FROM python:3.11-slim
WORKDIR /app

# Install system dependencies + redis-server for sidecar
RUN apt-get update && apt-get install -y --no-install-recommends \
    curl \
    git \
    build-essential \
    redis-server \
    && rm -rf /var/lib/apt/lists/*

# Install Python dependencies
# torch CPU FIRST (the PyPI default wheel is the CUDA build, ~2.5 GB) — the
# risk-scorer and advisory layers run on PyTorch (the ONNX export was unreliable).
RUN pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Part B Change 4 — persistent local-model cache.
# Model weights are downloaded ONCE at build time from the public HF Hub
# (free, unmetered — Hub downloads are NOT Inference API calls) into
# /app/models, and HF_HOME points there for the runtime. A running container
# therefore makes ZERO network calls for scoring: no Inference API, no token,
# no credits. If AI_SCORER_MODEL is changed at runtime without a rebuild, the
# pipeline transparently downloads the new checkpoint on first use.
ENV HF_HOME=/app/models
RUN python3 -c "from transformers import pipeline; pipeline('zero-shot-classification', model='typeform/distilbert-base-uncased-mnli', device=-1)" \
    && echo "[BUILD] local scorer checkpoint baked into image"

# Copy app source AND entrypoint
COPY app/ ./app/
COPY entrypoint.py .

EXPOSE 7860

# Use the entrypoint that starts redis + celery worker + uvicorn
CMD ["python3", "entrypoint.py"]