Update app.py

app.py

@@ -2,11 +2,16 @@ import gradio as gr
 import pandas as pd
 import numpy as np
 import pickle
+from sklearn.preprocessing import LabelEncoder
 
 # Load the model
 with open("apartment_price_model.pkl", mode="rb") as f:
     model = pickle.load(f)
 
+# Load the label encoder for neighborhoods
+with open("neighborhood_encoder.pkl", mode="rb") as f:
+    neighborhood_encoder = pickle.load(f)
+
 def predict_price(neighborhood, rooms, area, has_balcony, is_renovated):
     # Default values for other features
     pop = 420217
@@ -16,6 +21,9 @@ def predict_price(neighborhood, rooms, area, has_balcony, is_renovated):
     tax_income = 85446
     price_per_room = rooms / area if area != 0 else 0
 
+    # Encode the neighborhood
+    neighborhood_encoded = neighborhood_encoder.transform([neighborhood])[0]
+
     # Create input dataframe
     input_data = pd.DataFrame([{
         'rooms': rooms,
@@ -28,7 +36,7 @@ def predict_price(neighborhood, rooms, area, has_balcony, is_renovated):
         'price_per_room': price_per_room,
         'has_balcony': 1 if has_balcony else 0,
         'is_renovated': 1 if is_renovated else 0,
-        'neighborhood': neighborhood
+        'neighborhood': neighborhood_encoded
     }])
 
     # Define features in the correct order