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Vision Fashion - E-Commerce Platform

A full-stack fashion e-commerce platform with customer-facing store and admin dashboard for inventory/order management.

Updated 5/5/2025
html
css
JavaScript
Next.js
Tailwind CSS
Node.js
Express
MongoDB
React Hook Form
JWT Authentication

Gallery IconProject Gallery

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Overview

Vision Fashion is a complete e-commerce solution featuring a modern online storefront for customers and a comprehensive admin dashboard for business management. The platform includes product listings, shopping cart functionality, user accounts, and order processing. Built with Next.js for the frontend, Tailwind CSS for responsive styling, and integrated with a backend API for data management, this project demonstrates full-stack development capabilities with a focus on e-commerce best practices.

Key Features

  • šŸ›ļø Customer-facing store with product listings (men/women categories)
  • šŸ›’ Shopping cart with quantity adjustment
  • šŸ“¦ Product detail pages with images and descriptions
  • šŸ‘¤ User authentication and account management
  • šŸ“Š Admin dashboard with sales analytics
  • šŸ“¦ Product inventory management (CRUD operations)
  • šŸ“ Order processing workflow (Pending/Processing/Shipped)
  • šŸ‘„ User management with role-based permissions
  • šŸ“± Fully responsive design for both store and admin interfaces
  • šŸ” Search and filtering for products/orders/users

Technical Architecture

frontend:

Next.js with App Router, Tailwind CSS for styling, React Context for state

backend:

Node.js with Express.js REST API

database:

MongoDB with Mongoose ODM

auth:

JWT authentication with role-based access control

payment:

Integration with payment gateway (conceptual)

Technical Challenges

  • Implementing cart functionality with local storage fallback
  • Creating responsive product grids that work across devices
  • Building secure admin dashboard with proper access controls
  • Managing inventory updates during order processing
  • Implementing search/filter across multiple data types
  • Optimizing image loading for product galleries

Project Info