Build Interactive Apps with Google Assistant: Challenge Lab
Challenge scenario
As a junior developer in Jooli Inc. and recently trained with Google Cloud and Dialogflow you have been asked to help a new team (Taniwha) set up their environment. The team has asked for your help and has done some work, but needs you to complete the work.
You are expected to have the skills and knowledge for these tasks so don’t expect step-by-step guides.
Your challenge
You need to help the team with some of their initial work creating two new Dialogflow apps.
As soon as you sit down at your desk and open your new laptop you receive the following request to complete these tasks
Task 1: Initialize and configure a Cloud Function
Open the Navigation menu and select Cloud Functions, which is located under the compute header. Then click Create function.
This will open a template to create a new Cloud Function. Your page will resemble the following:
For the Cloud Function’s Name field, enter in magic_eight_ball
Then scroll down to the authentication section and check the box next to “Allow unauthenticated invocations”:
Forgetting to do the above will cause your simulation test to fail at the end!
Click SAVE
Now click NEXT and find the inline editor for MAIN.PY and REQUIREMENTS.TXT. Make sure that the MAIN.PY tab is open. If not already present create them. This file defines your fulfillment logic and is used to create and deploy a Cloud Function. Here are some specifics on its basic functioning:
- When Dialogflow intents are triggered, the intent’s action name (declared in the action area of the intent) is provided to you in the request to your fulfillment. You use this action name to determine what logic to carry out.
- Within every request to your fulfillment, if Dialogflow parsed parameters from the user input, you can access the parameter by name. Here, you declare the names of the parameters so you can access them later.
Now that you have a better understanding of MAIN.PY, you will build out the function's fulfillment logic. Remove the boilerplate code from the file. Then copy and paste the following code into MAIN.PY:
import random
import logging
import google.cloud.logging
from google.cloud import translate_v2 as translate
from flask import Flask, request, make_response, jsonify
def magic_eight_ball(request):
client = google.cloud.logging.Client()
client.get_default_handler()
client.setup_logging()
choices = [
."
]
magic_eight_ball_response = random.choice(choices)
logging.info(magic_eight_ball_response)
return make_response(jsonify({'fulfillmentText': magic_eight_ball_response }))
Now open the REQUIREMENTS.TXT. Replace the contents of the file with the following:
REQUIREMENTS.TXT
google-cloud-translate
google-cloud-logging
Once you have those files configured, now change the runtime to Python 3.7. Then find the Entry point field. Enter in magic_eight_ball for the value.
Now click the Deploy button below. It will take about a minute for your function to be built. When the creation completes, your overview page will resemble the following
Now click on the magic_eight_ball function to get more details about it's configuration. Then click on the trigger tab. You will see a function URL that resembles the following:
Copy the function URL in a text editor. You will use it as the URL for the Dialogflow webhook, which is configured in the next section.
Click Check my progress to verify your performed task.
Task 2: Create the Lab Magic 8 Ball app for Google Assistant
Regardless of the Assistant application you’re building, you will always have to create an Actions project so your app has an underlying organizational unit.
Open the Actions on Google Developer Console(see lab manual for link) in a new tab. Sign in with your Qwiklabs credentials if prompted. You should be looking at a clean Actions console that resembles the following:
Click New Project and agree to Actions on Google’s terms of service when prompted.
Click into the Project Name field and select your Qwiklabs Google Cloud project ID from the dropdown. Then click Import project:
Soon after you will be presented with a welcome page that resembles the following:
Build an Action
An action is an interaction you build for the Google Assistant. An action supports a specific intent (a goal or task that users want to accomplish), which is carried out by a corresponding fulfillment (logic that handles an intent and carries out the corresponding Action.) You will now build an Action that supports silly name generation.
Click on your project name. Then from the center menu click Build your Action . Add a display name with your initials + magic 8 ball. Click SAVE
Now click Actions > Get Started. Then select Custom Intent > BUILD:
This will take you to the Dialogflow console. Select your Qwiklabs account and click Allow when Dialogflow prompts you for permission to access your Google Account.
When you land on the Dialogflow account settings page, check the box next to Yes, I have read and accept the agreement and click Accept.
If you are brought to the following Dialogflow agent creation page, click CREATE:
If you are brought to this page instead:
Close the Dialogflow agent creation tab. You will return to the Actions Console.
Click Get Started > Custom Intent > BUILD.
Select your Qwiklabs account and click Allow when Dialogflow prompts you for permission to access your Google Account.
Now click CREATE:
An agent is an organizational unit that collects information needed to complete a user’s request, which it then forwards to a service that provides fulfillment logic.
You will now build the basic framework for fulfillment logic.
Click Fulfillment from the left-hand menu. Move the slider for Webhook to the right, setting it to Enabled.
Copy and paste the cloud function URL. Your page should resemble the following:
Click Save
Scroll down and click Save in the bottom right corner. Then click Intents from the left hand menu and select Default Welcome Intent:
Delete all text responses
Click add response > text response
Add this “Welcome to the lab magic 8 ball, ask me a yes or no question and I will predict the future!”
Click Save
Again click on indents
Click default fallback intent
Delete all the responses
Enable Set this intent as end of conversation and Now scroll down and expand the Fulfillment section and click Enable fulfillment. Then click the Enable webhook call for this intent slider:
Click Save.
This tells Dialogflow to call your fulfillment to generate a response to the user instead of using Dialogflow’s response feature.
Test your Assistant application with the Actions simulator
Now that you your Cloud Function has been deployed and your webhook has been properly set up, you can preview the app in the Actions simulator.
Check your Google permission settings
Switch to the develop browser tab
Click test
Click visit activity controls
Ensure that the following permissions are enabled by sliding the toggles to TURN ON the following cards:
- Web & App Activity
Now close the Activity Controls page.
Test the application with the simulator
Return to the Dialogflow console. Then from the left-hand menu, click Integrations. Then select Integration Settings.
Once you land on the following page, click TEST:
Now you should be on test browser tab. To invoke the Action, hit the enter key in the Talk to my test app box near the bottom of the simulator console. You should be presented with a similar response:
Now enter : Will I complete this challenge lab?
It will return a random reply.
Click Check my progress to verify your performed task.
Task 3: Add multilingual support to your magic_eight_ball Cloud Function
Switch to cloud function, edit the function, and add the following code to the line after :
magic_eight_ball_response = random.choice(choices)
request_json = request.get_json() if request_json and ‘queryResult’ in request_json: question = request_json.get(‘queryResult’).get(‘queryText’) # try to identify the language language = ‘en’ translate_client = translate.Client() detected_language = translate_client.detect_language(question) if detected_language[‘language’] == ‘und’: language = ‘en’ elif detected_language[‘language’] != ‘en’: language = detected_language[‘language’] # translate if not english if language != ‘en’: logging.info(‘translating from en to %s’ % language) translated_text = translate_client.translate( magic_eight_ball_response, target_language=language) magic_eight_ball_response = translated_text[‘translatedText’]
Click Deploy
Switch to test and try the app again using the following lines
· 我会完成这个挑战实验室吗？
· ¿Completaré este laboratorio de desafío?
· இந்த சவால் ஆய்வகத்தை நான் முடிக்கலாமா?
Each line should reply in the same language
Click Check my progress to verify your performed task.