Create app.py

app.py

@@ -0,0 +1,139 @@
+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 = 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 = input.reshape(1, 7)
+    prediction = model.predict(input)
+    return 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()
+)
+
+iface.launch()