Skip to main content
Glama
by mckinsey
test-e2e-dashboard-vizro-ai.yml4.64 kB
name: e2e dashboard tests for VizroAI defaults: run: working-directory: vizro-ai on: schedule: - cron: "30 10 * * 1" # run every Monday at 10:30 UTC workflow_dispatch: # helps to run this job manually from GitHub Actions UI env: PYTHONUNBUFFERED: 1 FORCE_COLOR: 1 jobs: test-e2e-dashboard-vizro-ai-fork: if: ${{ github.event.pull_request.head.repo.fork }} name: test-e2e-dashboard-vizro-ai on Py${{ matrix.config.python-version }} ${{ matrix.config.label }} runs-on: ubuntu-latest strategy: fail-fast: false matrix: config: - python-version: "3.13" hatch-env: all.py3.13 steps: - uses: actions/checkout@v5 - name: Passed fork step run: echo "Success!" test-e2e-dashboard-vizro-ai: if: ${{ ! github.event.pull_request.head.repo.fork }} name: test-e2e-dashboard-vizro-ai on Py${{ matrix.config.python-version }} ${{ matrix.config.label }} runs-on: ubuntu-latest strategy: fail-fast: false matrix: config: - python-version: "3.13" hatch-env: all.py3.13 steps: - uses: actions/checkout@v5 - name: Set up Python ${{ matrix.config.python-version }} uses: actions/setup-python@v6 with: python-version: ${{ matrix.config.python-version }} - name: Install Hatch run: pip install hatch - name: Show dependency tree run: hatch run ${{ matrix.config.hatch-env }}:pip tree - name: Run vizro-ai e2e dashboard tests with PyPI vizro run: hatch run ${{ matrix.config.hatch-env }}:test-e2e-dashboard env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} OPENAI_API_BASE: ${{ secrets.OPENAI_API_BASE }} ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} ANTHROPIC_BASE_URL: ${{ secrets.ANTHROPIC_BASE_URL }} VIZRO_TYPE: pypi BRANCH: ${{ github.head_ref }} PYTHON_VERSION: ${{ matrix.config.python-version }} - name: Send custom JSON data to Slack id: slack # used pinned commit hash for security reasons uses: slackapi/slack-github-action@91efab103c0de0a537f72a35f6b8cda0ee76bf0a # v2.1.1 if: failure() with: payload: | { "text": "Vizro-ai ${{ matrix.config.hatch-env }} e2e dashboard tests build result: ${{ job.status }}\nBranch: ${{ github.head_ref }}\n${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}" } webhook: ${{ env.SLACK_WEBHOOK_URL }} webhook-type: incoming-webhook - name: Report artifacts uses: actions/upload-artifact@v5 if: always() with: name: Report-${{ matrix.config.python-version }}-${{ matrix.config.label }} path: | /home/runner/work/vizro/vizro/vizro-ai/tests/e2e/reports/report*.csv test-e2e-dashboard-vizro-ai-report: needs: test-e2e-dashboard-vizro-ai runs-on: ubuntu-latest if: always() steps: - uses: actions/checkout@v5 - name: Download All Artifacts uses: actions/download-artifact@v6 - name: Set current date as env variable id: date run: | echo "::set-output name=date::$(date +'%Y-%m-%dT%H-%M-%S')" echo "TIME_NOW=$(date +'%Y-%m-%dT%H-%M-%S')" >> $GITHUB_ENV - name: Create one csv report run: | cd /home/runner/work/vizro/vizro/ ls */*.csv | head -n1 | xargs head -n1 > report-aggregated-${{ steps.date.outputs.date }}.csv && tail -n+2 -q */*.csv >> report-aggregated-${{ steps.date.outputs.date }}.csv # replace all timestamps in aggregated report to current date gawk -F, -i inplace 'FNR>1 {$1="${{ steps.date.outputs.date }}"} {print}' OFS=, report-aggregated-${{ steps.date.outputs.date }}.csv - name: Report artifacts uses: actions/upload-artifact@v5 if: always() with: name: Report-aggregated-${{ steps.date.outputs.date }} path: | /home/runner/work/vizro/vizro/report-aggregated-${{ steps.date.outputs.date }}.csv - name: Upload reports to artifactory run: | cd /home/runner/work/vizro/vizro/ curl -fL https://getcli.jfrog.io | sh ./jfrog config add vizro --artifactory-url ${{ secrets.ARTIFACTORY_URL}} --user ${{ secrets.ARTIFACTORY_USER}} --apikey ${{ secrets.ARTIFACTORY_PASS}} --interactive=false ./jfrog rt u --flat=false "report-aggregated-${{ steps.date.outputs.date }}.csv" vizx-generic-local/vizro-ai-reports/

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mckinsey/vizro'

If you have feedback or need assistance with the MCP directory API, please join our Discord server