Update app.py

app.py

@@ -11,6 +11,7 @@ from official.nlp import optimization
 tf.get_logger().setLevel('ERROR')
 
 import math
+from datetime import datetime
 
 import gradio as gr
 
@@ -129,18 +130,14 @@ def predict_CTH(txt):
         n = len(arr)
         pred_value_max_three=print3largest(arr, n)
 
-
-
+        now = datetime.now()
+        print("Time =", now)
         sum_all = pred_value_max_three[0] + pred_value_max_three[1] + pred_value_max_three[2]
 
         val_1 = pred_value_max_three[0]/sum_all
         val_2 = pred_value_max_three[1]/sum_all
         val_3 = pred_value_max_three[2]/sum_all
 
-        #val_1= 97            #random.randrange(95, 99, 1)
-        #val_2=(pred_value_max_three[1]/pred_value_max_three[0])*val_1
-        #val_3=(pred_value_max_three[2]/pred_value_max_three[0])*val_1
-
         if pred_value_max_three[0]<=0.000131:
             Var_CTH=[]
             Var_desc=[]
@@ -166,8 +163,14 @@ def predict_CTH(txt):
 
                 try:
                     pred_duty=df_report_DUTY[df_report_DUTY['CTH']==str(pred_CTH)]['DUTY_RATE'].iloc[0]
+                except:
+                    pred_duty=''
+                    pass
+
+                try:
                     pred_desc=df_report[df_report['CTH Code']==str(pred_CTH)]['Concat Description'].iloc[0]
                 except:
+                    pred_desc=''
                     pass
 
                 Var_CTH.append(pred_CTH)