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Ready for Render deployment - Heart Attack Risk Predictor Ensemble Model

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Files changed (45) hide show
  1. .DS_Store +0 -0
  2. .dockerignore +18 -0
  3. .gitignore +13 -0
  4. .streamlit/config.toml +11 -0
  5. Copy_of_oneLastTime.ipynb +0 -0
  6. DEPLOY.md +124 -0
  7. Dockerfile +37 -0
  8. README.md +38 -0
  9. content/.config/.last_opt_in_prompt.yaml +1 -0
  10. content/.config/.last_survey_prompt.yaml +1 -0
  11. content/.config/.last_update_check.json +1 -0
  12. content/.config/active_config +1 -0
  13. content/.config/config_sentinel +0 -0
  14. content/.config/configurations/config_default +6 -0
  15. content/.config/default_configs.db +0 -0
  16. content/.config/gce +1 -0
  17. content/.config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db +0 -0
  18. content/.config/logs/2025.11.03/14.39.05.026360.log +765 -0
  19. content/.config/logs/2025.11.03/14.39.27.468532.log +5 -0
  20. content/.config/logs/2025.11.03/14.39.37.422924.log +153 -0
  21. content/.config/logs/2025.11.03/14.39.39.770295.log +5 -0
  22. content/.config/logs/2025.11.03/14.39.49.932668.log +8 -0
  23. content/.config/logs/2025.11.03/14.39.50.786121.log +8 -0
  24. content/catboost_info/catboost_training.json +704 -0
  25. content/catboost_info/learn/events.out.tfevents +0 -0
  26. content/catboost_info/learn_error.tsv +701 -0
  27. content/catboost_info/time_left.tsv +701 -0
  28. content/models/cat_5cv_results.csv +21 -0
  29. content/models/fairness_subgroups.csv +10 -0
  30. content/models/hybrid_metrics.csv +4 -0
  31. content/models/hybrid_metrics_best.csv +4 -0
  32. content/models/hybrid_metrics_optionA.csv +4 -0
  33. content/models/lgb_5cv_results.csv +26 -0
  34. content/models/metrics_class_weights.csv +6 -0
  35. content/models/model_metrics.csv +6 -0
  36. content/models/model_metrics_best.csv +4 -0
  37. content/models/pr_auc_table.csv +5 -0
  38. content/models/xgb_5cv_results.csv +26 -0
  39. content/sample_data/README.md +19 -0
  40. content/sample_data/anscombe.json +49 -0
  41. model_assets/.gitkeep +2 -0
  42. render.yaml +11 -0
  43. requirements.txt +9 -0
  44. streamlit_app.py +1162 -0
  45. test_predict.py +218 -0
.DS_Store ADDED
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.dockerignore ADDED
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+ .venv
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+ __pycache__
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+ .ipynb_checkpoints
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+ .git
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+ .gitignore
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+ *.pyc
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+ *.pyo
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+ *.pyd
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+ *.swp
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+ *.tmp
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+ *.log
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+ node_modules
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+ dist
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+ build
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+ Dockerfile
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+ README.md
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+ Copy_of_oneLastTime.ipynb
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+
.gitignore ADDED
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+ # Python / Streamlit
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+ __pycache__/
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+ *.pyc
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+ .streamlit/secrets.toml
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+
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+ # Model assets (keep directory, ignore large binaries by default)
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+ model_assets/*
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+ !model_assets/.gitkeep
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+
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+ # Jupyter checkpoints
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+ .ipynb_checkpoints/
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+
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+
.streamlit/config.toml ADDED
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+ [theme]
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+ primaryColor = "#3B82F6"
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+ backgroundColor = "#0B1221"
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+ secondaryBackgroundColor = "#111827"
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+ textColor = "#E5E7EB"
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+ font = "sans serif"
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+
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+ [server]
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+ port = 8051
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+ headless = true
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+
Copy_of_oneLastTime.ipynb ADDED
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DEPLOY.md ADDED
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1
+ # 🚀 Deploy to Render - Step by Step Guide
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+
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+ ## Prerequisites
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+ - GitHub account
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+ - Render account (free - sign up at https://render.com)
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+
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+ ## 📋 Deployment Steps
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+
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+ ### Step 1: Push Code to GitHub
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+ ```bash
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+ # Initialize git if not already done
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+ git init
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+
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+ # Add all files
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+ git add .
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+
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+ # Commit
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+ git commit -m "Ready for Render deployment"
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+
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+ # Create a new repository on GitHub (https://github.com/new)
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+ # Then push:
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+ git remote add origin https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git
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+ git branch -M main
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+ git push -u origin main
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+ ```
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+
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+ ### Step 2: Deploy on Render
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+
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+ 1. **Go to Render Dashboard**
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+ - Visit https://dashboard.render.com
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+ - Sign in or create account (use GitHub login for easier setup)
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+
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+ 2. **Create New Web Service**
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+ - Click "New +" button → "Web Service"
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+ - Connect your GitHub account if not already connected
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+ - Select your repository
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+
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+ 3. **Configure Service** (Render will auto-detect render.yaml)
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+ - **Name**: heart-attack-risk-predictor (or your choice)
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+ - **Runtime**: Docker
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+ - **Plan**: Free
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+ - Click "Create Web Service"
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+
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+ 4. **Wait for Build & Deploy**
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+ - Render will automatically:
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+ - Build your Docker image
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+ - Deploy the container
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+ - Assign a public URL
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+ - Build takes 2-5 minutes
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+
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+ 5. **Access Your App**
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+ - Once deployed, you'll get a URL like:
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+ `https://heart-attack-risk-predictor.onrender.com`
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+ - Open it in your browser!
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+
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+ ## ⚠️ Important Notes
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+
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+ ### Free Tier Limitations
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+ - App sleeps after 15 minutes of inactivity
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+ - First request after sleep takes ~30 seconds to wake up
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+ - 750 hours/month free (enough for most usage)
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+
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+ ### Custom Domain (Optional)
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+ - Go to Settings → Custom Domain
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+ - Add your domain (requires DNS setup)
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+
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+ ### Environment Variables (if needed)
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+ - Go to Environment → Add Environment Variable
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+ - Currently none required for this app
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+
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+ ### Logs & Monitoring
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+ - View logs: Click "Logs" tab in dashboard
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+ - Monitor performance: "Metrics" tab
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+
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+ ## 🔄 Auto-Deploy on Updates
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+
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+ Once set up, any push to your GitHub main branch will automatically:
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+ 1. Trigger new build
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+ 2. Deploy updated version
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+ 3. Switch traffic to new version
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+
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+ No manual intervention needed!
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+
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+ ## 🐛 Troubleshooting
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+
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+ ### Build Fails
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+ - Check Render logs for errors
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+ - Verify Dockerfile builds locally: `docker build -t heart-app .`
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+ - Check all files are committed to Git
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+
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+ ### App Won't Start
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+ - Check port is 8051 (matches Dockerfile EXPOSE)
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+ - Verify model files are in model_assets/
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+ - Check logs for Python errors
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+
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+ ### Slow Response
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+ - Free tier sleeps after inactivity
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+ - Upgrade to paid plan ($7/month) for always-on
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+
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+ ## 📦 What's Included
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+
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+ Your repo now has:
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+ - ✅ `render.yaml` - Render configuration
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+ - ✅ `Dockerfile` - Container definition
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+ - ✅ `requirements.txt` - Python dependencies
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+ - ✅ `streamlit_app.py` - Main application
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+ - ✅ `model_assets/` - ML models
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+ - ✅ `.streamlit/config.toml` - Streamlit settings
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+
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+ ## 🎉 You're Done!
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+
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+ Your app is now live and accessible worldwide!
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+
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+ ### Next Steps:
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+ - Share your URL
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+ - Monitor usage in Render dashboard
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+ - Set up custom domain if needed
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+ - Consider upgrading if you need 24/7 uptime
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+
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+ ---
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+
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+ **Need Help?**
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+ - Render Docs: https://render.com/docs
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+ - Render Community: https://community.render.com
Dockerfile ADDED
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+ FROM python:3.11-slim
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+
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+ # Prevents Python from writing .pyc files and buffering stdout/stderr
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+ ENV PYTHONDONTWRITEBYTECODE=1 \
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+ PYTHONUNBUFFERED=1 \
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+ PIP_NO_CACHE_DIR=1
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+
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+ WORKDIR /app
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+
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+ # System deps for lightgbm, xgboost, catboost (runtime)
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+ RUN apt-get update && apt-get install -y --no-install-recommends \
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+ build-essential \
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+ libgomp1 \
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+ curl \
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+ && rm -rf /var/lib/apt/lists/*
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+
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+ # Copy dependency list and install
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+ COPY requirements.txt /app/requirements.txt
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+ RUN pip install --upgrade pip \
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+ && pip install -r requirements.txt
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+
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+ # Copy app code
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+ COPY streamlit_app.py /app/streamlit_app.py
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+ COPY .streamlit /app/.streamlit
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+
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+ # Copy model assets if present
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+ RUN mkdir -p /app/model_assets
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+ COPY model_assets/ /app/model_assets/
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+
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+ # Optional: copy test script for quick in-container verification
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+ COPY test_predict.py /app/test_predict.py
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+
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+ EXPOSE 8051
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+
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+ CMD ["streamlit", "run", "streamlit_app.py", "--server.headless=true", "--server.address=0.0.0.0", "--server.port=8051"]
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+
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+
README.md ADDED
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+ # Streamlit Inference App
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+
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+ This app loads your trained models (CatBoost/XGBoost/LightGBM) and optional sklearn preprocessor, then serves batch CSV inference with an optional simple ensemble. No dataset is required at deploy time; users upload CSVs.
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+
5
+ ## Project Layout
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+
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+ - `streamlit_app.py` – main app
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+ - `requirements.txt` – dependencies (installed on Streamlit Cloud)
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+ - `.streamlit/config.toml` – dark theme
10
+ - `model_assets/` – place your artifacts here:
11
+ - `CatBoost.joblib` / `XGBoost.joblib` / `LightGBM.joblib` (or any of the alternative names used in the notebook)
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+ - `preprocessor.joblib` (recommended)
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+ - optional: `feature_names.json`, `hybrid_metrics.csv`, `model_metrics_summary.csv`
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+
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+ ## Local Preview (optional)
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+
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+ If you want to run locally without installing ML libs on your laptop, skip. Otherwise:
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+
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+ ```bash
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+ pip install -r requirements.txt
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+ streamlit run streamlit_app.py
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+ ```
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+
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+ ## Deploy to Streamlit Cloud
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+
26
+ 1. Push this folder to a public GitHub repo.
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+ 2. On Streamlit Cloud, create a new app pointing to that repo, file `streamlit_app.py`.
28
+ 3. In the repo, put your artifacts inside `model_assets/`.
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+
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+ ## Preparing Artifacts in Colab
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+
32
+ Artifacts the app can use (any subset works):
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+ - Models: `CatBoost.joblib`, `XGBoost.joblib`, `LightGBM.joblib` (supports common alt names)
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+ - Preprocessor: `preprocessor.joblib`
35
+ - Optional: `feature_names.json`, `hybrid_metrics.csv`, `model_metrics_summary.csv`
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+
37
+ The app auto-detects assets and displays metrics if the CSVs are present.
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+
content/.config/.last_opt_in_prompt.yaml ADDED
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content/.config/.last_survey_prompt.yaml ADDED
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+ {"last_update_check_time": 1762180779.211006, "last_update_check_revision": 20251024121634, "notifications": [], "last_nag_times": {}}
content/.config/active_config ADDED
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+ default
content/.config/config_sentinel ADDED
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content/.config/configurations/config_default ADDED
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+ [component_manager]
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+ disable_update_check = true
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+
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+ [compute]
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+ gce_metadata_read_timeout_sec = 0
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+
content/.config/default_configs.db ADDED
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content/.config/gce ADDED
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+ 2025-11-03 14:39:17,055 DEBUG root Loaded Command Group: ['gcloud', 'components']
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+ 2025-11-03 14:39:17,060 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
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+ 2025-11-03 14:39:17,063 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
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+
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+ 2025-11-03 14:39:17,151 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/1.1" 200 233157
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+ 2025-11-03 14:39:17,162 INFO ___FILE_ONLY___
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+ 2025-11-03 14:39:17,162 INFO ___FILE_ONLY___
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+
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+ 2025-11-03 14:39:17,162 INFO ___FILE_ONLY___ Installing components from version: 545.0.0
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+ 2025-11-03 14:39:17,163 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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+ 2025-11-03 14:39:17,164 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
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+ 2025-11-03 14:39:17,213 INFO ___FILE_ONLY___ │
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+ 2025-11-03 14:39:17,214 INFO ___FILE_ONLY___ Google Cloud CRC32C Hash Tool (Platform Specific)
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+ 2025-11-03 14:39:17,214 INFO ___FILE_ONLY___
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+ 2025-11-03 14:39:17,214 INFO ___FILE_ONLY___ │
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+ 2025-11-03 14:39:17,214 INFO ___FILE_ONLY___ │
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+ 2025-11-03 14:39:17,214 INFO ___FILE_ONLY___ │
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+ 2025-11-03 14:39:17,215 INFO ___FILE_ONLY___ └─────────────────────────────────────────────────────┴────────────┴──────────┘
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+ 2025-11-03 14:39:17,218 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2025-11-03 14:39:17,270 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/RELEASE_NOTES HTTP/1.1" 200 1509806
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+ 2025-11-03 14:39:17,791 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
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+ https://cloud.google.com/sdk/release_notes
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+ 2025-11-03 14:39:17,792 INFO ___FILE_ONLY___ Performing in place update...
