# %%
import gradio as gr
import tensorflow as tf
import numpy as np
import pandas as pd

# %%

model_path = "kia_apartment_keras_model.keras"
model = tf.keras.models.load_model(model_path)

df_bfs_data = pd.read_csv('bfs_municipality_and_tax_data.csv', sep=',', encoding='utf-8')
df_bfs_data['tax_income'] = df_bfs_data['tax_income'].str.replace("'", "").astype(float)

# %%
locations = {
    "Zürich": 261,
    "Kloten": 62,
    "Uster": 198,
    "Illnau-Effretikon": 296,
    "Feuerthalen": 27,
    "Pfäffikon": 177,
    "Ottenbach": 11,
    "Dübendorf": 191,
    "Richterswil": 138,
    "Maur": 195,
    "Embrach": 56,
    "Bülach": 53,
    "Winterthur": 230,
    "Oetwil am See": 157,
    "Russikon": 178,
    "Obfelden": 10,
    "Wald (ZH)": 120,
    "Niederweningen": 91,
    "Dällikon": 84,
    "Buchs (ZH)": 83,
    "Rüti (ZH)": 118,
    "Hittnau": 173,
    "Bassersdorf": 52,
    "Glattfelden": 58,
    "Opfikon": 66,
    "Hinwil": 117,
    "Regensberg": 95,
    "Langnau am Albis": 136,
    "Dietikon": 243,
    "Erlenbach (ZH)": 151,
    "Kappel am Albis": 6,
    "Stäfa": 158,
    "Zell (ZH)": 231,
    "Turbenthal": 228,
    "Oberglatt": 92,
    "Winkel": 72,
    "Volketswil": 199,
    "Kilchberg (ZH)": 135,
    "Wetzikon (ZH)": 121,
    "Zumikon": 160,
    "Weisslingen": 180,
    "Elsau": 219,
    "Hettlingen": 221,
    "Rüschlikon": 139,
    "Stallikon": 13,
    "Dielsdorf": 86,
    "Wallisellen": 69,
    "Dietlikon": 54,
    "Meilen": 156,
    "Wangen-Brüttisellen": 200,
    "Flaach": 28,
    "Regensdorf": 96,
    "Niederhasli": 90,
    "Bauma": 297,
    "Aesch (ZH)": 241,
    "Schlieren": 247,
    "Dürnten": 113,
    "Unterengstringen": 249,
    "Gossau (ZH)": 115,
    "Oberengstringen": 245,
    "Schleinikon": 98,
    "Aeugst am Albis": 1,
    "Rheinau": 38,
    "Höri": 60,
    "Rickenbach (ZH)": 225,
    "Rafz": 67,
    "Adliswil": 131,
    "Zollikon": 161,
    "Urdorf": 250,
    "Hombrechtikon": 153,
    "Birmensdorf (ZH)": 242,
    "Fehraltorf": 172,
    "Weiach": 102,
    "Männedorf": 155,
    "Küsnacht (ZH)": 154,
    "Hausen am Albis": 4,
    "Hochfelden": 59,
    "Fällanden": 193,
    "Greifensee": 194,
    "Mönchaltorf": 196,
    "Dägerlen": 214,
    "Thalheim an der Thur": 39,
    "Uetikon am See": 159,
    "Seuzach": 227,
    "Uitikon": 248,
    "Affoltern am Albis": 2,
    "Geroldswil": 244,
    "Niederglatt": 89,
    "Thalwil": 141,
    "Rorbas": 68,
    "Pfungen": 224,
    "Weiningen (ZH)": 251,
    "Bubikon": 112,
    "Neftenbach": 223,
    "Mettmenstetten": 9,
    "Otelfingen": 94,
    "Flurlingen": 29,
    "Stadel": 100,
    "Grüningen": 116,
    "Henggart": 31,
    "Dachsen": 25,
    "Bonstetten": 3,
    "Bachenbülach": 51,
    "Horgen": 295
}

# %%
# Define the core prediction function
def predict_apartment(rooms, area, town):
    bfs_number = locations[town]
    df = df_bfs_data[df_bfs_data['bfs_number']==bfs_number]
    
    if len(df) != 1: # if there are more than two records with the same bfs_number reutrn -1
        return -1
    
    input_data = np.array([rooms, area, df['pop'].iloc[0], df['pop_dens'].iloc[0], df['frg_pct'].iloc[0], df['emp'].iloc[0], df['tax_income'].iloc[0]])
    input_data = input_data.reshape(1, 7)
    prediction = model.predict(input_data)
    return float(np.round(prediction[0][0], 0))

# %%
# Create the Gradio interface
iface = gr.Interface(
    fn=predict_apartment,
    inputs=["number", "number", gr.Dropdown(choices=locations.keys(), label="Town", type="value")],
    outputs=gr.Number(),
    examples=[[4.5, 120, "Dietlikon"], [3.5, 60, "Winterthur"]]
)

iface.launch()

# %%