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import gradio as gr |
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import pandas as pd |
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import numpy as np |
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import pickle |
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from math import radians, cos, sin, asin, sqrt |
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with open("apartment_price_model.pkl", mode="rb") as f: |
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model = pickle.load(f) |
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zurich_neighborhoods = { |
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"City Center (Altstadt)": {"lat": 47.3769, "lon": 8.5417, "distance": 0.0}, |
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"Oerlikon": {"lat": 47.4111, "lon": 8.5458, "distance": 3.8}, |
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"Altstetten": {"lat": 47.3908, "lon": 8.4889, "distance": 4.2}, |
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"Wiedikon": {"lat": 47.3708, "lon": 8.5128, "distance": 2.3}, |
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"Seefeld": {"lat": 47.3550, "lon": 8.5550, "distance": 2.7}, |
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"Schwamendingen": {"lat": 47.4053, "lon": 8.5648, "distance": 3.5}, |
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"Wollishofen": {"lat": 47.3517, "lon": 8.5304, "distance": 3.0}, |
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"Enge": {"lat": 47.3656, "lon": 8.5267, "distance": 1.2}, |
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"Fluntern": {"lat": 47.3797, "lon": 8.5611, "distance": 1.8}, |
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"Hottingen": {"lat": 47.3683, "lon": 8.5584, "distance": 1.5}, |
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"Custom Location": {"lat": 47.3769, "lon": 8.5417, "distance": 0.0} |
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} |
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def haversine_distance(lat1, lon1, lat2, lon2): |
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lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) |
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dlon = lon2 - lon1 |
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dlat = lat2 - lat1 |
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a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2 |
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c = 2 * asin(sqrt(a)) |
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r = 6371 |
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return c * r |
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def predict_price(neighborhood, rooms, area, has_balcony, is_renovated, proximity_to_transport, custom_lat=None, custom_lon=None): |
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if neighborhood == "Custom Location" and custom_lat is not None and custom_lon is not None: |
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lat = custom_lat |
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lon = custom_lon |
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else: |
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lat = zurich_neighborhoods[neighborhood]["lat"] |
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lon = zurich_neighborhoods[neighborhood]["lon"] |
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distance_to_center = haversine_distance(lat, lon, 47.3769, 8.5417) |
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input_data = pd.DataFrame([{ |
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'rooms': rooms, |
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'area': area, |
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'pop': 420217, |
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'pop_dens': 4778, |
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'frg_pct': 32.45, |
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'emp': 491193, |
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'tax_income': 85446, |
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'price_per_room': 0, |
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'distance_to_center': distance_to_center, |
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'has_balcony': 1 if has_balcony else 0, |
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'is_renovated': 1 if is_renovated else 0, |
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'proximity_to_transport': 1 if proximity_to_transport else 0 |
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}]) |
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features = [ |
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'rooms', 'area', 'pop', 'pop_dens', 'frg_pct', 'emp', 'tax_income', |
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'price_per_room', 'distance_to_center', 'has_balcony', 'is_renovated', 'proximity_to_transport' |
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] |
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predicted_price = model.predict(input_data[features])[0] |
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result = f"Predicted Monthly Rent: CHF {predicted_price:.0f}" |
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result += f"\n\nProperty Details:" |
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result += f"\n- Location: {neighborhood}" |
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result += f"\n- {rooms} rooms, {area} m²" |
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result += f"\n- {distance_to_center:.2f} km from city center" |
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result += f"\n- {'Has balcony' if has_balcony else 'No balcony'}" |
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result += f"\n- {'Renovated' if is_renovated else 'Not renovated'}" |
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result += f"\n- {'Close to transport' if proximity_to_transport else 'Far from transport'}" |
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return result |
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with gr.Blocks() as demo: |
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gr.Markdown("# Zurich Apartment Rent Prediction") |
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with gr.Row(): |
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with gr.Column(): |
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neighborhood = gr.Dropdown(label="Neighborhood", choices=list(zurich_neighborhoods.keys()), value="City Center (Altstadt)") |
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custom_lat = gr.Number(label="Custom Latitude", value=47.3769, visible=False) |
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custom_lon = gr.Number(label="Custom Longitude", value=8.5417, visible=False) |
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rooms = gr.Number(label="Number of Rooms", value=3.5) |
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area = gr.Number(label="Area (m²)", value=75) |
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has_balcony = gr.Checkbox(label="Has Balcony", value=True) |
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is_renovated = gr.Checkbox(label="Is Renovated", value=False) |
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proximity_to_transport = gr.Checkbox(label="Proximity to Transport", value=False) |
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submit_button = gr.Button("Submit") |
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output = gr.Textbox(label="Output") |
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submit_button.click( |
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fn=predict_price, |
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inputs=[neighborhood, rooms, area, has_balcony, is_renovated, proximity_to_transport, custom_lat, custom_lon], |
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outputs=output |
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) |
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demo.launch() |
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