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+ 2025-11-03 14:39:17,794 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
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+ 2025-11-03 14:39:17,794 INFO ___FILE_ONLY___ ╠═ Downloading: BigQuery Command Line Tool ═╣
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+ 2025-11-03 14:39:17,797 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2025-11-03 14:39:18,035 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bq-20251024121634.tar.gz HTTP/1.1" 200 1911793
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+ 2025-11-03 14:39:18,049 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:18,055 INFO ___FILE_ONLY___ ╝
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+ 2025-11-03 14:39:18,057 INFO ___FILE_ONLY___ ╠═ Downloading: BigQuery Command Line Tool (Platform Spe... ═╣
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+
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+ 2025-11-03 14:39:18,058 INFO ___FILE_ONLY___ ╚
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+ 2025-11-03 14:39:18,060 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
220
+ 2025-11-03 14:39:18,315 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bq-nix-20250523104322.tar.gz HTTP/1.1" 200 1935
221
+ 2025-11-03 14:39:18,316 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+
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+
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+ 2025-11-03 14:39:18,318 INFO ___FILE_ONLY___ ╠═ Downloading: Bundled Python 3.12 ═╣
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+
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+ 2025-11-03 14:39:18,318 INFO ___FILE_ONLY___ ╚
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+ 2025-11-03 14:39:18,318 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+ 2025-11-03 14:39:18,318 INFO ___FILE_ONLY___ ╝
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+
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+ 2025-11-03 14:39:18,320 INFO ___FILE_ONLY___ ╠═ Downloading: Bundled Python 3.12 (Platform Specific) ═╣
235
+
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+ 2025-11-03 14:39:18,320 INFO ___FILE_ONLY___ ╚
237
+ 2025-11-03 14:39:18,323 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
238
+ 2025-11-03 14:39:18,531 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bundled-python3-unix-linux-x86_64-20250502143716.tar.gz HTTP/1.1" 200 93610468
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+ 2025-11-03 14:39:18,871 INFO ___FILE_ONLY___ ═
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+
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+ 2025-11-03 14:39:19,004 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool ═╣
304
+
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+ 2025-11-03 14:39:19,007 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
307
+ 2025-11-03 14:39:19,060 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-20250627154417.tar.gz HTTP/1.1" 200 12962791
308
+ 2025-11-03 14:39:19,118 INFO ___FILE_ONLY___ ═
309
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+ 2025-11-03 14:39:19,136 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,137 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,138 INFO ___FILE_ONLY___ ╝
369
+
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+
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+ 2025-11-03 14:39:19,140 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool (Platfor... ═╣
373
+
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+ 2025-11-03 14:39:19,140 INFO ___FILE_ONLY___ ╚
375
+ 2025-11-03 14:39:19,143 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
376
+ 2025-11-03 14:39:19,347 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-nix-20250523104322.tar.gz HTTP/1.1" 200 1950
377
+ 2025-11-03 14:39:19,348 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+ 2025-11-03 14:39:19,348 INFO ___FILE_ONLY___ ╝
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+ 2025-11-03 14:39:19,350 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
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+ 2025-11-03 14:39:19,351 INFO ___FILE_ONLY___ ╠═ Downloading: Default set of gcloud commands ═╣
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+ 2025-11-03 14:39:19,351 INFO ___FILE_ONLY___ ╚
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+ 2025-11-03 14:39:19,351 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+ 2025-11-03 14:39:19,351 INFO ___FILE_ONLY___ ╝
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+ 2025-11-03 14:39:19,353 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
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+ 2025-11-03 14:39:19,353 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CLI Core Libraries (Platfor... ═╣
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+ 2025-11-03 14:39:19,353 INFO ___FILE_ONLY___ ╚
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+ 2025-11-03 14:39:19,356 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
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+ 2025-11-03 14:39:19,591 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20250523104322.tar.gz HTTP/1.1" 200 2325
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+ 2025-11-03 14:39:19,592 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+ 2025-11-03 14:39:19,592 INFO ___FILE_ONLY___ ╝
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+ 2025-11-03 14:39:19,594 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
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+ 2025-11-03 14:39:19,594 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool ═╣
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+ 2025-11-03 14:39:19,594 INFO ___FILE_ONLY___ ╚
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+ 2025-11-03 14:39:19,594 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
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+ 2025-11-03 14:39:19,594 INFO ___FILE_ONLY___ ╝
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+ 2025-11-03 14:39:19,596 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
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+ 2025-11-03 14:39:19,596 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool (Platform ... ═╣
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+ 2025-11-03 14:39:19,596 INFO ___FILE_ONLY___ ╚
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+ 2025-11-03 14:39:19,599 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
412
+ 2025-11-03 14:39:19,644 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-crc32c-linux-x86_64-20250613150750.tar.gz HTTP/1.1" 200 1525557
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+ 2025-11-03 14:39:19,656 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,657 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,657 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,658 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,658 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,658 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,658 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,658 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,658 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,658 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,658 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,658 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,658 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,658 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,659 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,659 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,659 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,659 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,659 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,659 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,659 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,660 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,660 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,660 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,660 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,660 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,660 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,660 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,660 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,660 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,660 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,660 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,661 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,661 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,661 INFO ___FILE_ONLY___ ═
460
+ 2025-11-03 14:39:19,661 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,661 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,661 INFO ___FILE_ONLY___ ═
463
+ 2025-11-03 14:39:19,661 INFO ___FILE_ONLY___ ═
464
+ 2025-11-03 14:39:19,661 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,661 INFO ___FILE_ONLY___ ═
466
+ 2025-11-03 14:39:19,661 INFO ___FILE_ONLY___ ═
467
+ 2025-11-03 14:39:19,661 INFO ___FILE_ONLY___ ═
468
+ 2025-11-03 14:39:19,662 INFO ___FILE_ONLY___ ═
469
+ 2025-11-03 14:39:19,662 INFO ___FILE_ONLY___ ═
470
+ 2025-11-03 14:39:19,662 INFO ___FILE_ONLY___ ═
471
+ 2025-11-03 14:39:19,662 INFO ___FILE_ONLY___ ═
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+ 2025-11-03 14:39:19,662 INFO ___FILE_ONLY___ ═
473
+ 2025-11-03 14:39:19,662 INFO ___FILE_ONLY___ ╝
474
+
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+ 2025-11-03 14:39:19,664 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
476
+
477
+ 2025-11-03 14:39:19,664 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud cli dependencies (Platform Specific) ═╣
478
+
479
+ 2025-11-03 14:39:19,664 INFO ___FILE_ONLY___ ╚
480
+ 2025-11-03 14:39:19,667 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
481
+ 2025-11-03 14:39:19,910 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-deps-linux-x86_64-20210416153011.tar.gz HTTP/1.1" 200 104
482
+ 2025-11-03 14:39:19,911 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
483
+ 2025-11-03 14:39:19,911 INFO ___FILE_ONLY___ ╝
484
+
485
+ 2025-11-03 14:39:19,913 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
486
+
487
+ 2025-11-03 14:39:19,914 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool ═╣
488
+
489
+ 2025-11-03 14:39:19,914 INFO ___FILE_ONLY___ ╚
490
+ 2025-11-03 14:39:20,014 INFO ___FILE_ONLY___ ═
491
+ 2025-11-03 14:39:20,017 INFO ___FILE_ONLY___ ═
492
+ 2025-11-03 14:39:20,019 INFO ___FILE_ONLY___ ═
493
+ 2025-11-03 14:39:20,022 INFO ___FILE_ONLY___ ═
494
+ 2025-11-03 14:39:20,025 INFO ___FILE_ONLY___ ═
495
+ 2025-11-03 14:39:20,028 INFO ___FILE_ONLY___ ═
496
+ 2025-11-03 14:39:20,030 INFO ___FILE_ONLY___ ═
497
+ 2025-11-03 14:39:20,032 INFO ___FILE_ONLY___ ═
498
+ 2025-11-03 14:39:20,035 INFO ___FILE_ONLY___ ═
499
+ 2025-11-03 14:39:20,038 INFO ___FILE_ONLY___ ═
500
+ 2025-11-03 14:39:20,040 INFO ___FILE_ONLY___ ═
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502
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503
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508
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509
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510
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511
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515
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528
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530
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531
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534
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535
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536
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537
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539
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541
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542
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543
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546
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+ 2025-11-03 14:39:20,174 INFO ___FILE_ONLY___ ═
548
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549
+ 2025-11-03 14:39:20,179 INFO ___FILE_ONLY___ ═
550
+ 2025-11-03 14:39:20,179 INFO ___FILE_ONLY___ ╝
551
+
552
+ 2025-11-03 14:39:20,188 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
553
+
554
+ 2025-11-03 14:39:20,188 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
555
+
556
+ 2025-11-03 14:39:20,188 INFO ___FILE_ONLY___ ╚
557
+ 2025-11-03 14:39:20,189 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
558
+ 2025-11-03 14:39:20,189 INFO ___FILE_ONLY___ ╝
559
+
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+ 2025-11-03 14:39:20,193 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
561
+
562
+ 2025-11-03 14:39:20,194 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 ═╣
563
+
564
+ 2025-11-03 14:39:20,194 INFO ___FILE_ONLY___ ╚
565
+ 2025-11-03 14:39:20,196 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
566
+ 2025-11-03 14:39:20,196 INFO ___FILE_ONLY___ ╝
567
+
568
+ 2025-11-03 14:39:20,198 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
569
+
570
+ 2025-11-03 14:39:20,198 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 (Platform Specific) ═╣
571
+
572
+ 2025-11-03 14:39:20,198 INFO ___FILE_ONLY___ ╚
573
+ 2025-11-03 14:39:22,375 INFO ___FILE_ONLY___ ═
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575
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576
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577
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578
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583
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585
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589
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591
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592
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593
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594
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595
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609
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610
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611
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613
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617
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620
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621
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622
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
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634
+
635
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636
+
637
+ 2025-11-03 14:39:25,406 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
638
+
639
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640
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649
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650
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659
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662
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+ 2025-11-03 14:39:26,283 INFO ___FILE_ONLY___ ═
665
+ 2025-11-03 14:39:26,295 INFO ___FILE_ONLY___ ═
666
+ 2025-11-03 14:39:26,307 INFO ___FILE_ONLY___ ═
667
+ 2025-11-03 14:39:26,321 INFO ___FILE_ONLY___ ═
668
+ 2025-11-03 14:39:26,339 INFO ___FILE_ONLY___ ═
669
+ 2025-11-03 14:39:26,352 INFO ___FILE_ONLY___ ═
670
+ 2025-11-03 14:39:26,365 INFO ___FILE_ONLY___ ═
671
+ 2025-11-03 14:39:26,387 INFO ___FILE_ONLY___ ═
672
+ 2025-11-03 14:39:26,404 INFO ___FILE_ONLY___ ═
673
+ 2025-11-03 14:39:26,424 INFO ___FILE_ONLY___ ═
674
+ 2025-11-03 14:39:26,439 INFO ___FILE_ONLY___ ═
675
+ 2025-11-03 14:39:26,457 INFO ___FILE_ONLY___ ═
676
+ 2025-11-03 14:39:26,468 INFO ___FILE_ONLY___ ═
677
+ 2025-11-03 14:39:26,479 INFO ___FILE_ONLY___ ═
678
+ 2025-11-03 14:39:26,517 INFO ___FILE_ONLY___ ═
679
+ 2025-11-03 14:39:26,532 INFO ___FILE_ONLY___ ═
680
+ 2025-11-03 14:39:26,545 INFO ___FILE_ONLY___ ═
681
+ 2025-11-03 14:39:26,556 INFO ___FILE_ONLY___ ═
682
+ 2025-11-03 14:39:26,568 INFO ___FILE_ONLY___ ═
683
+ 2025-11-03 14:39:26,579 INFO ___FILE_ONLY___ ═
684
+ 2025-11-03 14:39:26,591 INFO ___FILE_ONLY___ ═
685
+ 2025-11-03 14:39:26,602 INFO ___FILE_ONLY___ ═
686
+ 2025-11-03 14:39:26,612 INFO ___FILE_ONLY___ ═
687
+ 2025-11-03 14:39:26,622 INFO ___FILE_ONLY___ ═
688
+ 2025-11-03 14:39:26,633 INFO ___FILE_ONLY___ ═
689
+ 2025-11-03 14:39:26,647 INFO ___FILE_ONLY___ ═
690
+ 2025-11-03 14:39:26,662 INFO ___FILE_ONLY___ ═
691
+ 2025-11-03 14:39:26,676 INFO ___FILE_ONLY___ ═
692
+ 2025-11-03 14:39:26,691 INFO ___FILE_ONLY___ ═
693
+ 2025-11-03 14:39:26,705 INFO ___FILE_ONLY___ ═
694
+ 2025-11-03 14:39:26,718 INFO ___FILE_ONLY___ ═
695
+ 2025-11-03 14:39:26,734 INFO ___FILE_ONLY___ ═
696
+ 2025-11-03 14:39:26,752 INFO ___FILE_ONLY___ ═
697
+ 2025-11-03 14:39:26,773 INFO ___FILE_ONLY___ ═
698
+ 2025-11-03 14:39:26,791 INFO ___FILE_ONLY___ ═
699
+ 2025-11-03 14:39:26,810 INFO ___FILE_ONLY___ ═
700
+ 2025-11-03 14:39:26,811 INFO ___FILE_ONLY___ ╝
701
+
702
+ 2025-11-03 14:39:26,842 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
703
+
704
+ 2025-11-03 14:39:26,842 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣
705
+
706
+ 2025-11-03 14:39:26,842 INFO ___FILE_ONLY___ ╚
707
+ 2025-11-03 14:39:26,843 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
708
+ 2025-11-03 14:39:26,843 INFO ___FILE_ONLY___ ╝
709
+
710
+ 2025-11-03 14:39:26,848 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
711
+
712
+ 2025-11-03 14:39:26,848 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣
713
+
714
+ 2025-11-03 14:39:26,848 INFO ___FILE_ONLY___ ╚
715
+ 2025-11-03 14:39:26,851 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
716
+ 2025-11-03 14:39:26,851 INFO ___FILE_ONLY___ ╝
717
+
718
+ 2025-11-03 14:39:26,853 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
719
+
720
+ 2025-11-03 14:39:26,853 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
721
+
722
+ 2025-11-03 14:39:26,853 INFO ___FILE_ONLY___ ╚
723
+ 2025-11-03 14:39:26,854 INFO ___FILE_ONLY___ ══════════════════════════════
724
+ 2025-11-03 14:39:26,855 INFO ___FILE_ONLY___ ══════════════════════════════
725
+ 2025-11-03 14:39:26,855 INFO ___FILE_ONLY___ ╝
726
+
727
+ 2025-11-03 14:39:26,859 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
728
+
729
+ 2025-11-03 14:39:26,859 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
730
+
731
+ 2025-11-03 14:39:26,860 INFO ___FILE_ONLY___ ╚
732
+ 2025-11-03 14:39:26,862 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
733
+ 2025-11-03 14:39:26,862 INFO ___FILE_ONLY___ ╝
734
+
735
+ 2025-11-03 14:39:26,864 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
736
+
737
+ 2025-11-03 14:39:26,865 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool (Platform S... ═╣
738
+
739
+ 2025-11-03 14:39:26,865 INFO ___FILE_ONLY___ ╚
740
+ 2025-11-03 14:39:26,902 INFO ___FILE_ONLY___ ══════════════════════════════
741
+ 2025-11-03 14:39:26,903 INFO ___FILE_ONLY___ ══════════════════════════════
742
+ 2025-11-03 14:39:26,903 INFO ___FILE_ONLY___ ╝
743
+
744
+ 2025-11-03 14:39:26,908 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
745
+
746
+ 2025-11-03 14:39:26,908 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies (Platform Specific) ═╣
747
+
748
+ 2025-11-03 14:39:26,908 INFO ___FILE_ONLY___ ╚
749
+ 2025-11-03 14:39:26,909 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
750
+ 2025-11-03 14:39:26,909 INFO ___FILE_ONLY___ ╝
751
+
752
+ 2025-11-03 14:39:26,914 DEBUG root Updating notification cache...
