# Use a lightweight Python base
FROM python:3.10-slim

# Prevent interactive prompts & speed up Python
ENV DEBIAN_FRONTEND=noninteractive \
    PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PIP_NO_CACHE_DIR=1 \
    TOKENIZERS_PARALLELISM=false

# Set work directory
WORKDIR /code

# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    build-essential \
    git \
    curl \
    libopenblas-dev \
    libomp-dev \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements first (for Docker caching)
COPY requirements.txt .

# Install Python dependencies
RUN pip install --no-cache-dir -r requirements.txt

# Hugging Face tools
RUN pip install --no-cache-dir huggingface-hub accelerate

# Set Hugging Face cache inside container (persistent, not /tmp)
ENV HF_HOME=/models/huggingface
ENV TRANSFORMERS_CACHE=/models/huggingface
ENV HUGGINGFACE_HUB_CACHE=/models/huggingface
ENV HF_HUB_CACHE=/models/huggingface

# Create cache dir
RUN mkdir -p /models/huggingface

# Pre-download model at build time (BLIP captioning model)
RUN python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='Salesforce/blip-image-captioning-large')"

# Copy project files
COPY . .

# Expose FastAPI port (Hugging Face Spaces uses 7860)
EXPOSE 7860

# Run FastAPI app with uvicorn (single worker)
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]