Add Lambda handler

lambda_function.py

@@ -0,0 +1,72 @@
+from detection import ml_detection, ml_utils
+import base64
+import json
+import logging
+
+
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+
+
+# Find ML model type based on string request
+def get_model_type(query_string):
+    # Default ml model type
+    if query_string == "":
+        model_type = "facebook/detr-resnet-50"
+    # Assess query string value
+    elif "detr" in query_string:
+        model_type = "facebook/" + query_string
+    elif "yolos" in query_string:
+        model_type = "hustvl/" + query_string
+    else:
+        raise Exception('Incorrect model type.')
+    return model_type
+
+
+# Run detection pipeline: load ML model, perform object detection and return json object
+def detection_pipeline(model_type, image_bytes):
+    # Load correct ML model
+    processor, model = ml_detection.load_model(model_type)
+
+    # Perform object detection
+    results = ml_detection.object_detection(processor, model, image_bytes)
+
+    # Convert dictionary of tensors to JSON object
+    result_json_dict = ml_utils.convert_tensor_dict_to_json(results)
+
+    return result_json_dict
+
+
+def lambda_handler(event, context):
+    try:
+        # Get the model name from the query string parameters
+        # Condition for local testing
+        is_querystringparam = event.get('queryStringParameters')
+        if is_querystringparam is not None:
+            model_query = event['queryStringParameters'].get('model', '').lower()
+        else:
+            model_query = ""
+        model_type = get_model_type(model_query)
+        logger.info(f"Model query: {model_query}")
+        logger.info(f"Model type: {model_type}")
+
+        # Decode the base64-encoded image data from the event
+        image_data = base64.b64decode(event['body'])
+        result_dict = detection_pipeline(model_type, image_data)
+        logger.info(f"API Results: {result_dict}")
+        return {
+            'statusCode': 200,
+            'headers': {
+                'Content-Type': 'application/json'
+            },
+            'body': json.dumps(result_dict),
+        }
+    except Exception as e:
+        logger.info(f"API Error: {str(e)}")
+        return {
+            'statusCode': 500,
+            'headers': {
+                'Content-Type': 'application/json'
+            },
+            'body': json.dumps({'error': str(e)}),
+        }