Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: armanshirzad1998@gmail.com

Built with ❤️ for space exploration and scientific discovery

"The universe is not only stranger than we imagine, it is stranger than we can imagine." - J.B.S. Haldane

Name		Name	Last commit message	Last commit date
.github		.github
.vscode		.vscode
Docs		Docs
ML		ML
__pycache__		__pycache__
backend		backend
frontend		frontend
.env.example		.env.example
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
CONTRIBUTORS.md		CONTRIBUTORS.md
DOCKER_MIGRATION.md		DOCKER_MIGRATION.md
LICENSE		LICENSE
README.md		README.md
README_DEPLOYMENT.md		README_DEPLOYMENT.md
REPOSITORY_STATUS.md		REPOSITORY_STATUS.md
docker-compose.yml		docker-compose.yml
nasa.conf		nasa.conf
nasa.conf.example		nasa.conf.example
opencode_global_config.json		opencode_global_config.json
setup-github.sh		setup-github.sh
setup-nginx.sh		setup-nginx.sh

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