753
+ 2025-11-03 14:39:26,915 INFO ___FILE_ONLY___
754
+
755
+ 2025-11-03 14:39:26,917 INFO ___FILE_ONLY___ Performing post processing steps...
756
+ 2025-11-03 14:39:26,918 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
757
+ 2025-11-03 14:39:36,454 DEBUG ___FILE_ONLY___
758
+ 2025-11-03 14:39:36,455 DEBUG ___FILE_ONLY___
759
+ 2025-11-03 14:39:36,706 INFO root descriptor_list: [{'universeDomain': 'googleapis.com', 'universeShortName': '', 'authenticationDomain': 'auth.cloud.google.com', 'projectPrefix': '', 'cloudWebDomain': 'cloud.google.com', 'documentationDomain': 'cloud.google.com', 'version': '1.0.0', 'state': 'primary', 'artifactRegistryDomain': 'pkg.dev'}]
760
+ 2025-11-03 14:39:36,706 INFO ___FILE_ONLY___
761
+ Update done!
762
+
763
+
764
+ 2025-11-03 14:39:36,710 DEBUG root Chosen display Format:none
765
+ 2025-11-03 14:39:36,710 INFO root Display format: "none"
content/.config/logs/2025.11.03/14.39.27.468532.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-11-03 14:39:27,469 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-11-03 14:39:27,471 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-11-03 14:39:27,473 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-11-03 14:39:36,318 DEBUG root Chosen display Format:none
5
+ 2025-11-03 14:39:36,319 INFO root Display format: "none"
content/.config/logs/2025.11.03/14.39.37.422924.log ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2025-11-03 14:39:37,423 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-11-03 14:39:37,458 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
+ 2025-11-03 14:39:37,460 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
4
+ 2025-11-03 14:39:37,462 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
5
+
6
+ 2025-11-03 14:39:37,470 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
7
+ 2025-11-03 14:39:37,699 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/1.1" 200 233157
8
+ 2025-11-03 14:39:37,714 WARNING root Component [compute] no longer exists.
9
+ 2025-11-03 14:39:37,714 INFO ___FILE_ONLY___
10
+
11
+ 2025-11-03 14:39:37,715 INFO ___FILE_ONLY___
12
+ Your current Google Cloud CLI version is: 545.0.0
13
+
14
+ 2025-11-03 14:39:37,715 INFO ___FILE_ONLY___ Installing components from version: 545.0.0
15
+
16
+ 2025-11-03 14:39:37,715 INFO ___FILE_ONLY___
17
+
18
+ 2025-11-03 14:39:37,715 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
19
+ 2025-11-03 14:39:37,716 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
20
+ 2025-11-03 14:39:37,716 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
21
+ 2025-11-03 14:39:37,733 INFO ___FILE_ONLY___ ┌────────────────────────────────────────────────┐
22
+ 2025-11-03 14:39:37,733 INFO ___FILE_ONLY___
23
+
24
+ 2025-11-03 14:39:37,733 INFO ___FILE_ONLY___ │ These components will be installed. │
25
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___
26
+
27
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___ ├─────────────────────────┬────────────┬─────────┤
28
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___
29
+
30
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___ │ Name │ Version │ Size │
31
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___
32
+
33
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___ ├─────────────────────────┼────────────┼─────────┤
34
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___
35
+
36
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___ │
37
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___ gcloud Alpha Commands
38
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___
39
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___ │
40
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___ 2025.10.24
41
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___
42
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___ │
43
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___ < 1 MiB
44
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___
45
+ 2025-11-03 14:39:37,734 INFO ___FILE_ONLY___ │
46
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___
47
+
48
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___ │
49
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___ gcloud Beta Commands
50
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___
51
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___ │
52
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___ 2025.10.24
53
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___
54
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___ │
55
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___ < 1 MiB
56
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___
57
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___ │
58
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___
59
+
60
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___ │
61
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___ gcloud Preview Commands
62
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___
63
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___ │
64
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___
65
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___
66
+ 2025-11-03 14:39:37,735 INFO ___FILE_ONLY___ │
67
+ 2025-11-03 14:39:37,736 INFO ___FILE_ONLY___ < 1 MiB
68
+ 2025-11-03 14:39:37,736 INFO ___FILE_ONLY___
69
+ 2025-11-03 14:39:37,736 INFO ___FILE_ONLY___ │
70
+ 2025-11-03 14:39:37,736 INFO ___FILE_ONLY___
71
+
72
+ 2025-11-03 14:39:37,736 INFO ___FILE_ONLY___ └─────────────────────────┴──��─────────┴─────────┘
73
+ 2025-11-03 14:39:37,736 INFO ___FILE_ONLY___
74
+
75
+ 2025-11-03 14:39:37,736 INFO ___FILE_ONLY___
76
+
77
+ 2025-11-03 14:39:37,739 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
78
+ 2025-11-03 14:39:37,946 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/RELEASE_NOTES HTTP/1.1" 200 1509806
79
+ 2025-11-03 14:39:38,475 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
80
+ https://cloud.google.com/sdk/release_notes
81
+
82
+
83
+ 2025-11-03 14:39:38,476 INFO ___FILE_ONLY___ Performing in place update...
84
+
85
+
86
+ 2025-11-03 14:39:38,478 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
87
+
88
+ 2025-11-03 14:39:38,478 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Alpha Commands ═╣
89
+
90
+ 2025-11-03 14:39:38,478 INFO ___FILE_ONLY___ ╚
91
+ 2025-11-03 14:39:38,481 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
92
+ 2025-11-03 14:39:38,726 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-alpha-20251024121634.tar.gz HTTP/1.1" 200 800
93
+ 2025-11-03 14:39:38,727 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
94
+ 2025-11-03 14:39:38,727 INFO ___FILE_ONLY___ ╝
95
+
96
+ 2025-11-03 14:39:38,729 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
97
+
98
+ 2025-11-03 14:39:38,729 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Beta Commands ═╣
99
+
100
+ 2025-11-03 14:39:38,729 INFO ___FILE_ONLY___ ╚
101
+ 2025-11-03 14:39:38,732 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
102
+ 2025-11-03 14:39:38,938 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-beta-20251024121634.tar.gz HTTP/1.1" 200 797
103
+ 2025-11-03 14:39:38,939 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
104
+ 2025-11-03 14:39:38,939 INFO ___FILE_ONLY___ ╝
105
+
106
+ 2025-11-03 14:39:38,941 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
107
+
108
+ 2025-11-03 14:39:38,941 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Preview Commands ═╣
109
+
110
+ 2025-11-03 14:39:38,941 INFO ___FILE_ONLY___ ╚
111
+ 2025-11-03 14:39:38,944 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
112
+ 2025-11-03 14:39:39,187 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-preview-20241115154308.tar.gz HTTP/1.1" 200 823
113
+ 2025-11-03 14:39:39,188 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
114
+ 2025-11-03 14:39:39,188 INFO ___FILE_ONLY___ ╝
115
+
116
+ 2025-11-03 14:39:39,190 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
117
+
118
+ 2025-11-03 14:39:39,191 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Alpha Commands ═╣
119
+
120
+ 2025-11-03 14:39:39,191 INFO ___FILE_ONLY___ ╚
121
+ 2025-11-03 14:39:39,192 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
122
+ 2025-11-03 14:39:39,192 INFO ___FILE_ONLY___ ╝
123
+
124
+ 2025-11-03 14:39:39,197 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
125
+
126
+ 2025-11-03 14:39:39,197 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Beta Commands ═╣
127
+
128
+ 2025-11-03 14:39:39,197 INFO ___FILE_ONLY___ ╚
129
+ 2025-11-03 14:39:39,198 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
130
+ 2025-11-03 14:39:39,198 INFO ___FILE_ONLY___ ╝
131
+
132
+ 2025-11-03 14:39:39,204 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
133
+
134
+ 2025-11-03 14:39:39,204 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Preview Commands ═╣
135
+
136
+ 2025-11-03 14:39:39,204 INFO ___FILE_ONLY___ ╚
137
+ 2025-11-03 14:39:39,205 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
138
+ 2025-11-03 14:39:39,205 INFO ___FILE_ONLY___ ╝
139
+
140
+ 2025-11-03 14:39:39,210 DEBUG root Updating notification cache...
141
+ 2025-11-03 14:39:39,211 INFO ___FILE_ONLY___
142
+
143
+ 2025-11-03 14:39:39,213 INFO ___FILE_ONLY___ Performing post processing steps...
144
+ 2025-11-03 14:39:39,213 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
145
+ 2025-11-03 14:39:49,015 DEBUG ___FILE_ONLY___
146
+ 2025-11-03 14:39:49,015 DEBUG ___FILE_ONLY___
147
+ 2025-11-03 14:39:49,231 INFO root descriptor_list: [{'universeDomain': 'googleapis.com', 'universeShortName': '', 'authenticationDomain': 'auth.cloud.google.com', 'projectPrefix': '', 'cloudWebDomain': 'cloud.google.com', 'documentationDomain': 'cloud.google.com', 'version': '1.0.0', 'state': 'primary', 'artifactRegistryDomain': 'pkg.dev'}]
148
+ 2025-11-03 14:39:49,232 INFO ___FILE_ONLY___
149
+ Update done!
150
+
151
+
152
+ 2025-11-03 14:39:49,234 DEBUG root Chosen display Format:none
153
+ 2025-11-03 14:39:49,234 INFO root Display format: "none"
content/.config/logs/2025.11.03/14.39.39.770295.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-11-03 14:39:39,771 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-11-03 14:39:39,772 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-11-03 14:39:39,774 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-11-03 14:39:48,871 DEBUG root Chosen display Format:none
5
+ 2025-11-03 14:39:48,872 INFO root Display format: "none"
content/.config/logs/2025.11.03/14.39.49.932668.log ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ 2025-11-03 14:39:49,934 DEBUG root Loaded Command Group: ['gcloud', 'config']
2
+ 2025-11-03 14:39:50,033 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set']
3
+ 2025-11-03 14:39:50,036 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "component_manager/disable_update_check", VALUE: "true"]
4
+ 2025-11-03 14:39:50,037 INFO ___FILE_ONLY___ Updated property [component_manager/disable_update_check].
5
+
6
+ 2025-11-03 14:39:50,038 DEBUG root Chosen display Format:default
7
+ 2025-11-03 14:39:50,038 INFO root Display format: "default"
8
+ 2025-11-03 14:39:50,044 DEBUG root SDK update checks are disabled.
