|
|
|
|
|
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()
|
|
|
|
