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# 🚀 Running Streamlit App Locally
## ✅ What's Been Done
1. **Optimized models copied** to `model_assets/`:
- ✅ XGBoost_optimized.joblib
- ✅ CatBoost_optimized.joblib
- ✅ LightGBM_optimized.joblib
- ✅ ensemble_info_optimized.json
- ✅ model_metrics_optimized.csv
- ✅ hybrid_metrics.csv
2. **Streamlit app updated**:
- ✅ Uses optimized models
- ✅ Loads ensemble weights from config
- ✅ Displays optimized ensemble weights in sidebar
- ✅ All paths configured correctly
## 📋 To Run Locally
### Option 1: Using Docker (Recommended - Already Set Up)
```bash
# The Docker environment already has all dependencies
docker run --rm -p 8501:8501 \
-v "$(pwd)/model_assets:/app/model_assets" \
-v "$(pwd)/content:/app/content" \
-v "$(pwd)/streamlit_app.py:/app/streamlit_app.py" \
heart-optimization \
streamlit run streamlit_app.py --server.headless=true --server.address=0.0.0.0 --server.port=8501
```
Then open: http://localhost:8501
### Option 2: Install Dependencies Locally
**Note:** Python 3.14.0 may have compatibility issues. Consider using Python 3.11 or 3.12.
```bash
# Install dependencies
pip install streamlit pandas numpy scikit-learn xgboost catboost lightgbm joblib
# Run the app
streamlit run streamlit_app.py
```
### Option 3: Use Virtual Environment (Recommended)
```bash
# Create virtual environment with Python 3.11 or 3.12
python3.11 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Run the app
streamlit run streamlit_app.py
```
## 🎯 What to Test
1. **Model Loading**: Check sidebar shows "Using Optimized Ensemble" with correct weights
2. **Input Form**: Fill in patient information
3. **Prediction**: Click "Predict Heart Attack Risk" button
4. **Results**: Verify prediction and risk percentage display correctly
5. **Ensemble Info**: Check that ensemble weights match optimized config (XGB: 5%, CAT: 85%, LGB: 10%)
## 📊 Expected Results
- **Ensemble Weights**: XGBoost: 5.0% | CatBoost: 85.0% | LightGBM: 10.0%
- **Accuracy**: ~80.8% (from optimized metrics)
- **Recall**: ~93.3% (from optimized metrics)
- **ROC-AUC**: ~0.925
## 🐛 Troubleshooting
### If models don't load:
- Check `model_assets/` folder has all `.joblib` files
- Verify file permissions are readable
### If dependencies fail:
- Use Docker (Option 1) - already configured
- Or use Python 3.11/3.12 instead of 3.14
### If app doesn't start:
- Check port 8501 is not in use: `lsof -i :8501`
- Try different port: `streamlit run streamlit_app.py --server.port=8502`
## ✅ Once Working Locally
After confirming the app works locally, we'll proceed with Hugging Face deployment!
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