LectureLens 🎓

AI-Powered Study Tool for Students

LectureLens transforms your lecture recordings and transcripts into comprehensive study materials using AI. Upload your lectures and get instant topic breakdowns, flashcards, practice questions, and intelligent Q&A capabilities.

Current Status: Backend processing complete (Milestone 1.1) | Frontend auth & UI in development

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🚀 Features

✅ Currently Implemented

• Secure Authentication - User signup/signin with Supabase Auth
• Lecture Upload - Upload and store lecture transcripts
• AI Topic Extraction - Automatic extraction of 5-8 main topics from lectures using GPT-4o-mini
• Semantic Chunking - Intelligent transcript chunking with vector embeddings
• Row-Level Security - Full RLS policies for data isolation
• Processing Pipeline - Edge function-based lecture processing with status tracking

🚧 In Development (Planned Milestones)

Milestone 1: Auth UI & Processing Integration (Current)

• Complete auth UI with sign up/sign in forms
• Global auth context and protected routes
• Navigation component with responsive design
• Landing page with hero and CTAs
• Processing trigger integration in upload flow
• Dashboard with lecture status display
• Topic display in lecture detail views

Milestone 2: Lecture Detail Page (Next)

• Topic navigation and exploration
• Inline topic editing
• Lecture management (edit, delete, reprocess)
• Loading states and error handling

Milestone 3: Flashcard Generation

• AI-generated flashcards per topic
• Interactive study mode with card-flip UI
• Custom flashcard creation
• Spaced repetition tracking (optional)

Milestone 4: Q&A Generation

• Practice questions with step-by-step solutions
• Interactive quiz mode
• AI answer evaluation
• Custom question creation

Milestone 5: Semantic Search & RAG

• Semantic search across lectures
• RAG-powered Q&A (ask questions, get grounded answers)
• Search history tracking
• Citation links to source chunks

Milestone 6: Study Schedules

• AI-generated personalized study plans
• Calendar view with progress tracking
• Email reminders (optional)
• Study analytics

Milestone 7: Production Polish

• Deployment to Vercel
• Error monitoring (Sentry)
• Analytics integration
• Performance optimization
• SEO & onboarding flow

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🏗️ Architecture

┌─────────────────┐
│   Next.js App   │ (React, TypeScript, TailwindCSS)
│   Frontend      │
└────────┬────────┘
         │
         ├─── Authentication (Supabase Auth)
         │
         ├─── API Routes (Next.js API)
         │
         ▼
┌─────────────────┐
│   Supabase      │
│   Backend       │
├─────────────────┤
│ • PostgreSQL    │ (Lectures, Topics, Chunks, Users)
│ • Vector Store  │ (pgvector for embeddings)
│ • Edge Funcs    │ (Deno-based processing)
│ • Storage       │ (Future: audio files)
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Vercel AI GW    │
├─────────────────┤
│ • GPT-4o-mini   │ (Topic extraction, flashcards, Q&A)
│ • text-emb-3    │ (Semantic search embeddings)
└─────────────────┘

Database Schema

Core Tables

• lectures - User-uploaded lectures with processing status
• lecture_chunks - Semantically chunked transcript pieces with embeddings
• topics - AI-extracted main topics from lectures

Future Tables (Planned)

• flashcards - Generated study flashcards per topic
• qa_pairs - Practice questions with solutions
• search_history - User Q&A interaction history
• study_plans - Personalized study schedules
• study_sessions - Scheduled study blocks

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🛠️ Tech Stack

Layer	Technology
Frontend	Next.js 16, React 19, TypeScript, TailwindCSS v4
Backend	Supabase (PostgreSQL, Auth, Edge Functions)
AI/ML	OpenAI GPT-4o-mini, text-embedding-3-small (via Vercel AI Gateway)
Vector DB	pgvector (Supabase extension)
Hosting	Vercel (app), Supabase (backend)
Dev Tools	ESLint, Prettier, Husky, TypeScript

