Web App with Github Actions

Open Azure Portal → App Services → Create Web App

1- Basics

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2- Deployment

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AZURE FUNCTIONS FOR WEB DEPLOYMENT

Using Azure Portal

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Using VSCode Azure Extension (Recommended)

1- Install Azure extension on VSCode.

2- Then create function app. (Select advanced. Create everything for app as new and make plan as premium Elastic Premium (EP1: 1)) Linux. (Don’t use consumption plan) You can skip application insight creation.

3- From Function App’s deployment center part in Azure, connect to github repository for continious deployment.

5- Open Azure Portal -> Your Function App -> Overview -> Get Publish Profile and download.

6- Open Settings→Secrets of Github Repo and paste all content of downloaded file to it. Use AZURE_FUNCTION_PUBLISH_PROFILE name and put the same name in yml’s Azure/functions-action@v1 Deploy to Azure Functions section.

7- Select “Deploy to Function App” from cloud button as in right image.

If you get “Failed to deploy web package to App Service. Not Found 404” error, just open Function App and restart it.
If you get error while publishing, use “func azure functionapp publish <FUNC-NAME> -build remote”
In website, if you get app error remove slot-name: production part.
Set both set FUNCTIONS_WORKER_RUNTIME **and **ENABLE_ORYX_BUILD to true and WEBSITE_RUN_FROM_PACKAGEto 1 from configuration.
To activate local env, use “source .venv/bin/activate” command. To start locally, use “func start”.

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3- Workflow Configuration File

# Docs for the Azure Web Apps Deploy action: <https://github.com/Azure/webapps-deploy>
# More GitHub Actions for Azure: <https://github.com/Azure/actions>
# More info on Python, GitHub Actions, and Azure App Service: <https://aka.ms/python-webapps-actions>

name: Build and deploy Python app to Azure Web App - age-prediction

on:
  push:
    branches:
      - master
  workflow_dispatch:

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v2

      - name: Set up Python version
        uses: actions/setup-python@v1
        with:
          python-version: '3.8'

      - name: Create and start virtual environment
        run: |
          python -m venv venv
          source venv/bin/activate
      
      - name: Install dependencies
        run: pip install -r requirements.txt
        
      # Optional: Add step to run tests here (PyTest, Django test suites, etc.)
      
		  # Artifacts are model files in Azure ML Studio.
      - name: Upload artifact for deployment jobs
        uses: actions/upload-artifact@v2
        with:
          name: python-app
          path: |
            . 
            !venv/

  deploy:
    runs-on: ubuntu-latest
    needs: build
    environment:
      name: 'production'
      url: ${{ steps.deploy-to-webapp.outputs.webapp-url }}

    steps:
      - name: Download artifact from build job
        uses: actions/download-artifact@v2
        with:
          name: python-app
          path: .
          
      - name: 'Deploy to Azure Web App'
        uses: azure/webapps-deploy@v2
        id: deploy-to-webapp
        with:
          app-name: 'age-prediction'
          slot-name: 'production'
          publish-profile: ${{ secrets.AzureAppService_PublishProfile_1234 }}

### To access files in Azure Storage
- name: 'Deploy model to Azure Web App'
    uses: azure/webapps-deploy@v2
    with:
      app-name: 'age-prediction'
  slot-name: 'Production'
  publish-profile: ${{ secrets.AZUREAPPSERVICE_PUBLISHPROFILE_229FDF161AA347DB981BBE9BC915A41A }}
  local-artifact: true
  artifact-name: 'age_model_b/age_model_abhipsrajsahoo2.h5'
  artifact-type: 'folder'
  artifact-link: '<https://sgmlworkstorageef1c6630d.blob.core.windows.net/azureml-blobstore-825cc236-ef0c-4097-a4c3-c66b60e48acf/WebUpload/221208203447-1128685028/age_model_b>'
  #artifact-link: 'https://<storage-account-name>.blob.core.windows.net/<container-name>/<model-folder>'

If you get error in deployment step;

Redeploy and set “Always on” option to false.

If that doesn’t resolve your issue, https://<web-page-name>scm.azurewebsites.net/DebugConsole open this to access to Kudu debug console for more detail.

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