# MIT No Attribution

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

# Permission is hereby granted, free of charge, to any person obtaining a copy of this
# software and associated documentation files (the "Software"), to deal in the Software
# without restriction, including without limitation the rights to use, copy, modify,
# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
# PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

AWSTemplateFormatVersion: 2010-09-09
Description: >
  "CloudFormation for re:Invent 2022 - BIZ310 - Real-time churn prediction in AI powered contact centres - Lambdas, Kinesis, API Gateway, EventBridge, Step Functions, DynamoDB tables, Lex, S3 Bucket, and CloudFront Distribution"

Parameters:
  AmazonConnectInstanceARN:
    Default: "**** Enter the Full Amazon Connect Instance ARN ****"
    Description: The Amazon Connect Instance ARN
    Type: String
  SageMakerFeatureGroupName:
    Default: "fg-contact-center-data"
    Description: The Amazon SageMaker Feature Group Name
    Type: String
  SageMakerEndPointName:
    Default: "sagemaker-endpoint-xgboost-customerchurn"
    Description: The Amazon SageMaker Endpoint Name
    Type: String
  CustomerProfilesDomain:
    Default: "**** Enter the Customer Profiles Domain ****"
    Description: The Amazon Connect Customers Profile Domain (amazon-connect-[alias])
    Type: String
  EmailForNotifications:
    Default: "**** Enter an email address for Contact Lens Notifications ****"
    Description: Email for Contact Lens Notification
    Type: String
  S3bucketWorkshopFiles:
    Default: "**** Enter the S3 bucket where the Workshop Files were uploaded ****"
    Description: S3Bucket with Workshop Files
    Type: String
  KMSforSNS:
    Default: "**** KMS ID for SNS ****"
    Description: Enter the KMS ID for SNS
    Type: String

# API Gateway URLs, Queue Retention ARN, and LexV2Bot ARN to be used in the Website
Outputs:
  apiGatewayInvokeURLchurn:
    Description: API Endpoint to capture Churn updates
    Value: !Sub https://${reInvent2022AgentInterfaceRestApi}.execute-api.${AWS::Region}.amazonaws.com/prod
  apiGatewayInvokeURLcontract:
    Description: API Endpoint to update contracted months
    Value: !Sub https://${reInvent2022AgentInterfaceRestApi}.execute-api.${AWS::Region}.amazonaws.com/prod/update
  LexV2BotARN:
    Description: The ARN of the LexV2 Bot
    Value: !GetAtt IVR.Arn
  CloudfrontEndpoint:
      Description: Endpoint for Custom CCP distribution
      Value: !Join
        - ''
        - - 'https://'
          - !GetAtt [CFCloudFrontDistribution, DomainName]
  CustomCCPS3Bucket:
    Description: Bucket for the CustomCCP Website
    Value: !Ref createCustomCCPS3Bucket

Resources:

# Sentiments DynamoDB Table, partition key contact Id
  SentimentsDDBTable:
    Type: AWS::DynamoDB::Table
    Properties:
      AttributeDefinitions:
        - AttributeName: ContactId
          AttributeType: S
      KeySchema:
        - AttributeName: ContactId
          KeyType: HASH
      ProvisionedThroughput:
        ReadCapacityUnits: 5
        WriteCapacityUnits: 5
      TableName: reInvent2022-Sentiments
      TimeToLiveSpecification:
        AttributeName: ttl
        Enabled: true

# Contacts ID DynamoDB Table, store customer ID per contact ID
  ContactIdsDDBTable:
    Type: AWS::DynamoDB::Table
    Properties:
      AttributeDefinitions:
        - AttributeName: ContactId
          AttributeType: S
      KeySchema:
        - AttributeName: ContactId
          KeyType: HASH
      ProvisionedThroughput:
        ReadCapacityUnits: 5
        WriteCapacityUnits: 5
      TableName: reInvent2022-ContactIds
      TimeToLiveSpecification:
        AttributeName: ttl
        Enabled: true

# Churn prediction table storing all the predictions per contact ID,
# partition key Contact ID and sort key Time
  ChurnPredictionDDBTable:
    Type: AWS::DynamoDB::Table
    Properties:
      AttributeDefinitions:
        - AttributeName: ContactId
          AttributeType: S
        - AttributeName: When
          AttributeType: S
      KeySchema:
        - AttributeName: ContactId
          KeyType: HASH
        - AttributeName: When
          KeyType: RANGE
      ProvisionedThroughput:
        ReadCapacityUnits: 5
        WriteCapacityUnits: 5
      TableName: reInvent2022-ChurnPrediction
      TimeToLiveSpecification:
        AttributeName: ttl
        Enabled: true

# IAM Role for Lambda functions
  LambdasIAMRole:
    Type: "AWS::IAM::Role"
    DependsOn:
      - ChurnPredictionDDBTable
      - ContactIdsDDBTable
      - SentimentsDDBTable
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service:
                - lambda.amazonaws.com
            Action:
                - "sts:AssumeRole"
      Description: A role to allow all the functions in the re:Invent2022 BIZ310 Project
      Policies:
        - PolicyName: reInvent2022role
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action:
                  - dynamodb:GetItem
                  - dynamodb:UpdateItem
                  - dynamodb:PutItem
                  - dynamodb:PartiQLSelect
                Resource:
                  - !GetAtt ContactIdsDDBTable.Arn
                  - !GetAtt ChurnPredictionDDBTable.Arn
                  - !GetAtt SentimentsDDBTable.Arn
              - Effect: Allow
                Action:
                  - profile:SearchProfiles
                  - profile:UpdateProfile
                Resource:
                  - !Sub arn:aws:profile:${AWS::Region}:${AWS::AccountId}:domains/${CustomerProfilesDomain}
              - Effect: Allow
                Action:
                  - connect:AssociateInstanceStorageConfig
                  - connect:UpdateContactAttributes
                Resource:
                  - !Ref AmazonConnectInstanceARN
                  - !Sub ${AmazonConnectInstanceARN}/*
              - Effect: Allow
                Action:
                  - logs:CreateLogGroup
                  - logs:CreateLogStream
                  - logs:PutLogEvents
                  - sagemaker:*
                  - kinesis:*
                  - iam:PutRolePolicy
                Resource: "*"
              - Effect: Allow
                Action:
                  -  states:StartExecution
                Resource:
                  - !Sub arn:aws:states:${AWS::Region}:${AWS::AccountId}:stateMachine:ContractCancelled

# IAM Service Link Role for Lex
  LexServiceLinkRole:
    Type: 'AWS::IAM::ServiceLinkedRole'
    Properties:
      AWSServiceName: lexv2.amazonaws.com
      Description: AWSServiceRoleForLexV2Bots
      CustomSuffix: reInvent2022

