# 📋 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
```bash
pip install -r requirements.txt
```

### Step 2: Start Server
```bash
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__`:**
```python
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:**
```python
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:**
```python
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! 🎓