add image recognition api example

demo03-image-analysis/image-analysis-api/.gitignore

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+# package directories
+node_modules
+jspm_packages
+
+# Serverless directories
+.serverless

demo03-image-analysis/image-analysis-api/README.md

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+<!--
+title: 'AWS Simple HTTP Endpoint example in NodeJS'
+description: 'This template demonstrates how to make a simple HTTP API with Node.js running on AWS Lambda and API Gateway using the Serverless Framework.'
+layout: Doc
+framework: v3
+platform: AWS
+language: nodeJS
+authorLink: 'https://github.com/serverless'
+authorName: 'Serverless, inc.'
+authorAvatar: 'https://avatars1.githubusercontent.com/u/13742415?s=200&v=4'
+-->
+
+# Serverless Framework Node HTTP API on AWS
+
+This template demonstrates how to make a simple HTTP API with Node.js running on AWS Lambda and API Gateway using the Serverless Framework.
+
+This template does not include any kind of persistence (database). For more advanced examples, check out the [serverless/examples repository](https://github.com/serverless/examples/) which includes Typescript, Mongo, DynamoDB and other examples.
+
+## Usage
+
+### Deployment
+
+```
+$ serverless deploy
+```
+
+After deploying, you should see output similar to:
+
+```bash
+Deploying aws-node-http-api-project to stage dev (us-east-1)
+
+✔ Service deployed to stack aws-node-http-api-project-dev (152s)
+
+endpoint: GET - https://xxxxxxxxxx.execute-api.us-east-1.amazonaws.com/
+functions:
+  hello: aws-node-http-api-project-dev-hello (1.9 kB)
+```
+
+_Note_: In current form, after deployment, your API is public and can be invoked by anyone. For production deployments, you might want to configure an authorizer. For details on how to do that, refer to [http event docs](https://www.serverless.com/framework/docs/providers/aws/events/apigateway/).
+
+### Invocation
+
+After successful deployment, you can call the created application via HTTP:
+
+```bash
+curl https://xxxxxxx.execute-api.us-east-1.amazonaws.com/
+```
+
+Which should result in response similar to the following (removed `input` content for brevity):
+
+```json
+{
+  "message": "Go Serverless v2.0! Your function executed successfully!",
+  "input": {
+    ...
+  }
+}
+```
+
+### Local development
+
+You can invoke your function locally by using the following command:
+
+```bash
+serverless invoke local --function hello
+```
+
+Which should result in response similar to the following:
+
+```
+{
+  "statusCode": 200,
+  "body": "{\n  \"message\": \"Go Serverless v3.0! Your function executed successfully!\",\n  \"input\": \"\"\n}"
+}
+```
+
+
+Alternatively, it is also possible to emulate API Gateway and Lambda locally by using `serverless-offline` plugin. In order to do that, execute the following command:
+
+```bash
+serverless plugin install -n serverless-offline
+```
+
+It will add the `serverless-offline` plugin to `devDependencies` in `package.json` file as well as will add it to `plugins` in `serverless.yml`.
+
+After installation, you can start local emulation with:
+
+```
+serverless offline
+```
+
+To learn more about the capabilities of `serverless-offline`, please refer to its [GitHub repository](https://github.com/dherault/serverless-offline).

demo03-image-analysis/image-analysis-api/handler.js

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+"use strict";
+const imageAnalyse = require('./imageHandler')
+
+module.exports.analyse = async (event) => {
+  try { 
+
+    const imageURL = event?.queryStringParameters?.imageURL
+
+    if(!imageURL) {
+      return {
+        statusCode: 200,
+        body: 'No image analysed',
+      };
+    }
+
+    const analysedData = await imageAnalyse(imageURL)
+
+    return {
+      statusCode: 200,
+      body: analysedData
+    }
+
+  } catch (err) {
+    console.log('*** Error ***', err)
+    return {
+      statusCode: 500,
+      body: 'Internal server error'
+    }
+  }
+}

demo03-image-analysis/image-analysis-api/imageHandler.js

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+const axios = require('axios')
+const AWS = require('aws-sdk')
+
+module.exports = async (imageURL) => {
+    const { data } = await downloadImage(imageURL)
+    const labels = await imageRecognition(data)
+    const mostRelevantLabels = getMostRelevant(labels)
+    const parsedTextResult = parseTextResult(mostRelevantLabels)
+    return parsedTextResult.join('\n')
+}
+
+const downloadImage = (imageURL) => {
+    return axios.get(
+        imageURL,
+        {
+            responseType: 'arraybuffer'
+        }
+    )
+}
+
+const imageRecognition = (data) => {
+    var rekognition = new AWS.Rekognition({apiVersion: '2016-06-27'});
+    return rekognition.detectLabels({
+        Image: {
+            Bytes: data
+        }
+    }).promise()
+}
+
+const getMostRelevant = (labels) => {
+    return labels.Labels.slice(0, 3).map(item => ({
+        name: item.Name,
+        confidence: item.Confidence.toFixed(2)
+    }))
+}
+
+const parseTextResult = (labels) => {
+    return labels.map(item => {
+        return `This image has ${item.confidence} chance to be an ${item.name.toLowerCase()}`
+    })
+}