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apartment_price_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fca4899edfa139b54aedb3ed936a350a1dd51ea9326f84bdefb9b3d1138a6553
+size 14830517

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import pickle
+from math import radians, cos, sin, asin, sqrt
+
+# Load only 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'
+]
+
+# Function to calculate distance between two points using Haversine formula
+def haversine_distance(lat1, lon1, lat2, lon2):
+    """Calculate the great circle distance between two points on earth."""
+    # Convert decimal degrees to radians
+    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
+    
+    # Haversine formula
+    dlon = lon2 - lon1
+    dlat = lat2 - lat1
+    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
+    c = 2 * asin(sqrt(a))
+    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
+    distance_to_center = haversine_distance(lat, lon, zurich_center_lat, zurich_center_lon)
+    
+    # Create input dataframe
+    input_data = pd.DataFrame([{
+        'rooms': rooms,
+        'area': area,
+        'pop': population,
+        'pop_dens': pop_density,
+        'frg_pct': foreign_pct,
+        'emp': employment,
+        'tax_income': tax_income,
+        'price_per_room': price_per_room,
+        'distance_to_center': distance_to_center,
+        'has_balcony': has_balcony,
+        'is_renovated': is_renovated
+    }])
+    
+    # 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
+    
+    # Make another prediction with updated price_per_room
+    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'}"
+    
+    return result
+
+# Create Gradio interface
+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"),
+        gr.Checkbox(label="Is Renovated"),
+    ],
+    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],
+    ],
+    title="Zurich Apartment Rent Prediction",
+    description="Enter apartment details to predict the monthly rent in Swiss Francs (CHF)."
+)
+
+demo.launch()

requirements.txt

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+scikit-learn