Study Buddy Setup Guide:

Prerequisites

1. Python 3.8+ installed

2. Node.js 18+ installed

3. 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 Variables

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 Backend

Open a terminal and run:

python study_buddy_backend.py

The backend will start on http://localhost:8001

You should see:

• "Gemini client configured successfully!"

• "Application startup complete"

• Server running on http://0.0.0.0:8001

4. Start the Frontend

Open a new terminal and run:

npm run dev:client

The frontend will start on http://localhost:5173 (or another port if 5173 is taken)

5. Use the Application

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

2. Upload study notes (PDF, DOCX, or TXT) using the upload button

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

4. The AI will use your uploaded notes to provide context-aware answers

Features

File Upload

• Click the upload button (📤) in the chat input

• Supported formats: PDF, DOCX, DOC, TXT

• Files are processed and stored as embeddings

• You can upload multiple files

AI Chat

• Ask questions about your uploaded notes

• The AI will automatically use your notes when answering

• Choose from different study modes (Active Learning, Break Mode, Focused, Review)

• Select from multiple languages

Document Summarization

• After uploading notes, you can ask for a summary

• The AI will generate a comprehensive summary of your documents

API Endpoints

Chat

• POST /api/chat - Send a chat message and get AI response

Notes

• POST /api/notes/upload - Upload study notes (PDF, DOCX, TXT)

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

• POST /api/notes/summary - Get a summary of all uploaded notes

Other

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

• GET /health - Health check endpoint

Troubleshooting

Backend Issues

Error: "GEMINI_API_KEY is not set"

• Make sure you've created a .env file with your API key

• Check that the .env file is in the root directory

Port 8001 already in use

• Change the port in study_buddy_backend.py (line 609):

  uvicorn.run(app, host="0.0.0.0", port=8002, reload=True)

• Update client/src/lib/queryClient.ts to match the new port

Module not found errors

• Run pip install -r requirements.txt again

• Ensure you're using the correct Python environment

Frontend Issues

API request fails / Network error

• Verify the backend is running on http://localhost:8001

• Check the browser console for CORS errors

• The backend has CORS enabled for all origins in development

Connection refused

• Make sure both frontend and backend are running

• Check that the API_BASE in client/src/lib/queryClient.ts matches your backend URL

File Upload Issues

File upload fails

• Check that the file is not too large

• Ensure the file format is supported (PDF, DOCX, DOC, TXT)

• Check backend logs for error messages

Development

Running Both Services

You can run both backend and frontend together using:

# Terminal 1: Backend
python study_buddy_backend.py

# Terminal 2: Frontend
npm run dev:client

Testing the Backend

Test the health endpoint:

curl http://localhost:8001/health

Test chat endpoint:

curl -X POST http://localhost:8001/api/chat `
  -H "Content-Type: application/json" `
  -d '{\"user_id\": 1, \"session_id\": \"test\", \"message\": \"Hello!\", \"personality_mode\": \"1\"}'

Notes

• The backend stores notes and conversations in memory (not persisted to disk)

• For production, you should use a database and vector store

• The backend auto-reloads when code changes (development mode)

• CORS is enabled for all origins in development (restrict in production)