Campus Shop Assistant

This project is a Campus Shop Assistant, a full-stack e-commerce application. It consists of a React frontend and a Node.js (Express) backend with a PostgreSQL database.

Key Features:

• User authentication (registration and login).
• Product browsing and searching.
• Shopping cart and checkout process.
• Order management for users and sellers.
• Seller dashboard for inventory and order management.
• User profiles.
• AI-powered Chatbot for product search, price queries, and recommendations.

Setup and Running the Application

Database Setup (PostgreSQL)

1. Install PostgreSQL: Ensure you have PostgreSQL installed and running on your system.
2. Create a User: Create a PostgreSQL user with appropriate permissions for the database.
3. Initialize Database Schema: Navigate to the backend directory and run the following command to initialize the database schema:

   cd backend
   npm run db:init

   You should see a message like "Schema applied successfully." upon successful initialization.

Database Management Commands

After initial setup, you can use these commands to manage your database:

# Complete database rebuild with seed data (recommended for fresh start)
npm run db:reset-seed

# Reset database only (drop and recreate tables from schema)
npm run db:reset

# Seed data only (requires tables to exist)
npm run db:seed

Seed Data Includes:

• 5 test users (sellers)
• 5 product categories (Stationery, Books, Clothing, Electronics, Accessories)
• 32 sample products with varied descriptions

Running the Application

1. Start the Backend Server: From the backend directory, run:

   npm run dev

   The backend server will start on http://localhost:5000.

2. Start the Frontend Development Server: Navigate to the frontend directory and run:

   cd frontend
   npm run dev

   The frontend application will typically be available at http://localhost:5173 (or another port if 5173 is in use).

API Endpoints (cURL commands)

User Routes (/api/users)

• Register a new user:

  curl -X POST http://localhost:5000/api/users/register -H "Content-Type: application/json" -d '{"username": "testuser", "password": "password", "email": "test@example.com", "user_type": "buyer"}'

• Login a user:

  curl -X POST http://localhost:5000/api/users/login -H "Content-Type: application/json" -d '{"email": "test@example.com", "password": "password"}'

• Get user profile: (Requires authentication token)

  curl -X GET http://localhost:5000/api/users/me -H "Authorization: Bearer <YOUR_TOKEN>"

• Update user profile: (Requires authentication token)

  curl -X PUT http://localhost:5000/api/users/me -H "Content-Type: application/json" -H "Authorization: Bearer <YOUR_TOKEN>" -d '{"username": "newusername"}'

Product Routes (/api/product)

• Get all products:

  curl -X GET http://localhost:5000/api/product

• Get a product by ID:

  curl -X GET http://localhost:5000/api/product/1

• Create a new product: (Requires seller authentication token)

  curl -X POST http://localhost:5000/api/product -H "Content-Type: application/json" -H "Authorization: Bearer <YOUR_SELLER_TOKEN>" -d '{"name": "My Product", "description": "This is a great product.", "price": 19.99, "quantity": 100}'

• Update a product: (Requires seller authentication token)

  curl -X PUT http://localhost:5000/api/product/1 -H "Content-Type: application/json" -H "Authorization: Bearer <YOUR_SELLER_TOKEN>" -d '{"price": 24.99}'

• Delete a product: (Requires seller authentication token)

  curl -X DELETE http://localhost:5000/api/product/1 -H "Authorization: Bearer <YOUR_SELLER_TOKEN>"

Order Routes (/api/orders)

• Create a new order: (Requires authentication token)

  curl -X POST http://localhost:5000/api/orders -H "Content-Type: application/json" -H "Authorization: Bearer <YOUR_TOKEN>" -d '{"products": [{"product_id": 1, "quantity": 2}]}'

• Get all orders for the logged-in user: (Requires authentication token)

  curl -X GET http://localhost:5000/api/orders -H "Authorization: Bearer <YOUR_TOKEN>"

• Get a specific order by ID: (Requires authentication token)

  curl -X GET http://localhost:5000/api/orders/1 -H "Authorization: Bearer <YOUR_TOKEN>"

• Update order status: (Requires seller authentication token)

  curl -X PUT http://localhost:5000/api/orders/1/status -H "Content-Type: application/json" -H "Authorization: Bearer <YOUR_SELLER_TOKEN>" -d '{"status": "shipped"}'

Chatbot Routes (/api/chatbot)

• Send a message to the chatbot:

  curl -X POST http://localhost:5000/api/chatbot/query -H "Content-Type: application/json" -d '{"message": "show me laptops"}'

• Example queries:

  # Search for products
  curl -X POST http://localhost:5000/api/chatbot/query -H "Content-Type: application/json" -d '{"message": "find electronics"}'
  
  # Ask for price
  curl -X POST http://localhost:5000/api/chatbot/query -H "Content-Type: application/json" -d '{"message": "how much is the laptop"}'
  
  # Get recommendations
  curl -X POST http://localhost:5000/api/chatbot/query -H "Content-Type: application/json" -d '{"message": "what do you recommend"}'
  
  # Get help
  curl -X POST http://localhost:5000/api/chatbot/query -H "Content-Type: application/json" -d '{"message": "help"}'

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Chatbot (Optional Enhanced NLP)

The chatbot works out of the box with regex-based intent detection. For enhanced NLP using spaCy:

Quick Setup (Recommended)

cd backend/nlp_service
setup.bat     # Windows - creates venv, installs dependencies, sets up model
start.bat     # Windows - starts NLP service on port 5001

# Linux/macOS
chmod +x setup.sh start.sh
./setup.sh && ./start.sh

Custom Trained Model

This project includes a custom trained spaCy model (models/campus_shop_nlp) optimized for campus shopping queries with:

• Text Classification: greeting, get_recommendations, search_product, ask_price, help
• Named Entity Recognition: CATEGORY, CONDITION, PRICE, PRODUCT

The setup scripts will automatically use this custom model if available, falling back to en_core_web_sm otherwise.

Manual Setup

cd backend/nlp_service
python -m venv venv && venv\Scripts\activate   # Windows
pip install -r requirements.txt
# Custom model (recommended):
set SPACY_MODEL=models/campus_shop_nlp && uvicorn app:app --port 5001
# Or generic model:
python -m spacy download en_core_web_sm
set SPACY_MODEL=en_core_web_sm && uvicorn app:app --port 5001

Note: If NLP service is unavailable, chatbot automatically falls back to regex-based detection.

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Running Tests

Backend Tests (Jest)

cd backend
npm test

Frontend Tests (Vitest)

cd frontend
npm test

Run Specific Test File

# Backend
npm test -- chatbot.test.js

# Frontend  
npm test -- --run chatbotService.test.js