X-Git-Url: https://gerrit.o-ran-sc.org/r/gitweb?a=blobdiff_plain;f=ad%2Fmain.py;h=8156af3a1ab6bf4f3edad69b32dca5166b816dbf;hb=d3dff387ed728a6a74a236eb9b65d59d4929714a;hp=3298b5f8c0ce931d3770b43cd8d80875ddcaa4d2;hpb=f98ee76af036d60b8f5077105830ed61a13ed5aa;p=ric-app%2Fad.git

diff --git a/ad/main.py b/ad/main.py
index 3298b5f..8156af3 100644
--- a/ad/main.py
+++ b/ad/main.py
@@ -14,69 +14,60 @@
 #  limitations under the License.
 # ==================================================================================
 
+import warnings
 import json
 import os
 from ricxappframe.xapp_frame import Xapp
 import pandas as pd
 from ad_model.tb_format import parse
 from ad_model.ad_model import HDB_PREDICT
-import schedule
+import schedule, time
 from ad_train import train
 
-
 def entry(self):
     """
-      If RF model is not present in the path, run train() to train the model for the prediction.
-      Calls predict function for every 1 second(for now as we are using simulated data).
+     If RF model is not present in the path, run train() to train the model for the prediction.
+     Calls predict function for every 1 second(for now as we are using simulated data).
     """
-    if not os.path.isfile('ad/RF'):
+    if not os.path.isfile('/tmp/ad/RF'):
         train()
     schedule.every(1).seconds.do(predict, self)
     while True:
         schedule.run_pending()
 
-
 def predict(self):
     """
-      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.
+     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.
     """
-    val = predict_anomaly(self)
-    if len(val) > 2:
-        msg_to_ts(self, val)
-
 
-def predict_anomaly(self):
-    # The read_csv logic will be modified when we are going to fetch the data from database via sdl api.
-    # Read the input csv file
-    ue_data = pd.read_csv('ad/ue_test.csv')
+    #The read_csv logic will be modified when we are going to fetch the data from database via sdl api.
+    #Read the input csv file 
+    ue_data = pd.read_csv('/tmp/ad/ue_test.csv')
 
-    # Parse the ue data and predict the anomaly records for the randomly selected UEID
+    #Parse the ue data and predict the anomaly records for the randomly selected UEID
     data = parse(ue_data)
     db_df = HDB_PREDICT(data)
-    db_df = db_df.loc[db_df['Anomaly'] == 1][['UEID', 'MeasTimestampRF']].head(1)
-    db_df['MeasTimestampRF'] = db_df['MeasTimestampRF'].apply(lambda x: str(x))  # converts into string format
+    db_df = db_df.loc[db_df['Anomaly'] == 1][['UEID','MeasTimestampRF' ]].head(1)    
+    db_df['MeasTimestampRF'] = db_df['MeasTimestampRF'].apply(lambda x : str(x)) # converts into string format
+    #print("db_df: ", db_df) # For debug purpose, we can enable this print statement
 
     # rmr send 30003(TS_ANOMALY_UPDATE), should trigger registered callback
-    result = json.loads(db_df.to_json(orient='records'))
+    result = json.loads(db_df.to_json(orient = 'records'))    
     val = json.dumps(result).encode()
-    return val
-
-
-def msg_to_ts(self, val):
-    # send message from ad to ts
-    print("rmr send value:", val)
-    self.rmr_send(val, 30003)
 
-    # rmr receive to get the acknowledgement message from the traffic steering.
-    for (summary, sbuf) in self.rmr_get_messages():
-        print("TS_ANOMALY_ACK: {}".format(summary))
-        self.rmr_free(sbuf)
+    if len(val) > 2 :
+        print("val: ", val)
+        self.rmr_send(val, 30003)
 
+        # rmr receive to get the acknowledgement message from the traffic steering.
+        for (summary, sbuf) in self.rmr_get_messages():
+            print("TS_ANOMALY_ACK: {}".format(summary))
+            self.rmr_free(sbuf)
+    
+# Initiates xapp api and runs the entry() using xapp.run()
+xapp = Xapp(entrypoint=entry, rmr_port=4560, use_fake_sdl=True)
+xapp.run()
 
-def start():
-    # Initiates xapp api and runs the entry() using xapp.run()
-    xapp = Xapp(entrypoint=entry, rmr_port=4560, use_fake_sdl=True)
-    xapp.run()