Dockerfile

# Use Miniconda base image
FROM continuumio/miniconda3

# Update system and install required packages
RUN apt-get update && apt-get install -y \
    nano \
    unzip \
    curl \
 && rm -rf /var/lib/apt/lists/*

# Install AWS CLI v2
RUN curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip" && \
    unzip awscliv2.zip && \
    ./aws/install && \
    rm -rf awscliv2.zip aws

# Create a new user 'user' (Hugging Face requires a non-root user)
RUN useradd -m -u 1000 user

# Switch to this user
USER user

# Define environment variables
ENV HOME=/home/user \
    PATH=/home/user/.local/bin:$PATH

# Set the working directory
WORKDIR $HOME/app

# Copy and install Python dependencies
COPY requirements.txt /requirements.txt
RUN pip install --no-cache-dir --upgrade pip && \
    pip install --no-cache-dir -r /requirements.txt

# Copy all files into the container
COPY --chown=user . $HOME/app

# Hugging Face Spaces will set $PORT automatically
# Secrets (like AWS_ACCESS_KEY_ID) must be added via the Hugging Face UI

# Define environment variables for MLflow and S3
ARG BACKEND_STORE_URI
ARG ARTIFACT_STORE_URI
ARG AWS_ACCESS_KEY_ID
ARG AWS_SECRET_ACCESS_KEY
ENV MLFLOW_BACKEND_STORE_URI=$BACKEND_STORE_URI \
    MLFLOW_DEFAULT_ARTIFACT_ROOT=$ARTIFACT_STORE_URI \
    AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
    AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY

# Start the MLflow server
CMD mlflow server \
    --host 0.0.0.0 \
    --port $PORT \
    --backend-store-uri $BACKEND_STORE_URI \
    --default-artifact-root $ARTIFACT_STORE_URI \
    --serve-artifacts