# Use the specified platform and base image

FROM --platform=amd64 condaforge/miniforge3@sha256:5367f97080d9cebdead119133af9293aed736fd0499b93cd940c8b97240b7b19

# Install all necessary apt packages in a single RUN command to reduce layers

RUN apt-get update && \

apt-get install -y --no-install-recommends \

netcat \

nano \

git \

make \

gcc \

wget \

g++ \

libxrender1 \

libxext6 && \

apt-get autoremove -y && \

apt-get clean -y && \

rm -rf /var/lib/apt/lists/*

# Set Python environment variables

ENV PYTHONDONTWRITEBYTECODE=1 \

PYTHONUNBUFFERED=1

# Set the working directory

WORKDIR /code

# Copy environment.yml first to leverage Docker cache if dependencies haven't changed

COPY environment.yml .

# Clone the required Git repositories with shallow clones for efficiency

RUN git clone --single-branch --branch main --depth 1 https://github.com/ReactionMechanismGenerator/molecule.git /code/Molecule && \

git clone --single-branch --branch main --depth 1 https://github.com/ReactionMechanismGenerator/RMG-database.git /code/RMG-database

# Set PATH and PYTHONPATH in a single ENV command to minimize layers

ENV PATH=/opt/conda/envs/tck_env/bin:/code/Molecule:/code/RMG-database:$PATH \

PYTHONPATH=/code/tckdb:/code/Molecule:/code/RMG-database:/code

# Create the Conda environment and clean up to reduce image size

RUN mamba env create -f environment.yml && \

mamba env create -f /code/Molecule/environment.yml && \

mamba clean --all -y

# Use the new Conda environment for subsequent RUN commands

SHELL ["conda", "run", "--no-capture-output", "-n", "molecule_env", "/bin/bash", "-c"]

# Build RMG-Py within the Conda environment

WORKDIR /code/Molecule

RUN make

WORKDIR /code

# (Optional) Expose necessary ports

EXPOSE 8000

ENTRYPOINT ["python", "/code/tckdb/backend/app/main.py"]