Model/.github/workflows/cml.yml

name: model-training
on: 
  push:
    branches:
      - mlmodel
permissions: write-all
jobs:
  run:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-python@v4
      - uses: iterative/setup-cml@v1
      - name: Train model
        env:
          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          ls
          cd model_data/simulation_system 
          pip install seaborn matplotlib
          python3 -c "import seaborn as sns; import matplotlib.pyplot as plt; sns.scatterplot(data=[0,1,2,3]); plt.savefig('test.png')"
          # pip install -r requirements.txt
          # python3 training.py --train-filepath ./model_build_data/change_data/rdsap_full/train_validation_data.parquet --test-filepath ./model_build_data/change_data/rdsap_full/test_data.parquet

          echo "## Residuals plot from model" > report.md
          # metrics_location=$(find . -maxdepth 10 -name "residuals.png")
          # echo $metrics_location
          # cd $metric_location
          echo "![](./test.png)" >> report.md

          cml comment create report.md

          # cd model_directory/RDSAP_CHANGE
          # echo "## Model metrics from registry" >> report.md
          # cat model_registry.csv >> report.md

          # cml comment create report.md