# Deployment Fix Summary

## Critical Issues Identified and Fixed

### Issue 1: Incorrect Docker Working Directory and Python Path
**Problem**: The Dockerfile was trying to run `python backend/main.py` from `/app` directory, but the Python imports in main.py were relative imports (e.g., `from pdf_processor import PDFProcessor`) which required the backend directory to be in the Python path.

**Solution**: 
- Changed Dockerfile to copy backend files directly to `/app/` instead of `/app/backend/`
- Updated CMD to run `uvicorn main:app` directly since all files are now in the working directory
- This allows all relative imports to work correctly

### Issue 2: Missing System Dependencies
**Problem**: Some system libraries required for PDF processing and OCR were missing or incomplete.

**Solution**: 
- Added `tesseract-ocr-eng` for English language support
- Added `libsm6`, `libxext6`, `libxrender-dev` for OpenCV support
- Added `libgomp1` for multi-threading support
- Added `git` for potential package installations

### Issue 3: OpenCV Library Conflict
**Problem**: `opencv-python` package requires GUI libraries that aren't available in Docker, causing import errors.

**Solution**: 
- Changed `opencv-python==4.9.0.80` to `opencv-python-headless==4.9.0.80`
- Headless version doesn't require X11/GUI libraries and works in Docker environments

### Issue 4: Missing Dependencies
**Problem**: Some required packages for full functionality were missing.

**Solution**: 
- Added `requests==2.31.0` for HTTP requests
- Added `cryptography==42.0.0` for security features
- Ensured all transformers dependencies are present (protobuf, safetensors, etc.)

### Issue 5: No .dockerignore File
**Problem**: Docker was copying unnecessary files (node_modules, docs, etc.) which bloated the image and could cause conflicts.

**Solution**: 
- Created comprehensive `.dockerignore` file
- Excludes development files, documentation, frontend build artifacts, and deployment scripts
- Keeps Docker image lean and focused

### Issue 6: Incorrect Uvicorn Configuration
**Problem**: The startup command wasn't optimized for production deployment.

**Solution**: 
- Changed to use uvicorn directly with proper configuration
- Added `--workers 1` for consistent behavior with GPU
- Set explicit host and port parameters

## Updated Files

### 1. Dockerfile
```dockerfile
FROM python:3.10-slim
WORKDIR /app

# Comprehensive system dependencies
RUN apt-get update && apt-get install -y \
    tesseract-ocr tesseract-ocr-eng \
    poppler-utils libgl1-mesa-glx libglib2.0-0 \
    libsm6 libxext6 libxrender-dev libgomp1 git \
    && rm -rf /var/lib/apt/lists/*

# Install Python deps first (better caching)
COPY backend/requirements.txt /app/requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Copy backend code to /app (not /app/backend)
COPY backend/ /app/

# Create necessary directories
RUN mkdir -p /app/logs

# Environment configuration
ENV PYTHONUNBUFFERED=1
ENV PORT=7860
ENV TRANSFORMERS_CACHE=/app/.cache/huggingface
ENV HF_HOME=/app/.cache/huggingface

EXPOSE 7860

# Run with uvicorn
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1"]
```

### 2. backend/requirements.txt
Key changes:
- Changed `opencv-python` to `opencv-python-headless`
- Added `requests` for HTTP client
- Added `cryptography` for security features
- Organized by category with comments

### 3. .dockerignore (New File)
Excludes:
- Python bytecode and caches
- Frontend node_modules and build artifacts
- Documentation files (except README)
- Development tools and IDE files
- Temporary files and deployment scripts

## Deployment Configuration

### Hugging Face Space Settings
- **SDK**: Docker
- **Hardware**: T4 GPU (Small)
- **Port**: 7860
- **Python Version**: 3.10

### Environment Variables
The following environment variables are automatically set:
- `HF_TOKEN`: From Space secrets
- `PYTHONUNBUFFERED`: Set to 1 for proper logging
- `PORT`: Set to 7860
- `TRANSFORMERS_CACHE`: Hugging Face model cache location
- `HF_HOME`: Hugging Face home directory

## Verification Steps

### 1. Build Verification
The Docker build should complete successfully with:
- All system dependencies installed
- All Python packages installed without errors
- No import errors when starting the application

### 2. API Endpoints
Once deployed, verify these endpoints:

```bash
# Health check
GET /health
Expected: {"status": "healthy", "components": {...}}

# API root
GET /api
Expected: {"status": "healthy", "version": "2.0.0", ...}

# Compliance status
GET /compliance-status
Expected: {"compliance_score": "...", "features": {...}}

# Supported models
GET /supported-models
Expected: {"domains": {...}}
```

### 3. Upload Functionality
Test with a medical PDF:
```bash
POST /analyze
Content-Type: multipart/form-data
Body: file=<medical.pdf>

Expected: {"job_id": "...", "status": "processing", ...}
```

### 4. Static Files
The frontend should be accessible at the root URL and all assets should load correctly.

## Expected Build Time
- Initial build: 8-12 minutes (downloading and installing dependencies)
- Subsequent builds: 2-4 minutes (if using cached layers)

## Troubleshooting

### If Build Fails

1. **Check Space Logs**: Visit Settings > Logs in Hugging Face Space
2. **Common Issues**:
   - Out of memory: Reduce dependencies or request larger instance
   - Package conflicts: Check requirements.txt versions
   - System lib missing: Add to Dockerfile apt-get install
   
### If App Doesn't Start

1. **Check Application Logs**: Look for Python errors in Space logs
2. **Common Issues**:
   - Import errors: Verify all files copied correctly
   - Port binding: Ensure PORT=7860 is set
   - Permissions: Check file permissions in Docker

### If API Returns 404

1. **Verify Routes**: Check main.py route definitions
2. **Check Path**: Ensure requesting correct endpoint
3. **Check FastAPI App**: Verify app object is created and routes registered

## Deployment Status

**Space URL**: https://huggingface.co/spaces/snikhilesh/medical-report-analyzer

**Current Status**: Building

The deployment has been uploaded with all fixes. The Space should be building now. Wait approximately 8-12 minutes for the initial build to complete.

## Post-Deployment Verification

Once the build completes, verify:

1. Space shows "Running" status
2. Opening the URL shows the frontend interface
3. API endpoints respond correctly
4. Can upload a PDF and get analysis results

## Next Steps After Successful Deployment

1. Test with sample medical PDFs
2. Monitor logs for any runtime errors
3. Verify model loading works correctly
4. Test authentication if enabled
5. Verify audit logging is working

## Files Changed

1. `/workspace/medical-ai-platform/Dockerfile` - Complete rewrite for proper Docker setup
2. `/workspace/medical-ai-platform/backend/requirements.txt` - Updated dependencies
3. `/workspace/medical-ai-platform/.dockerignore` - New file to optimize Docker builds

All Python code remains unchanged and functional. The fixes were purely deployment/infrastructure related.