-* Initiates xapp api and runs the entry() using xapp.run()
-* If RF model is not present in the path, run train() to train the model for the prediction.
- Call predict function for every 1 second(for now as we are using simulated data).
-* Read the input csv file that has both normal and anomalous data.
-* Simulate diff UEIDs that participate in the anomaly by randomly selecting records from this scoring data set
-* Send the UEID and timestamp for the anomalous entries to the Traffic Steering (rmr with the message type as 30003)
-* Get the acknowledgement message from the traffic steering.
+* Initiates xapp api, populated influxDB with data and runs the entry() using xapp.run()
+* If Model is not present in the current path, run train() to train the model for the prediction.
+* Call predict function to perform the following activities for every 10 milisecond.
+ a) Currently read the input from "liveUE" measurments and iterate through it. (Needs to update: To iterate every 10 miliseconds and fetch latest sample from influxDB)
+ b) Detect anomalous records for the inputs
+ c) send the UEID, DU-ID, Degradation type and timestamp for the anomalous records to the Traffic Steering (via rmr with the message type as 30003)
+ d) Get the acknowledgement message from the traffic steering
+
+Note: Need to implement the logic if we do not get the acknowledgment from the TS. (How xapp api handle this?)