README.md

metadata

title: MindGap AI
emoji: 🧠
colorFrom: blue
colorTo: indigo
sdk: streamlit
sdk_version: 1.31.0
python_version: '3.10'
app_file: app.py
pinned: false
license: apache-2.0

MindGap AI - Adaptive Learning Companion

MindGap AI is a hackathon-ready application designed to detect student knowledge gaps and generate personalized micro-lessons using RAG and Streamlit.

Tech Stack

• Frontend/Backend: Streamlit
• Vector Search: FAISS
• Embeddings: Sentence-Transformers (all-MiniLM-L6-v2)
• LLM: Groq API (llama-3.3-70b-versatile)
• Database: SQLite (Performance tracking)

📂 Project Structure

• app.py: Main Streamlit application.
• rag_engine.py: Core RAG logic.
• database.py: SQLite storage.
• requirements.txt: Project dependencies.

🚀 Quick Start (Local)

1. Install Dependencies:

   pip install -r requirements.txt
   

2. Environment Variables: Create a .env file or set the environment variable:

   GROQ_API_KEY=your_key_here
   

3. Run:

   streamlit run app.py
   

🌍 Deployment (Hugging Face Spaces)

The app is optimized for Hugging Face Spaces.

1. Create a Space with the Streamlit SDK.
2. Connect your GitHub repository.
3. Add GROQ_API_KEY to the Space Secrets.
4. The app will automatically build and run.

💡 Hackathon Demo Tips

• Unified Flow: Streamlit provides a seamless experience for uploading notes, learning, and testing in one place.
• RAG & FAISS: Your documents are indexed locally for fast, contextual retrieval.
• Adaptive Learning: The LLM adjusts the lesson depth based on your queries and performance history.