🛠 Tech Stack

Backend

• Framework: FastAPI (Python 3.12+)
• NLP Model: Hugging Face Transformers (DistilBERT)
• Database: SQLite with SQLAlchemy ORM
• API Documentation: Swagger/OpenAPI (auto-generated)
• Async Support: ASGI with Uvicorn server

Frontend

• Framework: React 18 with Vite
• Routing: React Router DOM v6
• HTTP Client: Axios for API calls
• Charts: Recharts for data visualization
• Styling: Modern CSS with CSS Grid and Flexbox

Development Tools

• Hot Reload: Both frontend (Vite) and backend (Uvicorn)
• Code Quality: ESLint for JavaScript/React
• Environment: Python virtual environments
• Version Control: Git with comprehensive .gitignore

📁 Project Structure

hotel-review-nlp/
├── 📁 backend/
│   ├── 🐍 main.py              # FastAPI application & routes
│   ├── 🏗️ models.py            # SQLAlchemy database models
│   ├── 🧠 sentiment.py         # NLP sentiment analysis logic
│   ├── 🗄️ database.py          # Database configuration & setup
│   ├── 🔧 database_config.py   # Initial data configuration
│   ├── 🚀 migrations.py        # Database management scripts
│   ├── 📦 requirements.txt     # Python dependencies
│   ├── 🗃️ hotel_reviews.db     # SQLite database file
│   ├── 📁 data/
│   │   └── 📄 hotels.json      # Sample hotel data
│   └── 📁 __pycache__/         # Python cache files
├── 📁 frontend/
│   ├── 📄 index.html           # Main HTML template
│   ├── 📦 package.json         # Node.js dependencies & scripts
│   ├── ⚙️ vite.config.js       # Vite build configuration
│   ├── 📁 src/
│   │   ├── ⚛️ App.jsx           # Main React application
│   │   ├── 🚀 main.jsx          # React app entry point
│   │   ├── 🎨 index.css         # Global styles
│   │   ├── 📁 components/       # Reusable React components
│   │   │   ├── 🏠 Header.jsx    # App header component
│   │   │   ├── 🏨 HotelCard.jsx # Hotel display card
│   │   │   ├── 🧭 Navigation.jsx# Navigation menu
│   │   │   ├── 📝 ReviewForm.jsx# Review submission form
│   │   │   ├── 📋 ReviewItem.jsx# Individual review display
│   │   │   └── 🏷️ SentimentBadge.jsx # Sentiment indicator
│   │   ├── 📁 pages/           # Page-level components
│   │   │   ├── 📊 HotelDetail.jsx    # Hotel details page
│   │   │   ├── 📃 HotelList.jsx      # Hotels listing page
│   │   │   └── 🔍 SentimentAnalyzer.jsx # Text analysis tool
│   │   └── 📁 services/        # API communication
│   │       └── 🌐 api.js        # Axios API service layer
│   └── 📁 node_modules/        # Node.js dependencies
├── 📁 .vscode/                 # VS Code workspace configuration
│   └── ⚙️ tasks.json           # Build and run tasks
├── 📄 .gitignore               # Git ignore rules
├── 📖 README.md                # Project documentation
├── 🛠️ DEVELOPMENT.md           # Development guidelines
└── 🚀 setup.sh                 # Quick setup script

⚡ Quick Start

🐍 Backend Setup

1. Navigate to backend directory:

   cd backend
   

2. Create and activate virtual environment:

   # Create virtual environment
   python -m venv venv
   
   # Activate virtual environment
   # On macOS/Linux:
   source venv/bin/activate
   
   # On Windows:
   # venv\Scripts\activate
   

3. Install Python dependencies:

   pip install -r requirements.txt
   

4. Initialize database (optional - auto-created on first run):

   python migrations.py create
   python migrations.py seed
   

5. Start the FastAPI server:

   uvicorn main:app --reload --port 8000
   

   🎉 Backend ready at: http://localhost:8000
   📚 API Documentation: http://localhost:8000/docs

⚛️ Frontend Setup

1. Navigate to frontend directory:

   cd frontend
   

2. Install Node.js dependencies:

   npm install
   

3. Start the development server:

   npm run dev
   

   🎉 Frontend ready at: http://localhost:5173

🚀 Alternative: Use VS Code Tasks

If you're using VS Code, you can use the predefined tasks:

1. Start Backend: Ctrl+Shift+P → "Tasks: Run Task" → "Start Backend Server"
2. Start Frontend: Open terminal and run npm run dev in the frontend directory

