Dockerfile

# Use an official Python runtime as a parent image
FROM python:3.12.3

# Set up a new user named "user" with user ID 1000
RUN useradd -m -u 1000 user

# Switch to the "user" user
USER user

# Set home to the user's home directory
ENV HOME=/home/user \
	PATH=/home/user/.local/bin:$PATH

# Set the working directory to the user's home directory
WORKDIR $HOME/app

# Try and run pip command after setting the user with `USER user` to avoid permission issues with Python
RUN pip install --no-cache-dir --upgrade pip

# Copy the current directory contents into the container at $HOME/app setting the owner to the user
COPY --chown=user . $HOME/app

# Download the parquet file from github
RUN wget https://github.com/ImagingDataCommons/CloudSegmentatorResults/releases/download/0.0.1/qual_checks_and_quantitative_measurements.parquet

# Download the mapping parquet file from github
RUN wget https://github.com/ImagingDataCommons/CloudSegmentatorResults/releases/download/0.0.1/ct-seg-sr-map-with-series-uids-gcs-aws-ulrs.parquet

# Install any needed packages specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Create the .streamlit directory
RUN mkdir -p .streamlit

# Create the config.toml file and set the maxMessageSize
RUN echo "\
[server]\n\
maxMessageSize = 2000\n\
" > .streamlit/config.toml

# Make port 8501 available to the world outside this container
EXPOSE 8501

# Run filter_data_app.py when the container launches
CMD streamlit run filter_data_app.py