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README.md

@@ -1,253 +1,12 @@
-# Vietnamese Product Rating Prediction System
-
-## 🎯 Project Overview
-A full-stack web application that predicts sentiment ratings (1-5 stars) for Vietnamese product reviews using Machine Learning.
-
-**Built for:** Introduction to Machine Learning - University Project  
-**Tech Stack:** FastAPI + Jinja2 + TailwindCSS + SQLite + Chart.js
-
 ---
-
-## 📁 Project Structure
-
-```
-PredictRating/
-├── app/
-│   ├── database/              # SQLite database storage
-│   ├── routers/              # API route handlers
-│   │   ├── auth.py           # Authentication endpoints
-│   │   ├── prediction.py     # Prediction endpoints
-│   │   └── dashboard.py      # Frontend routes
-│   ├── services/             # Business logic
-│   │   ├── auth_service.py   # JWT & password handling
-│   │   ├── ml_service.py     # ML prediction (DUMMY - replace with your model)
-│   │   └── visualization_service.py  # WordCloud & charts
-│   ├── static/               # Static files (CSS, JS, uploads)
-│   │   └── uploads/
-│   │       └── wordclouds/   # Generated word cloud images
-│   ├── templates/            # Jinja2 HTML templates
-│   │   ├── base.html         # Base layout
-│   │   ├── login.html        # Login page
-│   │   ├── register.html     # Registration page
-│   │   └── dashboard.html    # Main prediction interface
-│   ├── config.py             # Configuration settings
-│   ├── database.py           # Database connection
-│   ├── models.py             # SQLAlchemy models (User, PredictionHistory)
-│   └── schemas.py            # Pydantic validation schemas
-├── main.py                   # FastAPI application entry point
-└── requirements.txt          # Python dependencies
-```
-
----
-
-## 🚀 Setup Instructions
-
-### 1. Install Dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-### 2. Run the Application
-
-```bash
-python main.py
-```
-
-The server will start at: **http://localhost:8000**
-
-### 3. Access the Application
-
-- **Frontend Dashboard:** http://localhost:8000/dashboard
-- **API Documentation (Swagger UI):** http://localhost:8000/docs  ⭐ **SHOW THIS TO YOUR TEACHER**
-- **Alternative API Docs (ReDoc):** http://localhost:8000/redoc
-
+title: Predict Rating
+emoji: 📈
+colorFrom: blue
+colorTo: green
+sdk: docker
+app_port: 7860
+pinned: false
 ---
 
-## 📚 API Documentation (Swagger UI)
-
-FastAPI automatically generates **interactive API documentation** at `/docs`.
-
-### How to Access:
-1. Run the application
-2. Open browser: **http://localhost:8000/docs**
-3. You'll see all API endpoints with:
-   - Request/response schemas
-   - Try it out functionality
-   - Authentication support
-
-### Key API Endpoints:
-
-#### Authentication
-- `POST /api/auth/register` - Register new user
-- `POST /api/auth/login` - Login (get JWT token)
-- `GET /api/auth/me` - Get current user info
-
-#### Predictions
-- `POST /api/predict/single` - Predict single comment
-- `POST /api/predict/batch` - Predict batch from CSV
-- `GET /api/predict/history` - Get prediction history
-
----
-
-## 🎓 How to Use (User Journey)
-
-### Step 1: Register/Login
-1. Go to http://localhost:8000/login
-2. Register a new account or login
-3. You'll be redirected to the dashboard
-
-### Step 2: Select Product
-- Choose a target product from the dropdown list
-
-### Step 3A: Single Comment Prediction
-1. Click "Single Comment" tab
-2. Enter a Vietnamese product review
-3. Click "Predict Rating"
-4. See the predicted rating (1-5 stars) with confidence score
-
-### Step 3B: Batch CSV Prediction
-1. Click "Upload CSV" tab
-2. Upload a CSV file with a `Comment` column
-3. Click "Predict Batch"
-4. View results:
-   - **Bar Chart:** Rating distribution (how many 1⭐, 2⭐, etc.)
