adjusted app

add_click_app.py

@@ -14,19 +14,19 @@ def predict_click(gender, device_type, ad_position, browsing_history, time_of_da
     return f"Clicked: {'Yes' if predicted_click == 1 else 'No'}"
 
 inputs = [
-    gr.Radio(["Male", "Female"], label="Gender"),
-    gr.Radio(["Mobile", "Desktop", "Tablet"], label="Device Type"),
-    gr.Radio(["Top", "Bottom", "Sidebar"], label="Ad Position"),
-    gr.Radio(["News", "Tech", "Other"], label="Browsing History"),
-    gr.Radio(["Morning", "Afternoon", "Evening", "Night"], label="Time of Day"),
+    gr.Radio(["Female", "Male"], label="Gender"),
+    gr.Radio(["Desktop", "Mobile", "Tablet"], label="Device Type"),
+    gr.Radio(["Bottom", "Sidebar", "Top"], label="Ad Position"),
+    gr.Radio(["News", "Other", "Sports", "Tech"], label="Browsing History"),
+    gr.Radio(["Afternoon", "Evening", "Morning", "Night"], label="Time of Day"),
     gr.Slider(18, 64, step=1, label="Age"),
 ]
 outputs = gr.Label()
 
 examples = [
-    ["Male", "Mobile", "Top", "News", "Morning", 25],
-    ["Female", "Desktop", "Sidebar", "Tech", "Evening", 37],
-    ["Male", "Tablet", "Bottom", "Other", "Night", 44],
+    ["Female", "Desktop", "Sidebar", "Sports", "Morning", 31],
+    ["Male", "Mobile", "Top", "News", "Afternoon", 29],
+    ["Female", "Tablet", "Bottom", "Other", "Night", 42],
 ]
 
 gr.Interface(

requirements.txt

@@ -2,4 +2,4 @@ gradio
 skops
 pandas
 scikit-learn
-numpy
+numpy