README

Check out the rest of this README to discover more about the project, its features, and how to get started

Study Buddy - AI-Powered Study Assistant

An intelligent study companion that helps students learn with AI-powered chat, document analysis, and personalized study modes.

Features

  • 🤖 AI-Powered Chat: Chat with an AI tutor that adapts to your learning style

  • 📄 Document Upload: Upload PDF, DOCX, or TXT files for AI to analyze

  • 📚 Smart Study Modes: Choose from Active Learning, Break Mode, Focused, or Review

  • 🌍 Multi-Language Support: Available in 8 languages

  • 💡 Document Summarization: Get AI-generated summaries of your study materials

  • 🎯 Context-Aware Answers: AI uses your uploaded notes to provide relevant answers

  • 😊 Emotion Detection: AI adapts its tone based on your emotional state

Prerequisites

  • Python 3.8+ installed

  • Node.js 18+ installed

  • Google Gemini API Key - Get one from Google AI Studio

Quick Start

1. Install Dependencies

Backend (Python)

pip install -r requirements.txt

Frontend (Node.js)

npm install

2. Configure Environment

Create a .env file in the root directory:

GEMINI_API_KEY=your_actual_gemini_api_key_here

Important: Replace your_actual_gemini_api_key_here with your actual Google Gemini API key.

3. Start the Application

Option A: Run Both Services (Recommended)

Terminal 1 - Backend:

python study_buddy_backend.py

Backend runs on http://localhost:8001

Terminal 2 - Frontend:

npm run dev:client

Frontend runs on http://localhost:5173

Option B: Use PowerShell Scripts

Backend:

.\start-backend.ps1

Frontend:

.\start-client.ps1

4. Use the Application

  1. Open your browser to http://localhost:5173

  2. Upload study notes using the upload button (📤)

  3. Ask questions about your notes or chat with the AI

  4. The AI will automatically use your uploaded notes when answering

How to Use

Uploading Documents

  1. Click the upload button (📤) in the chat input area

  2. Select a PDF, DOCX, DOC, or TXT file

  3. Wait for the upload confirmation

  4. Start asking questions about your documents!

Chatting with AI

  • Type your question or message in the chat input

  • The AI will respond using your uploaded notes (if available)

  • Choose different study modes to change the AI's teaching style

  • Select your preferred language for responses

Getting Document Summaries

After uploading notes, you can ask:

  • "Can you summarize my notes?"

  • "What are the key concepts in my documents?"

  • "Give me a summary of the uploaded material"

API Endpoints

  • POST /api/chat - Send chat messages

  • POST /api/notes/upload - Upload study notes

  • POST /api/notes/ask - Ask questions about notes

  • POST /api/notes/summary - Get document summary

  • GET /api/personality-modes - Get AI personality modes

  • GET /health - Health check

Troubleshooting

See SETUP.md for detailed troubleshooting guide.

Common Issues

Backend won't start:

  • Check that .env file exists with GEMINI_API_KEY

  • Ensure Python dependencies are installed: pip install -r requirements.txt

Frontend can't connect to backend:

  • Verify backend is running on port 8001

  • Check browser console for errors

  • Ensure CORS is enabled (already configured in backend)

File upload fails:

  • Check file format (PDF, DOCX, DOC, TXT only)

  • Ensure file is not corrupted

  • Check backend logs for errors

Project Structure

├── study_buddy_backend.py    # FastAPI backend server
├── client/                   # React frontend
│   └── src/
│       ├── components/      # React components
│       ├── lib/             # API client and utilities
│       └── contexts/         # React contexts
├── server/                   # Express server (optional)
├── requirements.txt          # Python dependencies
├── package.json              # Node.js dependencies
└── SETUP.md                  # Detailed setup guide

Development

The backend auto-reloads when code changes. The frontend uses Vite's hot module replacement for instant updates.

Notes

  • Backend stores data in memory (not persisted)

  • For production, use a database and vector store

  • CORS is enabled for all origins in development

License

MIT

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