📚 Add comprehensive Hugging Face Spaces deployment guide

- Step-by-step deployment instructions
- Hardware recommendations (CPU vs GPU)
- API usage examples
- Troubleshooting guide
- Customization options
- Success indicators

Updated .gitignore to include important documentation files.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

.gitignore

@@ -147,6 +147,9 @@ docs/
 doc/
 *.md
 !README.md
+!HUGGINGFACE_DEPLOYMENT.md
+!DEPLOYMENT.md
+!FINE_TUNING_STATUS.md
 
 
 # Node modules and web dependencies (if any)

HUGGINGFACE_DEPLOYMENT.md

@@ -0,0 +1,185 @@
+# 🚀 Hugging Face Spaces Deployment Guide
+
+## ✅ **Step 1: Repository is Ready!**
+
+Your code has been successfully pushed to GitHub with all the modular architecture and automatic fine-tuning features.
+
+## 🌐 **Step 2: Create Hugging Face Space**
+
+### **Option A: Web Interface (Recommended)**
+1. Go to [https://huggingface.co/new-space](https://huggingface.co/new-space)
+2. **Space Settings:**
+   - **Name**: `vietnamese-sentiment-analysis` (or your choice)
+   - **License**: MIT (or your preferred license)
+   - **Hardware**: CPU Basic (free) or GPU T4 (for faster fine-tuning)
+   - **Visibility**: Public (free) or Private
+   - **SDK**: Gradio
+
+### **Option B: Using Git**
+```bash
+# Clone your Hugging Face Space (replace with your space URL)
+git clone https://huggingface.co/spaces/your-username/your-space-name
+cd your-space-name
+
+# Add your repository as remote
+git remote add upstream https://github.com/your-username/SentimentAnalysis.git
+git pull upstream main
+git push origin main
+```
+
+## 📋 **Step 3: Files to Upload**
+
+Make sure these files are in your Space:
+
+```
+SentimentAnalysis/
+├── app.py                    # ✅ Main application
+├── requirements.txt          # ✅ Dependencies
+├── py/
+│   ├── __init__.py          # ✅ Package
+│   ├── api_controller.py    # ✅ API controller
+│   ├── fine_tune_sentiment.py # ✅ Fine-tuning script
+│   └── pages/               # ✅ UI pages
+│       ├── __init__.py
+│       ├── single_analysis.py
+│       ├── batch_analysis.py
+│       ├── model_info.py
+│       └── api_endpoints.py
+```
+
+## ⚙️ **Step 4: Space Configuration**
+
+### **Requirements.txt Check:**
+Ensure your `requirements.txt` includes:
+```
+torch>=2.0.0
+transformers>=4.21.0
+datasets>=2.0.0
+gradio>=4.44.1
+fastapi>=0.104.0
+uvicorn>=0.24.0
+pydantic>=2.5.0
+psutil>=5.9.0
+accelerate>=0.21.0
+```
+
+### **Hardware Selection:**
+- **CPU Basic**: Free, but fine-tuning will be slow (30-60 minutes)
+- **GPU T4**: Paid, but fine-tuning will be fast (5-10 minutes)
+- **GPU A10G**: More expensive, best performance
+
+## 🚀 **Step 5: Deployment Process**
+
+### **What Happens Automatically:**
+1. **Environment Detection**: App detects it's running on Hugging Face Spaces
+2. **Model Loading**: Tries to load fine-tuned model, runs fine-tuning if needed
+3. **API Server**: Starts REST API on port 7861 automatically
+4. **Gradio Interface**: Launches with all 4 tabs
+
+### **Expected Timeline:**
+- **Initial Build**: 2-5 minutes (installing dependencies)
+- **First Run**: 10-30 minutes (fine-tuning, depends on hardware)
+- **Subsequent Runs**: 1-2 minutes (model already created)
+
+## 📱 **Step 6: Testing Your Space**
+
+Once deployed, your Space will have:
+
+### **Main Interface** (Main URL):
+- 📝 **Single Text Analysis** - Analyze individual Vietnamese texts
+- 📊 **Batch Analysis** - Process multiple texts
+- ℹ️ **Model Information** - View model details and memory usage
+- 🌐 **REST API Endpoints** - API documentation and examples
+
+### **REST API** (Your Space URL + :7861):
+- **Interactive Docs**: `https://your-space.hf.space:7861/docs`
+- **Health Check**: `GET /health`
+- **Analysis**: `POST /analyze`
+- **Batch**: `POST /analyze/batch`
+
+## 🎯 **Step 7: API Usage Examples**
+
+### **Python Example:**
+```python
+import requests
+
+response = requests.post(
+    "https://your-space.hf.space:7861/analyze",
+    json={"text": "Giảng viên dạy rất hay và tâm huyết."}
+)
+result = response.json()
+print(f"Sentiment: {result['sentiment']}")
+print(f"Confidence: {result['confidence']:.2%}")
+```
+
+### **cURL Example:**
+```bash
+curl -X POST "https://your-space.hf.space:7861/analyze" \
+     -H "Content-Type: application/json" \
+     -d '{"text": "Giảng viên dạy rất hay và tâm huyết."}'
+```
+
+## ⚠️ **Step 8: Troubleshooting**
+
+### **Common Issues:**
+
+1. **Build Fails:**
+   - Check requirements.txt has all dependencies
+   - Ensure no syntax errors in Python files
+   - Check Space logs for specific error messages
+
+2. **Fine-Tuning Takes Too Long:**
+   - Consider upgrading to GPU hardware
+   - The process will complete eventually on CPU
+
+3. **API Not Working:**
+   - API runs on port 7861 (different from main Gradio port)
+   - Check if CORS is enabled (included in our setup)
+
+4. **Memory Issues:**
+   - The app includes automatic memory cleanup
+   - Batch size is limited to 10 texts
+   - GPU spaces have more memory available
+
+## 🔧 **Step 9: Advanced Configuration**
+
+### **Custom Fine-Tuned Model:**
+If you have your own fine-tuned model on Hugging Face Hub:
+1. Update line 32 in `app.py`:
+   ```python
+   self.finetuned_model = "your-username/your-model-name"
+   ```
+
+### **Custom Fine-Tuning Parameters:**
+Modify `py/fine_tune_sentiment.py` to adjust:
+- Learning rate
+- Number of epochs
+- Batch size
+- Dataset
+
+## 📊 **Step 10: Monitoring**
+
+### **Space Metrics:**
+- Usage statistics in your Space dashboard
+- API endpoint performance
+- Model loading time
+- Memory usage (shown in Model Info tab)
+
+## 🎉 **Success Indicators:**
+
+Your deployment is successful when you see:
+- ✅ Gradio interface loads with all 4 tabs
+- ✅ Model information shows "vietnamese_sentiment_finetuned"
+- ✅ API documentation is accessible at `/docs`
+- ✅ Test analysis returns correct sentiment predictions
+- ✅ Health check endpoint returns status "healthy"
+
+## 📞 **Support**
+
+If you encounter issues:
+1. Check the Space logs for error messages
+2. Verify all files are uploaded correctly
+3. Test locally with `python app.py` first
+4. Review the fine-tuning process in the logs
+
+Your modular Vietnamese Sentiment Analysis app is now ready for production deployment! 🚀