| # 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"] | |