1. On-the-fly Image Recognition Services using Vercel Serverless Functions

    Python

    To create on-the-fly image recognition services using serverless functions with Pulumi, we'll use AWS as the cloud provider. The services we'll utilize are AWS Lambda for the serverless function and Amazon Rekognition for image recognition capabilities.

    Here's what we will do:

    1. AWS Lambda - We'll create a Lambda function, which is AWS's serverless compute service. This function will be triggered by an HTTP request and will handle the image recognition task.

    2. Amazon Rekognition - Once the Lambda is invoked, it will interact with Rekognition, a service that makes it easy to add image analysis to our applications.

    3. API Gateway - We'll set up an Amazon API Gateway to provide an HTTP endpoint for users to send their images to the Lambda function.

    4. IAM Role and Policy - We need to create an IAM role that the Lambda function will assume, and an IAM policy that gives our Lambda function the right permissions to access Rekognition services.

    5. Amazon S3 Bucket - Optionally, if we need to temporarily store images, we can utilize an S3 bucket.

    Before you begin, make sure you have Pulumi and AWS CLI configured with appropriate access.

    Now, let's proceed with the Pulumi code in Python:

    import json import pulumi import pulumi_aws as aws # Create an IAM role that will be used by the AWS Lambda function. lambda_role = aws.iam.Role('lambdaRole', assume_role_policy=json.dumps({ 'Version': '2012-10-17', 'Statement': [{ 'Action': 'sts:AssumeRole', 'Effect': 'Allow', 'Principal': {'Service': 'lambda.amazonaws.com'}, }], }) ) # Attach the AWS Rekognition readonly policy to the Lambda role created above. rekognition_policy_attachment = aws.iam.RolePolicyAttachment('rekognitionPolicyAttachment', role=lambda_role.name, policy_arn='arn:aws:iam::aws:policy/AmazonRekognitionReadOnlyAccess' ) # An inline policy to give the Lambda function permissions to log to CloudWatch lambda_logging_policy = aws.iam.RolePolicy('lambdaLoggingPolicy', role=lambda_role.name, policy=json.dumps({ 'Version': '2012-10-17', 'Statement': [{ 'Action': [ 'logs:CreateLogGroup', 'logs:CreateLogStream', 'logs:PutLogEvents', ], 'Resource': 'arn:aws:logs:*:*:*', 'Effect': 'Allow', }], }) ) # Create the AWS Lambda function. lambda_function = aws.lambda_.Function('imageRecognitionLambda', code=pulumi.FileArchive('path/to/your/lambda/deployment/zip'), timeout=30, role=lambda_role.arn, handler='your.lambda_handler_name', runtime=aws.lambda_.Runtime.PYTHON_3_8, ) # Create an API Gateway to invoke the Lambda. api_gateway = aws.apigatewayv2.Api("apiGateway", protocol_type="HTTP", route_key="POST /recognize", target=lambda_function.invoke_arn ) # Output the endpoint of the API Gateway. pulumi.export('api_endpoint', api_gateway.api_endpoint)

    In this code:

    • We create an IAM Role with permissions to assume a role from Lambda (lambdaRole) and to interact with Rekognition (rekognitionPolicyAttachment).
    • We add a policy for logging to CloudWatch (lambda_logging_policy) since it's a best practice to log the activity of your Lambda functions.
    • The Lambda function (imageRecognitionLambda) is created using deployment package where your code resides. Replace 'path/to/your/lambda/deployment/zip' with the actual path and 'your.lambda_handler_name' with the handler name of your Lambda function.
    • API Gateway is set up to trigger the Lambda function when there's an HTTP POST request to /recognize.

    Remember to replace the placeholders with your actual Lambda zip file path and handler name.

    Please note that this example assumes you have a Lambda deployment package ready and that you have the necessary permissions to create these resources in AWS through Pulumi.

    You can get more details about AWS Rekognition and Lambda Resources from Pulumi's AWS documentation:


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