import gradio as gr
import pandas as pd
import numpy as np
import pickle
from math import radians, cos, sin, asin, sqrt

# Load the model
with open("apartment_price_model.pkl", mode="rb") as f:
    model = pickle.load(f)

# Zurich neighborhoods with coordinates and distances
zurich_neighborhoods = {
    "City Center (Altstadt)": {"lat": 47.3769, "lon": 8.5417, "distance": 0.0},
    "Oerlikon": {"lat": 47.4111, "lon": 8.5458, "distance": 3.8},
    "Altstetten": {"lat": 47.3908, "lon": 8.4889, "distance": 4.2},
    "Wiedikon": {"lat": 47.3708, "lon": 8.5128, "distance": 2.3},
    "Seefeld": {"lat": 47.3550, "lon": 8.5550, "distance": 2.7},
    "Schwamendingen": {"lat": 47.4053, "lon": 8.5648, "distance": 3.5},
    "Wollishofen": {"lat": 47.3517, "lon": 8.5304, "distance": 3.0},
    "Enge": {"lat": 47.3656, "lon": 8.5267, "distance": 1.2},
    "Fluntern": {"lat": 47.3797, "lon": 8.5611, "distance": 1.8},
    "Hottingen": {"lat": 47.3683, "lon": 8.5584, "distance": 1.5},
    "Custom Location": {"lat": 47.3769, "lon": 8.5417, "distance": 0.0}
}

def haversine_distance(lat1, lon1, lat2, lon2):
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
    dlon = lon2 - lon1
    dlat = lat2 - lat1
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a))
    r = 6371  # Radius of earth in kilometers
    return c * r

def predict_price(neighborhood, rooms, area, has_balcony, is_renovated, proximity_to_transport, custom_lat=None, custom_lon=None):
    if neighborhood == "Custom Location" and custom_lat is not None and custom_lon is not None:
        lat = custom_lat
        lon = custom_lon
    else:
        lat = zurich_neighborhoods[neighborhood]["lat"]
        lon = zurich_neighborhoods[neighborhood]["lon"]

    distance_to_center = haversine_distance(lat, lon, 47.3769, 8.5417)

    input_data = pd.DataFrame([{
        'rooms': rooms,
        'area': area,
        'pop': 420217,
        'pop_dens': 4778,
        'frg_pct': 32.45,
        'emp': 491193,
        'tax_income': 85446,
        'price_per_room': 0,
        'distance_to_center': distance_to_center,
        'has_balcony': 1 if has_balcony else 0,
        'is_renovated': 1 if is_renovated else 0,
        'proximity_to_transport': 1 if proximity_to_transport else 0
    }])

    features = [
        'rooms', 'area', 'pop', 'pop_dens', 'frg_pct', 'emp', 'tax_income',
        'price_per_room', 'distance_to_center', 'has_balcony', 'is_renovated', 'proximity_to_transport'
    ]

    predicted_price = model.predict(input_data[features])[0]

    result = f"Predicted Monthly Rent: CHF {predicted_price:.0f}"
    result += f"\n\nProperty Details:"
    result += f"\n- Location: {neighborhood}"
    result += f"\n- {rooms} rooms, {area} m²"
    result += f"\n- {distance_to_center:.2f} km from city center"
    result += f"\n- {'Has balcony' if has_balcony else 'No balcony'}"
    result += f"\n- {'Renovated' if is_renovated else 'Not renovated'}"
    result += f"\n- {'Close to transport' if proximity_to_transport else 'Far from transport'}"

    return result

with gr.Blocks() as demo:
    gr.Markdown("# Zurich Apartment Rent Prediction")

    with gr.Row():
        with gr.Column():
            neighborhood = gr.Dropdown(label="Neighborhood", choices=list(zurich_neighborhoods.keys()), value="City Center (Altstadt)")
            custom_lat = gr.Number(label="Custom Latitude", value=47.3769, visible=False)
            custom_lon = gr.Number(label="Custom Longitude", value=8.5417, visible=False)
            rooms = gr.Number(label="Number of Rooms", value=3.5)
            area = gr.Number(label="Area (m²)", value=75)
            has_balcony = gr.Checkbox(label="Has Balcony", value=True)
            is_renovated = gr.Checkbox(label="Is Renovated", value=False)
            proximity_to_transport = gr.Checkbox(label="Proximity to Transport", value=False)
            submit_button = gr.Button("Submit")
            output = gr.Textbox(label="Output")

        submit_button.click(
            fn=predict_price,
            inputs=[neighborhood, rooms, area, has_balcony, is_renovated, proximity_to_transport, custom_lat, custom_lon],
            outputs=output
        )

demo.launch()