FROM python:3.11-slim

# Set working directory
WORKDIR /app

# Install system dependencies including CUDA-related packages
RUN apt-get update && apt-get install -y \
    ffmpeg \
    git \
    wget \
    curl \
    build-essential \
    && rm -rf /var/lib/apt/lists/*

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

# Install CPU versions from requirements.txt first
RUN pip install --no-cache-dir -r requirements.txt

# Install CUDA-optimized PyTorch (overwrites CPU versions)
# Updated to torch 2.1.0+ for SpeechBrain 1.0 / pyannote diarization compatibility
RUN pip install --force-reinstall \
    torch==2.1.0+cu118 \
    torchvision==0.16.0+cu118 \
    torchaudio==2.1.0+cu118 \
    --index-url https://download.pytorch.org/whl/cu118

# Copy application code
COPY . .

# Create directories for mounted volumes
RUN mkdir -p /app/data/videos /app/data/outputs /app/data/cache

# Set environment variables
ENV STREAMLIT_SERVER_PORT=8501
ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
ENV STREAMLIT_SERVER_HEADLESS=true
ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false

# GPU-specific environment variables
ENV CUDA_VISIBLE_DEVICES=0
ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility

# Expose Streamlit port
EXPOSE 8501

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
    CMD curl -f http://localhost:8501/_stcore/health || exit 1

# Start the application
CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]