Project Done

project/lambda function/classify.py

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+import json
+import sagemaker
+import base64
+from sagemaker.serializers import IdentitySerializer
+from sagemaker.predictor import Predictor
+
+# Fill this in with the name of your deployed model
+ENDPOINT = 'image-classification-2023-02-14-13-25-18-287'  ## TODO: fill in
+
+def lambda_handler(event, context):
+
+    # Decode the image data
+    image = base64.b64decode(## TODO: fill in)
+
+    # Instantiate a Predictor
+    ## TODO: fill in
+    predictor = Predictor('image-classification-2023-02-14-13-25-18-287')
+
+    # For this model the IdentitySerializer needs to be "image/png"
+    predictor.serializer = IdentitySerializer("image/png")
+
+    # Make a prediction:
+    inferences = predictor.predict(predictor.serializer)  ## TODO: fill in
+
+    # We return the data back to the Step Function    
+    event["inferences"] = inferences.decode('utf-8')
+    return {
+        'statusCode': 200,
+        'body': json.dumps(event)
+    }

project/lambda function/filter.py

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+import json
+
+THRESHOLD = .93
+
+
+def lambda_handler(event, context):
+
+    # Grab the inferences from the event
+    inferences = event["inferences"]  ## TODO: fill in
+
+    # Check if any values in our inferences are above THRESHOLD
+    meets_threshold = (inferences>THRESHOLD) ## TODO: fill in
+
+    # If our threshold is met, pass our data back out of the
+    # Step Function, else, end the Step Function with an error
+    if meets_threshold:
+        pass
+    else:
+        raise("THRESHOLD_CONFIDENCE_NOT_MET")
+
+    return {
+        'statusCode': 200,
+        'body': json.dumps(event)
+    }
+def lambda_handler(event, context):
+    # TODO implement
+    return {
+        'statusCode': 200,
+        'body': json.dumps('Hello from Lambda!')
+    }

project/lambda function/serializeImageData.py

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+import json
+import boto3
+import base64
+
+s3 = boto3.client('s3')
+
+def lambda_handler(event, context):
+    """A function to serialize target data from S3"""
+
+    # Get the s3 address from the Step Function event input
+    key = event["s3_key"] ## TODO: fill in
+    bucket =event["s3_bucket"] ## TODO: fill in
+
+    # Download the data from s3 to /tmp/image.png
+    ## TODO: fill in
+
+    input_object = event['image_data']
+    file_name = '/tmp/image.png'
+    s3.download_file(bucket, input_object, file_name)
+
+    # We read the data from a file
+    with open("/tmp/image.png", "rb") as f:
+        image_data = base64.b64encode(f.read())
+
+    # Pass the data back to the Step Function
+    print("Event:", event.keys())
+    return {
+        'statusCode': 200,
+        'body': {
+            "image_data": image_data,
+            "s3_bucket": bucket,
+            "s3_key": key,
+            "inferences": []
+        }
+    }
+