Dockerfile

# Use Python 3.11 as base image (recommended for Spaces)
FROM python:3.11-slim

# Set environment variables for optimal performance
ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PIP_NO_CACHE_DIR=1 \
    PIP_DISABLE_PIP_VERSION_CHECK=1 \
    DEBIAN_FRONTEND=noninteractive \
    CUDA_VISIBLE_DEVICES=""

# Install system dependencies for optimal performance
RUN apt-get update && apt-get install -y --no-install-recommends \
    build-essential \
    curl \
    git \
    libgl1-mesa-glx \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    libgomp1 \
    libjpeg62-turbo-dev \
    zlib1g-dev \
    libfreetype6-dev \
    liblcms2-dev \
    libwebp-dev \
    tcl8.6-dev \
    tk8.6-dev \
    python3-tk \
    libharfbuzz-dev \
    libfribidi-dev \
    libxcb1-dev \
    libx11-dev \
    && rm -rf /var/lib/apt/lists/*

# Create non-root user for security
RUN groupadd -r appuser && useradd -r -g appuser appuser

# Set working directory
WORKDIR /app

# Copy requirements first for better layer caching
COPY requirements.txt .

# Install Python dependencies with optimizations
RUN pip install --upgrade pip setuptools wheel && \
    pip install -r requirements.txt && \
    pip cache purge && \
    # Install additional system-specific optimizations
    pip install nvidia-ml-py3 psutil || true

# Copy application code
COPY . .

# Ensure proper file permissions
RUN chown -R appuser:appuser /app && \
    chmod -R 755 /app

# Switch to non-root user
USER appuser

# Create directories for logs and cache
RUN mkdir -p /tmp/logs /tmp/cache /tmp/models && \
    chmod 755 /tmp/logs /tmp/cache /tmp/models

# Set up environment variables
ENV APP_ENV=production \
    LOG_LEVEL=INFO \
    MAX_CONCURRENT_REQUESTS=5 \
    CACHE_TTL=3600 \
    MODEL_CACHE_SIZE=1000 \
    METRICS_RETENTION_DAYS=7 \
    HOST=0.0.0.0 \
    PORT=7860

# Health check for monitoring
HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
    CMD curl -f http://localhost:7860/health || exit 1

# Expose port
EXPOSE 7860

# Set startup command
CMD ["python", "app.py"]

# Multi-stage build optimization notes:
# This Dockerfile uses a single-stage build for simplicity
# For production deployments with larger models, consider:
# 1. Pre-building model cache in build stage
# 2. Using multi-stage builds to separate build and runtime dependencies
# 3. Adding model quantization for faster loading
# 4. Implementing layer caching strategies for updates

# CUDA support can be enabled by using:
# FROM nvidia/cuda:11.8-devel-ubuntu20.04 as cuda-base
# Then install CUDA runtime and PyTorch with CUDA support

# Performance optimizations implemented:
# 1. Non-root user for security
# 2. Efficient layer caching with requirements.txt first
# 3. System dependencies optimized for ML workloads
# 4. Health checks for monitoring
# 5. Environment variables for runtime configuration
# 6. Proper file permissions and directory structure
# 7. Graceful handling of optional dependencies