# IAM Role for Step Functions
  StepsIAMRole:
    Type: 'AWS::IAM::Role'
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
        - Effect: Allow
          Principal:
            Service: !Sub 'states.${AWS::Region}.amazonaws.com'
          Action: 'sts:AssumeRole'
      Policies:
      - PolicyName: ConnectTasksAPI
        PolicyDocument:
          Statement:
          - Effect: Allow
            Action:
              - connect:StartTaskContact
            Resource:
              - !Sub ${AmazonConnectInstanceARN}/*
          - Effect: Allow
            Action:
              - logs:CreateLogGroup
              - logs:CreateLogStream
              - logs:PutLogEvents
            Resource: "*"

# SNS Topic to send email with notification about negative sentiment
  SNSforNegativeSentiment:
    Type: AWS::SNS::Topic
    Properties:
      TopicName: NegativeSentiment
      KmsMasterKeyId: !Ref KMSforSNS

# SNS Policy for Event Bridge
  SNSpolicyforEventBridge:
    Type: AWS::SNS::TopicPolicy
    Properties:
      PolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service:
                - events.amazonaws.com
            Action:
              - sns:Publish
            Resource:
              - !Ref 'SNSforNegativeSentiment'
      Topics:
        - !Ref 'SNSforNegativeSentiment'

# Subscription to send email with negative sentiment
  EmailSubscription:
    Type: AWS::SNS::Subscription
    Properties:
      Protocol: email
      Endpoint: !Ref EmailForNotifications
      TopicArn: !Ref 'SNSforNegativeSentiment'


# EventBridge Rule to capture Negative Sentiment based on Contact Lens Events
  NegSentimentEventBridgeRule:
    Type: AWS::Events::Rule
    Properties:
      Description: Rule to capture Negative Sentiment based on Contact Lens Events
      EventBusName: default
      EventPattern:
        source:
          - aws.connect
        detail-type:
          - "Contact Lens Realtime Rules Matched"
        detail:
          actionName:
            - "LongNegativeSentiment"
      State: ENABLED
      Targets:
        - Arn: !Ref SNSforNegativeSentiment
          Id: Id1234
          InputTransformer:
            InputPathsMap:
              Region: "$.region"
              ContactArn: "$.detail.contactArn"
              RuleName: "$.detail.ruleName"
              Time: "$.time"
            InputTemplate: |
              "There is a call in <Region> with <RuleName>, customer has been experiencing negative sentiment since <Time>. Please find out more details using the Contact Id <ContactArn> and help the agent ASAP to manage this siutation or call the customer back before they churn to the competition."


# PandasLayerForLambda
  PandasLayer:
    Type: AWS::Lambda::LayerVersion
    Properties:
      CompatibleRuntimes:
        - python3.9
      Content:
        S3Bucket: !Ref S3bucketWorkshopFiles
        S3Key: pandas/python.zip
      Description: Pandas Lambda Layer
      LayerName: pandas
      LicenseInfo: MIT

# NumpyLayerForLambda
  NumpyLayer:
    Type: AWS::Lambda::LayerVersion
    Properties:
      CompatibleRuntimes:
        - python3.9
      Content:
        S3Bucket: !Ref S3bucketWorkshopFiles
        S3Key: numpy/python.zip
      Description: Numpy Lambda Layer
      LayerName: numpy
      LicenseInfo: MIT

# Lambda Function to retrieve customer information from Profiles
  LambdareInvent2022CustomerProfiles:
    Type: AWS::Lambda::Function
    Properties:
      Description: Function to retrieve customer information from Profiles
      Environment:
        Variables:
          ContactIdTableName: reInvent2022-ContactIds
          ProfilesDomain: !Ref CustomerProfilesDomain
          InstanceARN: !Ref AmazonConnectInstanceARN
      Code:
        ZipFile: |
            # MIT No Attribution

            # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

            # Permission is hereby granted, free of charge, to any person obtaining a copy of this
            # software and associated documentation files (the "Software"), to deal in the Software
            # without restriction, including without limitation the rights to use, copy, modify,
            # merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
            # permit persons to whom the Software is furnished to do so.

            # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
            # INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
            # PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
            # HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
            # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
            # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

            import json
            import boto3
            import os
            import datetime
            import time

            profiles = boto3.client('customer-profiles')
            dynamodb = boto3.resource('dynamodb')
            dynamodbclient = boto3.client('dynamodb')

            ProfilesDomain= os.environ['ProfilesDomain']
            ContactIdTableName= os.environ['ContactIdTableName']
            InstanceArn = os.environ['InstanceARN']

            def lambda_handler(event, context):

              InstanceId = InstanceArn[-36:]
              #Customer phone number passed from Amazon Connect via JSON
              phoneNumber = event ['Details']['ContactData']['CustomerEndpoint']['Address']
              #ContactId passed from Amazon Connect via JSON
              ContactId = event ['Details']['ContactData']['ContactId']

              # expires after one day
              expiryTimestamp = int(time.time() + 24*60*60)

              #customer lookup in Customer Profiles via phone number
              response = profiles.search_profiles(
                  DomainName=ProfilesDomain,
                  KeyName='_phone',
                  Values=[phoneNumber])

              Items = response['Items']

              #If record exists write values to variables

              for Item in Items:

                if not "FirstName" in Item:
                    print("No FirstName in profile")
                else:
                  customerid = Item['AccountNumber']
                  firstName = Item['FirstName']
                  lastName = Item['LastName']
                  email = Item['EmailAddress']
                  contractedMonths = Item['AdditionalInformation']
                  customerProfile = Item["ProfileId"]

                  Input = json.dumps({'ContactId': ContactId, 'customerid':customerid, 'InstanceId': InstanceId})
                  print(Input)

                  Time = str(datetime.datetime.utcnow())

                  contactResp = dynamodbclient.put_item(
                     TableName=ContactIdTableName,
                     Item={
                       'ContactId':{
                         'S': ContactId
                       },
                       'customerid':{
                         'S': customerid
                       },
                       'When':{
                         'S': Time
                       },
                       'customerProfile':{
                           'S': customerProfile
                       },
                       'ttl': {
                          'N': str(expiryTimestamp)
                       }
                     },
                     ReturnConsumedCapacity='TOTAL'
                   )
                  print(contactResp)

                  #Return variables to Amazon Connect
                  return {'message': 'Success',
                           'customerid' : customerid,
                           'firstName' : firstName,
                           'lastName' : lastName,
                           'email' : email,
                           'contractedMonths': contractedMonths,
                           'customerProfile': customerProfile
                  }

      FunctionName: CustomerProfiles
      Handler: index.lambda_handler
      Role: !GetAtt LambdasIAMRole.Arn
      Runtime: python3.9

# Permission for Connect to access Lambda Function
  ConnectFunctionPolicyLambdaCustomerProfiles:
    Type: AWS::Lambda::Permission
    DependsOn: LambdareInvent2022CustomerProfiles
    Properties:
      FunctionName: !Sub 'CustomerProfiles'
      Action: "lambda:InvokeFunction"
      Principal:  "connect.amazonaws.com"
      SourceAccount: !Sub ${AWS::AccountId}
      SourceArn: !Ref AmazonConnectInstanceARN

