# Use a smaller base image
FROM python:3.9-slim

# Set the working directory in the container
WORKDIR /app

# Copy the requirements file into the container
COPY requirements.txt .

# Install any needed packages specified in requirements.txt
# Use --no-cache-dir to prevent caching of packages
# Use -r to install from requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Copy the application code and the trained model into the container
COPY app.py .
COPY best_sales_forecasting_model.joblib .

# Expose the port that the Flask app will run on
EXPOSE 7860

# Define environment variable
ENV FLASK_APP=app.py

# Run the Flask application using a production-ready WSGI server like Gunicorn
# Install gunicorn
RUN pip install gunicorn==22.0.0

# Use gunicorn to run the Flask app with specified workers and threads
CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superkart_sales_api"]