Update app.py

app.py

@@ -11,21 +11,110 @@ import streamlit as st
 #     result = model_2.predict([[]])
 #     st.write(f"The predicted price of the rental house is {result}")
 
+# 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'])
+# 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")
+
+# # 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}
+
+#     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 FileNotFoundError:
+#     st.error(f"Model file not found at: {model_path}")
+# except Exception as e:
+#     st.error(f"An error occurred: {e}")
+
+
 import joblib
 import streamlit as st
 
-st.title("User Behavior Using Mobile Prediction")
+# Set page configuration
+st.set_page_config(page_title="User Behavior Prediction", page_icon="📱", layout="wide")
+
+# Title and Description
+st.title("📱 User Behavior Prediction Using Mobile Data")
+st.markdown("""
+Welcome to the **User Behavior Prediction App**!  
+This tool predicts user behavior based on mobile usage data, powered by a machine learning model.  
+Fill in the details below and click **Submit** to see the results.
+""")
+
+# Inputs in columns for better alignment
+col1, col2 = st.columns(2)
 
-# 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'])
-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")
+with col1:
+    Device_Model = st.selectbox(
+        "📱 Device Model Type",
+        ['Xiaomi Mi 11', 'iPhone 12', 'Google Pixel 5', 'OnePlus 9', 'Samsung Galaxy S21'],
+        help="Select the type of mobile device being used."
+    )
+    Operating_System = st.selectbox(
+        "⚙️ Operating System Type",
+        ['Android', 'iOS'],
+        help="Select the mobile's operating system."
+    )
+    gender = st.radio(
+        "👤 Gender",
+        ['Male', 'Female'],
+        help="Select the gender of the user."
+    )
+    age = st.number_input(
+        "🎂 Age",
+        min_value=0, max_value=100, step=1, format="%d",
+        help="Enter the user's age."
+    )
+
+with col2:
+    App_Usage_Time = st.number_input(
+        "⏱️ App Usage Time (in hours)",
+        min_value=0.0, step=0.1,
+        help="Enter the total app usage time (e.g., 3.5 hours)."
+    )
+    Screen_On_Time = st.number_input(
+        "🔆 Screen On Time (in hours)",
+        min_value=0.0, step=0.1,
+        help="Enter the total screen on time (e.g., 5.2 hours)."
+    )
+    Battery_Drain = st.number_input(
+        "🔋 Battery Drain (in percentage)",
+        min_value=0.0, step=0.1,
+        help="Enter the battery drain percentage (e.g., 15.5%)."
+    )
+    Number_of_Apps_Installed = st.number_input(
+        "📱 Number of Apps Installed",
+        min_value=0, step=1,
+        help="Enter the total number of apps installed on the device."
+    )
+    Data_Usage = st.number_input(
+        "📶 Data Usage (in GB)",
+        min_value=0.0, step=0.1,
+        help="Enter the total data usage (e.g., 1.5 GB)."
+    )
 
 # Load the model
 model_path = "./rfc.pkl"  # Adjust if in a subdirectory
@@ -41,13 +130,18 @@ try:
     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}")
+    # Predict and display result
+    if st.button("🚀 Submit"):
+        with st.spinner("Running prediction..."):
+            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.success("🎉 Prediction Completed!")
+        st.markdown(f"**📊 Predicted User Behavior:** `{result[0]}`")
+
 except FileNotFoundError:
-    st.error(f"Model file not found at: {model_path}")
+    st.error(f"🚨 Model file not found at: `{model_path}`. Please upload the model.")
 except Exception as e:
-    st.error(f"An error occurred: {e}")
+    st.error(f"🚨 An error occurred: {e}")
+