Create app.py

app.py

@@ -0,0 +1,42 @@
+import streamlit as st
+import pandas as pd
+import joblib
+
+@st.cache_resource
+def load_model():
+    return joblib.load('logistic_regression_model.joblib')
+
+model = load_model()
+
+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'])
+
+if st.button('Predict'):
+    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]
+    })
+
+    
+    prediction = model.predict(input_data)
+    probability = model.predict_proba(input_data)[0][1]
+
+    if prediction[0] == 1:
+        st.success(f'This user is likely to click on the ad. Probability: {probability:.2f}')
+    else:
+        st.error(f'This user is unlikely to click on the ad. Probability: {probability:.2f}')
+
+    # Girdi değerlerini göster
+    st.write('Input values:')
+    st.write(input_data)