.github/workflows/pipeline.yml

name: SuperKart MLOps Pipeline

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main

jobs:
  build-and-deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout Code
      uses: actions/checkout@v3

    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.9'

    - name: Install Dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt

    - name: Run Model Training & Evaluation
      env:
        HF_TOKEN: ${{ secrets.HF_TOKEN }}
      run: |
        # In a real scenario, this would trigger a python script 
        # that executes your training logic
        python app.py --mode train 

    - name: Push to Hugging Face Spaces
      env:
        HF_TOKEN: ${{ secrets.HF_TOKEN }}
      run: |
        # This uses the huggingface_hub CLI or a script to sync files
        pip install huggingface_hub
        python -c "from huggingface_hub import HfApi; api = HfApi(); api.upload_folder(folder_path='.', repo_id='your_username/superkart-deployment', repo_type='space', token='${{ secrets.HF_TOKEN }}')"