Update Dockerfile

Dockerfile

@@ -1,26 +1,7 @@
-# This is the same dockerfile from `Helper_Files/Dockerfiles/tldw-nvidia_amd64_Dockerfile`. c/p here so people see a 'Dockerfile' in the root directory and know what to do.
-# Usage
-# docker build -t tldw-nvidia_amd64 .
-# docker run --gpus=all -p 7860:7860 -v tldw_volume:/tldw tldw-nvidia_amd64
-#
-# If the above command doesn't work and it hangs on start, use the following command:
-#
-#   sudo docker run -it -p 7860:7860 -v tldw_volume:/tdlw tldw-nvidia_amd64 bash
-#
-# Once in the container, run the following command:
-#
-#   python summarize.py -gui
-#
-# And you should be good.
-
-# Use Nvidia image:
+# Use Nvidia CUDA runtime as the base image
 FROM nvidia/cuda:12.6.1-cudnn-runtime-ubuntu24.04
 
-# Use an official Python runtime as a parent image
-#FROM python:3.10.15-slim-bookworm
-
-
-# Set build arguments
+# Set build arguments for repository configuration
 ARG REPO_URL=https://github.com/rmusser01/tldw.git
 ARG BRANCH=main
 ARG GPU_SUPPORT=cpu
@@ -35,29 +16,34 @@ RUN apt-get update && apt-get install -y \
     python3-pyaudio \
     portaudio19-dev \
     python3-pip \
-    portaudio19-dev \
     python3-venv \
     && rm -rf /var/lib/apt/lists/*
 
-# Set the working directory in the container
-WORKDIR /tldw
+# Create a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
 
-# Clone the repository
-RUN git clone -b ${BRANCH} ${REPO_URL} .
+# Switch to the "user" user
+USER user
 
-# Create and activate virtual environment
-RUN python3 -m venv ./venv
-ENV PATH="/tldw/venv/bin:$PATH"
+# Set environment variables for the user's home directory and PATH
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
 
-# Upgrade pip and install wheel
-RUN pip install --upgrade pip wheel
+# Set the working directory to the user's app directory
+WORKDIR $HOME/app
 
-# Install CUDA
-RUN pip install nvidia-cublas-cu12 nvidia-cudnn-cu12
+# Upgrade pip and install wheel as the non-root user
+RUN pip install --no-cache-dir --upgrade pip wheel
 
-# setup PATH
-RUN export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
+# Clone the repository into the working directory
+RUN git clone -b ${BRANCH} ${REPO_URL} .
+
+# Create and activate a virtual environment
+RUN python3 -m venv venv
+ENV PATH="/home/user/app/venv/bin:$PATH"
 
+# Install CUDA libraries
+RUN pip install --no-cache-dir nvidia-cublas-cu12 nvidia-cudnn-cu12
 
 # Install PyTorch based on GPU support
 RUN if [ "$GPU_SUPPORT" = "cuda" ]; then \
@@ -68,22 +54,26 @@ RUN if [ "$GPU_SUPPORT" = "cuda" ]; then \
         pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cpu; \
     fi
 
-# Install other requirements
-RUN pip install -r requirements.txt
+# Install other Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
 
 # Update config.txt for CPU if needed
 RUN if [ "$GPU_SUPPORT" = "cpu" ]; then \
         sed -i 's/cuda/cpu/' ./Config_Files/config.txt; \
     fi
 
+# Download a checkpoint (replace <SOME_ASSET_URL> and <SOME_ASSET_NAME> with actual values)
+RUN mkdir -p content
+ADD --chown=user https://<SOME_ASSET_URL> content/<SOME_ASSET_NAME>
+
 # Create a volume for persistent storage
-VOLUME /tldw
+VOLUME /home/user/app/tldw_volume
 
-# Make port 7860 available to the world outside this container
+# Expose port 7860 to the outside world
 EXPOSE 7860
 
-# Set listening to all interfaces
+# Set environment variable for Gradio to listen on all interfaces
 ENV GRADIO_SERVER_NAME="0.0.0.0"
 
-# Run the application
-CMD ["python", "summarize.py", "-gui", "-log DEBUG"]
+# Define the default command to run the application
+CMD ["python", "summarize.py", "-gui"]