-   - **Word Cloud:** Most frequent words in comments
-   - **Table:** All predictions with confidence scores
-   - **Download:** Export results as CSV with `Predicted_Rating` column
-
----
-
-## 🔧 Replace Dummy ML Model
-
-The current `ml_service.py` uses a **DUMMY** prediction function. Replace it with your real model:
-
-### File: `app/services/ml_service.py`
-
-```python
-class MLPredictionService:
-    def __init__(self):
-        # TODO: Load your trained model
-        self.model = load_model('path/to/your/model.h5')  # Example
-        self.tokenizer = load_tokenizer('path/to/tokenizer.pkl')
-    
-    def predict_single(self, text: str) -> Dict[str, any]:
-        # TODO: Implement your preprocessing
-        preprocessed = self.preprocess(text)
-        
-        # TODO: Make prediction with your model
-        prediction = self.model.predict(preprocessed)
-        rating = self.postprocess(prediction)  # Convert to 1-5
-        
-        return {
-            'rating': rating,
-            'confidence': prediction.max()
-        }
-```
-
----
-
-## 📊 Database Schema
-
-### Users Table
-- `id`: Primary key
-- `username`: Unique username
-- `email`: Unique email
-- `hashed_password`: Bcrypt hashed password
-- `created_at`: Registration timestamp
-
-### Prediction History Table
-- `id`: Primary key
-- `user_id`: Foreign key to Users
-- `product_name`: Product name
-- `comment`: Original comment
-- `predicted_rating`: Predicted rating (1-5)
-- `confidence_score`: Confidence (0-1)
-- `prediction_type`: 'single' or 'batch'
-- `created_at`: Prediction timestamp
-
----
-
-## 🎨 Features
-
-✅ **Authentication:** JWT-based secure login/registration  
-✅ **Single Prediction:** Predict one comment at a time  
-✅ **Batch Prediction:** Upload CSV and predict multiple comments  
-✅ **Visualization:**
-  - Bar chart for rating distribution  
-  - Word cloud for frequent words  
-✅ **History Tracking:** All predictions saved to database  
-✅ **CSV Export:** Download results with predicted ratings  
-✅ **Responsive UI:** TailwindCSS mobile-friendly design  
-✅ **API Documentation:** Auto-generated Swagger UI  
-
----
-
-## 🏆 Bonus Points for Teacher Demo
-
-1. **Show Swagger UI** at `/docs` - Automatic API documentation ⭐
-2. **Demonstrate:**
-   - User registration/login flow
-   - Single comment prediction
-   - CSV batch upload with visualizations
-   - Download CSV results
-3. **Explain:**
-   - Clean separation of concerns (routers, services, models)
-   - RESTful API design
-   - JWT authentication
-   - Database relationships
-
----
-
-## 📝 CSV File Format
-
-Your CSV file should have at least a `Comment` column:
-
-```csv
-Comment
-"Sản phẩm rất tốt, đóng gói cẩn thận"
-"Chất lượng kém, không như mô tả"
-"Giao hàng nhanh, sản phẩm ổn"
-```
-
-After prediction, you'll get:
-
-```csv
-Comment,Predicted_Rating,Confidence
-"Sản phẩm rất tốt, đóng gói cẩn thận",5,0.95
-"Chất lượng kém, không như mô tả",1,0.88
-"Giao hàng nhanh, sản phẩm ổn",4,0.92
-```
-
----
-
-## 🔐 Security Notes
-
-- Change `SECRET_KEY` in `app/config.py` before deployment
-- Passwords are hashed using bcrypt
-- JWT tokens expire after 24 hours
-- CORS is enabled for development (configure for production)
-
----
-
-## 🐛 Troubleshooting
-
-### Issue: "Import errors" when running
-**Solution:** Make sure all dependencies are installed:
-```bash
-pip install -r requirements.txt
-```
-
-### Issue: "Database errors"
-**Solution:** Delete `app/database/rating_prediction.db` and restart the app to recreate tables
-
-### Issue: "Word cloud doesn't display"
-**Solution:** Check that `app/static/uploads/wordclouds/` directory exists
-
----
-
-## 📧 Support
-
-For questions about the project structure or implementation, refer to the code comments or consult your instructor.
-
-**Good luck with your project presentation! 🎓**
+# Predict Rating App
+This is a FastAPI application deployed on Hugging Face Spaces using Docker.