🎓 Shayak - Comprehensive Educational Platform

Live Website Link : https://sahayak-deployment-frontend.onrender.com/

A modern, AI-powered educational platform designed to replace Google Classroom with advanced features including real-time student engagement monitoring, interactive quizzes, comprehensive attendance tracking, and seamless video class management.

Videos : https://www.youtube.com/watch?v=44cze2HiUXg,

https://drive.google.com/file/d/1EzHMsx9InIu8ZmWBII7oHxwfDYVXQJLA/view?usp=drivesdk

🌟 Key Features

📚 Core Educational Features

• Modern Classroom Management: Create and manage virtual classrooms with intuitive UI
• Interactive Assignments: Create, distribute, and grade assignments with rich media support
• Student Enrollment: Seamless student registration and classroom joining
• Real-time Collaboration: Discussion posts, comments, and peer interaction
• Grade Management: Comprehensive grading system with analytics

🎥 Video Class Management

• Live Video Classes: Integrated video conferencing for remote learning
• Class Scheduling: Advanced scheduling with timezone support
• Recording & Playback: Automatic class recording and playback functionality
• Interactive Whiteboard: Real-time collaborative whiteboard during classes
• Screen Sharing: Teacher and student screen sharing capabilities

📊 Advanced Attendance & Engagement

• AI-Powered Engagement Monitoring: Real-time student engagement analysis using computer vision
• Smart Attendance Tracking: Automatic attendance marking with join/leave detection
• Engagement Analytics: Detailed engagement reports with 6 classification categories:
  • Actively Looking (High engagement)
  • Confused (Medium engagement)
  • Talking to Peers (Medium engagement)
  • Distracted (Low engagement)
  • Bored (Very low engagement)
  • Drowsy (Very low engagement)
• Attendance Statistics: Comprehensive attendance analytics and reports

🧠 AI-Powered Assessment

• Adaptive Quizzes: Smart quiz generation with difficulty adjustment
• Refresher System: AI-powered topic refresher with personalized question generation
• Daily Practice Problems (DPP): Automated daily problem sets
• Real-time Quiz Analytics: Live quiz performance monitoring
• Batch Assessment: Efficient bulk quiz submission and grading

🎨 Modern UI/UX

• Responsive Design: Mobile-first design with seamless cross-device experience
• Advanced Animations: 120+ sophisticated scroll animations with Framer Motion
• Spring Physics: Natural, physics-based animations and interactions
• Dark/Light Mode: Comprehensive theme support
• Accessibility: WCAG compliant with screen reader support

🛠 Technology Stack

Frontend

• Framework: Next.js 14.2.32 with TypeScript
• Styling: Tailwind CSS with custom design system
• UI Components: Radix UI + shadcn/ui components
• Animations: Framer Motion with advanced spring physics
• State Management: Zustand for global state
• Data Fetching: TanStack React Query
• Forms: React Hook Form with validation
• Icons: Lucide React

Backend

• Runtime: Node.js with Express.js
• Database: MongoDB with Mongoose ODM
• Authentication: JWT with bcrypt encryption
• File Upload: Multer with AWS S3 integration
• Real-time: Socket.IO for live features
• Task Scheduling: Node-cron for automated tasks
• Email: Nodemailer for notifications
• Validation: Joi for data validation

AI/ML Services

• Engagement Analysis: TensorFlow/Keras CNN model
• Computer Vision: Python Flask API for real-time engagement detection
• Deployment: Render cloud platform for ML services
• Image Processing: Base64 encoding for privacy-compliant analysis

DevOps & Infrastructure

• Cloud Database: MongoDB Atlas
• File Storage: AWS S3 for media files
• Deployment: Ready for Vercel (frontend) and Railway/Heroku (backend)
• Environment: Docker-ready configuration
• Monitoring: Health check endpoints and error tracking

🚀 Quick Start

Prerequisites

• Node.js 18+ installed
• MongoDB Atlas account (or local MongoDB)
• AWS S3 bucket (for file uploads)
• Python 3.8+ (for engagement analysis)

1. Clone the Repository

git clone https://github.com/r1cksync/edu-a.git
cd edu-a

2. Environment Setup

Backend Environment (.env)

