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import gradio as gr
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import tensorflow as tf
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import numpy as np
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import pandas as pd
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model_path = "kia_apartment_keras_model.keras"
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model = tf.keras.models.load_model(model_path)
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df_bfs_data = pd.read_csv('bfs_municipality_and_tax_data.csv', sep=',', encoding='utf-8')
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df_bfs_data['tax_income'] = df_bfs_data['tax_income'].str.replace("'", "").astype(float)
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locations = {
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"Zürich": 261,
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"Kloten": 62,
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"Uster": 198,
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"Illnau-Effretikon": 296,
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"Feuerthalen": 27,
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"Pfäffikon": 177,
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"Ottenbach": 11,
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"Dübendorf": 191,
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"Richterswil": 138,
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"Maur": 195,
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"Embrach": 56,
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"Bülach": 53,
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"Winterthur": 230,
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"Oetwil am See": 157,
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"Russikon": 178,
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"Obfelden": 10,
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"Wald (ZH)": 120,
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"Niederweningen": 91,
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"Dällikon": 84,
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"Buchs (ZH)": 83,
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"Rüti (ZH)": 118,
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"Hittnau": 173,
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"Bassersdorf": 52,
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"Glattfelden": 58,
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"Opfikon": 66,
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"Hinwil": 117,
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"Regensberg": 95,
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"Langnau am Albis": 136,
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"Dietikon": 243,
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"Erlenbach (ZH)": 151,
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"Kappel am Albis": 6,
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"Stäfa": 158,
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"Zell (ZH)": 231,
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"Turbenthal": 228,
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"Oberglatt": 92,
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"Winkel": 72,
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"Volketswil": 199,
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"Kilchberg (ZH)": 135,
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"Wetzikon (ZH)": 121,
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"Zumikon": 160,
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"Weisslingen": 180,
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"Elsau": 219,
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"Hettlingen": 221,
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"Rüschlikon": 139,
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"Stallikon": 13,
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"Dielsdorf": 86,
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"Wallisellen": 69,
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"Dietlikon": 54,
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"Meilen": 156,
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"Wangen-Brüttisellen": 200,
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"Flaach": 28,
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"Regensdorf": 96,
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"Niederhasli": 90,
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"Bauma": 297,
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"Aesch (ZH)": 241,
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"Schlieren": 247,
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"Dürnten": 113,
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"Unterengstringen": 249,
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"Gossau (ZH)": 115,
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"Oberengstringen": 245,
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"Schleinikon": 98,
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"Aeugst am Albis": 1,
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"Rheinau": 38,
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"Höri": 60,
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"Rickenbach (ZH)": 225,
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"Rafz": 67,
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"Adliswil": 131,
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"Zollikon": 161,
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"Urdorf": 250,
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"Hombrechtikon": 153,
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"Birmensdorf (ZH)": 242,
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"Fehraltorf": 172,
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"Weiach": 102,
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"Männedorf": 155,
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"Küsnacht (ZH)": 154,
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"Hausen am Albis": 4,
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"Hochfelden": 59,
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"Fällanden": 193,
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"Greifensee": 194,
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"Mönchaltorf": 196,
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"Dägerlen": 214,
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"Thalheim an der Thur": 39,
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"Uetikon am See": 159,
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"Seuzach": 227,
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"Uitikon": 248,
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"Affoltern am Albis": 2,
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"Geroldswil": 244,
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"Niederglatt": 89,
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"Thalwil": 141,
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"Rorbas": 68,
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"Pfungen": 224,
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"Weiningen (ZH)": 251,
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"Bubikon": 112,
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"Neftenbach": 223,
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"Mettmenstetten": 9,
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"Otelfingen": 94,
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"Flurlingen": 29,
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"Stadel": 100,
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"Grüningen": 116,
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"Henggart": 31,
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"Dachsen": 25,
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"Bonstetten": 3,
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"Bachenbülach": 51,
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"Horgen": 295
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}
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def predict_apartment(rooms, area, town):
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bfs_number = locations[town]
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df = df_bfs_data[df_bfs_data['bfs_number']==bfs_number]
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if len(df) != 1:
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return -1
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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]])
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input_data = input_data.reshape(1, 7)
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prediction = model.predict(input_data)
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return float(np.round(prediction[0][0], 0))
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iface = gr.Interface(
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fn=predict_apartment,
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inputs=["number", "number", gr.Dropdown(choices=locations.keys(), label="Town", type="value")],
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outputs=gr.Number(),
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examples=[[4.5, 120, "Dietlikon"], [3.5, 60, "Winterthur"]]
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)
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iface.launch()
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