content/.config/logs/2025.11.03/14.39.50.786121.log ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ 2025-11-03 14:39:50,787 DEBUG root Loaded Command Group: ['gcloud', 'config']
2
+ 2025-11-03 14:39:50,884 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set']
3
+ 2025-11-03 14:39:50,886 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "compute/gce_metadata_read_timeout_sec", VALUE: "0"]
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+ 7.556349658966065,0.492997578315865,0.29904756546020506,0.009935959858090856,0.7,2.0,400,3,9,0.03,1.0,"{'subsample': 0.7, 'reg_lambda': 2.0, 'n_estimators': 400, 'min_child_weight': 3, 'max_depth': 9, 'learning_rate': 0.03, 'colsample_bytree': 1.0}",0.923549117089989,0.9237686165152279,0.9176716122144295,0.9219253906507094,0.921864851716761,0.9217559176374234,0.002190372551579654,19
17
+ 1.6358491897583007,0.18053804082539826,0.07599682807922363,0.023467825379196155,0.7,1.0,200,5,3,0.05,0.9,"{'subsample': 0.7, 'reg_lambda': 1.0, 'n_estimators': 200, 'min_child_weight': 5, 'max_depth': 3, 'learning_rate': 0.05, 'colsample_bytree': 0.9}",0.9256829506105407,0.925604889363648,0.9186989881470311,0.9242111858220337,0.9236872697384029,0.9235770567363314,0.0025595591002717513,9
18
+ 16.44065918922424,0.37946010336079455,0.624353551864624,0.027445557276670102,0.7,0.0,800,1,9,0.1,1.0,"{'subsample': 0.7, 'reg_lambda': 0.0, 'n_estimators': 800, 'min_child_weight': 1, 'max_depth': 9, 'learning_rate': 0.1, 'colsample_bytree': 1.0}",0.9119043433398945,0.9097529874928854,0.9051045167710666,0.9085129156953651,0.9089040032673487,0.908835753313312,0.002204022305213528,25
19
+ 1.8156661987304688,0.3086791422199985,0.05843620300292969,0.007786695767772957,0.7,1.0,200,1,3,0.05,1.0,"{'subsample': 0.7, 'reg_lambda': 1.0, 'n_estimators': 200, 'min_child_weight': 1, 'max_depth': 3, 'learning_rate': 0.05, 'colsample_bytree': 1.0}",0.9257397746064403,0.9260659545469894,0.9186667496173708,0.9240134178639887,0.9235538513029852,0.9236079495875549,0.0026521408177050056,8
20
+ 1.8150565147399902,0.28940547728094107,0.06025094985961914,0.012948825597809768,0.7,0.0,200,1,3,0.03,0.9,"{'subsample': 0.7, 'reg_lambda': 0.0, 'n_estimators': 200, 'min_child_weight': 1, 'max_depth': 3, 'learning_rate': 0.03, 'colsample_bytree': 0.9}",0.9244796977141989,0.9248090983189377,0.9170120616424924,0.9230044568900289,0.9222983500246684,0.9223207329180653,0.002810802786929396,17
21
+ 5.742648553848267,0.48947094333368485,0.30849599838256836,0.049372804698920324,0.9,0.0,400,5,9,0.1,0.7,"{'subsample': 0.9, 'reg_lambda': 0.0, 'n_estimators': 400, 'min_child_weight': 5, 'max_depth': 9, 'learning_rate': 0.1, 'colsample_bytree': 0.7}",0.9172082989437081,0.9166793128225068,0.9112397066695586,0.9149191689638616,0.9147376318814449,0.9149568238562159,0.002092965909930583,23
22
+ 2.8859848976135254,0.3807237286270843,0.12069888114929199,0.00521380073004172,1.0,0.0,200,5,6,0.03,1.0,"{'subsample': 1.0, 'reg_lambda': 0.0, 'n_estimators': 200, 'min_child_weight': 5, 'max_depth': 6, 'learning_rate': 0.03, 'colsample_bytree': 1.0}",0.9251876539122731,0.9257745960450051,0.9191768581821341,0.9242473146669972,0.9233050150790894,0.9235382875770999,0.00233669367744405,10
23
+ 16.142475175857545,0.4774098370001288,0.6894989013671875,0.15814375182569287,1.0,1.0,800,3,9,0.01,0.9,"{'subsample': 1.0, 'reg_lambda': 1.0, 'n_estimators': 800, 'min_child_weight': 3, 'max_depth': 9, 'learning_rate': 0.01, 'colsample_bytree': 0.9}",0.9239971401777507,0.9242880203632713,0.9175306523525468,0.9218788823361643,0.9216315926691799,0.9218652575797825,0.0024190200626282607,18
24
+ 4.1485659122467045,0.2762408573830072,0.19593615531921388,0.06019605958011852,0.7,0.0,200,5,9,0.03,0.7,"{'subsample': 0.7, 'reg_lambda': 0.0, 'n_estimators': 200, 'min_child_weight': 5, 'max_depth': 9, 'learning_rate': 0.03, 'colsample_bytree': 0.7}",0.9251376220101593,0.9250192386853683,0.9189546004652871,0.9229751201403674,0.9230960704571788,0.9230365303516722,0.00223674702769231,14
25
+ 6.216812801361084,0.5135274304462328,0.18406295776367188,0.005573472838052655,0.7,1.0,800,1,3,0.01,0.7,"{'subsample': 0.7, 'reg_lambda': 1.0, 'n_estimators': 800, 'min_child_weight': 1, 'max_depth': 3, 'learning_rate': 0.01, 'colsample_bytree': 0.7}",0.925100855417975,0.9255116495409708,0.9183594312893776,0.9238837781294786,0.9233042657164622,0.9232319960188529,0.0025636452760256327,13
26
+ 2.66218638420105,0.39313848266395596,0.12299108505249023,0.04645305646815818,1.0,1.0,200,3,6,0.03,1.0,"{'subsample': 1.0, 'reg_lambda': 1.0, 'n_estimators': 200, 'min_child_weight': 3, 'max_depth': 6, 'learning_rate': 0.03, 'colsample_bytree': 1.0}",0.9251786137311072,0.9259145195545496,0.9191782931315254,0.924167292304741,0.9231276234069506,0.9235132684257747,0.002362535167399179,11
content/sample_data/README.md ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ This directory includes a few sample datasets to get you started.
2
+
3
+ * `california_housing_data*.csv` is California housing data from the 1990 US
4
+ Census; more information is available at:
5
+ https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub
6
+
7
+ * `mnist_*.csv` is a small sample of the
8
+ [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
9
+ described at: http://yann.lecun.com/exdb/mnist/
10
+
11
+ * `anscombe.json` contains a copy of
12
+ [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
13
+ was originally described in
14
+
15
+ Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
16
+ Statistician. 27 (1): 17-21. JSTOR 2682899.
17
+
18
+ and our copy was prepared by the
19
+ [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
content/sample_data/anscombe.json ADDED
@@ -0,0 +1,49 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {"Series":"I", "X":10.0, "Y":8.04},
3
+ {"Series":"I", "X":8.0, "Y":6.95},
4
+ {"Series":"I", "X":13.0, "Y":7.58},
5
+ {"Series":"I", "X":9.0, "Y":8.81},
6
+ {"Series":"I", "X":11.0, "Y":8.33},
7
+ {"Series":"I", "X":14.0, "Y":9.96},
8
+ {"Series":"I", "X":6.0, "Y":7.24},
9
+ {"Series":"I", "X":4.0, "Y":4.26},
10
+ {"Series":"I", "X":12.0, "Y":10.84},
11
+ {"Series":"I", "X":7.0, "Y":4.81},
12
+ {"Series":"I", "X":5.0, "Y":5.68},
13
+
14
+ {"Series":"II", "X":10.0, "Y":9.14},
15
+ {"Series":"II", "X":8.0, "Y":8.14},
16
+ {"Series":"II", "X":13.0, "Y":8.74},
17
+ {"Series":"II", "X":9.0, "Y":8.77},
18
+ {"Series":"II", "X":11.0, "Y":9.26},
19
+ {"Series":"II", "X":14.0, "Y":8.10},
20
+ {"Series":"II", "X":6.0, "Y":6.13},
21
+ {"Series":"II", "X":4.0, "Y":3.10},
22
+ {"Series":"II", "X":12.0, "Y":9.13},
23
+ {"Series":"II", "X":7.0, "Y":7.26},
24
+ {"Series":"II", "X":5.0, "Y":4.74},
25
+
26
+ {"Series":"III", "X":10.0, "Y":7.46},
27
+ {"Series":"III", "X":8.0, "Y":6.77},
28
+ {"Series":"III", "X":13.0, "Y":12.74},
29
+ {"Series":"III", "X":9.0, "Y":7.11},
30
+ {"Series":"III", "X":11.0, "Y":7.81},
31
+ {"Series":"III", "X":14.0, "Y":8.84},
32
+ {"Series":"III", "X":6.0, "Y":6.08},
33
+ {"Series":"III", "X":4.0, "Y":5.39},
34
+ {"Series":"III", "X":12.0, "Y":8.15},
35
+ {"Series":"III", "X":7.0, "Y":6.42},
36
+ {"Series":"III", "X":5.0, "Y":5.73},
37
+
38
+ {"Series":"IV", "X":8.0, "Y":6.58},
39
+ {"Series":"IV", "X":8.0, "Y":5.76},
40
+ {"Series":"IV", "X":8.0, "Y":7.71},
41
+ {"Series":"IV", "X":8.0, "Y":8.84},
42
+ {"Series":"IV", "X":8.0, "Y":8.47},
43
+ {"Series":"IV", "X":8.0, "Y":7.04},
44
+ {"Series":"IV", "X":8.0, "Y":5.25},
45
+ {"Series":"IV", "X":19.0, "Y":12.50},
46
+ {"Series":"IV", "X":8.0, "Y":5.56},
47
+ {"Series":"IV", "X":8.0, "Y":7.91},
48
+ {"Series":"IV", "X":8.0, "Y":6.89}
49
+ ]
model_assets/.gitkeep ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+
2
+
render.yaml ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ services:
2
+ - type: web
3
+ name: heart-attack-risk-predictor
4
+ runtime: docker
5
+ plan: free
6
+ dockerfilePath: ./Dockerfile
7
+ dockerContext: .
8
+ envVars:
9
+ - key: PORT
10
+ value: 8051
11
+ healthCheckPath: /_stcore/health
requirements.txt ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ streamlit==1.39.0
2
+ pandas==2.2.2
3
+ numpy==2.0.2
4
+ scikit-learn==1.7.2
5
+ xgboost==3.1.1
6
+ catboost==1.2.8
7
+ lightgbm==4.6.0
8
+ joblib==1.5.2
9
+
streamlit_app.py ADDED
@@ -0,0 +1,1162 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Streamlit App for Heart Attack Risk Prediction
3
+ Based on ensemble model (XGBoost + CatBoost + LightGBM)
4
+ """
5
+
6
+ import streamlit as st
7
+ import pandas as pd
8
+ import numpy as np
9
+ import joblib
10
+ import json
11
+ import os
12
+ from pathlib import Path
13
+
14
+ # Page configuration
15
+ st.set_page_config(
16
+ page_title="Predicting Heart Attack Risk: An Ensemble Modeling Approach",
17
+ layout="wide",
18
+ initial_sidebar_state="expanded"
19
+ )
20
+
21
+ # Custom CSS for modern styling
22
+ st.markdown("""
23
+ <style>
24
+ /* Modern Design System */
25
+ :root {
26
+ --primary: #3B82F6;
27
+ --primary-dark: #2563EB;
28
+ --secondary: #8B5CF6;
29
+ --success: #10B981;
30
+ --warning: #F59E0B;
31
+ --danger: #EF4444;
32
+ --bg-card: rgba(30, 41, 59, 0.4);
33
+ --bg-card-hover: rgba(30, 41, 59, 0.6);
34
+ --border: rgba(148, 163, 184, 0.1);
35
+ --border-strong: rgba(148, 163, 184, 0.2);
36
+ --text-primary: #F1F5F9;
37
+ --text-secondary: #CBD5E1;
38
+ --shadow-sm: 0 1px 3px 0 rgb(0 0 0 / 0.1);
39
+ --shadow-md: 0 4px 6px -1px rgb(0 0 0 / 0.1);
40
+ --shadow-lg: 0 10px 15px -3px rgb(0 0 0 / 0.2);
41
+ --radius: 16px;
42
+ --radius-sm: 12px;
43
+ }
44
+
45
+ /* Hide Streamlit branding */
46
+ #MainMenu {visibility: hidden;}
47
+ footer {visibility: hidden;}
48
+
49
+ /* Main container improvements */
50
+ .main .block-container {
51
+ padding-top: 2rem;
52
+ padding-bottom: 2rem;
53
+ max-width: 1400px;
54
+ }
55
+
56
+ /* Header with gradient */
57
+ .main-header {
58
+ font-size: 2.5rem;
59
+ font-weight: 800;
60
+ text-align: center;
61
+ margin: 0 0 0.5rem;
62
+ letter-spacing: -0.02em;
63
+ background: linear-gradient(135deg, var(--primary) 0%, var(--secondary) 100%);
64
+ -webkit-background-clip: text;
65
+ background-clip: text;
66
+ color: transparent;
67
+ line-height: 1.2;
68
+ }
69
+
70
+ .subtitle {
71
+ text-align: center;
72
+ color: var(--text-secondary);
73
+ font-size: 0.95rem;
74
+ margin-bottom: 2rem;
75
+ font-weight: 400;
76
+ }
77
+
78
+ /* Section divider */
79
+ .section-divider {
80
+ height: 1px;
81
+ background: linear-gradient(90deg, transparent, var(--border-strong), transparent);
82
+ margin: 2rem 0;
83
+ border: none;
84
+ }
85
+
86
+ /* Modern cards */
87
+ .info-card {
88
+ padding: 1.5rem;
89
+ border-radius: var(--radius-sm);
90
+ background: var(--bg-card);
91
+ border: 1px solid var(--border);
92
+ backdrop-filter: blur(10px);
93
+ transition: all 0.3s ease;
94
+ box-shadow: var(--shadow-sm);
95
+ }
96
+
97
+ .info-card:hover {
98
+ background: var(--bg-card-hover);
99
+ border-color: var(--border-strong);
100
+ box-shadow: var(--shadow-md);
101
+ transform: translateY(-2px);
102
+ }
103
+
104
+ /* Metric cards */
105
+ div[data-testid="metric-container"] {
106
+ background: var(--bg-card);
107
+ padding: 1rem;
108
+ border-radius: var(--radius-sm);
109
+ border: 1px solid var(--border);
110
+ box-shadow: var(--shadow-sm);
111
+ }
112
+
113
+ div[data-testid="metric-container"]:hover {
114
+ background: var(--bg-card-hover);
115
+ border-color: var(--border-strong);
116
+ }
117
+
118
+ /* Buttons */
119
+ .stButton > button {
120
+ width: 100%;
121
+ background: linear-gradient(135deg, var(--primary) 0%, var(--primary-dark) 100%);
122
+ color: white;
123
+ border: none;
124
+ padding: 0.875rem 2rem;
125
+ font-size: 1.05rem;
126
+ font-weight: 600;
127
+ border-radius: var(--radius-sm);
128
+ transition: all 0.3s ease;
129
+ box-shadow: var(--shadow-md);
130
+ letter-spacing: 0.01em;
131
+ }
132
+
133
+ .stButton > button:hover {
134
+ transform: translateY(-2px);
135
+ box-shadow: var(--shadow-lg);
136
+ background: linear-gradient(135deg, var(--primary-dark) 0%, var(--primary) 100%);
137
+ }
138
+
139
+ /* Input fields */
140
+ .stTextInput > div > div > input,
141
+ .stNumberInput > div > div > input,
142
+ .stSelectbox > div > div,
143
+ .stRadio > div {
144
+ background: var(--bg-card);
145
+ border: 1px solid var(--border);
146
+ border-radius: var(--radius-sm);
147
+ color: var(--text-primary);
148
+ }
149
+
150
+ .stTextInput > div > div > input:focus,
151
+ .stNumberInput > div > div > input:focus,
152
+ .stSelectbox > div > div:focus-within {
153
+ border-color: var(--primary);
154
+ box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.1);
155
+ }
156
+
157
+ /* Sidebar */
158
+ section[data-testid="stSidebar"] {
159
+ background: linear-gradient(180deg, rgba(15, 23, 42, 0.95) 0%, rgba(30, 41, 59, 0.95) 100%);
160
+ border-right: 1px solid var(--border);
161
+ }
162
+
163
+ section[data-testid="stSidebar"] .block-container {
164
+ padding-top: 2rem;
165
+ }
166
+
167
+ /* Expander */
168
+ .streamlit-expanderHeader {
169
+ background: var(--bg-card);
170
+ border: 1px solid var(--border);
171
+ border-radius: var(--radius-sm);
172
+ font-weight: 600;
173
+ color: var(--text-primary);
174
+ }
175
+
176
+ .streamlit-expanderHeader:hover {
177
+ background: var(--bg-card-hover);
178
+ border-color: var(--border-strong);
179
+ }
180
+
181
+ /* Progress bars */
182
+ .stProgress > div > div > div {
183
+ background: linear-gradient(90deg, var(--primary), var(--secondary));
184
+ border-radius: 10px;
185
+ }
186
+
187
+ /* Tabs */
188
+ .stTabs [data-baseweb="tab-list"] {
189
+ gap: 8px;
190
+ background: transparent;
191
+ }
192
+
193
+ .stTabs [data-baseweb="tab"] {
194
+ background: var(--bg-card);
195
+ border: 1px solid var(--border);
196
+ border-radius: var(--radius-sm);
197
+ padding: 0.5rem 1.5rem;
198
+ color: var(--text-secondary);
199
+ }
200
+
201
+ .stTabs [aria-selected="true"] {
202
+ background: linear-gradient(135deg, var(--primary) 0%, var(--secondary) 100%);
203
+ color: white;
204
+ border-color: transparent;
205
+ }
206
+
207
+ /* Alerts */
208
+ .stAlert {
209
+ border-radius: var(--radius-sm);
210
+ border: 1px solid var(--border);
211
+ backdrop-filter: blur(10px);
212
+ }
213
+
214
+ /* Success/Error states */
215
+ .risk-high {
216
+ color: var(--danger);
217
+ font-size: 1.5rem;
218
+ font-weight: 700;
219
+ }
220
+
221
+ .risk-low {
222
+ color: var(--success);
223
+ font-size: 1.5rem;
224
+ font-weight: 700;
225
+ }
226
+
227
+ /* Section headers */
228
+ h1, h2, h3 {
229
+ color: var(--text-primary);
230
+ font-weight: 700;
231
+ letter-spacing: -0.01em;
232
+ }
233
+
234
+ h2 {
235
+ font-size: 1.875rem;
236
+ margin-top: 2rem;
237
+ margin-bottom: 1rem;
238
+ }
239
+
240
+ h3 {
241
+ font-size: 1.25rem;
242
+ margin-top: 1.5rem;
243
+ margin-bottom: 0.75rem;
244
+ }
245
+
246
+ /* Info boxes */
247
+ .stMarkdown p {
248
+ color: var(--text-secondary);
249
+ line-height: 1.6;
250
+ }
251
+
252
+ /* Radio buttons */
253
+ .stRadio > label {
254
+ color: var(--text-primary);
255
+ font-weight: 500;
256
+ }
257
+
258
+ /* Selectbox */
259
+ .stSelectbox > label {
260
+ color: var(--text-primary);
261
+ font-weight: 500;
262
+ }
263
+ </style>
264
+ """, unsafe_allow_html=True)
265
+
266
+ # Paths
267
+ BASE_DIR = os.path.dirname(__file__)
268
+ ASSETS_DIR = os.path.join(BASE_DIR, "model_assets")
269
+ os.makedirs(ASSETS_DIR, exist_ok=True)
270
+
271
+ def find_first_existing(names):
272
+ for n in names:
273
+ p = os.path.join(ASSETS_DIR, n)
274
+ if os.path.exists(p):
275
+ return p
276
+ return None
277
+
278
+ def load_performance_metrics():
279
+ """Load model and ensemble metrics from available CSVs.
