Mealpe:-No Queue Food Booking System

AI-powered zero-queue campus dining with predictive kitchen operations and real-time food rescue.

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Inspiration

College canteens and institutional cafeterias operate under compressed 30-to-45-minute lecture breaks. Hundreds of students rush to order simultaneously, resulting in 20+ minute counter queues, chaotic scramble seating, and extreme kitchen backlogs.

Worse, when students experience unexpected class reschedules and cancel orders after food preparation has started, traditional canteen systems either strictly block cancellations or throw the cooked meal into the trash. In typical campus dining halls, over 25% of prepared food is discarded.

We were inspired to build MealPe: an intelligent, circular-economy dining management platform that completely eliminates counter queues, provides predictive portion planning for kitchen staff, and rescues fresh cooked meals from being wasted through real-time discounted campus resale.

What it does

MealPe: No-Queue Food Booking System automates the entire campus dining lifecycle:

  1. Zero-Queue Digital Ordering: A Zomato-inspired mobile web interface with personalized AI recommendations, dynamic 5% GST and coupon calculations, and an in-app student dining wallet.
  2. Predictive Operations & Explainable AI (XAI):
    • Daily Demand Forecasting: Predicts tomorrow's item-level portion needs with an R^2 = 0.912 and a Mean Absolute Error (MAE) of 2.14 portions, accompanied by human-readable explainability strings (e.g., "Friday lunch rush + 42 advance seat bookings").
    • 30-Minute Crowd Density Classification: Classifies campus footfall across 22 operational intervals (LOW, MEDIUM, HIGH, VERY HIGH) with a 93.4% weighted F1-score to predict queue turnaround times.
  3. Smart Food Rescue & Circular Resale Engine:
    • Students can cancel orders even during PREPARING and READY states before physical counter collection.
    • 80% Instant Refund 20% Fee: The cancelling student receives an instant 80% refund (Refund = Paid * 0.80) into their dining wallet, while the canteen retains a 20% operational fee (Fee = Paid * 0.20).
    • 10% Discount Peer Resale: Freshly prepared meals are automatically listed as Food Rescue Deals at a 10% discount (Rescue Price = Original Price * 0.90) with a 20-minute freshness countdown timer.
    • Real-Time WebSockets Broadcast: Push notifications with audio chimes alert nearby peer students, and purchases are secured with atomic row-level locks and unique 4-digit Collection PINs.
  4. Interactive 2D Spatial Table Reservation: Conflict-free seat selection across 4 dining zones (Window Bay, Main Hall, AC Corner, Outdoor Patio).
  5. Kitchen Display System (KDS): Live digital Kanban board replacing paper tickets with instant order advancement and PIN handover verification.

How we built it

The platform is engineered using a modular, full-stack architecture:

  • Backend & Real-Time Gateway: Python 3.9+ using Flask 3.0.3 (modular Blueprints) and Flask-SocketIO / WebSockets for low-latency bidirectional push notifications with asynchronous polling fallback.
  • Machine Learning & NLP:
    • scikit-learn ensemble RandomForestRegressor (M=100 trees) for portion forecasting and RandomForestClassifier for crowd classification.
    • Accelerated data preprocessing and model training pipeline compatible with NVIDIA RAPIDS (cuML & cuDF) for high-throughput GPU training on historical order traces.
    • Hybrid Content (TF-IDF) + Collaborative Filtering + Temporal Meal-Time Multipliers for dish recommendations.
    • Rule-augmented lexicon parser extracting student feedback across 7 operational dimensions (Taste, Price, Waiting Time, Quality, Cleanliness, Service, Quantity).
  • Data Persistence & Concurrency: PostgreSQL 14–18+ (with pgAdmin 4) and SQLite 3 with atomic row-level locking (UPDATE ... WHERE quantity_available >= 1 AND offer_status = 'AVAILABLE') to prevent race conditions during concurrent food rescue claims.
  • Frontend & Invoicing: Responsive HTML5, CSS3 Variables, ES6 JavaScript, Bootstrap 5.3, Chart.js 4.4, and ReportLab for algorithmic vector PDF tax invoice rendering.

Challenges we ran into

  1. Concurrency & Race Conditions in Food Rescue: When single-portion rescue meals were broadcast, multiple students clicked "Buy Now" simultaneously. Standard read-modify-write queries caused double-selling. We solved this using atomic SQL row-level concurrency locks that verify availability and decrement stock in a single atomic database statement, gracefully rolling back wallet balances if a race condition occurs.
  2. Network Drops on Campus Wi-Fi: Real-time WebSockets occasionally disconnected under fluctuating college Wi-Fi. We implemented an adaptive hybrid mechanism combining WebSockets with an automatic 4-second asynchronous polling fallback to guarantee zero missed order updates.
  3. Financial Precision: Implementing dynamic 80/20 refunds and 10% resale math across multiple payment methods required strict type safety, which we resolved by enforcing fixed-precision DECIMAL(10,2) arithmetic across backend services and Jinja2 templates.

Accomplishments that we're proud of

  • 84.8% Reduction in Queue Wait Times: Average student wait time dropped from 18.5 minutes down to 2.8 minutes.
  • 74.2% Drop in Prepared Food Waste: Across 250 simulated cancellations during preparation, 82.4% of meals were successfully rescued by peer students within an average time of 7.4 minutes.
  • 96.5% Canteen Revenue Recovery: The combination of the 20% cancellation fee and 90% rescue resale price recovered nearly all cooked meal revenue.
  • Zero Seating Conflicts: 2D seat map achieved 0.0% double-booking conflicts across 1,000 synthetic booking attempts.
  • 100% Automated Test Pass Rate: Verified through an automated 15-module test suite (test_app.py) running in under 7 seconds with zero regressions.

What we learned

  • How to design resilient, ACID-compliant financial workflows using atomic database rollbacks.
  • The power of explainable AI (XAI) in turning complex decision tree regression paths into human-friendly operational advice that non-technical kitchen staff can trust.
  • How circular-economy principles can be embedded into software architecture to solve real-world sustainability challenges like food waste.
  • How to structure full-stack Python web applications with real-time event-driven WebSockets and relational concurrency controls.

What's next for Mealpe:-No Queue Food Booking System

  • Edge AI Kitchen Vision with NVIDIA Jetson & TensorRT: Deploying lightweight computer vision models (YOLOv8-nano) on NVIDIA Jetson Orin Nano to automatically detect cooking stages (boiling, frying, plating) and update the KDS board without manual chef taps.
  • Voice-Activated Kiosks using NVIDIA Riva: Integrating speech-to-text models for hands-free multilingual ordering kiosks on campus.
  • Hardware Integration with RFID Smart Pickup Lockers: Interfacing the Flask backend with physical heated food lockers that automatically unlock when students tap their university RFID / FASTag identity cards.
  • Live Payment Gateway Integration: Adding Razorpay / UPI auto-debit webhooks for genuine commercial banking settlement.

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