RUN_STREAMLIT_LOCAL.md

🚀 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)

# 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.

# 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)

# 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!