Changed Dockerfile to multi-stage build to further reduce size

Dockerfile

@@ -1,80 +1,68 @@
-# First stage: build dependencies
-#FROM public.ecr.aws/docker/library/python:3.11.9-slim-bookworm
-FROM python:3.11.9-slim-bookworm
+# Stage 1: Build dependencies and download models
+FROM python:3.11.9-slim-bookworm AS builder
 
 # 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 \
-	&& rm -rf /var/lib/apt/lists/*
-
-#wget \
-#curl \
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    && rm -rf /var/lib/apt/lists/*
 
-# Create a directory for the model
-RUN mkdir /model && mkdir /model/rep && mkdir /model/embed
+# Create directories (if needed for model download script)
+RUN mkdir -p /model/rep /model/embed
 
 WORKDIR /src
 
+# Copy requirements file and install dependencies
 COPY requirements_aws.txt .
-
 RUN pip install --no-cache-dir -r requirements_aws.txt
 
-# Gradio needs to be installed after due to conflict with spacy in requirements
+# Install Gradio
 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
+# Download models (using your download_model.py script)
+COPY download_model.py /src/download_model.py
+RUN python /src/download_model.py
 
-# Change ownership of /home/user directory
-RUN chown -R user:user /home/user
+# Stage 2: Final runtime image
+FROM python:3.11.9-slim-bookworm
 
-# Make output folder
-RUN mkdir -p /home/user/app/output && chown -R user:user /home/user/app/output \
-&& mkdir -p /home/user/.cache/huggingface/hub && chown -R user:user /home/user/.cache/huggingface/hub \
-&& mkdir -p /home/user/.cache/matplotlib && chown -R user:user /home/user/.cache/matplotlib \
-&& mkdir -p /home/user/app/model/rep && chown -R user:user /home/user/app/model/rep \
-&& mkdir -p /home/user/app/model/embed && chown -R user:user /home/user/app/model/embed \
-&& mkdir -p /home/user/app/cache && chown -R user:user /home/user/app/cache
+# Create a non-root user
+RUN useradd -m -u 1000 user
+
+# Create necessary directories and set ownership
+RUN mkdir -p /home/user/app/output /home/user/.cache/huggingface/hub /home/user/.cache/matplotlib /home/user/app/cache \
+    && chown -R user:user /home/user
 
 # 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 embedding model - 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
+# Copy models from the builder stage
+COPY --from=builder /model/rep /home/user/app/model/rep
+COPY --from=builder /model/embed /home/user/app/model/embed
 
-# Switch to the "user" user
+# Switch to the non-root user
 USER user
 
-# Set home to the user's home directory
+# Set environment variables
 ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH \
+    PATH=/home/user/.local/bin:$PATH \
     PYTHONPATH=/home/user/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/' \
-	NUMBA_CACHE_DIR=/home/user/app/cache \
-	SYSTEM=spaces
- 
-# Set the working directory to the user's home directory
+    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/' \
+    NUMBA_CACHE_DIR=/home/user/app/cache \
+    SYSTEM=spaces
+
+# Set working directory and copy application code
 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
 
+# Command to run your application
 CMD ["python", "app.py"]