Update app.py

app.py

@@ -5,6 +5,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 import pickle
+import os
 
 from datetime import datetime
 from datetime import timedelta
@@ -36,64 +37,64 @@ 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 = model1.predict(user_data)
+#     # make the prediction using the loaded model and input data
+#     predicted_unrolled_value = model1.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} ")