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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 = 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 = input.reshape(1, 7) |
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prediction = model.predict(input) |
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return 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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) |
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iface.launch() |
