Update lambda_yoga_project.py

@@ -6,6 +6,14 @@
 #*****************************************************
 #*****************************************************
 """ A sample lambda for bird detection"""
 """ A sample lambda for bird detection"""
 from threading import Thread, Event
 from threading import Thread, Event
+#*****************************************************
+#                                                    *
+# Copyright 2018 Amazon.com, Inc. or its affiliates. *
+# All Rights Reserved.                               *
+#                                                    *
+#*****************************************************
+
+from threading import Thread, Event
 import os
 import os
 import json
 import json
 import numpy as np
 import numpy as np
@@ -94,9 +102,9 @@ def infinite_infer_run():
         model_name = "image-classification"
         model_name = "image-classification"
         ret, model_path = mo.optimize(model_name, input_width, input_height, aux_inputs={'--epoch': 5,'--precision':'FP16'})
         ret, model_path = mo.optimize(model_name, input_width, input_height, aux_inputs={'--epoch': 5,'--precision':'FP16'})
         # Load the model onto the GPU.
         # Load the model onto the GPU.
-        client.publish(topic=iot_topic, payload='Loading bird detection model')
+        client.publish(topic=iot_topic, payload='Loading  model')
         model = awscam.Model(model_path, {'GPU': 1})
         model = awscam.Model(model_path, {'GPU': 1})
-        client.publish(topic=iot_topic, payload='Bird detection loaded')
+        client.publish(topic=iot_topic, payload=' loaded')
         # The number of top results to stream to IoT.
         # The number of top results to stream to IoT.
         num_top_k = 3
         num_top_k = 3
         # Define the detection region size.
         # Define the detection region size.
@@ -159,6 +167,6 @@ def infinite_infer_run():
                 cloud_output[output_map[obj['label']]] = obj['prob']
                 cloud_output[output_map[obj['label']]] = obj['prob']
             client.publish(topic=iot_topic, payload=json.dumps(cloud_output))
             client.publish(topic=iot_topic, payload=json.dumps(cloud_output))
     except Exception as ex:
     except Exception as ex:
-        client.publish(topic=iot_topic, payload='Error in bird detection lambda: {}'.format(ex))
+        client.publish(topic=iot_topic, payload='Error : {}'.format(ex))
 
 
 infinite_infer_run()
 infinite_infer_run()