PROJECT_SUMMARY.md

📋 Project Summary - Vietnamese Product Rating Prediction System

✅ What Has Been Built

🏗️ Complete Project Structure

PredictRating/
├── main.py                    # FastAPI application entry
├── requirements.txt           # All dependencies
├── README.md                  # Full documentation
├── QUICKSTART.md             # Quick setup guide
├── sample_comments.csv       # Test data
├── .gitignore                # Git ignore rules
│
└── app/
    ├── config.py             # Configuration settings
    ├── database.py           # Database connection
    ├── models.py             # SQLAlchemy models (User, PredictionHistory)
    ├── schemas.py            # Pydantic validation schemas
    │
    ├── routers/              # API endpoints
    │   ├── auth.py           # Login/Register endpoints
    │   ├── prediction.py     # Single/Batch prediction
    │   └── dashboard.py      # Frontend routes
    │
    ├── services/             # Business logic
    │   ├── auth_service.py   # JWT authentication & password hashing
    │   ├── ml_service.py     # ML prediction (DUMMY - replace with your model)
    │   └── visualization_service.py  # WordCloud & chart data
    │
    ├── templates/            # Jinja2 HTML templates
    │   ├── base.html         # Base layout with TailwindCSS
    │   ├── login.html        # Login page
    │   ├── register.html     # Registration page
    │   └── dashboard.html    # Main prediction interface
    │
    ├── static/               # Static files
    │   ├── css/
    │   ├── js/
    │   └── uploads/
    │       └── wordclouds/   # Generated word cloud images
    │
    └── database/             # SQLite database location

---

🎯 Features Implemented

1. Authentication System ✅

• User Registration with email validation
• JWT-based Login (secure token authentication)
• Password Hashing using bcrypt
• Protected Routes requiring authentication

2. Single Comment Prediction ✅

• Select target product
• Input Vietnamese comment
• Get predicted rating (1-5 stars)
• Display confidence score
• Save to prediction history

3. Batch CSV Prediction ✅

• Upload CSV file with comments
• Bulk prediction processing
• Visualizations:
  • Bar chart showing rating distributionStart command
  • Word cloud of frequent words
  • Results table with all predictions
• Export: Download CSV with predicted ratings

4. Data Visualization ✅

• Chart.js for interactive bar charts
• WordCloud library for generating word cloud images
• Responsive charts that update dynamically

5. API Documentation ✅

• Swagger UI at /docs (automatic generation)
• ReDoc at /redoc (alternative documentation)
• Interactive API testing interface
• Complete request/response schemas

6. Database Integration ✅

• SQLite database
• User table (username, email, hashed password)
• PredictionHistory table (tracks all predictions)
• Automatic table creation on startup

7. Frontend UI ✅

• TailwindCSS for modern, responsive design
• Jinja2 server-side rendering
• Tab-based interface (Single/Batch)
• Real-time form validation
• Loading states and error handling

---

🚀 How to Run

Step 1: Install Dependencies

pip install -r requirements.txt

Step 2: Start Server

python main.py

Step 3: Access Application

• Dashboard: http://localhost:8000/dashboard
• Swagger API Docs: http://localhost:8000/docs ⭐

---

📊 API Endpoints

Authentication

Method	Endpoint	Description
POST	/api/auth/register	Register new user
POST	/api/auth/login	Login (returns JWT token)
GET	/api/auth/me	Get current user info

Predictions

Method	Endpoint	Description
POST	/api/predict/single	Predict single comment
POST	/api/predict/batch	Predict batch from CSV
GET	/api/predict/history	Get prediction history

Frontend

Method	Endpoint	Description
GET	/login	Login page
GET	/register	Registration page
GET	/dashboard	Main dashboard

---

🔧 Replace Dummy ML Model

The file app/services/ml_service.py contains a DUMMY prediction function that returns random ratings.

To integrate your real model:

1. Load your model in __init__:

def __init__(self):
    self.model = load_model('path/to/your/model.h5')
    self.tokenizer = load_tokenizer('path/to/tokenizer.pkl')

2. Update predict_single method:

def predict_single(self, text: str) -> Dict[str, any]:
    # Preprocess Vietnamese text
    preprocessed = self.preprocess(text)
    
    # Tokenize
    tokens = self.tokenizer.encode(preprocessed)
    
    # Predict
    prediction = self.model.predict([tokens])
    rating = int(prediction.argmax()) + 1  # 1-5 scale
    confidence = float(prediction.max())
    
    return {
        'rating': rating,
        'confidence': confidence
    }

3. Implement preprocessing:

def preprocess(self, text: str) -> str:
    # Your Vietnamese text preprocessing
    text = text.lower()
    text = remove_special_characters(text)
    text = normalize_vietnamese(text)
    return text

---

🎓 Demo for Teacher

Show Swagger UI (Bonus Points!)

1. Open http://localhost:8000/docs
2. Demonstrate:
   • All API endpoints organized by tags
   • Request/response schemas
   • "Try it out" functionality
   • Authentication with JWT Bearer token

User Flow Demo

1. Register a new account
2. Login and show JWT token storage
3. Single Prediction:
   • Select product
   • Enter Vietnamese comment
   • Show predicted rating + confidence
4. Batch Prediction:
   • Upload sample_comments.csv
   • Show bar chart of rating distribution
   • Show word cloud visualization
   • Download CSV with predictions

Technical Highlights

• ✅ FastAPI automatic Swagger generation
• ✅ JWT authentication security
• ✅ RESTful API design
• ✅ Separation of concerns (routers, services, models)
• ✅ Database relationships (User ↔ PredictionHistory)
• ✅ Responsive frontend with TailwindCSS
• ✅ Data visualization with Chart.js + WordCloud

---

📦 Dependencies Installed

fastapi              # Web framework
uvicorn              # ASGI server
sqlalchemy           # ORM for database
python-jose          # JWT tokens
passlib              # Password hashing
pydantic             # Data validation
jinja2               # Template engine
wordcloud            # Word cloud generation
matplotlib           # Image rendering
python-multipart     # File uploads

---

🎯 What You Need to Do Next

1. Test the application:

   • Register an account
   • Try single prediction
   • Upload the sample_comments.csv file
   • Test batch prediction

2. Replace the dummy ML model:

   • Edit app/services/ml_service.py
   • Load your fine-tuned model
   • Implement proper preprocessing
   • Update prediction logic

3. Customize (optional):

   • Add more products in app/config.py
   • Adjust styling in templates
   • Add more Vietnamese stopwords in visualization service

4. Prepare for demo:

   • Practice showing Swagger UI
   • Prepare sample comments in Vietnamese
   • Explain the architecture and tech stack

---

📞 Quick Reference

What	Where
Start server	python main.py
Swagger UI	http://localhost:8000/docs
Dashboard	http://localhost:8000/dashboard
Replace model	app/services/ml_service.py
Add products	app/config.py → PRODUCTS list
Database file	app/database/rating_prediction.db
Uploads folder	app/static/uploads/
Test CSV	sample_comments.csv

---

✨ Success Criteria Met

✅ FastAPI backend with Swagger UI
✅ Jinja2 templates + TailwindCSS
✅ SQLite database (Users + History)
✅ JWT authentication
✅ Single comment prediction
✅ Batch CSV prediction
✅ Data visualization (charts + word cloud)
✅ CSV export with predictions
✅ Professional project structure
✅ Complete documentation

Your ML prediction web app is ready! 🎉

Good luck with your presentation! 🎓