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# First stage: build dependencies
FROM public.ecr.aws/docker/library/python:3.11.9-slim-bookworm
# Install Lambda web adapter in case you want to run with with an AWS Lamba function URL (not essential if not using Lambda)
#COPY --from=public.ecr.aws/awsguru/aws-lambda-adapter:0.8.3 /lambda-adapter /opt/extensions/lambda-adapter
# Install wget, curl, and build-essential
RUN apt-get update && apt-get install -y \
wget \
curl \
build-essential
# Create a directory for the model
RUN mkdir /model && mkdir /model/rep && mkdir /model/embed
WORKDIR /src
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Gradio needs to be installed after due to conflict with spacy in requirements
RUN pip install --no-cache-dir gradio==4.41.0
# Set up a new user named "user" with user ID 1000
RUN useradd -m -u 1000 user
# Change ownership of /home/user directory
RUN chown -R user:user /home/user
# Make output folder
RUN mkdir -p /home/user/app/output && chown -R user:user /home/user/app/output
RUN mkdir -p /home/user/.cache/huggingface/hub && chown -R user:user /home/user/.cache/huggingface/hub
RUN mkdir -p /home/user/.cache/matplotlib && chown -R user:user /home/user/.cache/matplotlib
RUN mkdir -p /home/user/app/model/rep && chown -R user:user /home/user/app/model/rep
RUN mkdir -p /home/user/app/model/embed && chown -R user:user /home/user/app/model/embed
# Download the quantised phi model directly with curl. Changed at it is so big - not loaded
#RUN curl -L -o /home/user/app/model/rep/Phi-3.1-mini-128k-instruct-Q4_K_M.gguf https://huggingface.co/bartowski/Phi-3.1-mini-128k-instruct-GGUF/tree/main/Phi-3.1-mini-128k-instruct-Q4_K_M.gguf
# Download the Mixed bread embedding model during the build process - changed as it is too big for AWS. Not loaded.
#RUN curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | bash
#RUN apt-get install git-lfs -y
#RUN git lfs install
#RUN git clone https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1 /home/user/app/model/embed
#RUN rm -rf /home/user/app/model/embed/.git
# Download the BGE embedding model during the build process. Create a directory for the model and download specific files using huggingface_hub
COPY download_model.py /src/download_model.py
RUN python /src/download_model.py
# Switch to the "user" user
USER user
# Set home to the user's home directory
ENV HOME=/home/user \
PATH=/home/user/.local/bin:$PATH \
PYTHONPATH=$HOME/app \
PYTHONUNBUFFERED=1 \
PYTHONDONTWRITEBYTECODE=1 \
GRADIO_ALLOW_FLAGGING=never \
GRADIO_NUM_PORTS=1 \
GRADIO_SERVER_NAME=0.0.0.0 \
GRADIO_SERVER_PORT=7860 \
GRADIO_THEME=huggingface \
AWS_STS_REGIONAL_ENDPOINT=regional \
GRADIO_OUTPUT_FOLDER='output/' \
#GRADIO_ROOT_PATH=/data-text-search \
SYSTEM=spaces
# Set the working directory to the user's home directory
WORKDIR $HOME/app
# Copy the current directory contents into the container at $HOME/app setting the owner to the user
COPY --chown=user . $HOME/app
CMD ["python", "app.py"] |