280
+ Returns:
281
+ metrics_rows: list of dicts with keys: model, accuracy, recall, f1, roc_auc
282
+ hybrid_rows: list of dicts with keys: version, accuracy, recall, f1, roc_auc
283
+ """
284
+ metrics_rows = []
285
+ hybrid_rows = []
286
+
287
+ # Candidate files in order of preference
288
+ candidate_model_metrics = [
289
+ os.path.join(BASE_DIR, "content", "models", "model_metrics_best.csv"),
290
+ os.path.join(BASE_DIR, "model_assets", "model_metrics.csv"),
291
+ os.path.join(BASE_DIR, "content", "models", "model_metrics.csv"),
292
+ ]
293
+ candidate_hybrid_metrics = [
294
+ os.path.join(BASE_DIR, "content", "models", "hybrid_metrics_best.csv"),
295
+ os.path.join(BASE_DIR, "model_assets", "hybrid_metrics.csv"),
296
+ os.path.join(BASE_DIR, "content", "models", "hybrid_metrics.csv"),
297
+ ]
298
+
299
+ # Load model metrics
300
+ for fp in candidate_model_metrics:
301
+ if os.path.exists(fp):
302
+ try:
303
+ df = pd.read_csv(fp)
304
+ except Exception:
305
+ try:
306
+ df = pd.read_csv(fp, index_col=0)
307
+ except Exception:
308
+ continue
309
+ cols = {c.lower(): c for c in df.columns}
310
+ # Normalize rows
311
+ for idx, row in df.iterrows():
312
+ mr = {}
313
+ mr["model"] = str(row.get(cols.get("model"), idx))
314
+ for k in ["accuracy", "precision", "recall", "f1", "roc_auc"]:
315
+ v = row.get(cols.get(k)) if cols.get(k) in row else None
316
+ try:
317
+ mr[k] = float(v)
318
+ except Exception:
319
+ mr[k] = None
320
+ metrics_rows.append(mr)
321
+ # Prefer first successful file then break
322
+ if metrics_rows:
323
+ break
324
+
325
+ # Load hybrid/ensemble metrics
326
+ for fp in candidate_hybrid_metrics:
327
+ if os.path.exists(fp):
328
+ try:
329
+ dfh = pd.read_csv(fp)
330
+ except Exception:
331
+ try:
332
+ dfh = pd.read_csv(fp, index_col=0)
333
+ except Exception:
334
+ continue
335
+ cols = {c.lower(): c for c in dfh.columns}
336
+ for idx, row in dfh.iterrows():
337
+ hr = {}
338
+ hr["version"] = str(row.get(cols.get("version", "version"), idx))
339
+ for k in ["accuracy", "precision", "recall", "f1", "roc_auc"]:
340
+ v = row.get(cols.get(k)) if cols.get(k) in row else None
341
+ try:
342
+ hr[k] = float(v)
343
+ except Exception:
344
+ hr[k] = None
345
+ hybrid_rows.append(hr)
346
+ if hybrid_rows:
347
+ break
348
+
349
+ return metrics_rows, hybrid_rows
350
+
351
+ def get_algo_metrics(metrics_rows, algo_name: str):
352
+ """Pick metrics for a given algo ('XGBoost', 'CatBoost', 'LightGBM').
353
+ Uses heuristics to match model names in CSV.
354
+ Returns best (highest accuracy) matching row or None.
355
+ """
356
+ if not metrics_rows:
357
+ return None
358
+ name_hints = {
359
+ "XGBoost": ["XGB", "XGBoost", "xgb"],
360
+ "CatBoost": ["CAT", "CatBoost", "cat"],
361
+ "LightGBM": ["LGBM", "LightGBM", "lgb"],
362
+ "LogReg": ["LogReg", "logreg", "logistic"],
363
+ "RandomForest": ["RF", "RandomForest", "random forest"],
364
+ }
365
+ hints = name_hints.get(algo_name, [algo_name])
366
+ best = None
367
+ for row in metrics_rows:
368
+ label = str(row.get("model", "")).upper()
369
+ if any(hint.upper() in label for hint in hints):
370
+ if best is None:
371
+ best = row
372
+ else:
373
+ acc_best = best.get("accuracy") or -1
374
+ acc_new = row.get("accuracy") or -1
375
+ if acc_new > acc_best:
376
+ best = row
377
+ return best
378
+
379
+ def get_ensemble_metrics(hybrid_rows):
380
+ """Return the preferred ensemble metrics row.
381
+ Preference: 'Ensemble_best@0.5' -> 'Ensemble@0.5' -> first Ensemble row.
382
+ """
383
+ if not hybrid_rows:
384
+ return None
385
+ # Normalize
386
+ rows = list(hybrid_rows)
387
+ # First preference
388
+ for r in rows:
389
+ ver = str(r.get("version", ""))
390
+ if ver.lower() == "ensemble_best@0.5" or ("ensemble_best" in ver.lower() and "@0.5" in ver.lower()):
391
+ return r
392
+ # Second preference
393
+ for r in rows:
394
+ ver = str(r.get("version", ""))
395
+ if ver.lower() == "ensemble@0.5" or ("ensemble" in ver.lower() and "@0.5" in ver.lower()):
396
+ return r
397
+ # Any ensemble row
398
+ for r in rows:
399
+ ver = str(r.get("version", ""))
400
+ if "ensemble" in ver.lower():
401
+ return r
402
+ return None
403
+
404
+ @st.cache_resource
405
+ def load_models():
406
+ """Load models and preprocessor (cached for performance). Robust per-model loading."""
407
+ preprocessor = None
408
+ try:
409
+ preproc_path = find_first_existing(["preprocessor.joblib"])
410
+ if preproc_path:
411
+ preprocessor = joblib.load(preproc_path)
412
+ except Exception as e:
413
+ st.warning(f"Preprocessor load skipped: {e}")
414
+
415
+ models = {}
416
+ # Resolve paths
417
+ xgb_path = find_first_existing([
418
+ "XGB_spw.joblib", "XGBoost.joblib", "xgb_model.joblib", "xgb_full.joblib", "XGBoost_best_5cv.joblib"
419
+ ])
420
+ cat_path = find_first_existing([
421
+ "CAT_cw.joblib", "CatBoost.joblib", "catboost.joblib", "cat_model.joblib", "cat_full.joblib", "CatBoost_best_5cv.joblib"
422
+ ])
423
+ lgb_path = find_first_existing([
424
+ "LGBM_cw.joblib", "LightGBM.joblib", "lgb_model.joblib", "LightGBM_best_5cv.joblib"
425
+ ])
426
+
427
+ # Load each model independently so one failure doesn't break others
428
+ if xgb_path:
429
+ try:
430
+ models["XGBoost"] = joblib.load(xgb_path)
431
+ except Exception as e:
432
+ st.warning(f"XGBoost model failed to load from {os.path.basename(xgb_path)}: {e}")
433
+ if cat_path:
434
+ try:
435
+ models["CatBoost"] = joblib.load(cat_path)
436
+ except Exception as e:
437
+ st.warning(f"CatBoost model failed to load from {os.path.basename(cat_path)}: {e}")
438
+ if lgb_path:
439
+ try:
440
+ models["LightGBM"] = joblib.load(lgb_path)
441
+ except Exception as e:
442
+ st.warning(f"LightGBM model failed to load from {os.path.basename(lgb_path)}: {e}")
443
+
444
+ # Do NOT restrict to CatBoost if preprocessor is missing; ensemble needs both.
445
+
446
+ # Load metrics paths for display/selection (optional)
447
+ metrics_paths = []
448
+ for mp in ["hybrid_metrics.csv", "model_metrics_summary.csv", "model_metrics.csv"]:
449
+ p = find_first_existing([mp])
450
+ if p:
451
+ metrics_paths.append(p)
452
+
453
+ return preprocessor, models, metrics_paths
454
+
455
+ def pick_best_model(models: dict, metrics_paths: list):
456
+ """Pick best model based on highest accuracy then recall from available metrics CSVs."""
457
+ fallback_order = [
458
+ ("CatBoost", ["CAT", "Cat", "cat"]),
459
+ ("XGBoost", ["XGB", "XGBoost", "xgb"]),
460
+ ("LightGBM", ["LGBM", "LightGBM", "lgbm"]),
461
+ ]
462
+
463
+ best_label = None
464
+ best_acc = -1.0
465
+ best_rec = -1.0
466
+
467
+ for mp in metrics_paths:
468
+ try:
469
+ dfm = pd.read_csv(mp)
470
+ except Exception:
471
+ try:
472
+ dfm = pd.read_csv(mp, index_col=0)
473
+ except Exception:
474
+ continue
475
+
476
+ cols = {c.lower(): c for c in dfm.columns}
477
+ if "accuracy" in cols and "recall" in cols:
478
+ acc_col = cols["accuracy"]
479
+ rec_col = cols["recall"]
480
+ if "model" in {c.lower() for c in dfm.columns}:
481
+ name_col = [c for c in dfm.columns if c.lower() == "model"][0]
482
+ iter_rows = dfm[[name_col, acc_col, rec_col]].itertuples(index=False, name=None)
483
+ else:
484
+ iter_rows = zip(dfm.index.astype(str).tolist(), dfm[acc_col].tolist(), dfm[rec_col].tolist())
485
+
486
+ for label, acc, rec in iter_rows:
487
+ try:
488
+ acc_f = float(acc)
489
+ rec_f = float(rec)
490
+ except Exception:
491
+ continue
492
+ if (acc_f > best_acc) or (np.isclose(acc_f, best_acc) and rec_f > best_rec):
493
+ best_acc = acc_f
494
+ best_rec = rec_f
495
+ best_label = str(label)
496
+
497
+ if best_label:
498
+ label_u = best_label.upper()
499
+ if "CAT" in label_u and "CatBoost" in models:
500
+ return "CatBoost"
501
+ if "XGB" in label_u and "XGBoost" in models:
502
+ return "XGBoost"
503
+ if ("LGBM" in label_u or "LGB" in label_u) and "LightGBM" in models:
504
+ return "LightGBM"
505
+
506
+ for key, hints in fallback_order:
507
+ if key in models:
508
+ return key
509
+ return None
510
+
511
+ # Load models
512
+ preprocessor, models, metrics_paths = load_models()
513
+
514
+ if not models:
515
+ st.error("⚠️ No models found in `model_assets/`. Please add your trained model files.")
