GITHUB_SETUP.md

📤 GitHub Setup Guide for Hugging Face Deployment

Step 1: Initialize Git Repository (if not done)

If you haven't initialized git yet, run:

cd /home/kbs/Documents/heart-attack-risk-ensemble
git init

Step 2: Using GitHub Desktop

Option A: Clone Existing Repository

1. Open GitHub Desktop
2. Click "File" → "Clone Repository"
3. If you already created a repo on GitHub.com:
   • Select "GitHub.com" tab
   • Choose your repository
   • Click "Clone"

Option B: Create New Repository

1. Open GitHub Desktop
2. Click "File" → "New Repository"
3. Fill in:
   • Name: heart-attack-risk-ensemble (or your choice)
   • Description: "Heart Attack Risk Prediction using Ensemble ML Models"
   • Local Path: /home/kbs/Documents/heart-attack-risk-ensemble
   • Initialize with README: ✅ Check this
   • Git Ignore: Python
   • License: MIT (optional)
4. Click "Create Repository"

Step 3: Add Files to GitHub Desktop

1. In GitHub Desktop, you'll see all your files listed
2. Review the changes:
   • ✅ Include: All Python files, requirements.txt, configs, documentation
   • ✅ Include: Model files (if under 100MB each)
   • ⚠️ Check: Large files (>100MB) - GitHub has limits

Files to Commit:

• ✅ streamlit_app.py
• ✅ requirements.txt
• ✅ Dockerfile
• ✅ render.yaml
• ✅ .streamlit/config.toml
• ✅ TEST_CASES.md
• ✅ DEPLOYMENT_CHECKLIST.md
• ✅ DEPLOYMENT_OPTIONS.md
• ✅ README.md
• ✅ model_assets/ (with optimized models)
• ✅ content/models/ (if needed)
• ✅ .gitignore

Step 4: Commit Changes

1. In GitHub Desktop, you'll see all changes
2. Summary: Write a commit message like:

   Initial commit: Heart Attack Risk Prediction App
   - Streamlit app with ensemble models (XGBoost, CatBoost, LightGBM)
   - Optimized models with 80.77% accuracy, 93.27% recall
   - Complete UI with model breakdown
   - Test cases and deployment documentation
   

3. Description (optional): Add more details
4. Click "Commit to main" (or your branch name)

Step 5: Publish to GitHub

1. Click "Publish repository" button (top right)
2. If creating new repo:
   • ✅ Keep code private: Uncheck (make it public for Hugging Face)
   • ✅ Add description: "Heart Attack Risk Prediction using Ensemble ML Models"
3. Click "Publish Repository"

Step 6: Verify on GitHub.com

1. Go to https://github.com/YOUR_USERNAME/heart-attack-risk-ensemble
2. Verify all files are there
3. Check that model files are uploaded (if they're not too large)

⚠️ Important Notes

File Size Limits:

• GitHub: 100MB per file (hard limit)
• GitHub LFS: For files >100MB, use Git LFS
• Model files: Usually 10-50MB each, should be fine

If Models Are Too Large:

1. Use Git LFS:

   git lfs install
   git lfs track "*.joblib"
   git add .gitattributes
   

2. Or exclude from git and upload separately to Hugging Face

Repository Visibility:

• Public: Required for Hugging Face Spaces (free tier)
• Private: Requires Hugging Face Pro for private spaces

✅ Next Steps After GitHub Push

Once your code is on GitHub:

1. Go to https://huggingface.co/spaces
2. Click "Create new Space"
3. Select "Streamlit"
4. Connect your GitHub repository
5. Deploy!

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🐛 Troubleshooting

GitHub Desktop Not Showing Files:

• Make sure you're in the correct directory
• Check if .git folder exists
• Try refreshing GitHub Desktop

Large File Warnings:

• If models are too large, use Git LFS or exclude them
• Hugging Face can pull models from other sources if needed

Commit Fails:

• Check file permissions
• Make sure you're not committing sensitive files
• Review .gitignore file