• 📍 Overview
• 👾 Features
• 📁 Project Structure
  • 📂 Project Index
• 🚀 Getting Started
  • ⚙️ Installation
  • 🤖 Usage
  • 🧪 Testing
• 🔰 Contributing
• 🎗 License
• 🙌 Acknowledgments

SpaceSage is a comprehensive astronomy toolkit that leverages advanced machine learning techniques to analyze astronomical data. The platform offers four main capabilities: galaxy classification, redshift analysis, exoplanet habitability assessment, and satellite orbit optimization. Built with React and TypeScript for the frontend interface and Python with various ML libraries for the backend models, SpaceSage provides astronomers, researchers, and space enthusiasts with powerful tools for space data analysis.

• CNN-based classification of galaxies into six distinct categories (Round Elliptical, In-between Elliptical, Cigar-shaped Elliptical, Edge-on Spiral, Unbarred Spiral, Barred Spiral)
• Utilizes the Galaxy10 dataset to achieve 82.56% accuracy
• Visual feedback with uploaded galaxy images

• Analyzes Doppler shift in spectral data to determine galactic motion
• Random Forest model for classifying approaching vs receding celestial objects
• Calculation of redshift values and radial velocities from wavelength data

• Assesses potential habitability of exoplanets based on atmospheric composition
• Analyzes key gases (O₂, CO₂, CH₄, H₂O, O₃) from spectral data
• Machine learning model synthesized from spectroscopic atmospheric analysis

• BERT-based NLP model for determining optimal satellite orbits
• Classification of satellite types into LEO, MEO, GEO, HEO, and SSO
• Integration with real-time satellite congestion data

└── SpaceSage/
    ├── README.md
    ├── eslint.config.js
    ├── index.html
    ├── models
    │   ├── andromeda.ipynb      # Redshift analysis model
    │   ├── atm_cond.ipynb       # Exoplanet habitability model
    │   ├── galaxy.ipynb         # Galaxy classification CNN
    │   └── orbita.ipynb         # Satellite orbit optimization model
    ├── package.json
    ├── src
    │   ├── App.tsx              # Main application routing
    │   ├── components
    │   │   └── Navbar.tsx       # Navigation component
    │   ├── index.css
    │   ├── main.tsx
    │   ├── pages
    │   │   ├── About.tsx        # About page
    │   │   ├── AndreShift.tsx   # Redshift analysis interface
    │   │   ├── AndreShiftInfo.tsx # Redshift information
    │   │   ├── ExoHabit.tsx     # Exoplanet habitability interface 
    │   │   ├── ExoHabitInfo.tsx # Exoplanet habitability information
    │   │   ├── GalaxyClassifier.tsx # Galaxy classification interface
    │   │   ├── GalaxyInfo.tsx   # Galaxy classification information
    │   │   ├── Home.tsx         # Landing page
    │   │   ├── Orbita.tsx       # Satellite orbit interface
    │   │   ├── OrbitaInfo.tsx   # Satellite orbit information
    │   │   └── app.py           # Flask backend for model integration
    │   └── vite-env.d.ts
    ├── tailwind.config.js
    ├── tsconfig.app.json
    ├── tsconfig.json
    ├── tsconfig.node.json
    └── vite.config.ts

Install SpaceSage using one of the following methods:

Build from source:

1. Clone the SpaceSage repository:

❯ git clone https://github.com/Riddhish1/SpaceSage

2. Navigate to the project directory:

❯ cd SpaceSage

3. Install the project dependencies:

Using npm  

❯ npm install

4. Install Python dependencies for the ML backend:

❯ pip install -r requirements.txt

1. Start the frontend development server:

❯ npm run dev

2. In a separate terminal, start the backend Flask server:

❯ cd src/pages
❯ python app.py

3. The application should now be running at http://localhost:5173, with the backend API at http://localhost:5000.

Run the test suite using the following command:

❯ npm test

• 💬 Join the Discussions: Share your insights, provide feedback, or ask questions.
• 🐛 Report Issues: Submit bugs found or log feature requests for the SpaceSage project.
• 💡 Submit Pull Requests: Review open PRs, and submit your own PRs.

This project is protected under the MIT License. For more details, refer to the LICENSE file.

• The Galaxy10 dataset used in the galaxy classification model
• NASA exoplanet data for habitability assessment model training
• CelesTrak for satellite orbit data
• HuggingFace Transformers library for BERT-based models
• TensorFlow and PyTorch for machine learning model development
• React, TypeScript, and TailwindCSS for the frontend