Update app.py

app.py

@@ -1,25 +1,58 @@
-import pandas as pd,numpy as np
+# import pandas as pd,numpy as np
+# import joblib
+# import streamlit as st
+
+# st.title("User Behavior Using Mobile Prediction")
+
+# Device_Model = st.selectbox("Enter Device Model Type",['Xiaomi Mi 11', 'iPhone 12', 'Google Pixel 5','Google Pixel 5','OnePlus 9','Samsung Galaxy S21'])
+# age = st.number_input("Enter age",min_value=0,max_value=1000,step=1,format="%d" )
+# gender = st.radio("Enter gender",['Male','Female'])
+# Operating_System= st.selectbox("Enter Operating System Type",['Android','iOS'])
+# App_Usage_Time = st.number_input("Enter the App Usage Time")
+# Screen_On_Time= st.number_input("Enter the Screen On Time")
+# Battery_Drain= st.number_input("Enter the Battery Drain")
+# Number_of_Apps_Installed = st.number_input("Enter the Number of Apps Installed")
+# Data_Usage = st.number_input("Enter the Data Usage")
+
+# model_2 = joblib.load(r"Mobile_data_user_behaviour/rfc.pkl") #pickle file path
+
+
+# if st.button("Submit"):
+#     result = model_2.predict([[Device_Model,Operating_System,App_Usage_Time,Screen_On_Time,Battery_Drain,Number_of_Apps_Installed,Data_Usage,age,gender]])
+#     st.write(f"The predicted price of the rental house is {result}")
+
+
+import pandas as pd
+import numpy as np
 import joblib
 import streamlit as st
 
 st.title("User Behavior Using Mobile Prediction")
 
-Device_Model = st.selectbox("Enter Device Model Type",['Xiaomi Mi 11', 'iPhone 12', 'Google Pixel 5','Google Pixel 5','OnePlus 9','Samsung Galaxy S21'])
-age = st.number_input("Enter age",min_value=0,max_value=1000,step=1,format="%d" )
-gender = st.radio("Enter gender",['Male','Female'])
-Operating_System= st.selectbox("Enter Operating System Type",['Android','iOS'])
+Device_Model = st.selectbox("Enter Device Model Type", ['Xiaomi Mi 11', 'iPhone 12', 'Google Pixel 5', 'OnePlus 9', 'Samsung Galaxy S21'])
+age = st.number_input("Enter age", min_value=0, max_value=100, step=1, format="%d")
+gender = st.radio("Enter gender", ['Male', 'Female'])
+Operating_System = st.selectbox("Enter Operating System Type", ['Android', 'iOS'])
 App_Usage_Time = st.number_input("Enter the App Usage Time")
-Screen_On_Time= st.number_input("Enter the Screen On Time")
-Battery_Drain= st.number_input("Enter the Battery Drain")
+Screen_On_Time = st.number_input("Enter the Screen On Time")
+Battery_Drain = st.number_input("Enter the Battery Drain")
 Number_of_Apps_Installed = st.number_input("Enter the Number of Apps Installed")
 Data_Usage = st.number_input("Enter the Data Usage")
 
-model_2 = joblib.load(r"Mobile_data_user_behaviour/rfc.pkl") #pickle file path
-
+try:
+    model_2 = joblib.load(r"Mobile_data_user_behaviour/rfc.pkl")  # Adjust path as needed
+except FileNotFoundError:
+    st.error("Model file 'rfc.pkl' not found. Please check the file path.")
 
 if st.button("Submit"):
-    result = model_2.predict([[Device_Model,Operating_System,App_Usage_Time,Screen_On_Time,Battery_Drain,Number_of_Apps_Installed,Data_Usage,age,gender]])
-    st.write(f"The predicted price of the rental house is {result}")
+    try:
+        # Encode categorical features if necessary
+        # Assuming you preprocess the data before prediction
+        result = model_2.predict([[Device_Model, Operating_System, App_Usage_Time, Screen_On_Time, Battery_Drain, Number_of_Apps_Installed, Data_Usage, age, gender]])
+        st.write(f"The predicted price of the rental house is {result}")
+    except Exception as e:
+        st.error(f"An error occurred during prediction: {e}")
+
     
     
 # age = st.number_input("Enter age",min_value=0,max_value=1000,step=1,format="%d" )