Dockerfile

# Base image: Python 3.9 slim version
FROM python:3.9-slim

# Set the working directory in the container
WORKDIR /app

# Install system dependencies
# - build-essential: for compiling Python packages if needed
# - curl: for the healthcheck
# - libgl1-mesa-glx: dependency for OpenCV (to fix libGL.so.1 error)
RUN apt-get update && \
    apt-get install -y \
    build-essential \
    curl \
    libgl1-mesa-glx \
    && rm -rf /var/lib/apt/lists/*

# Copy the requirements file first to leverage Docker cache
COPY requirements.txt ./

# Install Python dependencies
# Using --no-cache-dir to keep the image size smaller
RUN pip3 install --no-cache-dir -r requirements.txt

# Create necessary subdirectories within /app for your application's structure
# These directories will hold your models, CSS, and images
RUN mkdir -p /app/face_detector /app/css /app/images

# Copy your application files into the container,
# placing them in the correct subdirectories as expected by app.py
COPY app.py ./app.py
COPY deploy.prototxt /app/face_detector/deploy.prototxt
COPY res10_300x300_ssd_iter_140000.caffemodel /app/face_detector/res10_300x300_ssd_iter_140000.caffemodel
COPY mask_detector.h5 ./mask_detector.h5
COPY styles.css /app/css/styles.css
COPY out.jpg /app/images/out.jpg # Copies the out.jpg from your repo

# Expose the port Streamlit runs on (default is 8501)
EXPOSE 8501

# Healthcheck for Streamlit (optional but good practice)
HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health

# Set the entrypoint to run the Streamlit application
# This will execute: streamlit run app.py --server.port=8501 --server.address=0.0.0.0
ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]