# Lambda Function to get the Churn Prediction based on Lex Sentiment
  LambdareInvent2022LexSentimentUpdate:
    Type: AWS::Lambda::Function
    DependsOn:
      - PandasLayer
      - NumpyLayer
    Properties:
      Description: Function to get the Churn Prediction based on Lex Sentiment
      Environment:
        Variables:
          ChurnPredictionTable: reInvent2022-ChurnPrediction
          ContactIdTableName: reInvent2022-ContactIds
          EndPointName: !Ref SageMakerEndPointName
          SageMakerFGName: !Ref SageMakerFeatureGroupName
          SentimentsTable: reInvent2022-Sentiments
      Layers:
        - !Ref PandasLayer
        - !Ref NumpyLayer
      Code:
        ZipFile: |
            # MIT No Attribution

            # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

            # Permission is hereby granted, free of charge, to any person obtaining a copy of this
            # software and associated documentation files (the "Software"), to deal in the Software
            # without restriction, including without limitation the rights to use, copy, modify,
            # merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
            # permit persons to whom the Software is furnished to do so.

            # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
            # INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
            # PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
            # HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
            # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
            # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

            import json
            import base64
            import boto3
            import os
            # This funciton requires a pandas layer
            import pandas as pd
            import io
            import csv
            import datetime
            import numpy
            import time

            # Environment Variables including DynamoDB Churn predictions and sentiment tables, SageMaker
            # Feature Group and EndPoint

            ContactIdTableName= os.environ['ContactIdTableName']
            ChurnPredictionTable = os.environ['ChurnPredictionTable']
            SentimentsTable = os.environ['SentimentsTable']
            sagemakerFGname = os.environ['SageMakerFGName']
            endPointName = os.environ['EndPointName']

            boto_session = boto3.Session()
            ddbclient = boto3.client('dynamodb')
            dynamodb = boto3.resource('dynamodb')
            region = boto_session.region_name
            runtime_client = boto3.client("runtime.sagemaker")

            # This lambda reads the Event details from Kinesis, get customer features from SageMaker Feature Store, inference to the SageMaker endpoint and update
            # sentiments and churn prediction tables.
            def lambda_handler(event, context):
                print('Incoming event: ', event)

                # expires after one day
                expiryTimestamp = int(time.time() + 24*3600)

                # CALL UPDATE RECORD with the new Table
                def FeatureValue (**kwargs):
                    try:
                        result = dict()
                        for key, value in kwargs.items():
                            if value is not None:
                                result[key] = value
                                #print(result)
                        return result
                    except Exception as e:
                        print("error is: " + e)
                        return {"Error"}


                # This function returns the customerid for the specific ContactId
                def getcustomerid (ContactId):
                    try:
                        table = dynamodb.Table(ContactIdTableName)
                        response = table.get_item(Key={'ContactId' : ContactId})
                        if 'Item' in response:
                            customer = response['Item']['customerid']
                            #print(customer)
                            return customer
                    except Exception as e:
                        print(e)
                        return {"Error"}

                # Update the churn associated to the Contact ID in the churn prediction table
                def updateTransaction(ContactId,churn_pred,expiryTimestamp):
                    Time = str(datetime.datetime.utcnow())
                    print(Time)
                    responseContact = ddbclient.put_item(
                        TableName=ChurnPredictionTable,
                        Item={
                            'ContactId':{
                                'S': ContactId
                            },
                            'When':{
                                'S': Time
                            },
                            'ChurnPrediction':{
                                'N':churn_pred
                            },
                            'ttl': {
                               'N': str(expiryTimestamp)
                            }
                        },
                        ReturnConsumedCapacity='TOTAL'
                    )
                    print(responseContact)

                # Update historical customer sentiments in the dynamodb table
                def updateSentiments(ContactId,df,expiryTimestamp):
                    print(f'negative value:{df["pastSenti_neg"]["ValueAsString"]}')
                    print(f'neutral value:{df["pastSenti_nut"]["ValueAsString"]}')
                    print(f'positive value:{df["pastSenti_pos"]["ValueAsString"]}')
                    responseSentiment = ddbclient.put_item(
                        TableName=SentimentsTable,
                        Item={
                            'ContactId':{
                                'S': ContactId
                            },
                            'Neutrals':{
                                'N': str(df["pastSenti_nut"]["ValueAsString"])
                            },
                            'Negatives':{
                                'N': str(df["pastSenti_neg"]["ValueAsString"])
                            },
                            'Positives':{
                                'N': str(df["pastSenti_pos"]["ValueAsString"])
                            },
                            'ttl': {
                               'N': str(expiryTimestamp)
                            }
                        },
                        ReturnConsumedCapacity='TOTAL'
                    )
                    print(responseSentiment)

                # Main Function Starts
                try:

                    # Read the attributes provided by Amazon Connect
                    ContactId = event['Details']['ContactData']['ContactId']
                    customerid = event['Details']['ContactData']['Attributes']['customerid']
                    Sentiment = event['Details']['ContactData']['Attributes']['customerSentiment']
                    print(Sentiment)

                    # Calling API to get customer features from SageMaker Feature Store based on Customer ID for this Record
                    featurestore_runtime = boto_session.client(service_name='sagemaker-featurestore-runtime', region_name=region)
                    fsresponse = featurestore_runtime.get_record(FeatureGroupName=sagemakerFGname, RecordIdentifierValueAsString=customerid)
                    Record = fsresponse ['Record']

                    # Use pandas to read the Record
                    df = pd.DataFrame(Record).set_index('FeatureName').transpose()
                    print(df)

                    # Call function to update sentiments table
                    updateSentiments(ContactId, df,expiryTimestamp)

                    if Sentiment == "POSITIVE":
                        df['pastSenti_pos'] = df['pastSenti_pos'].astype(int) + 1
                        df['sentiment'] = 1
                    elif Sentiment == "NEUTRAL":
                        df['pastSenti_nut'] = df['pastSenti_nut'].astype(int) + 1
                        df['sentiment'] = 0
                    elif Sentiment == "MIXED":
                        df['pastSenti_nut'] = df['pastSenti_nut'].astype(int) + 1
                        df['sentiment'] = 0
                    elif Sentiment == "NEGATIVE":
                        df['pastSenti_neg'] = df['pastSenti_neg'].astype(int) + 1
                        df['sentiment'] = -1

                    # Update Feature Store
                    row = df.iloc[0]
                    recordUpdate = [
                        FeatureValue(
                            FeatureName=df.columns[index],
                            ValueAsString=str(row[index])
                        )
                        for index in range(0, len(row))
                        if pd.notna(row[index])
                    ]
                    #print(record)

                    # Update Feature Store
                    featurestore_runtime.put_record(
                        FeatureGroupName=sagemakerFGname,
                        Record=recordUpdate
                    )

