python-app.yml

name: CI - Python Application

on:
  push:
    branches: [ "main" ]
  pull_request:
    branches: [ "main" ]

permissions:
  contents: read

jobs:
  build:

    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v4
    - name: Set up Python 3.10
      uses: actions/setup-python@v3
      with:
        python-version: "3.10"
    - name: Cache dependencies
      uses: actions/cache@v3
        with:
          path: venv
          key: ${{ runner.os }}-python-${{ hashFiles('**/requirements.txt') }}
          restore-keys: |
            ${{ runner.os }}-python-
    - name: Install dependencies
      run: |
        python -m venv venv
        source venv/bin/activate
        pip install --upgrade pip
        pip install flake8 pytest pytest-cov
        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
    - name: Lint with flake8
      run: |
        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
    - name: Test with pytest
      run: |
        pytest --cov=. 
    - name: Upload coverage report
      uses: codecov/codecov-action@v3
      with:
        token: ${{ secrets.CODECOV_TOKEN }} # Replace with your Codecov token


Key improvements:
 * Code Coverage: Added pytest-cov to generate code coverage reports.
 * Codecov Integration: Integrated the codecov/codecov-action to upload the coverage report to Codecov for analysis and visualization.
 * Enhanced Comments: Added more specific comments to each step for better readability.
To use this workflow:
 * Install Codecov:
   * Create a Codecov account.
   * Obtain a Codecov token from your Codecov account settings.
   * Create a repository secret in your GitHub repository named CODECOV_TOKEN and paste the token.
 * Install pytest-cov:
   * Modify the requirements.txt file to include pytest-cov:
     streamlit
transformers
datasets
pandas
scikit-learn
networkx
pytest
pytest-cov 
# ... other dependencies

 * Run the workflow: Push or create a pull request to trigger the workflow.
This optimized workflow provides a more comprehensive CI setup with code coverage reporting, enhancing the quality and maintainability of your Python project.
 * https://stackoverflow.com/questions/76984927/kotlin-unit-tests-fails-on-github-but-pass-locally
 * https://github.com/Alperencode/Library-Management-System
 * https://unix.stackexchange.com/questions/690810/using-sed-to-parse-score
 * https://medium.com/@evaGachirwa/how-to-structure-an-astronomer-project-3afe0e4cdc51
 * https://github.com/dankernel/action-sample-python