Invoice recognition
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.gitignore
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.gitignore
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# Byte-compiled / optimized / DLL files
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__pycache__/
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cli.py
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*/*venv
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*$py.class
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# C extensions
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# aws
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# AWS image recognition with Textract
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### To deploy:
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- clone lambda.py code into your lambda trigger
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- Add S3 bucket to your Lambda Trigger
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- Add invoice to your bucket
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- Enjoy!
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invoice/lambda.py
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invoice/lambda.py
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import boto3
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from collections import defaultdict
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from urllib.parse import unquote_plus
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import json
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def print_labels_and_values(field, keys):
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if "LabelDetection" in field and "ValueDetection" in field:
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a, b = str(field.get('LabelDetection')['Text']), str(field.get('ValueDetection')['Text'])
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for w in keys:
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if w in a:
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print(f"{a}:{b}")
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return w, b
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return None, None
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def process_expense_analysis(response):
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wanted = {"NIP":"", "Sprzedawca":"", "brutto":""}
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for expense_doc in response["ExpenseDocuments"]:
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for summary_field in expense_doc["SummaryFields"]:
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a,b = print_labels_and_values(summary_field, wanted.keys())
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if a != None:
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wanted[a] = b
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print()
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return wanted
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def lambda_handler(event, context):
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file_obj = event["Records"][0]
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bucket = unquote_plus(str(file_obj["s3"]["bucket"]["name"]))
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file_name = unquote_plus(str(file_obj["s3"]["object"]["key"]))
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print(f'Bucket: {bucket}, file: {file_name}')
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client = boto3.client('textract')
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response = client.analyze_expense(Document={'S3Object': {'Bucket': bucket, "Name": file_name}})
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invoice_data = process_expense_analysis(response)
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invoice_data['name'] = file_name
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print(json.dumps(invoice_data, indent=4))
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dynamodb = boto3.resource('dynamodb')
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table = dynamodb.Table('texttract-s478874')
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table.put_item(Item=invoice_data)
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from cli import aws_access_key_id, aws_secret_access_key, aws_session_token, DEFAULT_VPC
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import boto3, time
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index = '478874'
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key_name = index + '_key'
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group_name = index + '_group'
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target_name = index + '_target'
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lb_name = index + '_loadbalancer'
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user_data=r'''
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#!/bin/bash
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sudo yum update -y
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sudo yum install git -y
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git clone https://git.wmi.amu.edu.pl/bikol/DPZC-2022-23.git
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cd 04_Public_cloud/zadania
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sudo yum install docker -y
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sudo service docker start
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sudo usermod -a -G docker ec2-user
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docker build -t webservice .
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docker run -d -p 80:8080 -t webservice
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'''
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if __name__ == '__main__':
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ec2 = boto3.resource(
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'ec2',
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region_name='us-east-1',
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aws_access_key_id=aws_access_key_id,
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aws_secret_access_key=aws_secret_access_key,
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aws_session_token=aws_session_token,
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)
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key_pair = ec2.create_key_pair(
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KeyName=key_name,
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KeyType='ed25519',
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KeyFormat='pem',
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)
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security_group = ec2.create_security_group(
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Description=group_name,
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GroupName=group_name,
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VpcId=DEFAULT_VPC,
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)
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inbound_rules = security_group.authorize_ingress(
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GroupId=security_group.group_id,
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CidrIp='0.0.0.0/0',
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IpProtocol='tcp',
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FromPort=80,
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ToPort=80,
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)
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#TODO instancja EC2
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instance1, instance2 = ec2.create_instances(
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ImageId='ami-0b5eea76982371e91',
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MinCount=2,
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MaxCount=2,
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InstanceType='t2.micro',
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KeyName=key_pair.name,
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UserData=user_data,
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SecurityGroups=[security_group.group_name],
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)
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while True:
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time.sleep(1)
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instance1 = ec2.Instance(instance1.id)
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instance2 = ec2.Instance(instance2.id)
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if instance1.state['Code'] == 16 and instance2.state['Code'] == 16:
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break
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#TODO target group
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elbv2 = boto3.client(
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'elbv2',
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region_name='us-east-1',
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aws_access_key_id=aws_access_key_id,
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aws_secret_access_key=aws_secret_access_key,
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aws_session_token=aws_session_token,
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)
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target_group = elbv2.create_target_group(
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Name=target_name,
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Protocol='TCP',
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Port=80,
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VpcId=DEFAULT_VPC,
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TargetType='instance',
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IpAddressType='ipv4',
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)
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registered_targets = elbv2.register_targets(
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TargetGroupArn=target_group['TargetGroups'][0]['TargetGroupArn'],
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Targets=[
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{
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'Id': instance1.id,
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'Port': 80,
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},
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{
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'Id': instance2.id,
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'Port': 80,
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},
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]
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)
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#TODO elastic IP
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ec2_client = boto3.client(
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'ec2',
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region_name='us-east-1',
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aws_access_key_id=aws_access_key_id,
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aws_secret_access_key=aws_secret_access_key,
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aws_session_token=aws_session_token,
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)
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allocation = ec2_client.allocate_address(
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Domain='vpc'
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)
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#TODO load balancer
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load_balancer = elbv2.create_load_balancer(
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Name=lb_name,
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SubnetMappings=[
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{
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'SubnetId': instance1.subnet_id,
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'AllocationId': allocation['AllocationId'],
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},
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],
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Scheme='internet-facing',
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Type='network',
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IpAddressType='ipv4',
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)
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listener = elbv2.create_listener(
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LoadBalancerArn=load_balancer['LoadBalancers'][0]['LoadBalancerArn'],
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Protocol='TCP',
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Port=80,
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DefaultActions=[
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{
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'Type': 'forward',
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'TargetGroupArn': target_group['TargetGroups'][0]['TargetGroupArn'],
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},
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],
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)
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print(f'{allocation["PublicIp"]}:80')
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#!/bin/bash
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echo "Creating virtual enviroment ..."
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python3 -m venv aws_lb_venv
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echo "Installing dependencies ..."
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pip install -q boto3, time
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echo "Initiating gitea cloud deploy ..."
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chmod +x a.py
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python3 a.py
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