6. Now we need to update the Lambda function with the bucket name we’ll be using. In lambda/index.js on line 8, update the Bucket property with the S3 bucket name you created earlier.

7. Finally, we need to add the actual Lambda function we will be running! In the lambda folder of this project, there is a Lambda function (index.js

yarn || npm install
zip -r ../translate.zip *

Upload this zip file to AWS Lambda as the function code and click Save.

A look at the Lambda function

Let’s take a look at and discuss the Lambda function we will be using:

• The first thing we do is import the aws-sdk and create new instances of Translate, Polly, & S3.
• Next, we create the handler function.
• The first thing we do in the handler function is create a params object for Translate that will describe the Text & the Language Code we have coming in from the API.
• Next, we call translate.translateText passing in this params object. This will return the translated text and we set the message variable to that text.
• Now we will use the language code to get the proper voice to go with the language. Each language has a couple of voices that work properly with the language so we make sure to choose the right voice for the right language.
• We then create a new params object for Polly containing a few things including the Text & VoiceID.
• Now we call polly.synthesizeSpeech & pass in the params object we just created.
• If polly.synthesizeSpeech is successful, we will get a data object returned with an AudioStream property.
• We use the data.AudioStream along with a unique ID to store the Audio in an S3 bucket using S3.putObject. If the item is stored without error we get a success callback function.
• If the S3.putObject method is successful, we call the callback of the Lambda function passing in the id of the S3 object (which we will use later to play back the MP3).

Building the GraphQL API

The last step is creating the GraphQL API.

1. Give the API a name, choose Custom Schema & click create.
2. Create a new AWS Lambda data source

• Click Data Sources
• Click NEW
• Give the data source a name, choose AWS Lambda Function as the Data source type
• For the region, choose the region where you created the AWS Lamba function
• For the Function ARN, choose the Lambda function you just created
• For the Role, choose New Role

4. Click on Schema and create the following schema:

type TranslatedSentence {
  sentence: String!
}

type Query {
  getTranslatedSentence(sentence: String!, code: String!): TranslatedSentence
}

5. Add a resolver to the getTranslatedSentence query

• From within the Schema editor, on the Right (under Data Types), click on Attach next to the getTranslatedSentence field.
• For the Data Source name, choose the new data source we just created
• Click Save

6. Add AWS AppSync configuration to the project

Next, we need to edit the aws-exports.js file to specify our AWS AppSync configuration. In the root directory of the React Native project, update aws-exports.js with your AWS AppSync credentials:

const awsmobile = {
  'aws_appsync_graphqlEndpoint': 'YOURENDPOINT',
  'aws_appsync_region': 'YOURREGION',
  'aws_appsync_authenticationType': 'API_KEY',
  'aws_appsync_apiKey': 'YOURAPIKEY',
}

7. Run the React Native app

If building for iOS, run the following command from the terminal:

react-native run-ios

If building for Android, first open an Android emulator and then run the following command from the terminal:

react-native run-android

Conclusion

With AWS AppSync you can leverage a single GraphQL endpoint to work with multiple databases, data sources & AWS Lambda functions giving you the power to do almost anything you would like. In this post, we’ve scratched the surface by demonstrating how we can use a GraphQL query to work with an AWS Lambda function to interact with other AWS services such as Amazon Polly, Amazon Translate, & Amazon S3.

To learn more about AWS AppSync, check out this repo.

I encourage you to experiment more and reach out to me to show me cool stuff you are building with this stack!

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