DEPLOYMENT_FIX.md

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

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:

# 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:

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.