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📦 Installation & Setup

Prerequisites

• Node.js 20+ and npm
• Supabase account (supabase.com)
• Vercel AI Gateway API key (vercel.com/ai)

1. Clone Repository

git clone https://github.com/egekaya1/LectureLens.git
cd LectureLens

2. Install Dependencies

npm install

3. Environment Variables

Create .env.local in the root directory:

# Supabase
NEXT_PUBLIC_SUPABASE_URL=https://your-project.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=your-anon-key
SERVICE_ROLE_KEY=your-service-role-key

# Vercel AI Gateway
AI_GATEWAY_API_KEY=your-vercel-ai-gateway-key

How to get these values:

• Supabase: Project Settings → API in Supabase Dashboard
• Vercel AI Gateway: Create API key at vercel.com/ai

4. Set Up Supabase

Initialize Supabase

npx supabase init
npx supabase link --project-ref your-project-ref

Run Migrations

npx supabase db push

This creates:

• lectures table with RLS policies
• lecture_chunks table with vector embeddings
• topics table
• Necessary indexes and triggers

Deploy Edge Function

npx supabase functions deploy process-lecture

Set edge function secrets:

npx supabase secrets set AI_GATEWAY_API_KEY=your-key
npx supabase secrets set SERVICE_ROLE_KEY=your-service-role-key
npx supabase secrets set NEXT_PUBLIC_SUPABASE_URL=your-url

5. Run Development Server

npm run dev

Open http://localhost:3000 in your browser.

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🧪 Testing the Current Features

1. Sign Up

Navigate to /login and create an account using email/password.

2. Upload a Lecture

# Navigate to /upload
# Enter a title and paste a transcript
# Click "Upload Lecture"

3. Trigger Processing (Manual - UI coming soon)

# Get your lecture ID from the database
# Call the edge function manually:
curl -X POST https://your-project.supabase.co/functions/v1/process-lecture \
  -H "Content-Type: application/json" \
  -d '{"lectureId": "your-lecture-id"}'

4. View Results

Check the database:

-- View lecture status
SELECT id, title, status, topic_count FROM lectures;

-- View extracted topics
SELECT title, summary FROM topics WHERE lecture_id = 'your-lecture-id';

-- View chunks with embeddings
SELECT chunk_index, content FROM lecture_chunks WHERE lecture_id = 'your-lecture-id';

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📁 Project Structure

lecturelens/
├── src/
│   ├── app/                    # Next.js app router
│   │   ├── page.tsx           # Landing page (to be updated)
│   │   ├── layout.tsx         # Root layout with providers
│   │   ├── globals.css        # Global styles
│   │   ├── dashboard/         # Dashboard page
│   │   ├── login/             # Auth page (to be built)
│   │   └── upload/            # Upload page
│   ├── components/            # React components (to be added)
│   │   ├── AuthForm.tsx      # (Planned) Auth UI
│   │   ├── Navbar.tsx        # (Planned) Navigation
│   │   └── TopicList.tsx     # (Planned) Topic display
│   ├── context/               # React context (to be added)
│   │   └── AuthContext.tsx   # (Planned) Global auth state
│   ├── lib/
│   │   └── supabaseClient.ts # Supabase client initialization
│   └── types/
│       └── supabase.ts        # Generated TypeScript types
├── supabase/
│   ├── functions/
│   │   └── process-lecture/   # Edge function for AI processing
│   │       ├── index.ts       # Main processing logic
│   │       └── deno.json      # Deno config
│   ├── migrations/            # Database migrations
│   │   ├── *_init_tables.sql
│   │   ├── *_add_user_id_to_lectures.sql
│   │   ├── *_add_processing_fields.sql
│   │   ├── *_create_lecture_chunks.sql
│   │   ├── *_create_topics.sql
│   │   └── *_add_lectures_rls_policies.sql
│   └── config.toml            # Supabase config
├── public/                    # Static assets
├── package.json
├── tsconfig.json
├── next.config.ts
├── tailwind.config.ts
└── README.md