                    # Call API Endpoint with the new sentiment value
                    df.drop(['event_time','customerID'], inplace=True, axis=1)
                    csv_buffer = io.StringIO()
                    csv_writer = csv.writer(csv_buffer, delimiter=",")
                    csv_writer.writerow(df.values[0][1:])
                    response = runtime_client.invoke_endpoint(
                        EndpointName=endPointName, ContentType="text/csv", Body=csv_buffer.getvalue().rstrip("\r\n")
                    )
                    churn_pred = response["Body"].read().decode("ascii")
                    updateTransaction(ContactId,str(churn_pred),expiryTimestamp)

                    print(churn_pred)

                    return {
                        'message': 'Success',
                        'ContactId': ContactId,
                        'customerid': customerid,
                        'Churn': churn_pred
                    }

                except Exception as a:
                    print (a)

      FunctionName: LexSentimentUpdate
      Handler: index.lambda_handler
      Role: !GetAtt LambdasIAMRole.Arn
      Runtime: python3.9
      Timeout: 8

# Permission for Connect to access Lambda Function
  ConnectFunctionPolicyLambdaLexSentiment:
    Type: AWS::Lambda::Permission
    DependsOn: LambdareInvent2022LexSentimentUpdate
    Properties:
      FunctionName: !Sub 'LexSentimentUpdate'
      Action: "lambda:InvokeFunction"
      Principal:  "connect.amazonaws.com"
      SourceAccount: !Sub ${AWS::AccountId}
      SourceArn: !Ref AmazonConnectInstanceARN

# Kinesis Data Streams for Contact Lens Real time
  KinesisContactLensRT:
    Type: AWS::Kinesis::Stream
    Properties:
      RetentionPeriodHours: 24
      StreamModeDetails:
        StreamMode: ON_DEMAND

# Lambda to Enable Kinesis for Real Time Contact Lens
  LambdareInvent2022EnableKinesis:
    Type: AWS::Lambda::Function
    DependsOn:
      - KinesisContactLensRT
    Properties:
      Description: Function to enable Kinesis for Real Time Contact Lens
      Environment:
        Variables:
          InstanceARN: !Ref AmazonConnectInstanceARN
          KinesisArn: !Sub arn:aws:kinesis:${AWS::Region}:${AWS::AccountId}:stream/${KinesisContactLensRT}
      Code:
        ZipFile: |
            # MIT No Attribution

            # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

            # Permission is hereby granted, free of charge, to any person obtaining a copy of this
            # software and associated documentation files (the "Software"), to deal in the Software
            # without restriction, including without limitation the rights to use, copy, modify,
            # merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
            # permit persons to whom the Software is furnished to do so.

            # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
            # INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
            # PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
            # HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
            # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
            # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

            import json
            import boto3
            import os
            import cfnresponse

            boto_session = boto3.Session()
            connect = boto3.client('connect')
            region = boto_session.region_name
            InstanceArn = os.environ['InstanceARN']
            KinesisArn = os.environ['KinesisArn']

            def lambda_handler(event, context):

              if event['RequestType'] == 'Create':

                InstanceId = InstanceArn[-36:]
                # Execute Request to associate Kinesis Streams with Contact Lens Real Time
                try:
                  response = connect.associate_instance_storage_config(
                      InstanceId= InstanceId,
                      ResourceType='REAL_TIME_CONTACT_ANALYSIS_SEGMENTS',
                      StorageConfig={
                          'StorageType': 'KINESIS_STREAM',
                          'KinesisStreamConfig': {
                              'StreamArn': KinesisArn
                          }
                      }
                  )
                  print(response)
                  responseValue = 200
                  responseData = {}
                  responseData['Data'] = responseValue
                  cfnresponse.send(event, context, cfnresponse.SUCCESS, responseData, "CustomResourcePhysicalID")

                except Exception as e:
                    print(e)

              else:
                  responseData = {}
                  responseData['Data'] = "Delete"
                  cfnresponse.send(event, context, cfnresponse.SUCCESS, responseData, "CustomResourcePhysicalID")

      Handler: index.lambda_handler
      Role: !GetAtt LambdasIAMRole.Arn
      Runtime: python3.9

# Invoke Lambda Function to associate Kinesis with Contact Lens Real Time
  LambdainvokeKinesisCLRealTime:
    Type: AWS::CloudFormation::CustomResource
    DependsOn:
      - LambdareInvent2022EnableKinesis
    Version: "1.0"
    Properties:
      ServiceToken: !GetAtt LambdareInvent2022EnableKinesis.Arn

# Lambda Function to capture contact lens realtime sentiments (it needs Pandas Layer)
  LambdareInvent2022CLStreamRT:
    Type: AWS::Lambda::Function
    DependsOn:
      - PandasLayer
      - NumpyLayer
    Properties:
      Description: Function to capture contact lens realtime sentiments (it needs Pandas Layer)
      Environment:
        Variables:
          ChurnPredictionTable: reInvent2022-ChurnPrediction
          ContactIdTableName: reInvent2022-ContactIds
          SentimentsTable: reInvent2022-Sentiments
          SageMakerFGName: !Ref SageMakerFeatureGroupName
          EndPointName: !Ref SageMakerEndPointName
      Layers:
        - !Ref PandasLayer
        - !Ref NumpyLayer
      Code:
        ZipFile: |
            # MIT No Attribution

            # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

            # Permission is hereby granted, free of charge, to any person obtaining a copy of this
            # software and associated documentation files (the "Software"), to deal in the Software
            # without restriction, including without limitation the rights to use, copy, modify,
            # merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
            # permit persons to whom the Software is furnished to do so.

            # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
            # INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
            # PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
            # HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
            # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
            # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

            import json
            import base64
            import boto3
            import os
            # This funciton requires a pandas layer
            import pandas as pd
            import io
            import csv
            import datetime
            import numpy
            import time

            # Environment Variables including DynamoDB Churn predictions and sentiment tables, SageMaker
            # Feature Group and EndPoint

            ContactIdTableName= os.environ['ContactIdTableName']
            ChurnPredictionTable = os.environ['ChurnPredictionTable']
            SentimentsTable = os.environ['SentimentsTable']
            sagemakerFGname = os.environ['SageMakerFGName']
            endPointName = os.environ['EndPointName']

            boto_session = boto3.Session()
            ddbclient = boto3.client('dynamodb')
            dynamodb = boto3.resource('dynamodb')
            region = boto_session.region_name
            runtime_client = boto3.client("runtime.sagemaker")

            # This lambda reads the Event details from Kinesis, get customer features from SageMaker Feature Store, inference to the SageMaker endpoint and update
            # sentiments and churn prediction tables.
            def lambda_handler(event, context):
                print('Incoming event: ', event)

