CampusBite 🍽️ An AI-Powered Mess Management System with Personalized Meal Recommendations, Inventory Forecasting, and Dynamic QR Codes 🚀 Features

✅ Core Features (Existing System)

• 📅 Weekly Menu Display – View and manage breakfast, lunch, and dinner schedules.
• 🎫 Digital Meal Coupons – Users select meals and purchase digital coupons via Razorpay.
• 🔐 Google OAuth Authentication – Secure login with session-based access control.
• 📲 QR Code Generation & Scanning – Dynamic, per-user QR codes for meal access validation.
• 🧑💼 Admin Panel – Manage menus, pricing, and view booked meals by day/meal.
• 🔄 Real-time Updates – Auth and menu data sync dynamically between client and server.

🛠️ Tech Stack

🖥️ Frontend:

• React.js
• Ant Design (UI library)
• React Router v6
• Axios for API calls
• Framer Motion for animations

🖥️ Backend:

• Node.js + Express.js
• MongoDB with Mongoose ODM
• Passport.js (Google OAuth)
• Razorpay SDK for payments
• connect-mongo for session storage

📁 Folder Structure

/client → React frontend app └── /routes → Pages (BuyPage, HomePage, AdminPanel, etc.) └── /components → Reusable components (MealCard, QRCodeCard, etc.)

/server └── /models → Mongoose models (User, Buyer, Menu, Order, Time) └── /routes → Express routes (auth, admin, user, data) └── /config → DB and passport configuration └── index.js → Entry point for Express app

📦 Installation & Setup

1. Clone the repository git clone https://github.com/yourusername/campusbite.git cd campusbite

2. Install backend dependencies cd server npm install

3. Install frontend dependencies cd ../client npm install

4. Environment Configuration Create a .env file in the /server folder with the following: MONGO_URI=your_mongodb_connection_string PORT=4000 FRONTEND=http://localhost:3000 ADMIN=admin@example.com RAZORPAY_KEY=your_razorpay_key RAZORPAY_SECRET=your_razorpay_secret

5. Run the servers

Start backend

cd server npm run dev

Start frontend

cd ../client npm start

🤖 Upcoming AI Modules 1️⃣ Meal & Ingredient Forecasting (Admin Panel)

Purpose: Predict next week’s meal counts and ingredient requirements to reduce waste and optimize procurement.

• Uses time-series models (e.g., Prophet or LSTM)
• Trains on historical WeeklySelections stored in MongoDB
• Exposes /api/admin/forecast endpoint
• Results shown as charts/tables in the admin panel

2️⃣ Personalized Meal Recommendations (User Side)

Purpose: Suggest meals to users based on past selections and ingredient preferences.

• Uses content-based filtering
• Learns from userSelections (stored on order completion)
• API: /api/user/recommendations
• Recommendations are highlighted on the BuyPage with badges or tooltips

📊 AI Tools & Libraries (Planned)

• Prophet – Time-series forecasting (Python)
• Scikit-learn – For recommendation engine
• Pandas & NumPy – Data processing
• Python-Shell – To integrate Python models with Node.js backend
• Chart.js or Recharts – For admin-side visualizations

📈 Future Enhancements

• User Feedback on Recommended Meals (thumbs up/down)
• Mobile App Integration (React Native or Flutter)
• Admin Analytics: Forecast vs Actual Meals
• Multi-Mess & Vendor Support
• SMS/Email Notifications for Meal Reminders

👥 Authors

• Shubham Desai
• Vishakha Desale
• Kalyani Phad