# Dockerfile

# Use a specific Python base image for stability and size optimization
FROM python:3.12-slim

# Set the working directory inside the container
WORKDIR /app

# Copy the requirements file and install dependencies
# Use --no-cache-dir to keep the image size small
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy the Flask application and the serialized model
COPY app.py .
COPY tuned_xgb_sales_forecaster.pkl .
COPY tuned_xgb_sales_forecaster.json .
COPY SuperKart.csv .

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

# Command to Start the Application (Gunicorn)
# This is the crucial part, borrowing from your colleague's working model:
# - `-w 4`: 4 worker processes for concurrency
# - `-b 0.0.0.0:7860`: Binds to the required port and all interfaces
# - `app:app`: The application target. It means:
#   - look in file 'app.py' (the first 'app')
#   - for the Flask instance named 'app' (the second 'app')
CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]