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AWS_PDF_Table_Textracts
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‎AWS_PDF_Table_Textracts/Pdf_Table_Textract.py

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import boto3
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import json
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import sys
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import time
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import urllib.parse
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class ProcessType:
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DETECTION = 1
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ANALYSIS = 2
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class DocumentProcessor:
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jobId = ''
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textract = boto3.client('textract')
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sqs = boto3.client('sqs')
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sns = boto3.client('sns')
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roleArn = ''
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bucket = ''
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document = ''
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sqsQueueUrl = ''
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snsTopicArn = ''
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processType = ''
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def main(self, bucketName, documentName):
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self.roleArn = 'arn:aws:iam::357171621133:role/ETLlambdaAccessRole'
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self.bucket = bucketName
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self.document = documentName
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self.CreateTopicandQueue()
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self.ProcessDocument(ProcessType.ANALYSIS)
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self.DeleteTopicandQueue()
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def ProcessDocument(self, type):
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jobFound = False
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self.processType = type
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validType = False
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# Determine which type of processing to perform
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if self.processType == ProcessType.DETECTION:
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response = self.textract.start_document_text_detection(DocumentLocation={'S3Object': {'Bucket': self.bucket, 'Name': self.document}},
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NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn})
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print('Processing type: Detection')
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validType = True
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if self.processType == ProcessType.ANALYSIS:
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response = self.textract.start_document_analysis(DocumentLocation={'S3Object': {'Bucket': self.bucket, 'Name': self.document}},
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FeatureTypes=[
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"TABLES"],
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NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn})
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print('Processing type: Analysis')
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validType = True
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if validType == False:
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print("Invalid processing type. Choose Detection or Analysis.")
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return
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print('Start Job Id: ' + response['JobId'])
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dotLine = 0
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while jobFound == False:
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sqsResponse = self.sqs.receive_message(QueueUrl=self.sqsQueueUrl, MessageAttributeNames=['ALL'],
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MaxNumberOfMessages=10)
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if sqsResponse:
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if 'Messages' not in sqsResponse:
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if dotLine < 40:
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print('.', end='')
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dotLine = dotLine+1
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else:
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print()
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dotLine = 0
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sys.stdout.flush()
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time.sleep(5)
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continue
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for message in sqsResponse['Messages']:
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notification = json.loads(message['Body'])
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textMessage = json.loads(notification['Message'])
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print(textMessage['JobId'])
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print(textMessage['Status'])
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if str(textMessage['JobId']) == response['JobId']:
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print('Matching Job Found:' + textMessage['JobId'])
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jobFound = True
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results = self.GetResults(textMessage['JobId'])
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self.StoreInS3(results)
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self.sqs.delete_message(QueueUrl=self.sqsQueueUrl,
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ReceiptHandle=message['ReceiptHandle'])
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else:
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print("Job didn't match:" +
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str(textMessage['JobId']) + ' : ' + str(response['JobId']))
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# Delete the unknown message. Consider sending to dead letter queue
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self.sqs.delete_message(QueueUrl=self.sqsQueueUrl,
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ReceiptHandle=message['ReceiptHandle'])
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print('Done!')
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# Store the result in a S3 bucket
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def StoreInS3(self, response):
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print('registering in s3 bucket...')
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outputInJsonText = str(response)
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pdfTextExtractionS3ObjectName = self.document.replace('.pdf', '')
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pdfTextExtractionS3Bucket = self.bucket
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s3 = boto3.client('s3')
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outputFileName = pdfTextExtractionS3ObjectName + '.json'
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s3.put_object(Body=outputInJsonText,
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Bucket=pdfTextExtractionS3Bucket, Key=outputFileName)
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print('file ' + outputFileName + ' registered successfully!')
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def CreateTopicandQueue(self):
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millis = str(int(round(time.time() * 1000)))
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# Create SNS topic
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snsTopicName = "AmazonTextractTopic" + millis
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topicResponse = self.sns.create_topic(Name=snsTopicName)
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self.snsTopicArn = topicResponse['TopicArn']
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# create SQS queue
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sqsQueueName = "AmazonTextractQueue" + millis
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self.sqs.create_queue(QueueName=sqsQueueName)
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self.sqsQueueUrl = self.sqs.get_queue_url(
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QueueName=sqsQueueName)['QueueUrl']
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attribs = self.sqs.get_queue_attributes(QueueUrl=self.sqsQueueUrl,
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AttributeNames=['QueueArn'])['Attributes']
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sqsQueueArn = attribs['QueueArn']
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# Subscribe SQS queue to SNS topic
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self.sns.subscribe(
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TopicArn=self.snsTopicArn,
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Protocol='sqs',
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Endpoint=sqsQueueArn)
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# Authorize SNS to write SQS queue
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policy = """{{
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"Version":"2012-10-17",
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"Statement":[
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{{
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"Sid":"MyPolicy",
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"Effect":"Allow",
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"Principal" : {{"AWS" : "*"}},
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"Action":"SQS:SendMessage",
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"Resource": "{}",
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"Condition":{{
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"ArnEquals":{{
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"aws:SourceArn": "{}"
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}}
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}}
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}}
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]
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}}""".format(sqsQueueArn, self.snsTopicArn)
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response = self.sqs.set_queue_attributes(
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QueueUrl=self.sqsQueueUrl,
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Attributes={
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'Policy': policy
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})
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def DeleteTopicandQueue(self):
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self.sqs.delete_queue(QueueUrl=self.sqsQueueUrl)
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self.sns.delete_topic(TopicArn=self.snsTopicArn)
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def GetResults(self, jobId):
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maxResults = 1000
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paginationToken = None
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finished = False
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pages = []
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while finished == False:
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response = None
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if self.processType == ProcessType.ANALYSIS:
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if paginationToken == None:
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response = self.textract.get_document_analysis(JobId=jobId,
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MaxResults=maxResults)
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else:
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response = self.textract.get_document_analysis(JobId=jobId,
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MaxResults=maxResults,
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NextToken=paginationToken)
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if self.processType == ProcessType.DETECTION:
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if paginationToken == None:
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response = self.textract.get_document_text_detection(JobId=jobId,
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MaxResults=maxResults)
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else:
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response = self.textract.get_document_text_detection(JobId=jobId,
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MaxResults=maxResults,
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NextToken=paginationToken)
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# Put response on pages list
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pages.append(response)
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print('Document Detected.')
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if 'NextToken' in response:
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paginationToken = response['NextToken']
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else:
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finished = True
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# convert pages as JSON pattern
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pages = json.dumps(pages)
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return pages
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def lambda_handler(event, context):
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analyzer = DocumentProcessor()
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# Get the object from the event and show its content type
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bucket = event['Records'][0]['s3']['bucket']['name']
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key = urllib.parse.unquote_plus(event['Records'][0]['s3']['object']['key'], encoding='utf-8')
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try:
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analyzer.main(bucket, key)
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return 'Processing Done!'
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except Exception as e:
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print(e)
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print('Error getting object {} from bucket {}. Make sure they exist and your bucket is in the same region as this function.'.format(key, bucket))
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raise e

