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e4e908a a9b944c e4e908a f7ab46a e4e908a f7ab46a e4e908a f7ab46a e4e908a f7ab46a e4e908a f7ab46a e4e908a f7ab46a e4e908a 0c469ca e4e908a 0c469ca e4e908a 0c469ca e4e908a f7ab46a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | import gradio as gr
import pandas as pd
import numpy as np
import pickle
MODEL_PATH = "model.pkl"
with open(MODEL_PATH, 'rb') as f:
model_package = pickle.load(f)
if isinstance(model_package, dict):
model = model_package['model']
scaler = model_package.get('scaler', None)
features = model_package.get('features', None)
else:
model = model_package
scaler = None
features = None
def create_features(rooms, area, postalcode, pop, pop_dens, frg_pct, emp, tax_income):
"""Create engineered features from input"""
data = {
'rooms': rooms,
'area': area,
'pop': pop,
'pop_dens': pop_dens,
'frg_pct': frg_pct,
'emp': emp,
'tax_income': tax_income,
'rooms_per_sqm': rooms / area,
'wealth_index': (tax_income / 100000) * (emp / 100000),
'is_zurich_city': 1 if (postalcode >= 8000 and postalcode < 8100) else 0,
'pop_emp_ratio': pop / (emp + 1),
'log_area': np.log1p(area),
'log_pop': np.log1p(pop),
'log_tax_income': np.log1p(tax_income)
}
return pd.DataFrame([data])
def predict_price(rooms, area, postalcode, pop, pop_dens, frg_pct, emp, tax_income):
"""Predict apartment rental price based on input features"""
input_data = create_features(rooms, area, postalcode, pop, pop_dens, frg_pct, emp, tax_income)
if features is not None:
input_data = input_data[features]
if scaler is not None:
input_data_scaled = scaler.transform(input_data)
prediction = model.predict(input_data_scaled)[0]
else:
prediction = model.predict(input_data)[0]
return f"Geschätzter Mietpreis: CHF {prediction:.2f}/Monat"
with gr.Blocks(title="Zürich Apartment Price Predictor") as demo:
gr.Markdown("# Zürich Apartment Rent Predictor")
gr.Markdown("Vorhersage von Mietpreisen für Wohnungen im Kanton Zürich")
with gr.Row():
with gr.Column():
rooms = gr.Number(label="Anzahl Zimmer", value=3.5, minimum=1, maximum=10)
area = gr.Number(label="Wohnfläche (m²)", value=75, minimum=10, maximum=500)
postalcode = gr.Number(label="Postleitzahl", value=8001, minimum=8000, maximum=8999)
with gr.Column():
pop = gr.Number(label="Bevölkerung (Gemeinde)", value=420000)
pop_dens = gr.Number(label="Bevölkerungsdichte", value=5000)
frg_pct = gr.Number(label="Ausländeranteil (%)", value=30)
emp = gr.Number(label="Anzahl Arbeitsplätze", value=490000)
tax_income = gr.Number(label="Durchschn. Steuereinkommen", value=85000)
predict_btn = gr.Button("Preis berechnen", variant="primary")
output = gr.Textbox(label="Ergebnis", lines=2)
predict_btn.click(
fn=predict_price,
inputs=[rooms, area, postalcode, pop, pop_dens, frg_pct, emp, tax_income],
outputs=output
)
if __name__ == "__main__":
demo.launch(theme=gr.themes.Soft())
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