516
+ st.stop()
517
+
518
+ # Enforce Ensemble-only usage: require both XGBoost and CatBoost
519
+ if not ("XGBoost" in models and "CatBoost" in models):
520
+ st.error("⚠️ Ensemble requires both XGBoost and CatBoost models. Please ensure both artifacts are present in `model_assets/`.")
521
+ st.stop()
522
+
523
+ # Main title
524
+ st.markdown('<h1 class="main-header">Predicting Heart Attack Risk: An Ensemble Modeling Approach</h1>', unsafe_allow_html=True)
525
+ st.markdown('<p class="subtitle">Advanced machine learning ensemble combining XGBoost and CatBoost for accurate cardiovascular risk assessment</p>', unsafe_allow_html=True)
526
+ st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
527
+
528
+ # Sidebar for model info
529
+ with st.sidebar:
530
+ st.header("📊 Ensemble")
531
+ st.success("✅ Using Ensemble Only (50% XGBoost + 50% CatBoost)")
532
+ _model_rows, _hybrid_rows = load_performance_metrics()
533
+ ens_row = get_ensemble_metrics(_hybrid_rows)
534
+ acc_text = f"{ens_row['accuracy']*100:.2f}%" if ens_row and ens_row.get('accuracy') is not None else "n/a"
535
+ rec_text = f"{ens_row['recall']*100:.2f}%" if ens_row and ens_row.get('recall') is not None else "n/a"
536
+ cols_side = st.columns(2)
537
+ with cols_side[0]:
538
+ st.metric("Accuracy", acc_text)
539
+ with cols_side[1]:
540
+ st.metric("Recall", rec_text)
541
+
542
+ if metrics_paths:
543
+ st.markdown("**Performance Metrics:**")
544
+ for mp in metrics_paths:
545
+ try:
546
+ dfm = pd.read_csv(mp, index_col=0) if mp.endswith('.csv') else pd.read_csv(mp)
547
+ st.dataframe(dfm.head(10), use_container_width=True)
548
+ except Exception:
549
+ pass
550
+
551
+ st.markdown("---")
552
+ st.info("""
553
+ **Note:** This is a prediction tool, not a medical diagnosis.
554
+ Always consult healthcare professionals for medical advice.
555
+ """)
556
+
557
+ # Input form with all features
558
+ st.header("📝 Patient Information")
559
+
560
+ col1, col2 = st.columns(2)
561
+
562
+ with col1:
563
+ st.subheader("Demographics")
564
+ gender = st.selectbox("Gender", options=[1, 2], format_func=lambda x: "Male" if x == 1 else "Female")
565
+ height = st.number_input("Height (cm)", min_value=100, max_value=220, value=170, step=1)
566
+ weight = st.number_input("Weight (kg)", min_value=30.0, max_value=200.0, value=70.0, step=0.1)
567
+
568
+ # Calculate BMI with category
569
+ bmi = weight / ((height / 100) ** 2) if height > 0 else 0
570
+ if bmi < 18.5:
571
+ bmi_status = "⚠️ Underweight"
572
+ bmi_color = "inverse"
573
+ elif bmi < 25:
574
+ bmi_status = "✅ Normal"
575
+ bmi_color = "normal"
576
+ elif bmi < 30:
577
+ bmi_status = "⚠️ Overweight"
578
+ bmi_color = "normal"
579
+ else:
580
+ bmi_status = "🔴 Obese"
581
+ bmi_color = "inverse"
582
+
583
+ st.metric("BMI", f"{bmi:.2f}", delta=bmi_status, delta_color=bmi_color,
584
+ help="Body Mass Index - Healthy range: 18.5-24.9")
585
+
586
+ with col2:
587
+ st.subheader("Blood Pressure")
588
+ ap_hi = st.number_input("Systolic BP (mmHg)", min_value=80, max_value=250, value=120, step=1)
589
+ ap_lo = st.number_input("Diastolic BP (mmHg)", min_value=40, max_value=150, value=80, step=1)
590
+
591
+ # Calculate BP_diff and category
592
+ bp_diff = ap_hi - ap_lo
593
+
594
+ # BP Status
595
+ if ap_hi < 120 and ap_lo < 80:
596
+ bp_status = "✅ Normal"
597
+ bp_color = "normal"
598
+ elif ap_hi < 130 and ap_lo < 80:
599
+ bp_status = "⚠️ Elevated"
600
+ bp_color = "normal"
601
+ elif ap_hi < 140 or ap_lo < 90:
602
+ bp_status = "🔴 Stage 1"
603
+ bp_color = "inverse"
604
+ else:
605
+ bp_status = "🚨 Stage 2"
606
+ bp_color = "inverse"
607
+
608
+ st.metric("Pulse Pressure", f"{bp_diff} mmHg", delta=bp_status, delta_color=bp_color,
609
+ help="Normal BP: <120/80 mmHg")
610
+
611
+ st.markdown("---")
612
+
613
+ col3, col4 = st.columns(2)
614
+
615
+ with col3:
616
+ st.subheader("Medical History")
617
+ cholesterol = st.selectbox("Cholesterol Level", options=[1, 2, 3],
618
+ format_func=lambda x: {1: "Normal", 2: "Above Normal", 3: "Well Above Normal"}.get(x))
619
+ gluc = st.selectbox("Glucose Level", options=[1, 2, 3],
620
+ format_func=lambda x: {1: "Normal", 2: "Above Normal", 3: "Well Above Normal"}.get(x))
621
+ smoke = st.radio("Smoking", options=[0, 1], format_func=lambda x: "No" if x == 0 else "Yes", horizontal=True)
622
+ alco = st.radio("Alcohol Consumption", options=[0, 1], format_func=lambda x: "No" if x == 0 else "Yes", horizontal=True)
623
+
624
+ with col4:
625
+ st.subheader("Activity & Derived Features")
626
+ active = st.radio("Physical Activity", options=[0, 1], format_func=lambda x: "No" if x == 0 else "Yes", horizontal=True)
627
+
628
+ # Age in years (for display)
629
+ age_years = st.number_input("Age (years)", min_value=20, max_value=100, value=50, step=1)
630
+ age_days = age_years * 365 # Convert to days for model compatibility
631
+
632
+ # Derived features
633
+ systolic_pressure = ap_hi
634
+ map_value = ap_lo + (bp_diff / 3) # Mean Arterial Pressure approximation
635
+ pulse_pressure_ratio = bp_diff / ap_hi if ap_hi > 0 else 0
636
+
637
+ # Additional derived features
638
+ st.markdown("---")
639
+ st.subheader("Additional Health Metrics")
640
+
641
+ col5, col6, col7 = st.columns(3)
642
+
643
+ with col5:
644
+ protein_level = st.number_input("Protein Level", min_value=0.0, max_value=200.0, value=14.0, step=0.1)
645
+
646
+ with col6:
647
+ ejection_fraction = st.number_input("Ejection Fraction (%)", min_value=0.0, max_value=100.0, value=60.0, step=0.1)
648
+
649
+ with col7:
650
+ # Calculate Lifestyle Score automatically
651
+ lifestyle_score = 0
652
+ risk_factors = []
653
+
654
+ if smoke == 1:
655
+ lifestyle_score += 1
656
+ risk_factors.append("Smoking")
657
+ if alco == 1:
658
+ lifestyle_score += 1
659
+ risk_factors.append("Alcohol")
660
+ if active == 0:
661
+ lifestyle_score += 1
662
+ risk_factors.append("Inactive")
663
+
664
+ if lifestyle_score == 0:
665
+ score_label = "✅ Low Risk"
666
+ delta_color = "normal"
667
+ elif lifestyle_score == 1:
668
+ score_label = "⚠️ Moderate Risk"
669
+ delta_color = "normal"
670
+ elif lifestyle_score == 2:
671
+ score_label = "🔴 High Risk"
672
+ delta_color = "inverse"
673
+ else:
674
+ score_label = "🚨 Very High Risk"
675
+ delta_color = "inverse"
676
+
677
+ st.metric(
678
+ "Lifestyle Risk Score",
679
+ f"{lifestyle_score}/3 - {score_label}",
680
+ help=f"Auto-calculated from lifestyle factors. Risk factors: {', '.join(risk_factors) if risk_factors else 'None'}"
681
+ )
682
+ if risk_factors:
683
+ st.caption(f"⚠️ Risk factors: {', '.join(risk_factors)}")
684
+
685
+ # Calculate additional derived features
686
+ obesity_flag = 1 if bmi >= 30 else 0
687
+ hypertension_flag = 1 if ap_hi >= 140 or ap_lo >= 90 else 0
688
+ health_risk_score = lifestyle_score + obesity_flag + hypertension_flag
689
+ smoker_alcoholic = 1 if (smoke == 1 or alco == 1) else 0
690
+
691
+ # Age group and BMI category
692
+ if age_years < 30:
693
+ age_group = "20-29"
694
+ elif age_years < 40:
695
+ age_group = "30-39"
696
+ elif age_years < 50:
697
+ age_group = "40-49"
698
+ elif age_years < 60:
699
+ age_group = "50-59"
700
+ else:
701
+ age_group = "60+"
702
+
703
+ if bmi < 18.5:
704
+ bmi_category = "Underweight"
705
+ elif bmi < 25:
706
+ bmi_category = "Normal"
707
+ elif bmi < 30:
708
+ bmi_category = "Overweight"
709
+ else:
710
+ bmi_category = "Obese"
711
+
712
+ # BP Category
713
+ if ap_hi < 120 and ap_lo < 80:
714
+ bp_category = "Normal"
715
+ elif ap_hi < 130 and ap_lo < 80:
716
+ bp_category = "Elevated"
717
+ elif ap_hi < 140 or ap_lo < 90:
718
+ bp_category = "Stage 1"
719
+ else:
720
+ bp_category = "Stage 2"
721
+
722
+ # Risk Level
723
+ if health_risk_score <= 2:
724
+ risk_level = "Low"
725
+ elif health_risk_score <= 4:
726
+ risk_level = "Medium"
727
+ else:
728
+ risk_level = "High"
729
+
730
+ # Risk Age (derived)
731
+ risk_age = age_years + (health_risk_score * 5)
732
+
733
+ # Generate Reason based on risk factors
734
+ reasons = []
735
+ if obesity_flag == 1:
736
+ reasons.append("High BMI (>30)")
737
+ if hypertension_flag == 1:
738
+ reasons.append("High BP")
739
+ if cholesterol == 3:
740
+ reasons.append("High cholesterol")
741
+ if gluc == 3:
742
+ reasons.append("High glucose")
743
+ if lifestyle_score > 0:
744
+ if smoke == 1:
745
+ reasons.append("Smoking")
746
+ if alco == 1:
747
+ reasons.append("Alcohol consumption")
748
+ if active == 0:
749
+ reasons.append("Inactive")
750
+ if not reasons:
751
+ reasons.append("Healthy indicators")
752
+ reason = ", ".join(reasons)
753
+
754
+ # Create feature dictionary matching the dataset structure
755
+ feature_dict = {
756
+ 'age': age_days,
757
+ 'gender': gender,
758
+ 'height': height,
759
+ 'weight': weight,
760
+ 'ap_hi': ap_hi,
761
+ 'ap_lo': ap_lo,
762
+ 'cholesterol': cholesterol,
763
+ 'gluc': gluc,
764
+ 'smoke': smoke,
765
+ 'alco': alco,
766
+ 'active': active,
767
+ 'BMI': bmi,
768
+ 'BP_diff': bp_diff,
769
+ 'Systolic_Pressure': systolic_pressure,
770
+ 'age_years': age_years,
771
+ 'Age_Group': age_group,
772
+ 'Lifestyle_Score': lifestyle_score,
773
+ 'Obesity_Flag': obesity_flag,
774
+ 'Hypertension_Flag': hypertension_flag,
775
+ 'Health_Risk_Score': health_risk_score,
776
+ 'Reason': reason,
777
+ 'Pulse_Pressure_Ratio': pulse_pressure_ratio,
778
+ 'MAP': map_value,
779
+ 'BMI_Category': bmi_category,
780
+ 'Smoker_Alcoholic': smoker_alcoholic,
781
+ 'BP_Category': bp_category,
782
+ 'Risk_Age': risk_age,
783
+ 'Risk_Level': risk_level,
784
+ 'Protein_Level': protein_level,
785
+ 'Ejection_Fraction': ejection_fraction
786
+ }
787
+
788
+ # Health Summary Card (before prediction)
789
+ st.markdown("---")
790
+ st.subheader("📊 Health Summary")
791
+
792
+ summary_col1, summary_col2, summary_col3, summary_col4 = st.columns(4)
793
+
794
+ with summary_col1:
795
+ if obesity_flag == 1:
796
+ st.error("🔴 Obesity Risk")
797
+ else:
798
+ st.success("✅ Healthy Weight")
799
+
800
+ with summary_col2:
801
+ if hypertension_flag == 1:
802
+ st.error("🔴 Hypertension")
803
+ else:
804
+ st.success("✅ Normal BP")
805
+
806
+ with summary_col3:
807
+ if lifestyle_score >= 2:
808
+ st.error(f"🔴 High Lifestyle Risk ({lifestyle_score}/3)")
809
+ elif lifestyle_score == 1:
810
+ st.warning(f"⚠️ Moderate Risk ({lifestyle_score}/3)")
811
+ else:
812
+ st.success("✅ Low Risk (0/3)")
813
+
814
+ with summary_col4:
815
+ if cholesterol == 3 or gluc == 3:
816
+ st.error("🔴 Elevated Levels")
817
+ elif cholesterol == 2 or gluc == 2:
818
+ st.warning("⚠️ Above Normal")
819
+ else:
820
+ st.success("✅ Normal Levels")
821
+
822
+ # Prediction button
823
+ st.markdown("---")
824
+ predict_button = st.button("🔮 Predict Heart Attack Risk", type="primary", use_container_width=True)
825
+
826
+ if predict_button:
827
+ try:
828
+ # Create DataFrame matching EXACT training data structure (excluding id, cardio, Reason)
829
+ feature_cols = ['age', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo', 'cholesterol', 'gluc',
830
+ 'smoke', 'alco', 'active', 'BMI', 'BP_diff', 'Systolic_Pressure', 'age_years',
831
+ 'Age_Group', 'Lifestyle_Score', 'Obesity_Flag', 'Hypertension_Flag', 'Health_Risk_Score',
832
+ 'Pulse_Pressure_Ratio', 'MAP', 'BMI_Category', 'Smoker_Alcoholic', 'BP_Category',
833
+ 'Risk_Age', 'Risk_Level', 'Protein_Level', 'Ejection_Fraction']
834
+
835
+ # Build input row with exact feature order
836
+ input_row = {
837
+ 'age': age_days,
838
+ 'gender': gender,
839
+ 'height': height,
840
+ 'weight': weight,
841
+ 'ap_hi': ap_hi,
842
+ 'ap_lo': ap_lo,
843
+ 'cholesterol': cholesterol,
844
+ 'gluc': gluc,
845
+ 'smoke': smoke,
846
+ 'alco': alco,
847
+ 'active': active,
848
+ 'BMI': bmi,
