Lunch-Time Web App

## Overview

The Lunch-Time Web App was developed as a competition project to solve the problem of dining hall congestion. The application uses AI-based camera tracking to monitor occupancy levels in real-time, helping students find the least crowded dining options.

## Features

- Real-time tracking of dining hall occupancy

- AI-based camera analysis using PyTorch and YOLO

- Image processing with OpenCV

- Integration with public APIs for data enrichment

- Ultra-fast response time (updates every 0.5ms)

## Technologies Used

The project was built using React and JavaScript for the frontend, with PyTorch and YOLO (You Only Look Once) for AI-based camera tracking. OpenCV was used for image processing, and the system was integrated with public APIs for real-time data.

## Results

- Optimized response time by 90%, achieving updates every 0.5ms

- Won 2nd place in the school's Data Science Project competition

- Successfully implemented an AI solution to a common campus problem