Azure Functions HOL (C#).md

Azure Functions

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Overview

Functions have been the basic building blocks of software since the first lines of code were written and the need for code organization and reuse became a necessity. Azure Functions expand on these concepts by allowing developers to create "serverless", event-driven functions that run in the cloud and can be shared across a wide variety of services and systems, uniformly managed, and easily scaled based on demand. In addition, Azure Functions can be written in a variety of languages, including C#, JavaScript, Python, Bash, and PowerShell, and they're perfect for building apps and nanoservices that employ a compute-on-demand model.

In this lab, you will create an Azure Function that monitors a blob container in Azure Storage for new images, and then performs automated analysis of the images using the Microsoft Cognitive Services Computer Vision API. Specifically, The Azure Function will analyze each image that is uploaded to the container for cats content and create a copy of the image in another container. Images that contain cat content will be copied to one container.

Objectives

In this hands-on lab, you will learn how to:

• Create an Azure Function App
• Write an Azure Function that uses a blob trigger
• Add application settings to an Azure Function App
• Use Microsoft Cognitive Services to analyze images and store the results in blob metadata

Prerequisites

The following are required to complete this hands-on lab:

• An active Microsoft Azure subscription. If you don't have one, sign up for a free trial.
• Microsoft Azure Storage Explorer (optional)

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Exercises

This hands-on lab includes the following exercises:

• Exercise 1: Create an Azure Function App
• Exercise 2: Add an Azure Function
• Exercise 3: Add a subscription key to application settings
• Exercise 4: Test the Azure Function
• Exercise 5: View blob metadata (optional)

Estimated time to complete this lab: 45 minutes.

Exercise 1: Create an Azure Function App

The first step in writing an Azure Function is to create an Azure Function App. In this exercise, you will create an Azure Function App using the Azure Portal. Then you will add the three blob containers to the storage account that is created for the Function App: one to store uploaded images, a second to store images that do not contain adult or racy content, and a third to contain images that do contain adult or racy content.

1. Open the Azure Portal in your browser. If asked to log in, do so using your Microsoft account.

2. Click + New, followed by Get Started and Serverless Function App.

Creating an Azure Function App

3. Enter an app name that is unique within Azure. Under Resource Group, select Create new and enter "FunctionsLabResourceGroup" (without quotation marks) as the resource-group name to create a resource group for the Function App. Choose the Location nearest you, and accept the default values for all other parameters. Then click Create to create a new Function App.

The app name becomes part of a DNS name and therefore must be unique within Azure. Make sure a green check mark appears to the name indicating it is unique. You probably won't be able to use "functionslab" as the app name.

Creating a Function App

4. Click Resource groups in the ribbon on the left side of the portal, and then click the resource group created for the Function App.

   

   Opening the resource group

5. Periodically click the Refresh button at the top of the blade until "Deploying" changes to "Succeeded," indicating that the Function App has been deployed. Then click the storage account that was created for the Function App.

   

   Opening the storage account

6. Click on Function Name, and then in the Platform features in the right pane. Then clik on Function app settings, select -1

   

   Chaning Function app setting

7. Click Blobs to view the contents of blob storage.

   

   Opening blob storage

8. Click + Container. Type "uploaded" into the Name box and set Access type to Private. Then click the OK button to create a new container.

   

   Adding a container

9. Repeat Step 7 to add containers named "accepted" and "rejected" to blob storage.

10. Confirm that all three containers were added to blob storage.

    

    The new containers

The Azure Function App has been created and you have added three containers to the storage account created for it. The next step is to add an Azure Function.

Exercise 2: Add an Azure Function

Once you have created an Azure Function App, you can add Azure Functions to it. In this exercise, you will add a function to the Function App you created in Exercise 1 and write C# code that uses the Computer Vision API to analyze images added to the "uploaded" container for adult or racy content.

1. Return to the blade for the "FunctionsLabResourceGroup" resource group and click the Azure Function App that you created in Exercise 1.

   

   Opening the Function App

2. Click the + sign to the right of Functions. Then click Custom function.

   

   Adding a function

3. Enter "Blob trigger" in Search box. Then click C#.

   

   Selecting a function template

4. Enter "BlobImageAnalysis" (without quotation marks) for the function name and "uploaded/{name}" into the Path box. (The latter applies the blob storage trigger to the "uploaded" container that you created in Exercise 1.) Then click the Create button to create the Azure Function.

   

   Creating an Azure Function

5. Replace the code shown in the code editor with the following statements:

#r "System.IO"
using Microsoft.ProjectOxford.Vision;
using System.Threading;
using System.Threading.Tasks;
using System.Linq;
using System;


public async static Task Run(Stream myBlob, Stream acceptedBlob, Stream rejectedBlob,string name, CancellationToken token, TraceWriter log)
{
    
    log.Info($"C# Blob trigger function Processed blob\n Name:{name} \n Size: {myBlob.Length} Bytes");
    VisualFeature[] VisualFeatures = { VisualFeature.Description };
    var key =Environment.GetEnvironmentVariable("SubscriptionKey").ToString();
    var searchCatTag = "cat";

    var vsClient = new VisionServiceClient(key, "https://westeurope.api.cognitive.microsoft.com/vision/v1.0");
    var result = await vsClient.AnalyzeImageAsync(myBlob, VisualFeatures);
    myBlob.Position = 0;
    bool containsCat = result.Description.Tags.Contains(searchCatTag);
    log.Info(containsCat.ToString());
    if(containsCat)
    {
        await myBlob.CopyToAsync(acceptedBlob, 4096, token);
    }
    else
    {
        await myBlob.CopyToAsync(rejectedBlob, 4096, token);
    }
}

1. Click the Save button at the top of the code editor to save your changes. Then click View files.

   

   Saving the function

2. Click + Add to add a new file, and name the file project.json.

   

   Adding a project file

3. Add the following statements to project.json:

{
    "frameworks": {
        "net46": {
            "dependencies": {					
                "Microsoft.ProjectOxford.Vision": "1.0.393",
                "WindowsAzure.Storage": "7.2.0"
            }
        }
    }
}

4. Click the Save button to save your changes. Then click run.csx to go back to that file in the code editor.

   

   Saving the project file

Add a new output to your function use acceptedBlob as your Blob Parameter Name and accepted for your path, and save this new output. Repeat this steps to add an another output with rejectedBlob as your Parameter Name and rejected for your path and save this new output.