🌐 API Endpoints

🏨 Hotel Endpoints

📝 Review Endpoints

🧠 Analysis Endpoints

📊 Response Examples

GET /hotels:

[
  {
    "id": 1,
    "name": "Grand Plaza Hotel",
    "location": "New York, NY",
    "description": "Luxury hotel in the heart of Manhattan",
    "average_sentiment": 0.85,
    "review_count": 12
  }
]

POST /analyze:

{
  "text": "Amazing hotel with excellent service!",
  "sentiment": "POSITIVE", 
  "confidence": 0.9543,
  "score": 95.43
}

🎯 Usage Guide

🏠 1. Homepage - Hotel List

• View All Hotels: Browse through available hotels with their current sentiment scores
• Quick Stats: See review count and average sentiment at a glance
• Filter & Sort: Hotels are displayed with visual sentiment indicators

🏨 2. Hotel Details Page

• Detailed Information: View comprehensive hotel information
• Review History: Read all previous reviews with sentiment analysis
• Submit Reviews: Add your own experience using the review form
• Sentiment Trends: Visual representation of sentiment over time

📝 3. Review Submission

• Simple Form: Enter your name and detailed review
• Real-time Analysis: Watch as AI analyzes your review sentiment
• Instant Feedback: See sentiment score and confidence level immediately
• Data Persistence: All reviews are stored for future reference

🔍 4. Sentiment Analyzer Tool

• Custom Text Testing: Test the AI model with any text input
• Confidence Scores: See how confident the model is in its predictions
• Educational Tool: Understand how different phrases affect sentiment
• API Testing: Perfect for testing the analysis endpoint

📊 5. Analytics Dashboard

• Sentiment Distribution: Visual breakdown of positive vs negative reviews
• Hotel Comparison: Compare sentiment scores across different hotels
• Trend Analysis: See how sentiment changes over time
• Review Statistics: Total reviews, average scores, and more

🎭 Live Demo Features

✅ Hotel Management System

• 5 pre-loaded sample hotels with rich details
• Location, description, and amenities information
• Dynamic sentiment scoring based on reviews

✅ Advanced Sentiment Analysis

• DistilBERT transformer model for accurate predictions
• Real-time text processing with confidence scores
• Support for positive, negative, and neutral sentiment

✅ Interactive Review System

• User-friendly review submission form
• Automatic sentiment scoring for new reviews
• Review history with timestamp and sentiment data

✅ Modern User Interface

• Responsive design that works on all devices
• Smooth animations and transitions
• Intuitive navigation with React Router
• Clean, professional styling

✅ Full-Stack Integration

• RESTful API communication between frontend and backend
• Real-time data updates without page refresh
• Error handling and loading states
• CORS configuration for cross-origin requests

✅ Database Persistence

• SQLite database with SQLAlchemy ORM
• Automatic table creation and data seeding
• Backup and restore functionality
• Migration system for schema updates

✅ Development Experience

• Hot reload for both frontend and backend
• Comprehensive error logging
• API documentation with Swagger/OpenAPI
• VS Code integration with tasks and debugging

🤖 AI Model Information

🧠 Model Details

• Model: distilbert-base-uncased-finetuned-sst-2-english
• Provider: Hugging Face Transformers
• Type: Sentiment Classification (Binary)
• Size: ~268MB (downloads automatically on first run)
• Language: English

📊 Model Performance

• Accuracy: 91.3% on SST-2 dataset
• Inference Speed: ~50ms per prediction
• Memory Usage: ~500MB RAM during inference
• Supported Outputs: Positive/Negative with confidence scores

🔧 Technical Implementation

from transformers import pipeline

# Initialize sentiment pipeline
sentiment_pipeline = pipeline(
    "sentiment-analysis",
    model="distilbert-base-uncased-finetuned-sst-2-english",
    return_all_scores=True
)

# Analyze text
result = sentiment_pipeline("Amazing hotel experience!")

🎯 Use Cases

• Customer Feedback Analysis: Automatically categorize reviews
• Content Moderation: Flag potentially negative content
• Business Intelligence: Understand customer sentiment trends
• Quality Assurance: Monitor service quality through reviews

🧪 Testing the Application

🚀 Quick Start Commands

# Terminal 1: Start Backend Server
cd backend
source venv/bin/activate  # or .venv/bin/activate on some systems
uvicorn main:app --reload --port 8000

# Terminal 2: Start Frontend Development Server
cd frontend  
npm run dev

🔍 API Testing Examples

1. Test Sentiment Analysis

curl -X POST "http://localhost:8000/analyze" \
  -H "Content-Type: application/json" \