app.py

import gradio as gr
import pandas as pd
import pickle
import numpy as np

# Load model from file
try:
    model_filename = "apartment_price_model.pkl"
    with open(model_filename, mode="rb") as f:
        model = pickle.load(f)
    model_loaded = True
except:
    print("Model file not found. App will still run but predictions will be simulated.")
    model_loaded = False

def predict(
    rooms, 
    area, 
    pop, 
    pop_dens, 
    frg_pct, 
    emp, 
    tax_income, 
    room_per_m2,
    luxurious, 
    temporary, 
    furnished, 
    area_cat_ecoded, 
    zurich_city,
    listing_complexity
):
    # Create input dataframe with all features
    input_data = pd.DataFrame([[
        rooms, 
        area, 
        pop, 
        pop_dens, 
        frg_pct, 
        emp, 
        tax_income,
        room_per_m2,
        luxurious,
        temporary, 
        furnished,
        area_cat_ecoded,
        zurich_city,
        listing_complexity
    ]], columns=[
        'rooms', 
        'area', 
        'pop', 
        'pop_dens', 
        'frg_pct', 
        'emp', 
        'tax_income', 
        'room_per_m2', 
        'luxurious', 
        'temporary', 
        'furnished', 
        'area_cat_ecoded', 
        'zurich_city',
        'listing_complexity'
    ])
    
    # Make prediction
    if model_loaded:
        prediction = model.predict(input_data)[0]
    else:
        # If model not loaded, simulate a basic prediction
        prediction = 1500 + (rooms * 500) + (area * 10)
    
    return f"Predicted monthly rent: CHF {prediction:.2f}"

# Create Gradio interface
demo = gr.Interface(
    fn=predict,
    inputs=[
        gr.Number(label="Number of Rooms", value=3),
        gr.Number(label="Area (m²)", value=80),
        gr.Number(label="Population", value=30000),
        gr.Number(label="Population Density", value=5000),
        gr.Number(label="Foreign Population Percentage", value=25),
        gr.Number(label="Employment Rate", value=0.75),
        gr.Number(label="Average Tax Income (CHF)", value=80000),
        gr.Number(label="Room per m²", value=26.7),
        gr.Number(label="Luxurious (0 or 1)", value=0),
        gr.Number(label="Temporary (0 or 1)", value=0),
        gr.Number(label="Furnished (0 or 1)", value=0),
        gr.Number(label="Area Category Encoded (0, 1, or 2)", value=1),
        gr.Number(label="Zurich City (0 or 1)", value=0),
        gr.Number(label="Listing Complexity", value=0.5)
    ],
    outputs="text",
    title="Swiss Apartment Price Predictor",
    description="Enter apartment details to predict the monthly rental price in Swiss Francs (CHF).",
    examples=[
        # 2-room apartment in Zurich
        [2, 65, 39647, 2574, 34.5, 0.82, 92000, 32.5, 1, 0, 1, 1, 1, 0.65],
        # 3.5-room apartment outside Zurich
        [3.5, 85, 25000, 3200, 22.3, 0.78, 75000, 24.3, 0, 0, 0, 1, 0, 0.5],
        # 1-room studio in Zurich
        [1, 35, 15874, 7942, 38.2, 0.83, 88000, 35.0, 0, 1, 1, 0, 1, 0.45]
    ]
)

if __name__ == "__main__":
    demo.launch()