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# %%
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()
# %%
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