Upload app.py

app.py

@@ -4,15 +4,13 @@ import numpy as np
 import pickle
 from math import radians, cos, sin, asin, sqrt
 
-# Load only the model
+# Load the model
 with open("apartment_price_model.pkl", mode="rb") as f:
     model = pickle.load(f)
 
-# Define features directly in the app
-features = [
-    'rooms', 'area', 'pop', 'pop_dens', 'frg_pct', 'emp', 'tax_income',
-    'price_per_room', 'distance_to_center', 'has_balcony', 'is_renovated'
-]
+# Zurich city center coordinates
+zurich_center_lat = 47.3769
+zurich_center_lon = 8.5417
 
 # Function to calculate distance between two points using Haversine formula
 def haversine_distance(lat1, lon1, lat2, lon2):
@@ -28,65 +26,58 @@ def haversine_distance(lat1, lon1, lat2, lon2):
     r = 6371  # Radius of earth in kilometers
     return c * r
 
-# Zurich city center coordinates
-zurich_center_lat = 47.3769
-zurich_center_lon = 8.5417
-
-def predict_price(rooms, area, population, pop_density, foreign_pct, 
-                 employment, tax_income, lat, lon, has_balcony, is_renovated):
-    
-    # Calculate derived features
-    price_per_room = 0  # This will be estimated by the model
+def predict_price(rooms, area, lat, lon, has_balcony, is_renovated):
+    # Calculate special feature: distance to city center
     distance_to_center = haversine_distance(lat, lon, zurich_center_lat, zurich_center_lon)
     
+    # Default values for other features
+    pop = 420217
+    pop_dens = 4778
+    frg_pct = 32.45
+    emp = 491193
+    tax_income = 85446
+    price_per_room = 0  # This will be updated by the model
+    
     # Create input dataframe
     input_data = pd.DataFrame([{
         'rooms': rooms,
         'area': area,
-        'pop': population,
-        'pop_dens': pop_density,
-        'frg_pct': foreign_pct,
-        'emp': employment,
+        'pop': pop,
+        'pop_dens': pop_dens,
+        'frg_pct': frg_pct,
+        'emp': emp,
         'tax_income': tax_income,
         'price_per_room': price_per_room,
         'distance_to_center': distance_to_center,
-        'has_balcony': has_balcony,
-        'is_renovated': is_renovated
+        'has_balcony': 1 if has_balcony else 0,
+        'is_renovated': 1 if is_renovated else 0
     }])
     
-    # Make prediction
-    predicted_price = model.predict(input_data[features])[0]
-    
-    # Update price_per_room with the predicted price
-    price_per_room = predicted_price / rooms
-    input_data['price_per_room'] = price_per_room
+    # Define features in the correct order
+    features = [
+        'rooms', 'area', 'pop', 'pop_dens', 'frg_pct', 'emp', 'tax_income',
+        'price_per_room', 'distance_to_center', 'has_balcony', 'is_renovated'
+    ]
     
-    # Make another prediction with updated price_per_room
+    # Make prediction
     predicted_price = model.predict(input_data[features])[0]
     
     # Format the result
     result = f"Predicted Monthly Rent: CHF {predicted_price:.0f}"
-    
-    # Additional insights
     result += f"\n\nProperty Details:"
     result += f"\n- {rooms} rooms, {area} m²"
     result += f"\n- {distance_to_center:.2f} km from city center"
     result += f"\n- {'Has balcony' if has_balcony else 'No balcony'}"
-    result += f"\n- {'Renovated' if is_renovated else 'Not mentioned as renovated'}"
+    result += f"\n- {'Renovated' if is_renovated else 'Not renovated'}"
     
     return result
 
-# Create Gradio interface
+# Create Gradio interface with fewer inputs
 demo = gr.Interface(
     fn=predict_price,
     inputs=[
         gr.Number(label="Number of Rooms"),
         gr.Number(label="Area (m²)"),
-        gr.Number(label="Population", value=420217),
-        gr.Number(label="Population Density", value=4778),
-        gr.Number(label="Foreign Percentage", value=32.45),
-        gr.Number(label="Employment", value=491193),
-        gr.Number(label="Tax Income", value=85446),
         gr.Number(label="Latitude", value=47.3769),
         gr.Number(label="Longitude", value=8.5417),
         gr.Checkbox(label="Has Balcony"),
@@ -94,12 +85,22 @@ demo = gr.Interface(
     ],
     outputs="text",
     examples=[
-        [3.5, 75, 420217, 4778, 32.45, 491193, 85446, 47.41106, 8.54654, True, True],
-        [2.0, 60, 420217, 4778, 32.45, 491193, 85446, 47.37624, 8.52814, False, False],
-        [4.5, 120, 420217, 4778, 32.45, 491193, 85446, 47.36368, 8.54678, True, False],
+        [3.5, 75, 47.41106, 8.54654, True, True],
+        [2.0, 60, 47.37624, 8.52814, False, False],
+        [4.5, 120, 47.36368, 8.54678, True, False],
     ],
     title="Zurich Apartment Rent Prediction",
-    description="Enter apartment details to predict the monthly rent in Swiss Francs (CHF)."
+    description="""
+    This app predicts apartment rental prices in Zurich with a special feature: Distance to City Center.
+    
+    **Special Feature Description:**
+    The app automatically calculates the apartment's distance from Zurich city center using the Haversine formula.
+    This distance is a critical factor in real estate pricing - properties closer to the city center typically
+    command higher rents due to convenience and accessibility to urban amenities.
+    
+    Simply enter the apartment's latitude and longitude, and the model will incorporate this distance
+    calculation to provide a more accurate rental price prediction.
+    """
 )
 
 demo.launch()