Update app.py

app.py

@@ -22,12 +22,12 @@
 #     st.write(f"The predicted price of the rental house is {result}")
 
 
-import os
 import joblib
 import streamlit as st
 
 st.title("User Behavior Using Mobile Prediction")
 
+# Inputs
 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'])
@@ -38,28 +38,27 @@ 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")
 
-# Path Debugging
-model_path = r"Mobile_data_user_behaviour/rfc.pkl"
-if not os.path.exists(model_path):
-    st.error(f"Model file not found at: {model_path}")
-else:
-    try:
-        model_2 = joblib.load(model_path)
+# Load the model
+model_path = "./rfc.pkl"  # Adjust if in a subdirectory
+try:
+    model_2 = joblib.load(model_path)
 
-        # Encode categorical variables
-        mapping_device = {'Xiaomi Mi 11': 0, 'iPhone 12': 1, 'Google Pixel 5': 2, 'OnePlus 9': 3, 'Samsung Galaxy S21': 4}
-        mapping_os = {'Android': 0, 'iOS': 1}
-        mapping_gender = {'Male': 0, 'Female': 1}
+    # Encode categorical variables
+    mapping_device = {'Xiaomi Mi 11': 0, 'iPhone 12': 1, 'Google Pixel 5': 2, 'OnePlus 9': 3, 'Samsung Galaxy S21': 4}
+    mapping_os = {'Android': 0, 'iOS': 1}
+    mapping_gender = {'Male': 0, 'Female': 1}
 
-        device_model_encoded = mapping_device[Device_Model]
-        operating_system_encoded = mapping_os[Operating_System]
-        gender_encoded = mapping_gender[gender]
+    device_model_encoded = mapping_device[Device_Model]
+    operating_system_encoded = mapping_os[Operating_System]
+    gender_encoded = mapping_gender[gender]
 
-        if st.button("Submit"):
-            result = model_2.predict([[device_model_encoded, operating_system_encoded, App_Usage_Time, Screen_On_Time, Battery_Drain, Number_of_Apps_Installed, Data_Usage, age, gender_encoded]])
-            st.write(f"The predicted behavior is: {result}")
-    except Exception as e:
-        st.error(f"Failed to load the model or predict: {e}")
+    if st.button("Submit"):
+        result = model_2.predict([[device_model_encoded, operating_system_encoded, App_Usage_Time, Screen_On_Time, Battery_Drain, Number_of_Apps_Installed, Data_Usage, age, gender_encoded]])
+        st.write(f"The predicted behavior is: {result}")
+except FileNotFoundError:
+    st.error(f"Model file not found at: {model_path}")
+except Exception as e:
+    st.error(f"An error occurred: {e}")