# Navigate to backend directory
cd backend

# Create .env file
cp .env.example .env

# Edit .env with your configurations:
NODE_ENV=development
PORT=3001
MONGODB_URI=your_mongodb_connection_string
JWT_SECRET=your_jwt_secret_key
JWT_EXPIRE=7d

# AWS S3 Configuration
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AWS_REGION=your_aws_region
AWS_S3_BUCKET=your_s3_bucket_name

# Email Configuration (Nodemailer)
EMAIL_FROM=your_email@domain.com
EMAIL_USER=your_smtp_username
EMAIL_PASS=your_smtp_password
EMAIL_HOST=your_smtp_host
EMAIL_PORT=587

# Engagement API
ENGAGEMENT_API_URL=http://localhost:5000

Frontend Environment (.env.local)

# Navigate to frontend directory
cd ../frontend

# Create .env.local file
NEXT_PUBLIC_API_URL=http://localhost:3001/api
NEXT_PUBLIC_APP_URL=http://localhost:3000

3. Install Dependencies

Backend

cd backend
npm install

Frontend

cd frontend
npm install

4. Database Setup

# The application will automatically create necessary collections
# Make sure your MongoDB Atlas cluster is running and accessible

5. AI Engagement Service Setup (Optional)

cd "online class attendence model and training ipynb/engagement-api-service"

# Install Python dependencies
pip install -r requirements.txt

# Start the engagement analysis service
python app.py

6. Start Development Servers

Start Backend Server

cd backend
npm run dev
# Server will run on http://localhost:3001

Start Frontend Server

cd frontend
npm run dev
# Application will run on http://localhost:3000

📁 Project Structure

edu-a/
├── frontend/                          # Next.js React frontend
│   ├── src/
│   │   ├── app/                      # Next.js app router pages
│   │   │   ├── auth/                 # Authentication pages
│   │   │   ├── dashboard/            # Main dashboard
│   │   │   ├── quiz/                 # Quiz system
│   │   │   └── page.tsx              # Landing page with animations
│   │   ├── components/               # React components
│   │   │   ├── ui/                   # shadcn/ui components
│   │   │   ├── classroom/            # Classroom management
│   │   │   ├── video-classes/        # Video conferencing
│   │   │   ├── attendance/           # Attendance tracking
│   │   │   ├── engagement/           # AI engagement monitoring
│   │   │   ├── quizzes/              # Quiz components
│   │   │   ├── refresher/            # AI refresher system
│   │   │   └── dpp/                  # Daily practice problems
│   │   ├── lib/                      # Utility libraries
│   │   │   ├── api.ts                # API client
│   │   │   └── utils.ts              # Helper functions
│   │   └── store/                    # Global state management
│   ├── public/                       # Static assets
│   └── package.json
├── backend/                          # Node.js Express backend
│   ├── src/
│   │   ├── controllers/              # Route controllers
│   │   │   ├── authController.js     # Authentication
│   │   │   ├── classroomController.js # Classroom management
│   │   │   ├── attendanceController.js # Attendance tracking
│   │   │   ├── engagementController.js # AI engagement analysis
│   │   │   └── quizController.js     # Quiz management
│   │   ├── models/                   # MongoDB schemas
│   │   │   ├── User.js               # User model
│   │   │   ├── Classroom.js          # Classroom model
│   │   │   ├── Attendance.js         # Attendance records
│   │   │   ├── EngagementAnalysis.js # Engagement data
│   │   │   └── Quiz.js               # Quiz model
│   │   ├── routes/                   # API route definitions
│   │   ├── middleware/               # Custom middleware
│   │   ├── services/                 # Business logic
│   │   └── utils/                    # Helper utilities
│   └── package.json
├── online class attendence model and training ipynb/
│   ├── engagement-api-service/       # Python Flask ML API
│   │   ├── app.py                    # Flask application
│   │   ├── Student_Engagement_Model.h5 # Trained TensorFlow model
│   │   ├── requirements.txt          # Python dependencies
│   │   ├── README.md                 # ML service documentation
│   │   └── DEPLOYMENT_GUIDE.md       # Deployment instructions
│   └── student-engagement-score.ipynb # Model training notebook
└── README.md                         # This file