                # expires after one day
                expiryTimestamp = int(time.time() + 24*60*60)

                # CALL UPDATE RECORD with the new Table
                def FeatureValue (**kwargs):
                    try:
                        result = dict()
                        for key, value in kwargs.items():
                            if value is not None:
                                result[key] = value
                                #print(result)
                        return result
                    except Exception as e:
                        print("error is: " + e)
                        return {"Error"}


                # This function returns the customerid for the specific ContactId
                def getcustomerid (ContactId):
                    try:
                        table = dynamodb.Table(ContactIdTableName)
                        response = table.get_item(Key={'ContactId' : ContactId})
                        if 'Item' in response:
                            customer = response['Item']['customerid']
                            #print(customer)
                            return customer
                    except Exception as e:
                        print(e)
                        return {"Error"}

                # Update the churn associated to the Contact ID in the churn prediction table
                def updateTransaction(ContactId,churn_pred,expiryTimestamp):
                    Time = str(datetime.datetime.utcnow())
                    print(Time)
                    responseContact = ddbclient.put_item(
                        TableName=ChurnPredictionTable,
                        Item={
                            'ContactId':{
                                'S': ContactId
                            },
                            'When':{
                                'S': Time
                            },
                            'ChurnPrediction':{
                                'N':churn_pred
                            },
                            'ttl': {
                               'N': str(expiryTimestamp)
                            }
                        },
                        ReturnConsumedCapacity='TOTAL'
                    )
                    print(responseContact)

                # Update historical customer sentiments in the dynamodb table
                def updateSentiments(ContactId,df,expiryTimestamp):
                    print(f'negative value:{df["pastSenti_neg"]["ValueAsString"]}')
                    print(f'neutral value:{df["pastSenti_nut"]["ValueAsString"]}')
                    print(f'positive value:{df["pastSenti_pos"]["ValueAsString"]}')
                    responseSentiment = ddbclient.put_item(
                        TableName=SentimentsTable,
                        Item={
                            'ContactId':{
                                'S': ContactId
                            },
                            'Neutrals':{
                                'N': str(df["pastSenti_nut"]["ValueAsString"])
                            },
                            'Negatives':{
                                'N': str(df["pastSenti_neg"]["ValueAsString"])
                            },
                            'Positives':{
                                'N': str(df["pastSenti_pos"]["ValueAsString"])
                            },
                            'ttl': {
                               'N': str(expiryTimestamp)
                            }
                        },
                        ReturnConsumedCapacity='TOTAL'
                    )
                    print(responseSentiment)

                # Main Function Starts
                try:

                    #Assign the data stream from Kinesis to 'records'
                    records = event['Records']

                    for record in records:
                        # Read the 'data' section of the record
                        data = record['kinesis']['data']

                        # Decode the Kinesis stream based on base64
                        payload = base64.b64decode(data)
                        payload_str = json.loads(payload)

                        eventType = payload_str['EventType']
                        ContactId = payload_str['ContactId']
                        customerid = getcustomerid(ContactId)

                        print(payload_str)
                        print(eventType)
                        print(ContactId)
                        print(customerid)

                        # Calling API to get customer features from SageMaker Feature Store based on Customer ID for this Record
                        featurestore_runtime = boto_session.client(service_name='sagemaker-featurestore-runtime', region_name=region)
                        fsresponse = featurestore_runtime.get_record(FeatureGroupName=sagemakerFGname, RecordIdentifierValueAsString=customerid)
                        Record = fsresponse ['Record']

                        # Use pandas to read the Record
                        df = pd.DataFrame(Record).set_index('FeatureName').transpose()
                        print(df)

                        # SEGMENTS eventType includes the Sentiment
                        if eventType == "SEGMENTS":
                            print("it is a Segment")

                            Segments = []
                            Segments = payload_str['Segments']

                            for segment in Segments:
                                if "Utterance" in segment:
                                    print("This segment is an Utterance")

                                # Transcript Segment includes the Sentiment
                                elif "Transcript" in segment:
                                    if segment ['Transcript'] ['ParticipantId'] == "CUSTOMER":
                                        Sentiment = segment ['Transcript'] ['Sentiment']
                                        print("it is a TRANSCRIPT and it is a CUSTOMER")
                                        print("Sentiment is: " + Sentiment)
                                        if Sentiment == "POSITIVE":
                                            df['pastSenti_pos'] = df['pastSenti_pos'].astype(int) + 1
                                            df['sentiment'] = 1
                                        elif Sentiment == "NEUTRAL":
                                            df['pastSenti_nut'] = df['pastSenti_nut'].astype(int) + 1
                                            df['sentiment'] = 0
                                        elif Sentiment == "MIXED":
                                            df['pastSenti_nut'] = df['pastSenti_nut'].astype(int) + 1
                                            df['sentiment'] = 0
                                        elif Sentiment == "NEGATIVE":
                                            df['pastSenti_neg'] = df['pastSenti_neg'].astype(int) + 1
                                            df['sentiment'] = -1

                                        # Update Feature Store
                                        row = df.iloc[0]
                                        recordUpdate = [
                                            FeatureValue(
                                                FeatureName=df.columns[index],
                                                ValueAsString=str(row[index])
                                            )
                                            for index in range(0, len(row))
                                            if pd.notna(row[index])
                                        ]
                                        #print(record)

                                        # Update Feature Store
                                        featurestore_runtime.put_record(
                                            FeatureGroupName=sagemakerFGname,
                                            Record=recordUpdate
                                        )

                                        # Call function to update sentiments table
                                        updateSentiments(ContactId, df, expiryTimestamp)

                                        # Call API Endpoint with the new sentiment value
                                        df.drop(['event_time','customerID'], inplace=True, axis=1)
                                        csv_buffer = io.StringIO()
                                        csv_writer = csv.writer(csv_buffer, delimiter=",")
                                        csv_writer.writerow(df.values[0][1:])
                                        response = runtime_client.invoke_endpoint(
                                            EndpointName=endPointName, ContentType="text/csv", Body=csv_buffer.getvalue().rstrip("\r\n")
                                        )
                                        churn_pred = response["Body"].read().decode("ascii")
                                        updateTransaction(ContactId,str(churn_pred),expiryTimestamp)


                                        print(churn_pred)
                                    else:
                                        print("Not a CUSTOMER")
                                else:
                                    print("Neither a TRANSCRIPT nor an UTTERANCE")
                        else:
                            print("Not a SEGMENT")

                    return {
                        'message': 'Success',
                        'ContactId': ContactId,
                        'customerid': customerid,
                        'Churn': churn_pred
                    }

                except Exception as a:
                    print (a)

      Handler: index.lambda_handler
      Role: !GetAtt LambdasIAMRole.Arn
      Runtime: python3.9
      Timeout: 30

# Permission for Kinesis to call Lambda
  KinesisTriggertoLambdaRTStream:
    Type: AWS::Lambda::Permission
    DependsOn:
      - LambdareInvent2022CLStreamRT
      - KinesisContactLensRT
    Properties:
      FunctionName: !Ref LambdareInvent2022CLStreamRT
      Action: "lambda:InvokeFunction"
      Principal:  "kinesis.amazonaws.com"
      SourceAccount: !Sub ${AWS::AccountId}
      SourceArn: !Sub arn:aws:kinesis:${AWS::Region}:${AWS::AccountId}:stream/${KinesisContactLensRT}

