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🚀 Streamlit Cloud Deployment Checklist

✅ Pre-Deployment Checks

1. Local Testing

  • All deployment tests pass (python test_deployment.py)
  • App startup test passes (python test_app_startup.py)
  • Fallback vector store test passes (python test_fallback_store.py)
  • App runs locally without errors

2. Code Quality

  • No syntax errors in Python files
  • All imports resolve correctly
  • No missing dependencies
  • Proper error handling in place

3. Dependencies

  • requirements.txt contains all necessary packages
  • pysqlite3-binary>=0.5.0 included for SQLite compatibility
  • All package versions are compatible with Python 3.13
  • No conflicting package versions

🎯 Deployment Steps

1. GitHub Repository

  • All changes committed and pushed
  • Repository is public or accessible to Streamlit Cloud
  • Main branch contains the latest code

2. Streamlit Cloud Setup

  • Go to share.streamlit.io
  • Sign in with GitHub account
  • Click "New app"
  • Select repository: karthiksuresh007/legal-research-assistant
  • Set branch: main
  • Set main file path: app.py

3. Environment Variables (Secrets)

  • Add OPENAI_API_KEY with your OpenAI API key
  • Add GOOGLE_API_KEY with your Google Gemini API key (or GEMINI_API_KEY)
  • Add ANONYMIZED_TELEMETRY = False
  • Add CHROMA_TELEMETRY_ENABLED = 0

Note: The app will automatically use GOOGLE_API_KEY if GEMINI_API_KEY is not set.

4. Deploy

  • Click "Deploy"
  • Wait for build to complete
  • Check logs for any errors
  • Test basic functionality

🔧 Troubleshooting Common Issues

API Key Issues

  • Problem: "Gemini API key is required" error
  • Solution: Set either GOOGLE_API_KEY or GEMINI_API_KEY in Streamlit secrets
  • Check: Verify the API key is correctly set in Streamlit Cloud secrets

Import Errors

  • Problem: Module not found errors
  • Solution: Check that all files are in the correct directories
  • Check: Run python test_app_startup.py locally

SQLite Compatibility Issues

  • Problem: ChromaDB SQLite version errors
  • Solution: The app automatically falls back to FAISS vector store
  • Check: Look for "Using fallback vector store" in logs

Package Installation Issues

  • Problem: Build fails during package installation
  • Solution: Check requirements.txt for version conflicts
  • Check: Verify all packages are available for Python 3.13

Memory Issues

  • Problem: App crashes due to memory limits
  • Solution: Consider upgrading to paid Streamlit Cloud tier
  • Check: Monitor memory usage in Streamlit Cloud dashboard

📊 Expected Log Output

After successful deployment, you should see:

✅ SQLite compatibility fix applied
✅ ChromaDB telemetry fixes applied
✅ Logging setup completed
✅ Main application imported successfully
✅ ChromaDB telemetry disabled
✅ Fallback vector store initialized with FAISS (if ChromaDB fails)
✅ API keys loaded successfully

🎉 Success Indicators

  • ✅ App loads without errors
  • ✅ No telemetry errors in logs
  • ✅ Document upload works
  • ✅ Legal question answering works
  • ✅ Vector store operations successful
  • ✅ API keys are properly loaded

📞 If Issues Persist

  1. Check Streamlit Cloud logs for specific error messages
  2. Verify environment variables are set correctly
  3. Test locally to ensure code works
  4. Check package versions for compatibility issues

Your Legal Research Assistant is now optimized for Streamlit Cloud deployment! 🚀