# Use an official Python runtime as a parent image
FROM python:3.10-slim-buster

# Set the working directory in the container
WORKDIR /app

# Install system dependencies needed for MLC LLM (e.g., git, cmake for build tools if compiling on the fly)
# These are necessary for MLC LLM's build process if models are compiled within the container
RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    build-essential \
    cmake \
    && rm -rf /var/lib/apt/lists/*

# Copy the requirements file and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Install torch specifically for CUDA if using GPU, otherwise use CPU
# For HuggingFace Spaces with GPU, CUDA 11.8 is a common and recommended version.
# Users might need to adjust 'cu118' to 'cpu' or a different CUDA version based on their target hardware.
# MLC-LLM might have its own torch dependency, ensure compatibility or remove this line if MLC-LLM handles it.
RUN pip uninstall -y torch && pip install torch==2.1.0 --extra-index-url https://download.pytorch.org/whl/cu118

# Copy the Flask application files
COPY app.py .

# Copy the model artifacts. This assumes model_artifacts exists and is populated.
# For large models, consider using git-lfs for HuggingFace Spaces or downloading at runtime
# if the model is too large for the Docker image or needs dynamic loading.
COPY model_artifacts ./model_artifacts

# Expose the port the app runs on
EXPOSE 5000

# Define environment variables for MLC LLM model paths
ENV MLC_MODEL_ARTIFACTS_DIR="./model_artifacts"
ENV MLC_MODEL_NAME="Llama-2-7b-chat-hf-q4f16_1" # Ensure this matches your downloaded model

# Command to run the Flask application
# Using Flask's built-in server for simplicity in development and small deployments.
# For production-grade deployments, consider a WSGI server like Gunicorn (e.g., gunicorn --bind 0.0.0.0:5000 app:app)
CMD ["python", "app.py"]