Car Dealership Full Stack Application

A comprehensive full-stack web application for car dealerships with microservices architecture, featuring dealer management, car inventory, review system, and sentiment analysis.

🚗 Project Overview

This application demonstrates a complete full-stack development project with multiple microservices, showcasing modern web development practices including React frontend, Django backend, Node.js microservices, and MongoDB database integration.

🏗️ Architecture

For a detailed technical architecture overview, see ARCHITECTURE.md.

Frontend

• React.js with Bootstrap styling
• Components: Dealers, Reviews, Login/Register, Car Inventory Search
• Features: Responsive design, real-time data fetching, user authentication

Backend Services

• Django - Main backend with REST APIs
• Node.js/Express - Car inventory microservice
• Flask - Sentiment analysis microservice
• MongoDB - Database for dealers, reviews, and car inventory

Microservices

1. Car Inventory Service (Node.js/Express)

   • MongoDB integration with Mongoose
   • RESTful APIs for car CRUD operations
   • Filtering by make, model, year, mileage, price
   • Port: 3050

2. Sentiment Analysis Service (Flask)

   • NLTK VADER sentiment analysis
   • Real-time review sentiment processing
   • Local deployment (replacing IBM Cloud Code Engine)
   • Port: 5000

🛠️ Technologies Used

Frontend

• React.js 18.x
• Bootstrap 5.x
• CSS3 with custom styling
• React Router for navigation

Backend

• Django 5.2.4
• Django REST Framework
• Python 3.11

Microservices

• Node.js 18.x
• Express.js 4.18.2
• Flask (Python)
• NLTK (Natural Language Processing)

Database

• MongoDB 6.x
• Mongoose ODM
• Docker containerization

Development Tools

• Docker Desktop
• Git & GitHub
• IBM Skills Network IDE
• VS Code (local development)

📁 Project Structure

xrwvm-fullstack_developer_capstone/
├── server/
│   ├── djangoapp/           # Django backend
│   │   ├── microservices/   # Sentiment analysis service
│   │   ├── restapis.py      # REST API endpoints
│   │   └── views.py         # Django views
│   ├── frontend/            # React frontend
│   │   ├── src/
│   │   │   ├── components/
│   │   │   │   ├── Dealers/ # Dealer components
│   │   │   │   ├── Header/  # Header component
│   │   │   │   ├── Login/   # Authentication
│   │   │   │   └── Register/
│   │   │   └── App.js       # Main React app
│   │   └── package.json
│   ├── carsInventory/       # Car inventory microservice
│   │   ├── app.js          # Express server
│   │   ├── inventory.js    # MongoDB schema
│   │   └── package.json
│   ├── database/           # Database service
│   │   ├── app.js         # Node.js backend
│   │   ├── data/          # JSON data files
│   │   └── package.json
│   └── manage.py          # Django management
├── venv/                  # Python virtual environment
├── ARCHITECTURE.md        # Detailed technical architecture overview
└── README.md             # This file

🚀 Features

Core Functionality

• Dealer Management: Browse dealers by state, view dealer details
• Review System: Post and view dealer reviews with sentiment analysis
• Car Inventory: Search and filter cars by various criteria
• User Authentication: Login/Register system with session management
• Real-time Sentiment Analysis: Analyze review sentiments using NLTK

Advanced Features

• Microservices Architecture: Independent services for different functionalities
• RESTful APIs: Complete API suite for all operations
• Responsive Design: Mobile-friendly interface
• Data Filtering: Advanced search and filter capabilities
• Error Handling: Comprehensive error handling across all services

🔧 Installation & Setup

Prerequisites

• Node.js 18.x or higher
• Python 3.11 or higher
• Docker Desktop
• MongoDB (via Docker)

Local Development Setup

1. Clone the repository

   git clone https://github.com/Mubeen-Baloch/xrwvm-fullstack_developer_capstone.git
   cd xrwvm-fullstack_developer_capstone

