# syntax=docker/dockerfile:1.7
FROM python:3.12-slim

# Install system dependencies
RUN apt-get update && apt-get install -y \
    # Build dependencies
    gcc \
    g++ \
    make \
    # Image processing libraries
    libvips42 \
    libvips-dev \
    # ExifTool for metadata extraction
    libimage-exiftool-perl \
    # FFmpeg for video processing
    ffmpeg \
    # Git for some Python packages
    git \
    # PostgreSQL client (for potential future use)
    postgresql-client \
    # Clean up
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

# Install PyTorch CPU-only FIRST, in its own layer, so open-clip-torch
# doesn't pull the full CUDA build (~7 GB). CPU inference is all we need
# — the heavy lifting happens through ONNX Runtime.
#
# Two cache wins here:
#   1. Its own RUN layer means edits to requirements.txt don't force a
#      re-pull of the ~200MB torch wheel.
#   2. The buildkit cache mount keeps pip's download cache on disk
#      across builds even when the layer itself is invalidated, so a
#      torch-version bump or a builder cache eviction still reuses the
#      wheel from local cache instead of re-fetching from pytorch.org.
RUN --mount=type=cache,target=/root/.cache/pip \
    pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu

COPY requirements.txt .
RUN --mount=type=cache,target=/root/.cache/pip \
    pip install -r requirements.txt

# Copy application code
COPY . .

# Create necessary directories
RUN mkdir -p /data/thumbs /data/db /data/proxies /data/models /app/config

# Expose port
EXPOSE 8000

# Run the application
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]