# Event Source Mapping
  LambdaKinesisEventSourceMapping:
    Type: AWS::Lambda::EventSourceMapping
    DependsOn:
      - LambdareInvent2022CLStreamRT
      - KinesisContactLensRT
    Properties:
        EventSourceArn: !Sub arn:aws:kinesis:${AWS::Region}:${AWS::AccountId}:stream/${KinesisContactLensRT}
        FunctionName: !GetAtt LambdareInvent2022CLStreamRT.Arn
        StartingPosition: "TRIM_HORIZON"

# Lambda Function to Return churn values to API Gateway Tables with Time
  LambdareInvent2022APIGatewayResponse:
    Type: AWS::Lambda::Function
    Properties:
      Description: Function to Return churn values to API Gateway Tables with Time
      Environment:
        Variables:
          ChurnTableName: reInvent2022-ChurnPrediction
          SentimentsTableName: reInvent2022-Sentiments
      Code:
        ZipFile: |
            # MIT No Attribution

            # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

            # Permission is hereby granted, free of charge, to any person obtaining a copy of this
            # software and associated documentation files (the "Software"), to deal in the Software
            # without restriction, including without limitation the rights to use, copy, modify,
            # merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
            # permit persons to whom the Software is furnished to do so.

            # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
            # INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
            # PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
            # HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
            # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
            # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

            import json
            import boto3
            import os

            ChurnTableName= os.environ['ChurnTableName']
            SentimentsTableName= os.environ['SentimentsTableName']

            dynamodb = boto3.resource('dynamodb')
            dynamodbclient = boto3.client('dynamodb')

            # This function reads the information in the contact ID and sentiments dynamodb tables based on Contact ID
            # provided by the web interface via API Gateway and return an array with sentiments and churn predictions
            def lambda_handler(event, context):

              try:
                print(event)
                # ContactId passed from Agent CCP via API Gateway
                ContactId = event ['ContactId']

                print(ContactId)

                myArray = []
                mySentiment = []

                # Churn prediction lookup on contact ID DynamoDB table using PartiQL
                response = dynamodbclient.execute_statement(
                  Statement='SELECT * FROM \"' + ChurnTableName + '\" WHERE \"ContactId\" = \'' + ContactId + '\''
                  )

                for x in response['Items']:
                  myArray.append(float(x['ChurnPrediction']['N']))

                # Sentiments lookup on sentiments DynamoDB table using PartiQL
                response1 = dynamodbclient.execute_statement(
                  Statement='SELECT * FROM \"' + SentimentsTableName + '\" WHERE \"ContactId\" = \'' + ContactId + '\''
                  )
                print(response1)
                for x in response1['Items']:
                  mySentiment.append(int(x['Negatives']['N']))
                  mySentiment.append(int(x['Positives']['N']))
                  mySentiment.append(int(x['Neutrals']['N']))

                print(mySentiment)

                # Return to the Agent CCP including Churn predictions and sentiments via API Gateway
                return {
                        "myArray" : myArray,
                        "mySentiment" : mySentiment
                        }
                print(myArray)
                print(mySentiment)

              except Exception as e:
                print (e)
                return { 'message': 'Fail'}

      Handler: index.lambda_handler
      Role: !GetAtt LambdasIAMRole.Arn
      Runtime: python3.9

# Lambda Function to update customer Features from Agent Interface (it needs Pandas Layer)
  LambdareInvent2022AgentUpdate:
    Type: AWS::Lambda::Function
    DependsOn:
      - PandasLayer
      - NumpyLayer
    Properties:
      Description: Function to update customer Features from Agent Interface (it needs Pandas Layer)
      Environment:
        Variables:
          ContactTableName: reInvent2022-ContactIds
          InstanceARN: !Ref AmazonConnectInstanceARN
          ProfilesDomain: !Ref CustomerProfilesDomain
          SageMakerFGName: !Ref SageMakerFeatureGroupName
          StepFunctions: !GetAtt CreateTaskStateMachine.Arn
          TaskContactFlowId: TaskContactFlowId
      Layers:
        - !Ref PandasLayer
        - !Ref NumpyLayer
      Code:
        ZipFile: |
            # MIT No Attribution

            # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

            # Permission is hereby granted, free of charge, to any person obtaining a copy of this
            # software and associated documentation files (the "Software"), to deal in the Software
            # without restriction, including without limitation the rights to use, copy, modify,
            # merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
            # permit persons to whom the Software is furnished to do so.

            # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
            # INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
            # PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
            # HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
            # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
            # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

            import json
            import boto3
            import os
            # This funciton requires a pandas layer
            import pandas as pd
            import numpy

            ContactTableName= os.environ['ContactTableName']
            sagemakerFGname = os.environ['SageMakerFGName']
            InstanceArn = os.environ ['InstanceARN']
            ProfilesDomain= os.environ['ProfilesDomain']
            stepsFunctionARN = os.environ['StepFunctions']
            TaskContactFlowId = os.environ['TaskContactFlowId']

            boto_session = boto3.Session()
            region = boto_session.region_name
            connect = boto3.client('connect')
            dynamodbclient = boto3.client('dynamodb')
            profiles = boto3.client('customer-profiles')
            sfn = boto3.client('stepfunctions')

            # This lambda update the number of months contracted by the customer based on an update from
            # the agent in the interface agent CCP via API Gateway
            def lambda_handler(event, context):

              try:
                print(event)
                InstanceId = InstanceArn[-36:]
                # Values provided by API Gateway asigned to variables
                ContactId = event ['ContactId']
                phoneNumber = event ['phoneNumber']
                email = event['email']
                firstName = event['firstName']
                Months = str(event['Months'])

                # Update Amazon Connect Contact Attributes with new contracted months
                responseConnect = connect.update_contact_attributes(
                    InitialContactId=ContactId,
                    InstanceId=InstanceId,
                    Attributes={
                        'contractedMonths': Months
                    }
                )

                # Start StepFunctions when contract has been cancelled
                if Months == "0" :
                    response = sfn.start_execution(
                        stateMachineArn=stepsFunctionARN,
                        input=json.dumps({"InstanceId": InstanceId, "ContactFlowId": TaskContactFlowId, "phoneNumber": phoneNumber, "email": email, "firstName": firstName }
                        ))
                    print(response.get('executionArn'))

                # Churn prediction lookup on contact ID DynamoDB table using PartiQL
                response = dynamodbclient.execute_statement(
                  Statement='SELECT * FROM \"' + ContactTableName + '\" WHERE \"ContactId\" = \'' + ContactId + '\''
                  )
                print(response)

