# Use a base image with Python and JupyterLab already installed for convenience
# This saves some steps and reduces image size by leveraging existing layers
FROM jupyter/scipy-notebook:latest

LABEL maintainer="Your Name <your.email@example.com>"

# Install Hugging Face Transformers and other common NLP/ML libraries
# Use --no-cache-dir to prevent caching pip packages, reducing image size
# Use a specific version for reproducibility, e.g., transformers==4.41.2
# Add other libraries you commonly use (e.g., datasets, evaluate, accelerate, sentencepiece, bitsandbytes)
RUN pip install --no-cache-dir \
    transformers \
    datasets \
    evaluate \
    accelerate \
    sentencepiece \
    tokenizers \
    diffusers \
    scikit-learn \
    pandas \
    numpy \
    matplotlib \
    seaborn

# Set the working directory inside the container
WORKDIR /home/jovyan/work

# You can expose the Jupyter Lab port (default is 8888)
EXPOSE 8888

# Command to run Jupyter Lab
# --ip=0.0.0.0 makes it accessible from outside the container
# --no-browser prevents it from trying to open a browser inside the container
# --allow-root is often used in Docker environments, but it's better to run as a non-root user if possible.
# The `jupyter/scipy-notebook` image usually sets up a `jovyan` user, so `--allow-root` might not be necessary.
# If you want to set a password/token:
# --NotebookApp.token='your_secret_token'
# --NotebookApp.password='your_hashed_password' (generate with `jupyter lab password`)
CMD ["jupyter", "lab", "--ip=0.0.0.0", "--port=7860", "--no-browser"]

# Optional: Copy your notebooks or data into the container if needed
# COPY notebooks/ /home/jovyan/work/notebooks/
# COPY data/ /home/jovyan/work/data/