Dockerfile

# Prometheus backend (FastAPI + YOLO + distance sampling) container.
# Weights are baked in and served ONLY through the API — never exposed for
# download. Run this on a PRIVATE host (HF Space / Cloud Run / VPS); the public
# React dashboard talks to it over HTTPS.
FROM python:3.11-slim

# System libs: OpenCV runtime + ffmpeg (video jobs). Headless OpenCV needs no GUI libs.
RUN apt-get update && apt-get install -y --no-install-recommends \
        libglib2.0-0 ffmpeg \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

# CPU-only torch — keeps the image ~1.5 GB instead of ~6 GB with CUDA.
RUN pip install --no-cache-dir \
        torch torchvision --index-url https://download.pytorch.org/whl/cpu

# App dependencies (lean: the API set, not the Streamlit/pandas dev extras).
RUN pip install --no-cache-dir \
        ultralytics opencv-python-headless numpy scipy pyyaml lap pillow \
        fastapi "uvicorn[standard]" python-multipart imageio imageio-ffmpeg

# Application code + trained weights + (optional) the built dashboard.
COPY src ./src
COPY api ./api
COPY config ./config
COPY weights ./weights
COPY web ./web

# Avoid the OpenMP duplicate-runtime crash; honour the platform's $PORT.
ENV KMP_DUPLICATE_LIB_OK=TRUE
ENV PORT=8080
EXPOSE 8080

# Shell form so ${PORT} expands (Cloud Run injects PORT; HF Spaces expects 7860).
CMD uvicorn api.main:app --host 0.0.0.0 --port ${PORT}