| import gradio as gr |
| import pandas as pd |
| import numpy as np |
| import pickle |
| from math import radians, cos, sin, asin, sqrt |
|
|
| |
| with open("apartment_price_model.pkl", mode="rb") as f: |
| model = pickle.load(f) |
|
|
| |
| zurich_neighborhoods = { |
| "City Center (Altstadt)": {"lat": 47.3769, "lon": 8.5417, "distance": 0.0}, |
| "Oerlikon": {"lat": 47.4111, "lon": 8.5458, "distance": 3.8}, |
| "Altstetten": {"lat": 47.3908, "lon": 8.4889, "distance": 4.2}, |
| "Wiedikon": {"lat": 47.3708, "lon": 8.5128, "distance": 2.3}, |
| "Seefeld": {"lat": 47.3550, "lon": 8.5550, "distance": 2.7}, |
| "Schwamendingen": {"lat": 47.4053, "lon": 8.5648, "distance": 3.5}, |
| "Wollishofen": {"lat": 47.3517, "lon": 8.5304, "distance": 3.0}, |
| "Enge": {"lat": 47.3656, "lon": 8.5267, "distance": 1.2}, |
| "Fluntern": {"lat": 47.3797, "lon": 8.5611, "distance": 1.8}, |
| "Hottingen": {"lat": 47.3683, "lon": 8.5584, "distance": 1.5}, |
| "Custom Location": {"lat": 47.3769, "lon": 8.5417, "distance": 0.0} |
| } |
|
|
| def haversine_distance(lat1, lon1, lat2, lon2): |
| lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) |
| 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 |
| return c * r |
|
|
| def predict_price(neighborhood, rooms, area, has_balcony, is_renovated, proximity_to_transport, custom_lat=None, custom_lon=None): |
| if neighborhood == "Custom Location" and custom_lat is not None and custom_lon is not None: |
| lat = custom_lat |
| lon = custom_lon |
| else: |
| lat = zurich_neighborhoods[neighborhood]["lat"] |
| lon = zurich_neighborhoods[neighborhood]["lon"] |
|
|
| distance_to_center = haversine_distance(lat, lon, 47.3769, 8.5417) |
|
|
| input_data = pd.DataFrame([{ |
| 'rooms': rooms, |
| 'area': area, |
| 'pop': 420217, |
| 'pop_dens': 4778, |
| 'frg_pct': 32.45, |
| 'emp': 491193, |
| 'tax_income': 85446, |
| 'price_per_room': 0, |
| 'distance_to_center': distance_to_center, |
| 'has_balcony': 1 if has_balcony else 0, |
| 'is_renovated': 1 if is_renovated else 0, |
| 'proximity_to_transport': 1 if proximity_to_transport else 0 |
| }]) |
|
|
| features = [ |
| 'rooms', 'area', 'pop', 'pop_dens', 'frg_pct', 'emp', 'tax_income', |
| 'price_per_room', 'distance_to_center', 'has_balcony', 'is_renovated', 'proximity_to_transport' |
| ] |
|
|
| predicted_price = model.predict(input_data[features])[0] |
|
|
| result = f"Predicted Monthly Rent: CHF {predicted_price:.0f}" |
| result += f"\n\nProperty Details:" |
| result += f"\n- Location: {neighborhood}" |
| 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 renovated'}" |
| result += f"\n- {'Close to transport' if proximity_to_transport else 'Far from transport'}" |
|
|
| return result |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("# Zurich Apartment Rent Prediction") |
|
|
| with gr.Row(): |
| with gr.Column(): |
| neighborhood = gr.Dropdown(label="Neighborhood", choices=list(zurich_neighborhoods.keys()), value="City Center (Altstadt)") |
| custom_lat = gr.Number(label="Custom Latitude", value=47.3769, visible=False) |
| custom_lon = gr.Number(label="Custom Longitude", value=8.5417, visible=False) |
| 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) |
| is_renovated = gr.Checkbox(label="Is Renovated", value=False) |
| proximity_to_transport = gr.Checkbox(label="Proximity to Transport", value=False) |
| submit_button = gr.Button("Submit") |
| output = gr.Textbox(label="Output") |
|
|
| submit_button.click( |
| fn=predict_price, |
| inputs=[neighborhood, rooms, area, has_balcony, is_renovated, proximity_to_transport, custom_lat, custom_lon], |
| outputs=output |
| ) |
|
|
| demo.launch() |
|
|