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🔒 Security Features

Row-Level Security (RLS)

All tables have RLS enabled with policies ensuring:

• Users can only access their own data
• Service role (edge functions) has full access
• Automatic user_id filtering on SELECT queries

Authentication

• Supabase Auth handles secure password hashing
• JWT-based session management
• Protected routes via middleware (to be implemented)

Environment Variables

• Service role keys never exposed to client
• API keys stored in Supabase secrets for edge functions

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🗺️ Development Roadmap

Timeline: 2-3 months (part-time)

Milestone	Duration	Status
M1: Auth UI & Processing	1.5-2 weeks	🟡 In Progress (15% done)
M2: Lecture Detail Pages	1-1.5 weeks	⚪ Not Started
M3: Flashcard Generation	1.5-2 weeks	⚪ Not Started
M4: Q&A Generation	1.5-2 weeks	⚪ Not Started
M5: Semantic Search & RAG	2-2.5 weeks	⚪ Not Started
M6: Study Schedules	1-1.5 weeks	⚪ Not Started
M7: Polish & Deploy	1.5-2 weeks	⚪ Not Started

Total Estimated Effort: 74-104 hours

See Development Plan below for detailed task breakdown.

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🎯 Development Plan

Milestone 1: Auth UI & Processing Integration (CURRENT)

Goal: Enable complete user flow from signup → upload → view topics

Tasks

• 1.1 Codify RLS Migration ✅ DONE
• 1.2 Build Complete Auth UI (3-4 hours)
  • Sign up/sign in forms with validation
  • Password reset flow
  • Error handling
• 1.3 Create Global Auth Context (1-2 hours)
  • AuthProvider component
  • useAuth() hook
  • Session persistence
• 1.4 Build Navigation Component (1-2 hours)
  • Responsive navbar
  • Protected route links
  • Sign out functionality
• 1.5 Replace Home Page with Landing (1 hour)
  • Hero section
  • Feature showcase
  • CTA buttons
• 1.6 Add Processing Trigger to Upload Flow (2-3 hours) ⭐
  • Title input field
  • Edge function integration
  • Status feedback
• 1.7 Display Topics in Dashboard (2-3 hours) ⭐
  • Lecture cards with status badges
  • Topic previews
  • Expandable topic lists
• 1.8 Add Route Protection Middleware (30 min)
  • Protected route redirects
  • Auth state checking

Acceptance Criteria: Users can sign up, upload lectures, trigger processing, and view extracted topics in dashboard.

Milestones 2-7: Feature Development

Full task breakdowns available in the comprehensive development plan document. Key features:

• M2: Lecture detail pages with topic navigation
• M3: AI flashcard generation + study mode
• M4: Practice questions with quiz interface
• M5: Semantic search + RAG Q&A
• M6: Study schedule generation
• M7: Production deployment + monitoring

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🤝 Contributing

This is currently a portfolio project. Contributions, issues, and feature requests are welcome!

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add some AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Code Style

• Run npm run format before committing (Prettier)
• Run npm run lint to check for issues (ESLint)
• Husky pre-commit hooks enforce formatting

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• OpenAI - GPT-4o-mini and text-embedding-3-small models
• Supabase - Backend infrastructure and edge functions
• Vercel - AI Gateway and Next.js hosting
• pgvector - Vector similarity search in PostgreSQL

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📧 Contact

Ege Kaya - @egekaya1

Project Repository: https://github.com/egekaya1/LectureLens

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🐛 Known Issues

• Auth UI not yet implemented (manual auth via Supabase dashboard)
• Processing must be triggered manually via curl (UI integration pending)
• No error handling for failed processing in UI
• Dashboard shows raw data (needs styled components)

Track all issues on GitHub Issues.

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📚 Additional Resources

• Next.js Documentation
• Supabase Docs
• OpenAI API Reference
• pgvector Guide
• TailwindCSS v4 Docs

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