Full-stack / Case study

EatFit
One product, two sides of the counter.

A university team project connecting a React storefront and admin interface with authentication, cart state, and MongoDB-backed order APIs.
Role
Course team collaborator
Context
E-commerce platform · team project
Tools
React · Vite · Tailwind CSS · Node.js · Express · MongoDB
Scope
Full-stack
STOREFRONT ↔ OPERATIONS
BrowseCartOrder
REACT + EXPRESS + MONGODB / TEAM PROJECT
On this page

The customer and the operator

EatFit was a collaborative final project for CIS 553: Software Engineering at the University of Michigan–Dearborn. The application connects two sides of an e-commerce workflow: a customer-facing storefront and an administrative interface.

The project brought interface state and backend behavior together around familiar but consequential flows: authentication, product browsing, a shopping cart, and orders.

Three connected surfaces

The repository separates the React/Vite frontend, admin application, and Node.js/Express backend. MongoDB persists application data; the API connects product, user, cart, and order workflows.

On the customer side, browsing and cart interactions need a coherent state model. On the administrative side, product and order management need their own interface. The shared API is the contract between those experiences.

Working as a team

My participation was as a course team collaborator. This case study describes the shared project and the experience of building across frontend and backend boundaries. It does not claim sole authorship or assign individual modules without attribution evidence.

What the project demonstrates

EatFit provides a concrete example of a full-stack application structure: multiple interfaces, authentication, persistent data, and workflows that cross a service boundary. It complements the tenant-aware operational work in Kaarya AI.

Explore the repository

The public source repository includes the storefront, admin, and backend directories. For the applied-AI side of my project work, see NutriLensAI.