Update app.py

app.py

@@ -7,7 +7,7 @@ import pickle
 with open("apartment_price_model.pkl", mode="rb") as f:
     model = pickle.load(f)
 
-def predict_price(rooms, area, has_balcony, is_renovated):
+def predict_price(neighborhood, rooms, area, has_balcony, is_renovated):
     # Default values for other features
     pop = 420217
     pop_dens = 4778
@@ -27,13 +27,14 @@ def predict_price(rooms, area, has_balcony, is_renovated):
         'tax_income': tax_income,
         'price_per_room': price_per_room,
         'has_balcony': 1 if has_balcony else 0,
-        'is_renovated': 1 if is_renovated else 0
+        'is_renovated': 1 if is_renovated else 0,
+        'neighborhood': neighborhood
     }])
 
     # Define features in the correct order
     features = [
         'rooms', 'area', 'pop', 'pop_dens', 'frg_pct', 'emp', 'tax_income',
-        'price_per_room', 'has_balcony', 'is_renovated'
+        'price_per_room', 'has_balcony', 'is_renovated', 'neighborhood'
     ]
 
     # Make prediction
@@ -42,6 +43,7 @@ def predict_price(rooms, area, has_balcony, is_renovated):
     # Format the result
     result = f"Predicted Monthly Rent: CHF {predicted_price:.0f}"
     result += f"\n\nProperty Details:"
+    result += f"\n- Neighborhood: {neighborhood}"
     result += f"\n- {rooms} rooms, {area} m²"
     result += f"\n- {'Has balcony' if has_balcony else 'No balcony'}"
     result += f"\n- {'Renovated' if is_renovated else 'Not renovated'}"
@@ -49,13 +51,22 @@ def predict_price(rooms, area, has_balcony, is_renovated):
     return result
 
 def reset_inputs():
-    return [3.5, 75, True, False, ""]
+    return ["City Center (Altstadt)", 3.5, 75, True, False, ""]
 
 with gr.Blocks() as demo:
     gr.Markdown("# Zurich Apartment Rent Prediction")
 
     with gr.Row():
         with gr.Column():
+            neighborhood = gr.Dropdown(
+                label="Neighborhood",
+                choices=[
+                    "City Center (Altstadt)", "Oerlikon", "Altstetten", "Wiedikon",
+                    "Seefeld", "Schwamendingen", "Wollishofen", "Enge",
+                    "Fluntern", "Hottingen"
+                ],
+                value="City Center (Altstadt)"
+            )
             rooms = gr.Number(label="Number of Rooms", value=3.5)
             area = gr.Number(label="Area (m²)", value=75)
             has_balcony = gr.Checkbox(label="Has Balcony", value=True)
@@ -70,14 +81,14 @@ with gr.Blocks() as demo:
 
     submit_button.click(
         fn=predict_price,
-        inputs=[rooms, area, has_balcony, is_renovated],
+        inputs=[neighborhood, rooms, area, has_balcony, is_renovated],
         outputs=output
     )
 
     clear_button.click(
         fn=reset_inputs,
         inputs=None,
-        outputs=[rooms, area, has_balcony, is_renovated, output]
+        outputs=[neighborhood, rooms, area, has_balcony, is_renovated, output]
     )
 
 demo.launch()