                # Retrieve customerProfile from Item returned from Profiles
                if 'Items' in response:
                  ProfileId= response['Items'][0]['customerProfile']['S']
                  CustomerId= response['Items'][0]['customerid']['S']
                  print(ProfileId)
                  print(CustomerId)
                else:
                  print('No profile found for ContactId: ' + ContactId)

                # Update Contracted months in the Customer Profiles
                response = profiles.update_profile(
                    DomainName=ProfilesDomain,
                    ProfileId=ProfileId,
                    AdditionalInformation=Months
                    )

                # Update SageMaker Feature Store with new contracted months using Customer ID
                featurestore_runtime = boto_session.client(service_name='sagemaker-featurestore-runtime', region_name=region)
                resp = featurestore_runtime.get_record(FeatureGroupName=sagemakerFGname, RecordIdentifierValueAsString=CustomerId)
                Record = resp ['Record']

                # use pandas to read Record
                df = pd.DataFrame(Record).set_index('FeatureName').transpose()
                df['mth_remain'] = Months

                # Update record with the new Table
                def FeatureValue(**kwargs):
                    result = dict()
                    for key, value in kwargs.items():
                        if value is not None:
                            result[key] = value
                    return result

                row = df.iloc[0]
                record = [
                    FeatureValue(
                        FeatureName=df.columns[index],
                        ValueAsString=str(row[index]),
                    )
                    for index in range(0, len(row))
                    if pd.notna(row[index])
                ]
                print(record)
                featurestore_runtime.put_record(
                    FeatureGroupName=sagemakerFGname,
                    Record=record,
                )

                # return consumed by API Gateway
                return {
                    'statusCode': 200,
                    'body': json.dumps(event)
                }

              except Exception as e:
                print (e)
                return { 'message': 'Fail'}

      Handler: index.lambda_handler
      Role: !GetAtt LambdasIAMRole.Arn
      Runtime: python3.9

# API Gateway for Agent Interface
  reInvent2022AgentInterfaceRestApi:
    Type: AWS::ApiGateway::RestApi
    DependsOn:
      - LambdareInvent2022APIGatewayResponse
      - LambdareInvent2022AgentUpdate
    Properties:
      Description: Return churn predictions for a contactID
      Name: reInvent2022-AgentInterface
      Parameters:
        endpointConfigurationTypes: REGIONAL

  reInvent2022ApiMethod:
    Type: AWS::ApiGateway::Method
    DependsOn:
      - reInvent2022AgentInterfaceRestApi
    Properties:
      AuthorizationType: NONE
      HttpMethod: POST
      Integration:
        IntegrationHttpMethod: POST
        Type: AWS
        Uri: !Sub arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${LambdareInvent2022APIGatewayResponse.Arn}/invocations
        IntegrationResponses:
          - StatusCode: 200
            ResponseParameters:
              method.response.header.Access-Control-Allow-Headers: "'Content-Type,X-Amz-Date,Authorization,X-Api-Key,X-Amz-Security-Token'"
              method.response.header.Access-Control-Allow-Methods: "'POST,OPTIONS'"
              method.response.header.Access-Control-Allow-Origin: "'*'"
            ResponseTemplates:
              application/json: ''
        PassthroughBehavior: WHEN_NO_MATCH
      ResourceId: !GetAtt reInvent2022AgentInterfaceRestApi.RootResourceId
      RestApiId: !Ref reInvent2022AgentInterfaceRestApi
      MethodResponses:
        - StatusCode: 200
          ResponseModels:
            application/json: 'Empty'
          ResponseParameters:
            method.response.header.Access-Control-Allow-Headers: false
            method.response.header.Access-Control-Allow-Methods: false
            method.response.header.Access-Control-Allow-Origin: false

  reInvent2022ApiOptionsMethod:
    Type: AWS::ApiGateway::Method
    DependsOn:
      - reInvent2022AgentInterfaceRestApi
    Properties:
      AuthorizationType: NONE
      RestApiId: !Ref reInvent2022AgentInterfaceRestApi
      ResourceId: !GetAtt reInvent2022AgentInterfaceRestApi.RootResourceId
      HttpMethod: OPTIONS
      Integration:
        IntegrationResponses:
        - StatusCode: 200
          ResponseParameters:
            method.response.header.Access-Control-Allow-Headers: "'Content-Type,X-Amz-Date,Authorization,X-Api-Key,X-Amz-Security-Token'"
            method.response.header.Access-Control-Allow-Methods: "'POST,OPTIONS'"
            method.response.header.Access-Control-Allow-Origin: "'*'"
          ResponseTemplates:
            application/json: ''
        PassthroughBehavior: WHEN_NO_MATCH
        Type: MOCK
      MethodResponses:
      - StatusCode: 200
        ResponseModels:
          application/json: 'Empty'
        ResponseParameters:
          method.response.header.Access-Control-Allow-Headers: false
          method.response.header.Access-Control-Allow-Methods: false
          method.response.header.Access-Control-Allow-Origin: false

  APIupdatePath:
    Type: 'AWS::ApiGateway::Resource'
    Properties:
      RestApiId: !Ref reInvent2022AgentInterfaceRestApi
      ParentId: !GetAtt
        - reInvent2022AgentInterfaceRestApi
        - RootResourceId
      PathPart: update

  reInvent2022ApiUpdateMethod:
    Type: AWS::ApiGateway::Method
    DependsOn:
      - reInvent2022AgentInterfaceRestApi
      - APIupdatePath
    Properties:
      AuthorizationType: NONE
      HttpMethod: POST
      Integration:
        IntegrationHttpMethod: POST
        Type: AWS
        Uri: !Sub arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${LambdareInvent2022AgentUpdate.Arn}/invocations
        IntegrationResponses:
          - StatusCode: 200
            ResponseParameters:
              method.response.header.Access-Control-Allow-Headers: "'Content-Type,X-Amz-Date,Authorization,X-Api-Key,X-Amz-Security-Token'"
              method.response.header.Access-Control-Allow-Methods: "'POST,OPTIONS'"
              method.response.header.Access-Control-Allow-Origin: "'*'"
            ResponseTemplates:
              application/json: ''
        PassthroughBehavior: WHEN_NO_MATCH
      ResourceId: !Ref APIupdatePath
      RestApiId: !Ref reInvent2022AgentInterfaceRestApi
      MethodResponses:
        - StatusCode: 200
          ResponseModels:
            application/json: 'Empty'
          ResponseParameters:
            method.response.header.Access-Control-Allow-Headers: false
            method.response.header.Access-Control-Allow-Methods: false
            method.response.header.Access-Control-Allow-Origin: false

