Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import pandas as pd
 import joblib
 
+
 @st.cache_resource
 def load_model():
     return joblib.load('logistic_regression_model.joblib')
@@ -13,22 +14,24 @@ st.title('Ad Click Prediction')
 st.write('Adjust the sliders below to predict if a user will click on an ad:')
 
 
-daily_time = st.slider('Daily Time Spent on Site (hours)', min_value=0.0, max_value=24.0, value=1.0, step=0.1)
-age = st.slider('Age', min_value=18, max_value=100, value=30)
-area_income = st.slider('Area Income ($)', min_value=10000, max_value=150000, value=50000, step=1000)
-daily_internet = st.slider('Daily Internet Usage (hours)', min_value=0.0, max_value=24.0, value=2.0, step=0.1)
-gender = st.radio('Gender', ['Male', 'Female'])
+daily_time = st.slider('Daily Time Spent on Site (minutes)', min_value=32.6, max_value=91.43, value=65.0, step=0.1)
+age = st.slider('Age', min_value=19, max_value=61, value=36, step=1)
+area_income = st.slider('Area Income ($)', min_value=13996, max_value=79485, value=55000, step=100)
+daily_internet = st.slider('Daily Internet Usage (minutes)', min_value=104.78, max_value=269.96, value=180.0, step=0.1)
+gender = st.radio('Gender', ['Male', 'Not Male'])
 
 if st.button('Predict'):
+    # Girişleri bir DataFrame'e dönüştür
     input_data = pd.DataFrame({
         'Daily Time Spent on Site': [daily_time],
         'Age': [age],
         'Area Income': [area_income],
         'Daily Internet Usage': [daily_internet],
-        'Male_not_male': [1 if gender == 'Female' else 0]
+        'Male_male': [1 if gender == 'Male' else 0],
+        'Male_not_male': [1 if gender == 'Not Male' else 0]
     })
 
-    
+    # Tahmin yap
     prediction = model.predict(input_data)
     probability = model.predict_proba(input_data)[0][1]