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README.md
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---
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title: Kepler Automated Detection
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emoji: 🪐
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colorFrom: blue
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colorTo: purple
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sdk: gradio
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sdk_version: 4.44.0
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app_file: app.py
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pinned: false
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license: mit
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---
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# 🪐 Kepler Automated Exoplanet Detection
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An AI-powered tool for detecting exoplanets using NASA Kepler mission data. This application uses machine learning to predict whether astronomical objects are likely to be exoplanets based on their observational characteristics.
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## 🚀 Features
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- **Automated Column Mapping**: Intelligent mapping of uploaded CSV columns to training dataset features using AI
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- **Machine Learning Predictions**: Trained model for exoplanet classification
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- **Interactive UI**: Easy-to-use Gradio interface for uploading data and viewing results
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- **Detailed Statistics**: Comprehensive analysis of prediction results
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## 📊 How It Works
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1. Upload a CSV file containing astronomical observation data
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2. The system automatically maps your columns to the required features
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3. Machine learning model analyzes the data
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4. Get predictions with confidence scores and detailed statistics
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## 🛠️ Technology Stack
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- **Machine Learning**: scikit-learn
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- **Model Persistence**: joblib
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- **UI Framework**: Gradio
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- **Data Processing**: pandas, numpy
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- **AI Mapping**: Together AI API
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## 📝 Usage
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1. Prepare your CSV file with astronomical observation data
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2. Upload the file through the interface
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3. Review the column mapping (automatic)
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4. Get predictions and statistics
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## 🔑 Environment Variables
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The application requires a `TOGETHER_API_KEY` for AI-powered column mapping. This should be set in your Hugging Face Space secrets.
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## 📄 License
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MIT License
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## 🌟 About
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This project was developed to assist astronomers and researchers in analyzing Kepler mission data for exoplanet detection. The machine learning model is trained on validated exoplanet data from NASA's Kepler mission.
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---
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Built with ❤️ for astronomy and space exploration
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