def lambda_handler()

in lambda/comprehend-create-human-classification-tasks.py [0:0]
37 lines of code
4 McCabe index (conditional complexity)

def lambda_handler(event, context):
    #print(event)
    print("Creating human review taks")

    # Get the object from the event
    bucketName = event['Records'][0]['s3']['bucket']['name']
    keyName = unquote_plus(event['Records'][0]['s3']['object']['key'])
    fileName = keyName[keyName.rindex('/')+1:]
   
    # Read the S3 Object
    bucket = s3.Bucket(bucketName)
    body = bucket.Object(keyName).get()['Body'].read().decode("utf-8", 'ignore')

    categories = os.environ['categories']

    if not categories:
        raise Exception('The environment variable categories cannot be empty')

    rowCount = 0

    print('Splitting file for pairs')    
    
    sentences = body.split("\n")
    
    # Create the human loop input JSON object
    humanLoopInput = {
        'comprehendPredictions' : [],
        'labels': categories.split(','),
        'rowCount': 0,
        'bucketName': bucketName,
        'keyName': keyName
    }

    # split the body by period to get individual sentences
    for sentence in sentences:
        print(sentence)
        if len(sentence.strip()) > 0:
            humanLoopInput['comprehendPredictions'].append(json.loads(sentence))
            rowCount+=1
           

    humanLoopInput['rowCount'] = rowCount
    
    humanLoopName = 'Comprehend-Text' + str(int(round(time.time() * 1000)))
    print('Starting human loop - ' + humanLoopName)
    response = a2i.start_human_loop(
                                HumanLoopName=humanLoopName,
                                FlowDefinitionArn= flowDefnARN,
                                HumanLoopInput={
                                    'InputContent': json.dumps(humanLoopInput)
                                    }
                                )
    

    print('Success')
    return 0