  reInvent2022ApiUpdateOptionsMethod:
    Type: AWS::ApiGateway::Method
    DependsOn:
      - reInvent2022AgentInterfaceRestApi
      - APIupdatePath
    Properties:
      AuthorizationType: NONE
      RestApiId: !Ref reInvent2022AgentInterfaceRestApi
      ResourceId: !Ref APIupdatePath
      HttpMethod: OPTIONS
      Integration:
        IntegrationResponses:
        - StatusCode: 200
          ResponseParameters:
            method.response.header.Access-Control-Allow-Headers: "'Content-Type,X-Amz-Date,Authorization,X-Api-Key,X-Amz-Security-Token'"
            method.response.header.Access-Control-Allow-Methods: "'POST,OPTIONS'"
            method.response.header.Access-Control-Allow-Origin: "'*'"
          ResponseTemplates:
            application/json: ''
        PassthroughBehavior: WHEN_NO_MATCH
        Type: MOCK
      MethodResponses:
      - StatusCode: 200
        ResponseModels:
          application/json: 'Empty'
        ResponseParameters:
          method.response.header.Access-Control-Allow-Headers: false
          method.response.header.Access-Control-Allow-Methods: false
          method.response.header.Access-Control-Allow-Origin: false

  reInvent2022ApiDeployment:
    Type: AWS::ApiGateway::Deployment
    DependsOn:
      - reInvent2022ApiMethod
      - reInvent2022ApiOptionsMethod
      - reInvent2022ApiUpdateMethod
      - reInvent2022ApiUpdateOptionsMethod
    Properties:
      RestApiId: !Ref reInvent2022AgentInterfaceRestApi
      StageName: prod

  lambdaApiGatewayInvoke1:
    Type: AWS::Lambda::Permission
    Properties:
      FunctionName: !GetAtt LambdareInvent2022APIGatewayResponse.Arn
      Action: lambda:InvokeFunction
      Principal: apigateway.amazonaws.com
      SourceArn: !Sub arn:aws:execute-api:${AWS::Region}:${AWS::AccountId}:${reInvent2022AgentInterfaceRestApi}/*/POST/

  lambdaApiGatewayInvoke2:
   Type: AWS::Lambda::Permission
   Properties:
     FunctionName: !GetAtt LambdareInvent2022AgentUpdate.Arn
     Action: lambda:InvokeFunction
     Principal: apigateway.amazonaws.com
     SourceArn: !Sub arn:aws:execute-api:${AWS::Region}:${AWS::AccountId}:${reInvent2022AgentInterfaceRestApi}/*/POST/update


# Step Functions
  CreateTaskStateMachine:
    Type: AWS::StepFunctions::StateMachine
    Properties:
      StateMachineName: ContractCancelled
      DefinitionString: !Sub |
        {
          "Comment": "Create a Task to Connect via API when Contract Cancelled",
          "StartAt": "StartTaskContact",
          "States": {
            "StartTaskContact": {
              "Type": "Task",
              "End": true,
              "Parameters": {
                "InstanceId.$": "$.InstanceId",
                "Name": "Contract Cancelled",
                "ContactFlowId.$": "$.ContactFlowId",
                "Attributes": {
                  "Task": "Urgently Call Customer Back",
                  "Name.$": "$.firstName",
                  "Message": "Contract has been cancelled",
                  "PhoneNumber.$": "$.phoneNumber"
                  },
                "References": {
                  "Email": {
                    "Value.$": "$.email",
                    "Type": "EMAIL"
                  }
                }
              },
              "Resource": "arn:aws:states:::aws-sdk:connect:startTaskContact"
            }
          }
        }
      RoleArn: !GetAtt StepsIAMRole.Arn

# LexV2 Bot
  IVR:
    Type: AWS::Lex::Bot
    DependsOn:
      - LexServiceLinkRole
    Properties:
      AutoBuildBotLocales: false
      BotFileS3Location:
        S3Bucket: !Ref S3bucketWorkshopFiles
        S3ObjectKey: reInvent2022-LexBot-DRAFT-RATIIENAXZ-LexJson.zip
      DataPrivacy:
        ChildDirected: false
      IdleSessionTTLInSeconds: 300
      Description: "Lex for Initial Customer Sentiment - reInvent2022 - BIZ310"
      Name: "reInvent2022-LexBot"
      RoleArn: !Sub arn:aws:iam::${AWS::AccountId}:role/aws-service-role/lexv2.amazonaws.com/${LexServiceLinkRole}
      TestBotAliasSettings:
        BotAliasLocaleSettings:
          - BotAliasLocaleSetting:
              Enabled: true
            LocaleId: en_US
        SentimentAnalysisSettings:
          DetectSentiment: true

# S3 Bucket for Custom CCP Website
  createCustomCCPS3Bucket:
      Type: AWS::S3::Bucket
      Properties:
        BucketName: !Sub customccp-${AWS::AccountId}-${AWS::Region}
        VersioningConfiguration:
          Status: Enabled
        PublicAccessBlockConfiguration:
            BlockPublicAcls: True
            BlockPublicPolicy: True
            IgnorePublicAcls: True
            RestrictPublicBuckets: True
        WebsiteConfiguration:
            IndexDocument: CCPcustom.html
            ErrorDocument: error.html

# S3 Bucket Policy
  CFS3BucketPolicy:
      Type: AWS::S3::BucketPolicy
      DependsOn:
          - CFCloudFrontDistributionAccessIdentity
      Properties:
          Bucket: !Ref createCustomCCPS3Bucket
          PolicyDocument:
              Statement:
                  -
                      Action:
                          - "s3:GetObject"
                      Effect: "Allow"
                      Principal:
                          CanonicalUser:
                              Fn::GetAtt: [ CFCloudFrontDistributionAccessIdentity , S3CanonicalUserId ]
                      Resource:
                            !Sub ${createCustomCCPS3Bucket.Arn}/*

# CloudFront Distribution Access Identity
  CFCloudFrontDistributionAccessIdentity:
    Type: AWS::CloudFront::CloudFrontOriginAccessIdentity
    Properties:
      CloudFrontOriginAccessIdentityConfig:
        Comment: 'reInvent BIZ310 Workshop - Custom CCP'

# CloudFront Distribution
  CFCloudFrontDistribution:
      Type: AWS::CloudFront::Distribution
      Properties:
        DistributionConfig:
          Origins:
          - DomainName:
              !Join
                  - ''
                  - - !Ref createCustomCCPS3Bucket
                    - .s3.
                    - !Sub ${AWS::Region}
                    - .amazonaws.com
            Id: !Ref createCustomCCPS3Bucket
            S3OriginConfig:
              OriginAccessIdentity:
                  !Join
                      - ''
                      - - 'origin-access-identity/cloudfront/'
                        - !Ref CFCloudFrontDistributionAccessIdentity
          Enabled: 'true'
          Comment: reInvent BIZ310 Workshop - Custom CCP
          DefaultRootObject: CCPcustom.html
          DefaultCacheBehavior:
              AllowedMethods:
                  - GET
                  - HEAD
                  - OPTIONS
              TargetOriginId: !Ref createCustomCCPS3Bucket
              ForwardedValues:
                  QueryString: true
                  Cookies:
                      Forward: all
              ViewerProtocolPolicy: redirect-to-https