Dockerfile

# Image Processor Pro — Hugging Face Spaces (Docker SDK).
# Mirrors the Python 3.9 environment the app was built and tested on locally.
FROM python:3.9-slim

# Shared libs that opencv-python needs at runtime.
RUN apt-get update && apt-get install -y --no-install-recommends \
        libgl1 \
        libglib2.0-0 \
    && rm -rf /var/lib/apt/lists/*

# Hugging Face Spaces runs the container as uid 1000.
RUN useradd -m -u 1000 user
USER user
ENV HOME=/home/user \
    PATH=/home/user/.local/bin:$PATH \
    PYTHONUNBUFFERED=1 \
    HF_HUB_DISABLE_TELEMETRY=1

WORKDIR /home/user/app

# Install CPU-only PyTorch explicitly so we don't pull the ~2 GB CUDA build.
RUN pip install --no-cache-dir --upgrade pip \
    && pip install --no-cache-dir \
         torch==2.8.0 torchvision==0.23.0 \
         --index-url https://download.pytorch.org/whl/cpu

# Then the application dependencies (Flask, OpenCV, LaMa, ...).
COPY --chown=user:user requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Application code (see .dockerignore for what is excluded).
COPY --chown=user:user . .

# Bake the LaMa weights into the image so the first request after a cold start
# doesn't have to download ~200 MB.
RUN python -c "from simple_lama_inpainting.utils import download_model; \
from simple_lama_inpainting.models.model import LAMA_MODEL_URL; download_model(LAMA_MODEL_URL)"

# HF Spaces routes traffic to this port (also declared as app_port in README.md).
EXPOSE 7860
CMD ["python", "webapp.py", "--host", "0.0.0.0", "--port", "7860"]