View on GitHub

faast-path-to-beautiful-apps

Adobe Summit 2019 - Lab 745 workbook

Chapter 4. Call Sensei API from Runtime

Learning Objectives

Lab Tasks


1. Add Adobe Content Sensei Services as a Service

GOAL: We will add Adobe Content Sensei Services as a service in your integration

  1. Navigate to Adobe I/O Console at https://console.adobe.io in your browser
  2. Find the Integration that you created during Chapter 1 and open it
  3. Select the Services tab in your Integration
  4. Under Available Services, select Content AI (Beta) from the Adobe Sensei category.
  5. Click Add Service to complete
  6. You should now see 2 services under Configured Services, Adobe Content AI (Beta) and I/O Management API

2. Create Runtime action to make an API call to Sensei

GOAL: We will start with an action that gets an access token for Sensei, then add an API call to the action so that your action can get tags for an image using Sensei Content Service.

Set up an action that gets an Access Token for Sensei Content AI

Any API that accesses a service or content on behalf of an end user authenticates using the OAuth and JSON Web Token standards. For service-to-service integrations, you will also need a JSON Web Token (JWT) that encapsulates your client credentials and authenticates the identity of your integration. You exchange the JWT for the OAuth token that authorizes access.

For more details, please refer to Service Account Integration documentation

The JWT Workflow contains 6 steps

Let’s set up an action that helps you create and store an Access Token in Runtime

  1. In the resources you have downloaded, navigate to adobeio-cna-lib-auth-ims-master folder
     $ cd ~/Desktop/adobeio-cna-lib-auth-ims-master/
    

    For Git users, you can also get the latest copy off our GitHub repo.

  2. We’ll create a .env file to configure this library to your integration. Run the following commands
     $ touch .env
     $ open .env
    
  3. Copy the following block in as place holder:
     OW_NAMESPACE="<change-me>"
     # Optional parameters (should use 'npm run configure' if used)
     IMS_AUTH_TYPE=<code/jwt>
    
  4. You can find the first two values using your Adobe I/O CLI
     $ aio runtime property get
    

    Update OW_NAMESPACE in the .env file to match to your whisk namespace.

  5. Save your .env file.
  6. Now, we’ll also populate the jwt.json file.
     $ open jwt.json
    

    Most values can be found in the Overview tab in Console. The metascope can be seen in your JWT tab, it should look something like this

     "meta_scopes": ["https://ims-na1.adobelogin.com/s/ent_adobeio_sdk", "https://ims-na1.adobelogin.com/s/ent_sensei_image_sdk"]
    

    9

  7. Next, run the following command,
     awk -v ORS='\\n' '1' ~/Desktop/private.key | pbcopy
    

    The private key has now been copied to your clipboard. Paste it into jwt_secret field. Starting from -----BEGIN PRIVATE KEY----- to -----END PRIVATE KEY-----.

  8. Save your jwt.json file.
  9. Your AUTH library is now set up. Let’s deploy it into your namespace by running the following command.
     $ npm install
     $ npm run deploy-jwt
    

    Your should get something that looks like

     endpoints (web actions): https://https://adobeioruntime.net/api/v1/web/<YOUR_NAMESPACE>/jwtauthp/jwtauthenticate
    

Run the action to get an Access Token

  1. You now have 2 new packages for Auth in your namespace. You can look them up by
     $ aio runtime package list
    

    You should see

     /NAMESPACE/jwtauthp-shared                      private 
     /NAMESPACE/jwtauthp                             private 
    
  2. Now, let’s invoke the Auth aciton to get a token
     $ aio runtime action invoke jwtauthp/jwtauthenticate --blocking
    

    You can see that this is actually a sequence, and has invoked multiple actions.

  3. You can now look up your access token at
     $ aio runtime action invoke jwtauthp-shared/jwtauth --result
    

    You should see your access token returned.
    ``` { “accessToken”: “xxxxxxxxxxxxxxxxxxx”, “accessTokenExpiry”: xxxxxxxxxxxxxxxxxxx } }

3. Invoke Sensei API using this Access Token

  1. There’s a sample action on your Desktop sensei.js. Let’s create a new action with it.
     $ aio runtime action update sensei sensei.js
    
  2. Now let’s try to invoke this action manually by passing in some parameters. Build the following command in your text editor
     aio runtime action invoke sensei --param accessToken <insert accessToken from above> --param ent_api_key <insert API key from Console Integration> --param image_url https://www.jcpportraits.com/sites/jcpportraits.com/files/portrait/1712/7_182-456_FamilyGallery6.jpg --param results 10 --param confidence 0.5 --result
    
  3. When ready, paste this command into your Terminal to invoke the action, you should get something that looks like
     {
     "tags": [
     {
       "confidence": 0.9170970402413429,
       "tag": "family"
     },
     {
       "confidence": 0.8392684959100764,
       "tag": "child"
     },
     {
       "confidence": 0.8171461274057861,
       "tag": "happy"
     }....
    

    This is a list of tags for the image and the confidence score for each tag

  4. Open a browser window and go to https://www.jcpportraits.com/sites/jcpportraits.com/files/portrait/1712/7_182-456_FamilyGallery6.jpg Does the tags look accurate?

4. Create Runtime action to make an API call to Sensei Content AI

Challenge: Using the sample call above, can you figure out how to use the access token you just create to make an API call to Sensei Content API? (Hint: default parameter and sequences are your best friends.)

If you got it right, the result should look something similar to your last call.


Next:
Lesson 5 - Use Project Starter to build an app with UI

Return Home: Workbook Index