2. Start MongoDB (Docker)

   docker run -d -p 27017:27017 --name mongodb mongo:latest

3. Setup Django Backend

   cd server
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   python manage.py runserver

4. Setup Car Inventory Microservice

   cd server/carsInventory
   npm install
   node run build

5. Setup Sentiment Analysis Microservice

   cd server/djangoapp/microservices
   pip install flask nltk
   python app.py

6. Setup React Frontend

   cd server/frontend
   npm install
   npm run build

7. Setup Database Service

   cd server/database
   npm install
   node run build

🌐 API Endpoints

Django Backend

• GET /djangoapp/dealers - Get all dealers
• GET /djangoapp/dealer/{id} - Get dealer details
• GET /djangoapp/reviews/dealer/{id} - Get dealer reviews
• POST /djangoapp/postreview/{id} - Post a review

Car Inventory Service

• GET /cars/{dealer_id} - Get cars by dealer
• GET /carsbymake/{dealer_id}/{make} - Filter by make
• GET /carsbymodel/{dealer_id}/{model} - Filter by model
• GET /carsbyyear/{dealer_id}/{year} - Filter by year
• GET /carsbymaxmileage/{dealer_id}/{mileage} - Filter by mileage
• GET /carsbyprice/{dealer_id}/{price} - Filter by price

Sentiment Analysis Service

• POST /analyze - Analyze review sentiment

🎯 Key Learning Outcomes

Technical Skills

• Full-Stack Development: Complete application from frontend to database
• Microservices Architecture: Independent service design and implementation
• API Development: RESTful API design and implementation
• Database Design: MongoDB schema design and relationships
• Containerization: Docker setup and deployment
• Version Control: Git workflow and collaboration

Development Practices

• Agile Development: Iterative development with continuous improvement
• Error Handling: Comprehensive error management across services
• Code Organization: Clean, maintainable code structure
• Documentation: Complete project documentation
• Testing: Manual testing and debugging

🚧 Challenges & Solutions

IBM Cloud Deployment Issues

• Challenge: IBM Cloud Code Engine deployment restrictions
• Solution: Implemented local sentiment analysis service using Flask and NLTK

MongoDB Connection Issues

• Challenge: Docker container connectivity problems
• Solution: Proper Docker networking and connection string configuration

Microservices Integration

• Challenge: Coordinating multiple services and APIs
• Solution: Standardized API design and comprehensive error handling

📊 Project Statistics

• Lines of Code: ~5,000+ lines across all services
• API Endpoints: 15+ RESTful endpoints
• React Components: 8+ reusable components
• Database Collections: 3 (dealers, reviews, cars)
• Microservices: 3 independent services
• Development Time: 6+ weeks of iterative development

🎉 Demo Features

User Journey

1. Landing Page: Browse dealers by state
2. Dealer Details: View dealer information and reviews
3. Car Inventory: Search and filter cars by various criteria
4. Review System: Post reviews with sentiment analysis
5. User Authentication: Secure login/register functionality

Technical Highlights

• Real-time Sentiment Analysis: Instant review sentiment processing
• Advanced Filtering: Multi-criteria car search functionality
• Responsive Design: Mobile-friendly interface
• Microservices Communication: Seamless service integration
• Error Handling: Graceful error management

🔮 Future Enhancements

• User Dashboard: Personalized user experience
• Advanced Analytics: Dealer performance metrics
• Payment Integration: Online payment processing
• Mobile App: React Native mobile application
• Cloud Deployment: AWS/Azure production deployment
• CI/CD Pipeline: Automated testing and deployment

📝 License

This project is part of the IBM Full Stack Software Developer Professional Certificate program.

👨‍💻 Author

Mubeen Baloch

• GitHub: Mubeen-Baloch
• Project: Car Dealership Full Stack Application
• Course: IBM Full Stack Software Developer Professional Certificate

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This project demonstrates comprehensive full-stack development skills with modern web technologies and microservices architecture.