Update app.py

app.py

@@ -23,12 +23,12 @@ st.title("Predict Unrolled Values")
 st.sidebar.header('Enter the Details here')
 st.write("""Regression Model""")
 
-# # load the saved model using pickle
-# with open('aajTak_model.pkl', 'rb') as f1:
-#     model1 = pickle.load(f1)
+# load the saved model using pickle
+with open('aajTak_model.pkl', 'rb') as f1:
+    model = pickle.load(f1)
 
-# # load the saved model using joblib
-model3 = joblib.load('aajTak_model.joblib')
+# # # load the saved model using joblib
+# model3 = joblib.load('aajTak_model.joblib')
 
 # Load the saved weekDay label encoder object using pickle
 with open('weekDay_le.pkl','rb') as file1:
@@ -41,64 +41,64 @@ with open('timeBand_le.pkl','rb') as file2:
 # previous_number_of_repairs = 
 # st.sidebar.number_input('Enter the Previous Number of Repairs Undergone  0 to 5 )',min_value=0,max_value=5,step=1)  
     
-# # DATA from user
-# def user_report():
+# DATA from user
+def user_report():
   
-#   Share = round(float(st.sidebar.slider('Share', 0.000000, 100.000000, 0.611246)), 6)
-#   AMA = round(float(st.sidebar.slider('AMA', 0.000000, 45.000000, 4.196084)), 6)
-#   rate = round(float(st.sidebar.slider('rate', 0.000000, 1.500000, 0.018516)), 6)  
-#   daily_reach = round(float(st.sidebar.slider('daily reach', 0.000000, 300.000000, 36.23)), 6) 
-#   cume_reach = round(float(st.sidebar.slider('cume reach', 0.000000, 300.000000, 36.231006)), 6)
+  Share = round(float(st.sidebar.slider('Share', 0.000000, 100.000000, 0.611246)), 6)
+  AMA = round(float(st.sidebar.slider('AMA', 0.000000, 45.000000, 4.196084)), 6)
+  rate = round(float(st.sidebar.slider('rate', 0.000000, 1.500000, 0.018516)), 6)  
+  daily_reach = round(float(st.sidebar.slider('daily reach', 0.000000, 300.000000, 36.23)), 6) 
+  cume_reach = round(float(st.sidebar.slider('cume reach', 0.000000, 300.000000, 36.231006)), 6)
     
-#   # Output: {'Friday': 0, 'Monday': 1, 'Saturday': 2, 'Sunday': 3, 'Thursday': 4, 'Tuesday': 5, 'Wednesday': 6}
+  # Output: {'Friday': 0, 'Monday': 1, 'Saturday': 2, 'Sunday': 3, 'Thursday': 4, 'Tuesday': 5, 'Wednesday': 6}
     
-#   Week_Day_Encoded = st.sidebar.selectbox("Week Day",
-#         ("Monday", "Tuesday","Wednesday","Thursday","Friday", "Saturday", "Sunday" ))
-#   if Week_Day_Encoded=='Monday':
-#       Week_Day_Encoded=1
-#   elif Week_Day_Encoded=="Tuesday":
-#       Week_Day_Encoded=5
-#   elif Week_Day_Encoded=="Wednesday":
-#       Week_Day_Encoded=6
-#   elif Week_Day_Encoded=="Thursday":
-#       Week_Day_Encoded =4
-#   elif Week_Day_Encoded=="Friday":
-#       Week_Day_Encoded =0
-#   elif Week_Day_Encoded=="Saturday":
-#       Week_Day_Encoded =2
-#   else:
-#       Week_Day_Encoded=3
+  Week_Day_Encoded = st.sidebar.selectbox("Week Day",
+        ("Monday", "Tuesday","Wednesday","Thursday","Friday", "Saturday", "Sunday" ))
+  if Week_Day_Encoded=='Monday':
+      Week_Day_Encoded=1
+  elif Week_Day_Encoded=="Tuesday":
+      Week_Day_Encoded=5
+  elif Week_Day_Encoded=="Wednesday":
+      Week_Day_Encoded=6
+  elif Week_Day_Encoded=="Thursday":
+      Week_Day_Encoded =4
+  elif Week_Day_Encoded=="Friday":
+      Week_Day_Encoded =0
+  elif Week_Day_Encoded=="Saturday":
+      Week_Day_Encoded =2
+  else:
+      Week_Day_Encoded=3
   
-#   user_report_data = {
-#        'Share': Share, 
-#        'AMA': AMA, 
-#        'rate': rate, 
-#        'daily reach': daily_reach, 
-#        'cume reach': cume_reach, 
-#        'Week_Day_Encoded': Week_Day_Encoded, 
-#        'Time_Band_Encoded': Time_Band_Encoded}
-#   report_data = pd.DataFrame(user_report_data, index=[0])
+  user_report_data = {
+       'Share': Share, 
+       'AMA': AMA, 
+       'rate': rate, 
+       'daily reach': daily_reach, 
+       'cume reach': cume_reach, 
+       'Week_Day_Encoded': Week_Day_Encoded, 
+       'Time_Band_Encoded': Time_Band_Encoded}
+  report_data = pd.DataFrame(user_report_data, index=[0])
   
-#   return report_data    
+  return report_data    
     
-# #Customer Data
-# user_data = user_report()
-# st.subheader("Entered Details")
-# st.write(user_data)
+#Customer Data
+user_data = user_report()
+st.subheader("Entered Details")
+st.write(user_data)
 
 
-# # define the prediction function
-# def predict_unrolled_value(user_data):
+# define the prediction function
+def predict_unrolled_value(user_data):
     
-#     # make the prediction using the loaded model and input data
-#     predicted_unrolled_value = model3.predict(user_data)
+    # make the prediction using the loaded model and input data
+    predicted_unrolled_value = model.predict(user_data)
     
-#     # return the predicted max number of repairs as output
-#     return np.round(predicted_unrolled_value[0])
+    # return the predicted max number of repairs as output
+    return np.round(predicted_unrolled_value[0])
 
     
-# # Function calling
-# y_pred = int(predict_unrolled_value(user_data))
-# st.write("Click here to see the Predictions")
-# if st.button("Predict"):
-#     st.subheader(f"Predicted Unrolled Value: {y_pred} ")
+# Function calling
+y_pred = int(predict_unrolled_value(user_data))
+st.write("Click here to see the Predictions")
+if st.button("Predict"):
+    st.subheader(f"Predicted Unrolled Value: {y_pred} ")