Cosmic Twin is a project developed for the NASA Space Apps Challenge 2024, specifically under the challenge titled Chronicles of Exoplanet Exploration. This challenge encourages participants to create innovative and engaging solutions that educate students about the wonders of exoplanets.
Cosmic Twin is an innovative educational project that bridges the harmony between the exploration of exoplanets and the creativity of ExoVibe. Our interactive website offers a unique and fun way for students and users to learn about exoplanets by connecting their personal preferences to the characteristics of distant worlds, creating their own Cosmic Twin.
The harmony between the Exploration of Exoplanets and the Innovation of ExoVibe creates a Cosmic Twin. This concept encapsulates the fusion of scientific discovery with an interactive, personalized learning approach that both educates and entertains.
- 📚 Educational Engagement: To inspire curiosity and learning about exoplanets in a simple, accessible format.
- 🎮 Interactive Learning: Use interactivity to turn scientific data into fun experiences through quizzes and an AI-powered chatbot.
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Purpose: Engages students by matching their preferences to planetary traits, making the vast subject of exoplanet science more personal and relatable.
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How it Works: Users answer a series of questions. Based on their answers, an algorithm classifies them into different types of exoplanets, helping them explore and learn in a playful, interactive manner.
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Outcome: The user is presented with an exoplanet profile that showcases scientific data in a simplified, fun way.
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Purpose: Helps students and educators explore exoplanet-related topics by answering their questions directly.
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Technology: The chatbot is powered by the Llama model, trained to provide information about exoplanets and link users to related NASA resources for further exploration.
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How it Works: Users can ask questions such as “How are exoplanets discovered?” or “What is the closest exoplanet to Earth?” The chatbot responds with accurate, concise answers and relevant NASA links.
- Frontend: React.js
- Backend: Flask (integrates with the exoplanet prediction model and user inputs)
- AI Model: Llama model trained to provide informative responses and NASA links.
- Data: Exoplanet data for personality matching and chatbot is sourced from open NASA resources and datasets.
To run the frontend locally, follow these steps:
- Navigate to the
frontendfolder:cd frontend - Install the required dependencies:
npm i
- Start the development server:
npm start
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