app.py

import joblib
import pandas as pd
import gradio as gr

# 📌 Lade das neue Modell
model = joblib.load("random_forest_regression_neu.pkl")

# 📌 Funktion zur Vorhersage
def predict_price(rooms, area, pop_dens, has_balcony):
    input_data = pd.DataFrame([[rooms, area, pop_dens, has_balcony]],
                              columns=model.feature_names_in_)
    
    prediction = model.predict(input_data)[0]
    return f"Predicted price: {prediction:.2f} CHF"

# 📌 Gradio UI
demo = gr.Interface(
    fn=predict_price,
    inputs=[
        gr.Number(label="Rooms"),
        gr.Number(label="Area (m²)"),
        gr.Number(label="Population Density"),
        gr.Number(label="Has Balcony (0 = No, 1 = Yes)")
    ],
    outputs="text",
)

# 📌 Starte die Web-App
demo.launch()