‎Run_Sample_Flask_App_on_AzureWebApp/Helpfile.json

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## Login AZ Accounts
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az login --use-device-code
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## Create a web app in Azure
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1) Create RG
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$LOCATION='eastus'
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$RESOURCE_GROUP_NAME='python-flask-webapp-rg'
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# Create a resource group
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az group create `
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--location $LOCATION `
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--name $RESOURCE_GROUP_NAME
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2) Create App Service Plan
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$APP_SERVICE_PLAN_NAME='python-flask-webapp-plan'
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az appservice plan create `
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--name $APP_SERVICE_PLAN_NAME `
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--resource-group $RESOURCE_GROUP_NAME `
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--sku B1 `
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--is-linux
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3) Create App service web app
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$APP_SERVICE_NAME='python-flask-webapp-quicklabs'
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az webapp create `
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--name $APP_SERVICE_NAME `
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--runtime 'PYTHON:3.9' `
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--plan $APP_SERVICE_PLAN_NAME `
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--resource-group $RESOURCE_GROUP_NAME `
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--query 'defaultHostName' `
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--output table
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4) Enable build automation.
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az webapp config appsettings set `
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--resource-group $RESOURCE_GROUP_NAME `
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--name $APP_SERVICE_NAME `
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--settings SCM_DO_BUILD_DURING_DEPLOYMENT=true
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5) Zip file upload.
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az webapp deploy `
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--name $APP_SERVICE_NAME `
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--resource-group $RESOURCE_GROUP_NAME `
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--src-path F:\RekhuAll\Azure\Run_Sample_Flask_App_on_AzureWebApp\msdocs-python-flask-webapp-quickstart\msdocs-python-flask-webapp-quickstart.zip
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6) Stream logs - Configuration
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az webapp log config `
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--web-server-logging filesystem `
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--name $APP_SERVICE_NAME `
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--resource-group $RESOURCE_GROUP_NAME
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7) Stream the log trail
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az webapp log tail `
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--name $APP_SERVICE_NAME `
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--resource-group $RESOURCE_GROUP_NAME

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