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app.py

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+import pandas as pd
+import numpy as np
+from geopy.distance import geodesic
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_absolute_error
+import gradio as gr
+import joblib
+
+# Load dataset
+df = pd.read_csv("week2/Updated_Apartment_Data.csv")
+
+# Define Zürich city center coordinates
+zurich_center = (47.378177, 8.540192)
+
+# Compute distance to city center
+df["distance_to_center_km"] = df.apply(lambda row: geodesic((row["lat"], row["lon"]), zurich_center).km, axis=1)
+
+# Define features and target
+features = ["rooms", "area", "pop", "pop_dens", "frg_pct", "emp", "tax_income", "distance_to_center_km"]
+target = "price"
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(df[features], df[target], test_size=0.2, random_state=42)
+
+# Train model
+model = RandomForestRegressor(random_state=42)
+model.fit(X_train, y_train)
+
+# Evaluate model
+y_pred = model.predict(X_test)
+print("Mean Absolute Error:", mean_absolute_error(y_test, y_pred))
+
+# Save model
+joblib.dump(model, "apartment_price_model.pkl")
+
+def predict_price(rooms, area, pop, pop_dens, frg_pct, emp, tax_income, distance_to_center_km):
+    model = joblib.load("apartment_price_model.pkl")
+    input_data = np.array([[rooms, area, pop, pop_dens, frg_pct, emp, tax_income, distance_to_center_km]])
+    prediction = model.predict(input_data)
+    return f"Estimated Price: CHF {prediction[0]:,.2f}"
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=predict_price,
+    inputs=[
+        gr.Number(label="Rooms"),
+        gr.Number(label="Area (sqm)"),
+        gr.Number(label="Population"),
+        gr.Number(label="Population Density"),
+        gr.Number(label="Foreign Percentage"),
+        gr.Number(label="Employment"),
+        gr.Number(label="Taxable Income"),
+        gr.Number(label="Distance to City Center (km)"),
+    ],
+    outputs="text",
+    title="Apartment Price Predictor",
+    description="Enter apartment details to estimate its price.",
+)
+
+# Launch the Gradio App
+iface.launch(share=True)