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‎README.md

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# AWS Step Function Example
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This repository serves as an example use-case of AWS Step Functions, through the use of AWS Lambda and integration with various other AWS products.

‎lambda-SNS.py

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import boto3
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def lambda_handler(event, context):
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'''
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Sends notification of negative results from sentiment analysis via SNS
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'''
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#construct message from input data and publish via SNS
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sns = boto3.client('sns')
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sns.publish(
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TopicArn = 'arn:aws:sns:XXXXXXXXXXXXXXXX:my-SNS-topic',
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Subject = 'Negative Review Received',
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Message = 'Review (ID = %i) of %s (ID = %i) received with negative results from sentiment analysis. Feedback from Customer (ID = %i): "%s"' % (int(event['reviewID']),
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event['reviewType'], int(event['productID']), int(event['customerID']), event['feedback'])
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)
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#pass through values
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return {
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'sentiment': event['sentiment'],
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'reviewType': event['reviewType'],
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'reviewID': event['reviewID'],
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'customerID': event['customerID'],
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'productID': event['productID'],
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'feedback': event['feedback'],
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}

‎lambda-invoke-step-function.py

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import json
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import boto3
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import pprint
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#initialize
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pp = pprint.PrettyPrinter(indent=4)
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def lambda_handler(event, context):
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'''
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triggers a step function from the S3 landing event,
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passing along the S3 file info
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'''
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#Fallback tests for initializations outside scope
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try:
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pp
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except NameError:
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pp = pprint.PrettyPrinter(indent=4)
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bucket_name = event['Records'][0]['s3']['bucket']['name']
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file_key = event['Records'][0]['s3']['object']['key']
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input= {
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'bucket_name': bucket_name,
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'file_key': file_key
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}
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stepFunction = boto3.client('stepfunctions')
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response = stepFunction.start_execution(
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stateMachineArn='arn:aws:states:XXXXXXXXXXXXXXXX:stateMachine:my-state-machine',
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input = json.dumps(input, indent=4)
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)
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return pp.pprint(response)

‎lambda-read-CSV.py

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import boto3
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import csv
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import json
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#initialize
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s3 = boto3.client('s3')
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def lambda_handler(event, context):
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'''
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reads the .csv from the S3 landing event, returns a json-formatted
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dict formed from the first line (and column headers)
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'''
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#Fallback tests for initializations outside scope
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try:
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s3
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except NameError:
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s3 = boto3.client('s3')
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#read s3 object
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bucket_name = event['bucket_name']
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file_key = event['file_key']
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response = s3.get_object(Bucket=bucket_name, Key=file_key)
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#convert response to lines of CSV
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lines = response['Body'].read().decode('utf-8').split('\n')
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#DictReader -> convert lines of CSV to OrderedDict
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for row in csv.DictReader(lines):
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#return just the first loop (row) results!
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return json.loads(json.dumps(row))

‎lambda-sentiment.py

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import os, boto3
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client = boto3.client('comprehend')
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def lambda_handler(event, context):
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'''
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perform sentiment analysis on the input feedback
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'''
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#use comprehend to perform sentiment analysis
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feedback = event['feedback']
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sentiment=client.detect_sentiment(Text=feedback,LanguageCode='en')['Sentiment']
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#pass through values
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return {
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'sentiment': sentiment,
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'reviewType': event['reviewType'],
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'reviewID': event['reviewID'],
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'customerID': event['customerID'],
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'productID': event['productID'],
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'feedback': event['feedback'],
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}

‎lambda-write-db.py

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import boto3
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def lambda_handler(event, context):
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'''
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Adds the review data to the review database table
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'''
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#select correct table based on input data
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dynamodb = boto3.client('dynamodb')
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if event['reviewType'] == 'Product':
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tableName = 'my-products-table'
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elif event['reviewType'] == 'Service':
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tableName = 'my-services-table'
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else:
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raise Exception("Input review is neither Product nor Service")
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#construct response to put data in table
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response = dynamodb.put_item(
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TableName=tableName,
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Item={
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'reviewID': {"N": event['reviewID'] },
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'customerID': {"N": event['customerID'] },
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'productID': {"N": event['productID'] },
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'feedback': {"S": event['feedback'] },
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'sentiment': {"S": event['sentiment'] }
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},
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)
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#pass through values
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response['reviewType'] = event['reviewType']
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response['reviewID'] = event['reviewID']
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response['customerID'] = event['customerID']
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response['productID'] = event['productID']
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response['feedback'] = event['feedback']
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response['sentiment'] = event['sentiment']
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return response

‎state-machine

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{
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"StartAt": "ReadCSV",
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"States": {
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"ReadCSV": {
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"Type": "Task",
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"Resource": "arn:aws:lambda:XXXXXXXXXXXXXXXX:function:lambda-read-CSV",
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"Next": "ProductOrServiceChoice"
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},
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"ProductOrServiceChoice": {
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"Type": "Choice",
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"Choices": [
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{
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"Variable": "$.reviewType",
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"StringEquals": "Product",
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"Next": "ProductSentimentAnalysis"
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},
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{
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"Variable": "$.reviewType",
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"StringEquals": "Service",
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"Next": "ServiceSentimentAnalysis"
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}
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],
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"Default": "CategorisationFail"
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},
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"CategorisationFail": {
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"Type": "Fail",
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"Cause": "Input CSV could not be categorised into 'Product' or 'Service'."
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},
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"ProductSentimentAnalysis": {
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"Type" : "Task",
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"Resource": "arn:aws:lambda:XXXXXXXXXXXXXXXX:function:lambda-sentiment",
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"Next": "ProductResultChoice"
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},
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"ServiceSentimentAnalysis": {
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"Type" : "Task",
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"Resource": "arn:aws:lambda:XXXXXXXXXXXXXXXX:function:lambda-sentiment",
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"Next": "ServiceResultChoice"
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},
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"ProductResultChoice": {
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"Type": "Choice",
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"Choices": [
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{
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"Or": [
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{
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"Variable": "$.sentiment",
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"StringEquals": "POSITIVE"
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},
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{
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"Variable": "$.sentiment",
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"StringEquals": "NEUTRAL"
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}
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],
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"Next": "WriteToDB"
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},
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{
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"Variable": "$.sentiment",
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"StringEquals": "NEGATIVE",
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"Next": "PublishToSNS"
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}
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],
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"Default": "SentimentFail"
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},
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"SentimentFail": {
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"Type": "Fail",
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"Cause": "Sentiment Analysis Failed!"
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},
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"ServiceResultChoice": {
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"Type": "Choice",
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"Choices": [
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{
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"Or": [
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{
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"Variable": "$.sentiment",
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"StringEquals": "POSITIVE"
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},
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{
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"Variable": "$.sentiment",
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"StringEquals": "NEUTRAL"
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}
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],
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"Next": "WriteToDB"
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},
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{
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"Variable": "$.sentiment",
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"StringEquals": "NEGATIVE",
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"Next": "PublishToSNS"
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}
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],
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"Default": "SentimentFail"
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},
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"PublishToSNS":
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{
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"Type": "Task",
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"Resource": "arn:aws:lambda:XXXXXXXXXXXXXXXX:function:lambda-SNS",
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"Next": "WriteToDB"
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},
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"WriteToDB":
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{
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"Type": "Task",
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"Resource": "arn:aws:lambda:XXXXXXXXXXXXXXXX:function:lambda-write-db",
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"Next": "Success"
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},
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"Success":
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{
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"Type": "Succeed"
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}
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}
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}

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