🎯 Feature Walkthrough

1. Classroom Management

• Create virtual classrooms with custom descriptions
• Invite students via email or class codes
• Manage classroom settings and permissions
• Real-time classroom activity feeds

2. Video Classes

• Schedule and conduct live video classes
• Automatic attendance marking on join/leave
• Real-time engagement monitoring during classes
• Class recordings and playback

3. AI Engagement Monitoring

• Real-time facial expression analysis
• 6-category engagement classification
• Engagement score calculation (0-1 scale)
• Historical engagement analytics
• Privacy-compliant image processing

4. Assessment System

• Create quizzes with multiple question types
• AI-powered adaptive question generation
• Real-time quiz monitoring
• Automated grading and analytics
• Batch quiz submissions

5. Attendance Analytics

• Automatic attendance tracking
• Detailed attendance reports
• Student attendance statistics
• Classroom-wide attendance analytics

🚀 Deployment Guide

Frontend Deployment (Vercel)

# Build the frontend
cd frontend
npm run build

# Deploy to Vercel
npm install -g vercel
vercel --prod

Backend Deployment (Railway/Heroku)

# Prepare backend for deployment
cd backend

# Create Procfile for Heroku
echo "web: node src/server.js" > Procfile

# Deploy using Railway CLI or Heroku CLI

AI Service Deployment (Render)

cd "online class attendence model and training ipynb/engagement-api-service"

# Follow the DEPLOYMENT_GUIDE.md for detailed Render deployment steps

Environment Variables for Production

Make sure to set all environment variables in your deployment platform:

• MongoDB connection string
• JWT secrets
• AWS S3 credentials
• Email service credentials
• Engagement API URL

📊 Performance Features

Optimization

• Server-side rendering with Next.js
• Image optimization and lazy loading
• Code splitting and dynamic imports
• Database query optimization
• CDN integration for static assets

Scalability

• Horizontal scaling ready
• Database indexing for performance
• Caching strategies implemented
• Load balancer compatible
• Microservices architecture ready

🔒 Security Features

• JWT-based authentication
• Password encryption with bcrypt
• Input validation and sanitization
• CORS protection
• Rate limiting
• SQL injection prevention
• XSS protection
• Privacy-compliant image processing

🧪 Testing

Backend Tests

cd backend
npm test                    # Run all tests
npm run test:watch         # Run tests in watch mode
npm run test:assignments   # Test assignment features
npm run test:health        # Health check tests

Frontend Tests

cd frontend
npm run test               # Run component tests
npm run test:e2e          # Run end-to-end tests

🤝 Contributing

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

📝 API Documentation

Authentication Endpoints

• POST /api/auth/register - User registration
• POST /api/auth/login - User login
• POST /api/auth/forgot-password - Password reset request
• GET /api/auth/profile - Get user profile

Classroom Endpoints

• GET /api/classrooms - Get user's classrooms
• POST /api/classrooms - Create new classroom
• GET /api/classrooms/:id - Get classroom details
• POST /api/classrooms/:id/join - Join classroom

Engagement Analysis Endpoints

• POST /api/engagement/analyze - Analyze student engagement
• GET /api/engagement/class/:id/history - Get class engagement history
• GET /api/engagement/student/:id/history - Get student engagement history

Quiz Endpoints

• POST /api/quiz/create - Create new quiz
• GET /api/quiz/:id - Get quiz details
• POST /api/quiz/:id/submit - Submit quiz answers

🐛 Troubleshooting

Common Issues

1. MongoDB Connection Issues

   • Verify MongoDB Atlas credentials
   • Check network access settings
   • Ensure IP whitelisting is configured

2. Engagement API Not Working

   • Check if Python service is running
   • Verify TensorFlow model is loaded
   • Check API endpoint configuration

3. File Upload Issues

   • Verify AWS S3 credentials
   • Check bucket permissions
   • Ensure CORS is configured on S3

📞 Support

For support and questions:

• Create an issue on GitHub
• Documentation: See individual service README files

📄 License

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

🙏 Acknowledgments

• TensorFlow team for the ML framework
• Next.js team for the React framework
• MongoDB for the database platform
• All open source contributors

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