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# %%
import gradio as gr
import pandas as pd
import pickle
with open("raoul_aufgabe_mit_distance_to_hb.pkl", "rb") as f:
model = pickle.load(f)
features = ['rooms', 'area', 'pop', 'pop_dens', 'frg_pct', 'emp', 'tax_income', 'room_per_m2', 'luxurious', 'temporary', 'furnished', 'area_cat_ecoded',
'(LUXURIÖS)', '(POOL)', '(SEESICHT)',
'(EXKLUSIV)', '(ATTIKA)', '(LOFT)', 'Kreis 6', 'Kreis 11', 'Kreis 12', 'Kreis 10',
'Kreis 4', 'Kreis 1', 'Kreis 9', 'Kreis 5', 'Kreis 7', 'Kreis 3',
'Kreis 2', 'Kreis 8', 'distance_to_hb']
from math import radians, sin, cos, sqrt, atan2
def haversine_distance(lat, lon, center_lat=47.3769, center_lon=8.5417):
R = 6371
dlat = radians(lat - center_lat)
dlon = radians(lon - center_lon)
a = sin(dlat / 2) ** 2 + cos(radians(lat)) * cos(radians(center_lat)) * sin(dlon / 2) ** 2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
return R * c
def predict_price(rooms, area, pop, pop_dens, frg_pct, emp, tax_income, room_per_m2,
luxurious, temporary, furnished, area_cat_ecoded, lux, pool, seesicht,
exklusiv, attika, loft, k6, k11, k12, k10, k4, k1, k9, k5, k7, k3, k2, k8,
lat, lon):
distance = haversine_distance(lat, lon)
input_data = pd.DataFrame([[rooms, area, pop, pop_dens, frg_pct, emp, tax_income, room_per_m2,
luxurious, temporary, furnished, area_cat_ecoded, lux, pool,
seesicht, exklusiv, attika, loft, k6, k11, k12, k10, k4, k1,
k9, k5, k7, k3, k2, k8, distance]],
columns=features)
pred = model.predict(input_data)[0]
return f"Estimated Rent Price: CHF {pred:.2f}"
inputs = [
gr.Number(label="Rooms"),
gr.Number(label="Area (m²)"),
gr.Number(label="Population"),
gr.Number(label="Population Density"),
gr.Number(label="Foreigners (%)"),
gr.Number(label="Employment"),
gr.Number(label="Taxable Income"),
gr.Number(label="Room per m²"),
gr.Checkbox(label="Luxurious"),
gr.Checkbox(label="Temporary"),
gr.Checkbox(label="Furnished"),
gr.Number(label="Area Category Encoded"),
gr.Checkbox(label="(LUXURIÖS)"),
gr.Checkbox(label="(POOL)"),
gr.Checkbox(label="(SEESICHT)"),
gr.Checkbox(label="(EXKLUSIV)"),
gr.Checkbox(label="(ATTIKA)"),
gr.Checkbox(label="(LOFT)"),
gr.Checkbox(label="Kreis 6"),
gr.Checkbox(label="Kreis 11"),
gr.Checkbox(label="Kreis 12"),
gr.Checkbox(label="Kreis 10"),
gr.Checkbox(label="Kreis 4"),
gr.Checkbox(label="Kreis 1"),
gr.Checkbox(label="Kreis 9"),
gr.Checkbox(label="Kreis 5"),
gr.Checkbox(label="Kreis 7"),
gr.Checkbox(label="Kreis 3"),
gr.Checkbox(label="Kreis 2"),
gr.Checkbox(label="Kreis 8"),
gr.Number(label="Latitude"),
gr.Number(label="Longitude")
]
examples = [
[3, 75, 100000, 1200, 30, 150000, 25000, 1.2, True, False, True, 1, True, False, True, False, True, False,
True, False, False, False, False, False, False, False, False, False, False, False,
47.3830, 8.5470],
[2, 55, 90000, 1500, 40, 140000, 22000, 1.3, False, True, True, 2, False, True, False, True, True, False,
False, False, False, False, True, False, False, False, False, False, False, False,
47.3750, 8.5275],
[4, 100, 130000, 1900, 28, 160000, 27000, 1.5, True, False, False, 3, True, True, True, False, False, True,
False, False, False, False, False, True, False, False, False, False, False, False,
47.3660, 8.5445],
[2, 60, 85000, 1100, 35, 135000, 21000, 1.1, False, False, True, 0, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False, False, False, False,
47.4100, 8.4900]
]
demo = gr.Interface(
fn=predict_price,
inputs=inputs,
outputs="text",
examples=examples,
title="Zürich Apartment Rent Estimator",
description="Predicts the estimated monthly rent (CHF) for an apartment in Zürich based on various features."
)
demo.launch()
# %% |