849
+ 'BP_diff': bp_diff,
850
+ 'Systolic_Pressure': systolic_pressure,
851
+ 'age_years': age_years,
852
+ 'Age_Group': age_group,
853
+ 'Lifestyle_Score': lifestyle_score,
854
+ 'Obesity_Flag': obesity_flag,
855
+ 'Hypertension_Flag': hypertension_flag,
856
+ 'Health_Risk_Score': health_risk_score,
857
+ 'Pulse_Pressure_Ratio': pulse_pressure_ratio,
858
+ 'MAP': map_value,
859
+ 'BMI_Category': bmi_category,
860
+ 'Smoker_Alcoholic': smoker_alcoholic,
861
+ 'BP_Category': bp_category,
862
+ 'Risk_Age': risk_age,
863
+ 'Risk_Level': risk_level,
864
+ 'Protein_Level': protein_level,
865
+ 'Ejection_Fraction': ejection_fraction
866
+ }
867
+
868
+ # Create DataFrame with exact column order
869
+ X_input = pd.DataFrame([input_row])[feature_cols]
870
+
871
+ # The model expects numeric features - categorical columns were one-hot encoded during training
872
+ # Load sample data to get all possible categorical values for proper one-hot encoding
873
+ sample_csv = os.path.join(BASE_DIR, "content", "cardio_train_extended.csv")
874
+ cat_cols = ['Age_Group', 'BMI_Category', 'BP_Category', 'Risk_Level']
875
+
876
+ if os.path.exists(sample_csv):
877
+ # Load sample to get all categorical values
878
+ sample_df = pd.read_csv(sample_csv, nrows=1000)
879
+ # Get all unique values for each categorical column
880
+ cat_values = {}
881
+ for col in cat_cols:
882
+ if col in sample_df.columns:
883
+ cat_values[col] = sorted(sample_df[col].unique().tolist())
884
+ else:
885
+ # Fallback to known values
886
+ cat_values = {
887
+ 'Age_Group': ['20-29', '30-39', '40-49', '50-59', '60+'],
888
+ 'BMI_Category': ['Underweight', 'Normal', 'Overweight', 'Obese'],
889
+ 'BP_Category': ['Normal', 'Elevated', 'Stage 1', 'Stage 2'],
890
+ 'Risk_Level': ['Low', 'Medium', 'High']
891
+ }
892
+
893
+ # Separate numeric and categorical columns
894
+ numeric_cols = [col for col in X_input.columns if col not in cat_cols]
895
+ X_numeric = X_input[numeric_cols].copy()
896
+
897
+ # One-hot encode categorical columns with all possible categories
898
+ X_cat_encoded_list = []
899
+ for col in cat_cols:
900
+ if col in X_input.columns:
901
+ # Create one-hot columns for all possible values
902
+ for val in cat_values.get(col, []):
903
+ col_name = f"{col}_{val}"
904
+ X_cat_encoded_list.append(pd.Series([1 if X_input[col].iloc[0] == val else 0], name=col_name))
905
+
906
+ if X_cat_encoded_list:
907
+ X_cat_encoded = pd.concat(X_cat_encoded_list, axis=1)
908
+ # Combine numeric and encoded categorical features
909
+ X_processed = pd.concat([X_numeric, X_cat_encoded], axis=1)
910
+ else:
911
+ X_processed = X_numeric.copy()
912
+
913
+ # Ensure all columns are numeric (float)
914
+ X_processed = X_processed.astype(float)
915
+
916
+ # Use ensemble model (50% XGBoost + 50% CatBoost) if both available, otherwise use best model
917
+ predictions = {}
918
+ ensemble_probs = []
919
+ ensemble_weights = []
920
+
921
+ # Try ensemble: XGBoost + CatBoost (0.5 each)
922
+ if "XGBoost" in models and "CatBoost" in models:
923
+ try:
924
+ # Predict with XGBoost
925
+ xgb_model = models["XGBoost"]
926
+
927
+ # Get expected features from XGBoost model
928
+ if hasattr(xgb_model, 'feature_names_in_'):
929
+ expected_features = list(xgb_model.feature_names_in_)
930
+ elif hasattr(xgb_model, 'get_booster'):
931
+ try:
932
+ booster = xgb_model.get_booster()
933
+ if hasattr(booster, 'feature_names') and booster.feature_names:
934
+ expected_features = list(booster.feature_names)
935
+ else:
936
+ # Check n_features_in_ to create placeholder columns
937
+ if hasattr(xgb_model, 'n_features_in_'):
938
+ n_features = xgb_model.n_features_in_
939
+ expected_features = [f"f{i}" for i in range(n_features)]
940
+ else:
941
+ expected_features = None
942
+ except:
943
+ expected_features = None
944
+ else:
945
+ expected_features = None
946
+
947
+ if expected_features:
948
+ # Align features exactly as XGBoost expects
949
+ X_aligned = pd.DataFrame(0.0, index=X_processed.index, columns=expected_features, dtype=float)
950
+ # Match columns by name
951
+ for col in X_processed.columns:
952
+ if col in X_aligned.columns:
953
+ X_aligned[col] = X_processed[col].values
954
+ X_xgb = X_aligned[expected_features] # Ensure exact order
955
+ else:
956
+ X_xgb = X_processed
957
+
958
+ if hasattr(xgb_model, 'predict_proba'):
959
+ xgb_prob = float(xgb_model.predict_proba(X_xgb)[0, 1])
960
+ ensemble_probs.append(xgb_prob)
961
+ ensemble_weights.append(0.5)
962
+ predictions["XGBoost"] = xgb_prob
963
+ except Exception as e:
964
+ st.warning(f"⚠️ XGBoost prediction failed (using CatBoost only): {str(e)}")
965
+ # Don't add to predictions, but continue with CatBoost
966
+
967
+ # Predict with CatBoost
968
+ if "CatBoost" in models:
969
+ try:
970
+ cat_model = models["CatBoost"]
971
+ if hasattr(cat_model, 'feature_names_in_'):
972
+ expected_features = list(cat_model.feature_names_in_)
973
+ X_aligned = pd.DataFrame(0, index=X_processed.index, columns=expected_features)
974
+ for col in X_processed.columns:
975
+ if col in X_aligned.columns:
976
+ X_aligned[col] = X_processed[col]
977
+ X_cat = X_aligned
978
+ else:
979
+ X_cat = X_processed
980
+
981
+ if hasattr(cat_model, 'predict_proba'):
982
+ cat_prob = float(cat_model.predict_proba(X_cat)[0, 1])
983
+ ensemble_probs.append(cat_prob)
984
+ ensemble_weights.append(0.5)
985
+ predictions["CatBoost"] = cat_prob
986
+ except Exception as e:
987
+ st.warning(f"CatBoost prediction failed: {e}")
988
+
989
+ # Ensemble-only: require both model probabilities
990
+ if len(ensemble_probs) >= 2:
991
+ # Ensemble prediction (weighted average)
992
+ ensemble_prob = np.average(ensemble_probs, weights=ensemble_weights)
993
+ predictions["Ensemble"] = ensemble_prob
994
+ else:
995
+ st.error("Ensemble prediction requires both XGBoost and CatBoost probabilities.")
996
+ with st.expander("Debug Info"):
997
+ st.write("XGBoost available:", "XGBoost" in models)
998
+ st.write("CatBoost available:", "CatBoost" in models)
999
+ st.write("Ensemble probs count:", len(ensemble_probs))
1000
+ st.stop()
1001
+
1002
+ if not predictions:
1003
+ st.error("No models with predict_proba available.")
1004
+ st.stop()
1005
+
1006
+ # Use ensemble prediction only
1007
+ if "Ensemble" in predictions:
1008
+ ensemble_prob = predictions["Ensemble"]
1009
+ else:
1010
+ st.error("Ensemble prediction missing.")
1011
+ st.stop()
1012
+
1013
+ # Binary prediction
1014
+ prediction = 1 if ensemble_prob >= 0.5 else 0
1015
+ risk_percentage = ensemble_prob * 100
1016
+
1017
+ # Display results
1018
+ st.markdown("---")
1019
+ st.header("🎯 Prediction Results")
1020
+
1021
+ # Main result with visual indicator
1022
+ if prediction == 1:
1023
+ st.error(f"⚠️ **HIGH RISK DETECTED** - {risk_percentage:.1f}% probability of heart disease")
1024
+ else:
1025
+ st.success(f"✅ **LOW RISK** - {risk_percentage:.1f}% probability of heart disease")
1026
+
1027
+ col_result1, col_result2, col_result3 = st.columns(3)
1028
+
1029
+ with col_result1:
1030
+ st.metric("Risk Probability", f"{risk_percentage:.2f}%",
1031
+ delta=f"{'High' if risk_percentage >= 70 else 'Moderate' if risk_percentage >= 50 else 'Low'} Risk",
1032
+ delta_color="inverse" if risk_percentage >= 70 else "normal")
1033
+
1034
+ with col_result2:
1035
+ if risk_percentage >= 70:
1036
+ risk_level_display = "🚨 Very High"
1037
+ elif risk_percentage >= 50:
1038
+ risk_level_display = "🔴 High"
1039
+ elif risk_percentage >= 30:
1040
+ risk_level_display = "⚠️ Moderate"
1041
+ else:
1042
+ risk_level_display = "✅ Low"
1043
+ st.metric("Risk Level", risk_level_display)
1044
+
1045
+ with col_result3:
1046
+ st.metric("Prediction", "Heart Disease Detected" if prediction == 1 else "No Heart Disease",
1047
+ delta="Consult Doctor" if prediction == 1 else "Continue Monitoring",
1048
+ delta_color="inverse" if prediction == 1 else "normal")
1049
+
1050
+ # Enhanced progress bar with color coding
1051
+ risk_bar_color = "#FF1744" if risk_percentage >= 70 else "#FF9800" if risk_percentage >= 50 else "#4CAF50"
1052
+ st.markdown(f"""
1053
+ <div style="background-color: #f0f0f0; border-radius: 5px; padding: 10px; margin: 10px 0;">
1054
+ <div style="background-color: {risk_bar_color}; width: {risk_percentage}%; height: 30px; border-radius: 5px; display: flex; align-items: center; justify-content: center; color: white; font-weight: bold;">
1055
+ {risk_percentage:.1f}%
1056
+ </div>
1057
+ </div>
1058
+ """, unsafe_allow_html=True)
1059
+
1060
+ # Display Reason
1061
+ st.info(f"**Key Risk Factors Identified:** {reason}")
1062
+
1063
+ # Detailed breakdown with visual bars
1064
+ with st.expander("📊 Model Details & Breakdown"):
1065
+ # Ensemble-only display
1066
+ display_order = ["Ensemble"] if "Ensemble" in predictions else []
1067
+
1068
+ # Load accuracy/recall metrics for display under each model
1069
+ _model_rows_all, _hybrid_rows_all = load_performance_metrics()
1070
+ xgb_m_all = get_algo_metrics(_model_rows_all, "XGBoost")
1071
+ cat_m_all = get_algo_metrics(_model_rows_all, "CatBoost")
1072
+ avg_acc_all = None
1073
+ if xgb_m_all and cat_m_all and (xgb_m_all.get("accuracy") is not None) and (cat_m_all.get("accuracy") is not None):
1074
+ avg_acc_all = (xgb_m_all["accuracy"] + cat_m_all["accuracy"]) / 2.0
1075
+ ens_best_all = None
1076
+ for hr in _hybrid_rows_all or []:
1077
+ if "ENSEMBLE" in hr.get("version", "").upper() and "@0.5" in hr.get("version", ""):
1078
+ ens_best_all = hr
1079
+ break
1080
+
1081
+ # Explicit ensemble header with models and average accuracy
1082
+ header_text = "Ensemble uses: XGBoost + CatBoost"
1083
+ if avg_acc_all is not None:
1084
+ st.markdown(f"**{header_text}** · Average@0.5 Accuracy: {avg_acc_all*100:.2f}%")
1085
+ else:
1086
+ st.markdown(f"**{header_text}**")
1087
+
1088
+ # Create columns for display
1089
+ if len(display_order) > 0:
1090
+ cols = st.columns(len(display_order))
1091
+ for idx, name in enumerate(display_order):
1092
+ with cols[idx]:
1093
+ if name == "Ensemble":
1094
+ st.write(f"**🎯 {name} (Final)**")
1095
+ risk_prob = float(predictions[name])
1096
+ risk_pct = risk_prob * 100
1097
+
1098
+ # Custom progress bar that fills proportionally to risk
1099
+ st.markdown(f"""
1100
+ <div style="background: rgba(148, 163, 184, 0.1); border-radius: 10px; height: 32px; width: 100%; position: relative; overflow: hidden; border: 1px solid rgba(148, 163, 184, 0.2);">
1101
+ <div style="background: linear-gradient(90deg, {'#EF4444' if risk_pct >= 50 else '#F59E0B' if risk_pct >= 30 else '#10B981'}, {'#DC2626' if risk_pct >= 50 else '#D97706' if risk_pct >= 30 else '#059669'}); width: {risk_pct}%; height: 100%; border-radius: 10px; transition: width 0.3s ease; display: flex; align-items: center; justify-content: center; color: white; font-weight: 600; font-size: 0.9rem;">
1102
+ {risk_pct:.1f}%
1103
+ </div>
1104
+ </div>
1105
+ """, unsafe_allow_html=True)
1106
+ st.caption(f"Risk Level: {risk_pct:.2f}%")
1107
+
1108
+ # Show ensemble accuracy from average and recorded best if available
1109
+ if avg_acc_all is not None:
1110
+ st.caption(f"Accuracy (Average@0.5): {avg_acc_all*100:.2f}%")
1111
+ if ens_best_all and ens_best_all.get("accuracy") is not None:
1112
+ st.caption(f"Recorded Ensemble_best@0.5: {ens_best_all['accuracy']*100:.2f}%")
1113
+ st.success("✅ Final decision uses Ensemble (50% XGBoost + 50% CatBoost)")
1114
+ else:
1115
+ pass # No individual model cards in ensemble-only mode
1116
+
1117
+ # Show ensemble info
1118
+ if "Ensemble" in predictions:
1119
+ st.info("💡 **Ensemble Method**: Weighted average (50% XGBoost + 50% CatBoost). Final decision uses the Ensemble output.")