An Azure Function written in C# has been created, complete with a JSON project file containing information regarding project dependencies. The next step is to add an application setting that the Azure Function relies on.

Exercise 3: Add a subscription key to application settings

The Azure Function you created in Exercise 2 loads a subscription key for the Microsoft Cognitive Services Computer Vision API from application settings. This key is required in order for your code to call the Computer Vision API, and is transmitted in an HTTP header in each call. In this exercise, you will subscribe to the Computer Vision API, and then add an access key for the subscription to application settings.

1. In the Azure Portal, click + New, followed by AI + Machine Learning and Computer Vision.

   

   Creating a new Computer Vision subscription

2. Enter "VisionAPI" into the Name box and select F0 as the Pricing tier. Under Resource Group, select Use existing and select the "FunctionsLabResourceGroup" that you created for the Function App in Exercise 1. Check the I confirm box, and then click Create.

   

   Subcribing to the Computer Vision

3. Return to the blade for the "FunctionsLabResourceGroup" resource group and click the Computer Vision API subscription that you just created.

   

   Opening the Computer Vision subscription

4. Click Overview in left pane. Click Show access keys.

   

   Viewing the access keys

5. Click the Copy button to the right of KEY 1 to copy the access key to the clipboard. Copy the Endpoint URL, you may need to change the VisionServiceClient object in the code.

   

   Copying the access key

6. Return to the Function App in the Azure Portal and click the function name in the ribbon on the left. Then click Platform features, followed by Configuration.

   

   Viewing configuration

7. Scroll down to the "App settings" section. Add a new app setting named "SubscriptionKey" (without quotation marks), and paste the subscription key that is on the clipboard into the Value box. Then click Save at the top of the blade.

   

   Adding a subscription key

8. The app settings are now configured for your Azure Function. It's a good idea to validate those settings by running the function and ensuring that it compiles without errors. Click BlobImageAnalysis. Then click Run to compile and run the function. Confirm that "Compilation succeeded" appears in the output log, and ignore any exceptions that are reported.

   

   Compiling the function

The work of writing and configuring the Azure Function is complete. Now comes the fun part: testing it out.

Exercise 4: Test the Azure Function

Your function is configured to listen for changes to the blob container named "uploaded" that you created in Exercise 1. Each time an image appears in the container, the function executes and passes the image to the Computer Vision API for analysis. To test the function, you simply upload images to the container. In this exercise, you will use the Azure Portal to upload images to the "uploaded" container and verify that copies of the images are placed in the "accepted" and "rejected" containers.

1. In the Azure Portal, go to the resource group created for your Function App. Then click the storage account that was created for it.

   

   Opening the storage account

2. Click Blobs to view the contents of blob storage.

   

   Opening blob storage

3. Click uploaded to open the "uploaded" container.

   

   Opening the "uploaded" container

4. Click Upload.

   

   Uploading images to the "uploaded" container

5. Click the button with the folder icon to the right of the Files box. Select all of the files in this lab's "Resources" folder. Then click the Upload button to upload the files to the "uploaded" container.

   

   Uploading images to the "uploaded" container

6. Return to the blade for the "uploaded" container and verify that eight images were uploaded.

   

   Images uploaded to the "uploaded" container

7. Close the blade for the "uploaded" container and open the "accepted" container.

   

   Opening the "accepted" container

8. Verify that the "accepted" container holds two images. These are the images that were classified as cats by the Computer Vision API.

It may take a minute or more for all of the images to appear in the container. If necessary, click Refresh every few seconds until you see all seven images.

Images in the "accepted" container

9. Close the blade for the "accepted" container and open the blade for the "rejected" container. Verify that the "rejected" container holds few images. These images were not classified as cats by the Computer Vision API.

   

   Images in the "rejected" container

The presence of seven images in the "accepted" container and one in the "rejected" container is proof that your Azure Function executed each time an image was uploaded to the "uploaded" container. If you would like, return to the BlobImageAnalysis function in the portal and click Monitor. You will see a log detailing each time the function executed.

Summary

In this hands-on lab you learned how to:

• Create an Azure Function App
• Write an Azure Function that uses a blob trigger
• Add application settings to an Azure Function App
• Use Microsoft Cognitive Services to analyze images and store the results in blob metadata

This is just one example of how you can leverage Azure Functions to automate repetitive tasks. Experiment with other Azure Function templates to learn more about Azure Functions and to identify additional ways in which they can aid your research or business.

-- Modified by Aymeric Weinbach for AzugFr For RedShirtTour 2018 Copyright 2016 Microsoft Corporation. All rights reserved. Except where otherwise noted, these materials are licensed under the terms of the MIT License. You may use them according to the license as is most appropriate for your project. The terms of this license can be found at https://opensource.org/licenses/MIT.