Edu Simplify

AI-powered lecture summarization and learning assistant built with FastAPI and React.

Edu Simplify converts lecture sources into structured study material and interactive practice:

• Topic-structured summaries
• Retrieval-grounded contextual chat
• Solver chat with optional image input
• MCQ generation with explanations
• Exportable PDF revision sheet

Video Explanation

Project walkthrough video:

• Watch or download the video explanation

Highlights

• Multiple input modes: YouTube lecture URL, pasted text, text file, PDF
• AI fallback chain for reliability: Gemini -> Ollama -> offline local logic
• Session-based learning workflow: summarize -> chat -> MCQ -> PDF
• JWT authentication with MongoDB-backed user profiles
• Responsive frontend built with Vite + TailwindCSS + Framer Motion

Tech Stack

• Backend: FastAPI, Pydantic, PyJWT, MongoDB (PyMongo)
• Frontend: React 18, Vite, TailwindCSS, Framer Motion
• AI Integration: Google Gemini, optional Ollama local model
• Document Export: ReportLab

Architecture

• backend/ exposes REST APIs for summarization, chat, MCQ, PDF export, and auth.
• frontend/ provides the single-page learning interface.
• Session state for lecture context is held in backend memory with TTL.
• Auth users are persisted in MongoDB.

Quick Start

Prerequisites

• Python 3.10+
• Node.js 18+ and npm
• MongoDB connection string (for auth endpoints)
• Gemini API key (recommended)
• Optional: Ollama running locally for local LLM fallback

1) Backend setup (http://localhost:8000)

cd backend
python -m venv .venv

# Windows (PowerShell)
.venv\Scripts\Activate.ps1
# macOS/Linux
source .venv/bin/activate

pip install -r requirements.txt
cp .env.example .env

Set values in backend/.env, then run:

uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

2) Frontend setup (http://localhost:3000)

cd frontend
npm install
cp .env.example .env
npm run dev

Environment Variables

Backend (backend/.env)

GEMINI_API_KEY=your_gemini_api_key_here
GEMINI_MODEL=models/gemini-2.5-flash
OLLAMA_ENABLED=true
OLLAMA_BASE_URL=http://127.0.0.1:11434
OLLAMA_MODEL=llama3.2:3b
CORS_ORIGINS=http://localhost:3000,http://127.0.0.1:3000
SESSION_TTL_MINUTES=240
JWT_SECRET=replace_with_secure_random_secret
JWT_EXP_MINUTES=10080
MONGO_URI=mongodb+srv://username:password@cluster.mongodb.net/?retryWrites=true&w=majority
MONGO_DB_NAME=edu_simplify

Frontend (frontend/.env)

VITE_API_BASE_URL=http://localhost:8000

API Overview

• GET /health - service health check
• POST /extract_captions - extract transcript from YouTube URL
• POST /video_meta - fetch YouTube title/thumbnail/channel metadata
• POST /summarize - generate structured notes from transcript text
• POST /chat - contextual Q&A against lecture summary and chunks
• POST /solver_chat - solver assistant (supports base64 image data URL)
• POST /mcq - generate lecture-grounded MCQs
• GET /pdf?session_id=... - download summary and MCQ PDF
• POST /auth/register - register user
• POST /auth/login - sign in and get JWT
• GET /auth/me - validate JWT and fetch user profile

Reliability Model

For summarization, chat, solver chat, and MCQ generation, the backend uses:

1. Gemini (primary)
2. Ollama local model (secondary)
3. Built-in offline local logic (final fallback)

This keeps core functionality available even when cloud APIs fail.

Docker (Backend)

cd backend
docker build -t edu-simplify-api .
docker run --rm -p 8000:8000 --env-file .env edu-simplify-api

Build Validation

# Backend
cd backend
python -m compileall app

# Frontend
cd ../frontend
npm run build

Project Structure

backend/
  app/
    api/
    core/
    models/
    services/
    main.py
  .env.example
  requirements.txt
  Dockerfile

frontend/
  src/
    components/
    context/
    hooks/
    pages/
    services/
  .env.example
  package.json
  vite.config.js
  tailwind.config.js

sample/
  sample_corpus.txt

docs/
  video-explanation.mp4

License

This project is licensed under the MIT License. See LICENSE.