Create the Lambda Function

import boto3
from decimal import Decimal
import json
import os
from helper import AwsHelper, S3Helper, DynamoDBHelper
from og import OutputGenerator
import datastore

def callTextract(bucketName, objectName, detectText, detectForms, detectTables):
    textract = AwsHelper().getClient('textract')
    if(not detectForms and not detectTables):
        response = textract.detect_document_text(
            Document={
                'S3Object': {
                    'Bucket': bucketName,
                    'Name': objectName
                }
            }
        )
    else:
        features  = []
        if(detectTables):
            features.append("TABLES")
        if(detectForms):
            features.append("FORMS")
        
        response = textract.analyze_document(
            Document={
                'S3Object': {
                    'Bucket': bucketName,
                    'Name': objectName
                }
            },
            FeatureTypes=features
        )

    return response


def processImage(documentId, features, bucketName, objectName, outputTableName, documentsTableName):

    detectText = "Text" in features
    detectForms = "Forms" in features
    detectTables = "Tables" in features

    response = callTextract(bucketName, objectName, detectText, detectForms, detectTables)

    dynamodb = AwsHelper().getResource("dynamodb")
    ddb = dynamodb.Table(outputTableName)

    print("Generating output for DocumentId: {}".format(documentId))

    opg = OutputGenerator(documentId, response, bucketName, objectName, detectForms, detectTables, ddb)
    opg.run()

    print("DocumentId: {}".format(documentId))

    ds = datastore.DocumentStore(documentsTableName, outputTableName)
    ds.markDocumentComplete(documentId)

# --------------- Main handler ------------------

def processRequest(request):

    output = ""

    print("request: {}".format(request))

    bucketName = request['bucketName']
    objectName = request['objectName']
    features = request['features']
    documentId = request['documentId']
    outputTable = request['outputTable']
    documentsTable = request['documentsTable']
    documentsTable = request["documentsTable"]
    
    if(documentId and bucketName and objectName and features):
        print("DocumentId: {}, features: {}, Object: {}/{}".format(documentId, features, bucketName, objectName))

        processImage(documentId, features, bucketName, objectName, outputTable, documentsTable)

        output = "Document: {}, features: {}, Object: {}/{} processed.".format(documentId, features, bucketName, objectName)
        print(output)

    return {
        'statusCode': 200,
        'body': output
    }

def lambda_handler(event, context):

    print("event: {}".format(event))
    message = json.loads(event['Records'][0]['body'])
    print("Message: {}".format(message))

    request = {}
    request["documentId"] = message['documentId']
    request["bucketName"] = message['bucketName']
    request["objectName"] = message['objectName']
    request["features"] = message['features']    
    request["outputTable"] = os.environ['OUTPUT_TABLE']
    request["documentsTable"] = os.environ['DOCUMENTS_TABLE']

    return processRequest(request)

#### Important code snippets in the Lambda function

* Below is where we call the textract ***detect_document_text*** API. This API call will return the raw text in an image in a JSON structure.

```python 
response = textract.detect_document_text(
            Document={
                'S3Object': {
                    'Bucket': bucketName,
                    'Name': objectName
                }
            }
```

* Below if we detect more complicated structures like forms and tables we call the ***analyze_document*** API. This API call will return the raw text and the also the relationships between detected text.

```python 
response = textract.analyze_document(
        Document={
            'S3Object': {
                'Bucket': bucketName,
                'Name': objectName
            }
        }
```