Update app.py

app.py

@@ -20,7 +20,7 @@ warnings.filterwarnings('ignore')
 
 
 st.title("Prection of Maimum Number of Repairs")
-
+st.sidebar.header('Enter the Components Details here')
 import pandas as pd
 import numpy as np
 import pickle
@@ -33,31 +33,70 @@ with open('max_repair_model.pkl', 'rb') as file:
 with open('manufacturer_le.pkl', 'rb') as file1:
     le = pickle.load(file1)
     
+# DATA from user
+def user_report():
+  manufacturer = st.sidebar.selectbox("Manufacturer",
+        ("JKL Company", "GHI Company","DEF Company","ABC Company","XYZ Company" ))
+  if manufacturer=='JKL Company':
+      manufacturer=3
+  elif manufacturer=="GHI Company":
+      manufacturer=2
+  elif manufacturer=="DEF Company":
+      manufacturer=1
+  elif manufacturer=="ABC Company":
+      manufacturer =0
+  else:
+      manufacturer=4
+  component_age = st.sidebar.slider('Component Age (in hours)', 100,250, 300 )
+  total_operating_hours = st.sidebar.slider('Total Operating Hours)', 400,1500, 500 )
+  operating_temperature = st.sidebar.slider('Operating Temperature', 70,80, 75 )  
+  humidity = st.sidebar.slider('Humidity', 50,70, 55 ) 
+  Vibration_Level = st.sidebar.slider('Vibration Level', 2,4, 2 )
+  Pressure = st.sidebar.slider('Pressure', 28,32, 30 ) 
+  Power_Input_Voltage= st.sidebar.slider('Power Input Voltage (V)',105,120,115)
+  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)  
+  load_factor = st.sidebar.slider('Load Factor',3,10,4)
+  engine_speed=st.sidebar.slider('Engine Speed',7000,8000,7800)
+  Oil_Temperature=st.sidebar.slider('Oil Temperature',170,185,172)  
+    
+  
+  user_report_data = {
+       'Manufacturer': manufacturer, 
+       'Component_Age': component_age, 
+       'Total_Operating_Hours': total_operating_hours, 
+       'Operating_Temperature': operating_temperature, 
+       'Humidity': humidity, 
+       'Vibration_Level': Vibration_Level, 
+       'Pressure': Pressure, 
+       'Power_Input_Voltage': Power_Input_Voltage, 
+       'Previous_number_of_repairs': previous_number_of_repairs, 
+       'Load_Factor': load_factor, 
+       'Engine_Speed': engine_speed, 
+       'Oil_Temperature':Oil_Temperature      
+  }
+  report_data = pd.DataFrame(user_report_data, index=[0])
+  return report_data    
+    
+#Customer Data
+user_data = user_report()
+st.header("Component Details")
+st.write(user_data)
+
 
 # define the prediction function
-def predict_max_number_of_repairs(manufacturer, component_age, total_operating_hours, operating_temperature, humidity, vibration_level, pressure, power_input_voltage, previous_number_of_repairs, load_factor, engine_speed, oil_temperature):
+def predict_max_number_of_repairs(user_data):
     
     # encode the manufacturer using the loaded LabelEncoder object
-    manufacturer_encoded = le.transform([manufacturer])[0]
-    
-    # create a DataFrame with the input variables
-    input_data = pd.DataFrame({'Manufacturer': [manufacturer_encoded], 
-                               'Component_Age': [component_age], 
-                               'Total_Operating_Hours': [total_operating_hours], 
-                               'Operating_Temperature': [operating_temperature], 
-                               'Humidity': [humidity], 
-                               'Vibration_Level': [vibration_level], 
-                               'Pressure': [pressure], 
-                               'Power_Input_Voltage': [power_input_voltage], 
-                               'Previous_number_of_repairs': [previous_number_of_repairs], 
-                               'Load_Factor': [load_factor], 
-                               'Engine_Speed': [engine_speed], 
-                               'Oil_Temperature': [oil_temperature]})
+    #manufacturer_encoded = le.transform([manufacturer])[0]
+    
+    
     
     # make the prediction using the loaded model and input data
-    predicted_max_number_of_repairs = model.predict(input_data)
+    predicted_max_number_of_repairs = model.predict(user_data)
     
     # return the predicted max number of repairs as output
     return np.round(predicted_max_number_of_repairs[0])
 # Function calling
-predict_max_number_of_repairs('ABC Company',100.00,1135,70.0,65,3.43,29.90,120,4,0.59,7398,170)
+y_pred = predict_max_number_of_repairs(user_data)
+st.header(y_pred)
+#predict_max_number_of_repairs('ABC Company',100.00,1135,70.0,65,3.43,29.90,120,4,0.59,7398,170)