Dockerfile.gpu

# ── GPU image (requires NVIDIA Container Toolkit) ──
FROM pytorch/pytorch:2.3.0-cuda12.1-cudnn8-runtime

WORKDIR /app

RUN apt-get update && apt-get install -y --no-install-recommends \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    curl \
    && rm -rf /var/lib/apt/lists/*

# torch + torchvision are already in the base image — install only remaining deps
RUN pip install --no-cache-dir \
    "timm>=0.9.12" \
    "fastapi>=0.110.0" \
    "uvicorn[standard]>=0.29.0" \
    "python-multipart>=0.0.9" \
    "pillow>=10.0.0" \
    "numpy>=1.24.0" \
    "opencv-python-headless>=4.9.0"

COPY app/ ./app/
COPY static/ ./static/
COPY models/ ./models/

EXPOSE 8000

CMD ["sh", "-c", "echo '' && echo '  CardioAI running at: http://localhost:8000' && echo '' && uvicorn app.main:app --host 0.0.0.0 --port 8000 --workers 1"]