1120
+
1121
+ # Metrics breakdown: show per-model accuracy and averaged accuracy (concise)
1122
+ st.markdown("---")
1123
+ st.subheader("Ensemble Metrics")
1124
+ ens_row_bd = get_ensemble_metrics(_hybrid_rows_all)
1125
+ acc_bd = f"{ens_row_bd['accuracy']*100:.2f}%" if ens_row_bd and ens_row_bd.get('accuracy') is not None else "n/a"
1126
+ rec_bd = f"{ens_row_bd['recall']*100:.2f}%" if ens_row_bd and ens_row_bd.get('recall') is not None else "n/a"
1127
+ cols_acc = st.columns(2)
1128
+ with cols_acc[0]:
1129
+ st.metric("Accuracy", acc_bd)
1130
+ with cols_acc[1]:
1131
+ st.metric("Recall", rec_bd)
1132
+
1133
+ # Recommendations
1134
+ st.markdown("---")
1135
+ if prediction == 1 or risk_percentage > 70:
1136
+ st.warning("⚠️ **High Risk Detected!** Please consult with a healthcare professional immediately.")
1137
+ st.info("""
1138
+ **Recommendations:**
1139
+ - Schedule an appointment with a cardiologist
1140
+ - Monitor blood pressure regularly
1141
+ - Maintain a healthy diet and exercise routine
1142
+ - Avoid smoking and limit alcohol consumption
1143
+ - Follow up with regular health checkups
1144
+ """)
1145
+ elif risk_percentage > 50:
1146
+ st.warning("⚠️ **Moderate Risk** - Consider consulting a healthcare professional.")
1147
+ else:
1148
+ st.success("✅ **Low Risk** - Continue maintaining a healthy lifestyle!")
1149
+
1150
+ except Exception as e:
1151
+ st.error(f"Error making prediction: {str(e)}")
1152
+ with st.expander("Error Details"):
1153
+ st.exception(e)
1154
+
1155
+ # Footer
1156
+ st.markdown("---")
1157
+ st.markdown("""
1158
+ <div style='text-align: center; color: #666; padding: 2rem;'>
1159
+ <p>⚠️ <strong>Disclaimer:</strong> This tool is for educational purposes only and should not be used as a substitute for professional medical advice, diagnosis, or treatment.</p>
1160
+ <p>Always seek the advice of qualified health providers with any questions you may have regarding a medical condition.</p>
1161
+ </div>
1162
+ """, unsafe_allow_html=True)
test_predict.py ADDED
@@ -0,0 +1,218 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import json
3
+ import joblib
4
+ import pandas as pd
5
+ import numpy as np
6
+
7
+ # Paths inside the container image
8
+ APP_DIR = "/app"
9
+ ASSETS_DIR = os.path.join(APP_DIR, "model_assets")
10
+
11
+ # Resolve model paths with fallbacks
12
+ XGB_CANDIDATES = [
13
+ "XGB_spw.joblib", "XGBoost_best_5cv.joblib", "XGBoost_best.joblib",
14
+ "XGBoost.joblib", "xgb_model.joblib", "xgb_full.joblib"
15
+ ]
16
+ CAT_CANDIDATES = [
17
+ "CAT_cw.joblib", "CatBoost_best_5cv.joblib", "CatBoost_best.joblib",
18
+ "CatBoost.joblib", "catboost.joblib", "cat_model.joblib", "cat_full.joblib"
19
+ ]
20
+
21
+
22
+ def find_first(path_list):
23
+ for name in path_list:
24
+ p = os.path.join(ASSETS_DIR, name)
25
+ if os.path.exists(p):
26
+ return p
27
+ return None
28
+
29
+
30
+ def build_sample_input():
31
+ # Use values close to the UI defaults
32
+ gender = 1
33
+ height = 170
34
+ weight = 70.0
35
+ ap_hi = 120
36
+ ap_lo = 80
37
+ cholesterol = 1
38
+ gluc = 1
39
+ smoke = 0
40
+ alco = 0
41
+ active = 1
42
+ age_years = 50
43
+ age_days = age_years * 365
44
+
45
+ # Derived features
46
+ bmi = weight / ((height / 100) ** 2)
47
+ bp_diff = ap_hi - ap_lo
48
+ systolic_pressure = ap_hi
49
+ map_value = ap_lo + (bp_diff / 3)
50
+ pulse_ratio = bp_diff / ap_hi if ap_hi > 0 else 0
51
+
52
+ obesity_flag = 1 if bmi >= 30 else 0
53
+ hypertension_flag = 1 if (ap_hi >= 140 or ap_lo >= 90) else 0
54
+ lifestyle_score = (1 if smoke == 1 else 0) + (1 if alco == 1 else 0) + (1 if active == 0 else 0)
55
+ health_risk_score = lifestyle_score + obesity_flag + hypertension_flag
56
+ smoker_alcoholic = 1 if (smoke == 1 or alco == 1) else 0
57
+
58
+ age_group = "50-59"
59
+ bmi_category = (
60
+ "Underweight" if bmi < 18.5 else "Normal" if bmi < 25 else "Overweight" if bmi < 30 else "Obese"
61
+ )
62
+ if ap_hi < 120 and ap_lo < 80:
63
+ bp_category = "Normal"
64
+ elif ap_hi < 130 and ap_lo < 80:
65
+ bp_category = "Elevated"
66
+ elif ap_hi < 140 or ap_lo < 90:
67
+ bp_category = "Stage 1"
68
+ else:
69
+ bp_category = "Stage 2"
70
+
71
+ risk_level = "Low" if health_risk_score <= 2 else "Medium" if health_risk_score <= 4 else "High"
72
+ risk_age = age_years + (health_risk_score * 5)
73
+
74
+ protein_level = 14.0
75
+ ejection_fraction = 60.0
76
+
77
+ feature_cols = [
78
+ 'age','gender','height','weight','ap_hi','ap_lo','cholesterol','gluc','smoke','alco','active','BMI','BP_diff',
79
+ 'Systolic_Pressure','age_years','Age_Group','Lifestyle_Score','Obesity_Flag','Hypertension_Flag','Health_Risk_Score',
80
+ 'Pulse_Pressure_Ratio','MAP','BMI_Category','Smoker_Alcoholic','BP_Category','Risk_Age','Risk_Level','Protein_Level','Ejection_Fraction'
81
+ ]
82
+
83
+ row = {
84
+ 'age': age_days,
85
+ 'gender': gender,
86
+ 'height': height,
87
+ 'weight': weight,
88
+ 'ap_hi': ap_hi,
89
+ 'ap_lo': ap_lo,
90
+ 'cholesterol': cholesterol,
91
+ 'gluc': gluc,
92
+ 'smoke': smoke,
93
+ 'alco': alco,
94
+ 'active': active,
95
+ 'BMI': bmi,
96
+ 'BP_diff': bp_diff,
97
+ 'Systolic_Pressure': systolic_pressure,
98
+ 'age_years': age_years,
99
+ 'Age_Group': age_group,
100
+ 'Lifestyle_Score': lifestyle_score,
101
+ 'Obesity_Flag': obesity_flag,
102
+ 'Hypertension_Flag': hypertension_flag,
103
+ 'Health_Risk_Score': health_risk_score,
104
+ 'Pulse_Pressure_Ratio': pulse_ratio,
105
+ 'MAP': map_value,
106
+ 'BMI_Category': bmi_category,
107
+ 'Smoker_Alcoholic': smoker_alcoholic,
108
+ 'BP_Category': bp_category,
109
+ 'Risk_Age': risk_age,
110
+ 'Risk_Level': risk_level,
111
+ 'Protein_Level': protein_level,
112
+ 'Ejection_Fraction': ejection_fraction,
113
+ }
114
+
115
+ X = pd.DataFrame([row])[feature_cols]
116
+
117
+ # One-hot encode categoricals using the same fallback values as app
118
+ cat_cols = ['Age_Group', 'BMI_Category', 'BP_Category', 'Risk_Level']
119
+ cat_values = {
120
+ 'Age_Group': ['20-29', '30-39', '40-49', '50-59', '60+'],
121
+ 'BMI_Category': ['Underweight', 'Normal', 'Overweight', 'Obese'],
122
+ 'BP_Category': ['Normal', 'Elevated', 'Stage 1', 'Stage 2'],
123
+ 'Risk_Level': ['Low', 'Medium', 'High'],
124
+ }
125
+ numeric_cols = [c for c in X.columns if c not in cat_cols]
126
+ Xn = X[numeric_cols].copy()
127
+
128
+ parts = []
129
+ for col in cat_cols:
130
+ if col in X.columns:
131
+ for v in cat_values[col]:
132
+ parts.append(pd.Series([1 if X[col].iloc[0] == v else 0], name=f"{col}_{v}"))
133
+ Xe = pd.concat(parts, axis=1) if parts else pd.DataFrame(index=X.index)
134
+ Xp = pd.concat([Xn, Xe], axis=1).astype(float)
135
+
136
+ return Xp
137
+
138
+
139
+ def align_for_model(model, Xp):
140
+ # Align dataframe columns to model expectations (by name when available)
141
+ X_aligned = Xp
142
+ if hasattr(model, 'feature_names_in_'):
143
+ expected = list(model.feature_names_in_)
144
+ Xa = pd.DataFrame(0.0, index=Xp.index, columns=expected)
145
+ for c in Xp.columns:
146
+ if c in Xa.columns:
147
+ Xa[c] = Xp[c].values
148
+ X_aligned = Xa[expected]
149
+ else:
150
+ try:
151
+ # xgboost booster feature names
152
+ booster = getattr(model, 'get_booster', lambda: None)()
153
+ if booster is not None and getattr(booster, 'feature_names', None):
154
+ expected = list(booster.feature_names)
155
+ Xa = pd.DataFrame(0.0, index=Xp.index, columns=expected)
156
+ for c in Xp.columns:
157
+ if c in Xa.columns:
158
+ Xa[c] = Xp[c].values
159
+ X_aligned = Xa[expected]
160
+ elif hasattr(model, 'n_features_in_'):
161
+ n = int(getattr(model, 'n_features_in_', Xp.shape[1]))
162
+ # Fallback: trim or pad to match expected number of features
163
+ if Xp.shape[1] >= n:
164
+ X_aligned = Xp.iloc[:, :n].copy()
165
+ else:
166
+ # pad with zero columns
167
+ pad = pd.DataFrame(0.0, index=Xp.index, columns=[f"pad_{i}" for i in range(n - Xp.shape[1])])
168
+ X_aligned = pd.concat([Xp, pad], axis=1)
169
+ except Exception:
170
+ pass
171
+ return X_aligned
172
+
173
+
174
+ def main():
175
+ xgb_path = find_first(XGB_CANDIDATES)
176
+ cat_path = find_first(CAT_CANDIDATES)
177
+
178
+ assert xgb_path and os.path.exists(xgb_path), f"XGBoost artifact not found in {ASSETS_DIR}"
179
+ assert cat_path and os.path.exists(cat_path), f"CatBoost artifact not found in {ASSETS_DIR}"
180
+
181
+ xgb = joblib.load(xgb_path)
182
+ cat = joblib.load(cat_path)
183
+
184
+ Xp = build_sample_input()
185
+ # Force shape match for XGBoost using n_features_in_
186
+ n_xgb = int(getattr(xgb, 'n_features_in_', Xp.shape[1]))
187
+ X_xgb = Xp.iloc[:, :n_xgb].values
188
+ print(f"DBG: n_xgb={n_xgb}, Xp.shape={Xp.shape}, X_xgb.shape={X_xgb.shape}")
189
+ # Align for CatBoost (by names if available), otherwise force shape
190
+ if hasattr(cat, 'feature_names_in_'):
191
+ X_cat = align_for_model(cat, Xp)
192
+ else:
193
+ # CatBoost models often don't expose names; pass full matrix
194
+ X_cat = Xp.values
195
+ print(f"DBG: X_cat.shape={X_cat.shape}")
196
+
197
+ if hasattr(xgb, 'predict_proba'):
198
+ px = float(xgb.predict_proba(X_xgb)[0, 1])
199
+ else:
200
+ px = float(xgb.predict(X_xgb)[0])
201
+
202
+ if hasattr(cat, 'predict_proba'):
203
+ pc = float(cat.predict_proba(X_cat)[0, 1])
204
+ else:
205
+ pc = float(cat.predict(X_cat)[0])
206
+
207
+ pe = 0.5 * px + 0.5 * pc
208
+ out = {
209
+ 'xgb_prob': px,
210
+ 'cat_prob': pc,
211
+ 'ensemble_prob': pe,
212
+ 'ensemble_risk_percent': pe * 100.0,
213
+ }
214
+ print(json.dumps(out, indent=2))
215
+
216
+
217
+ if __name__ == "__main__":
218
+ main()