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apartment_price_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3163528f3e621c77826714fcd0eba88e2fd33323c2ce77aec659b21d50983ce6
+size 14735024

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import pickle
+
+# Load the model
+with open("apartment_price_model.pkl", mode="rb") as f:
+    model = pickle.load(f)
+
+def predict_price(neighborhood, rooms, area, has_balcony, is_renovated):
+    # Default values for other features
+    pop = 420217
+    pop_dens = 4778
+    frg_pct = 32.45
+    emp = 491193
+    tax_income = 85446
+    price_per_room = rooms / area if area != 0 else 0
+
+    # Create input dataframe
+    input_data = pd.DataFrame([{
+        'rooms': rooms,
+        'area': area,
+        'pop': pop,
+        'pop_dens': pop_dens,
+        'frg_pct': frg_pct,
+        'emp': emp,
+        'tax_income': tax_income,
+        'price_per_room': price_per_room,
+        'has_balcony': 1 if has_balcony else 0,
+        'is_renovated': 1 if is_renovated else 0
+    }])
+
+    # Define features in the correct order
+    features = [
+        'rooms', 'area', 'pop', 'pop_dens', 'frg_pct', 'emp', 'tax_income',
+        'price_per_room', 'has_balcony', 'is_renovated'
+    ]
+
+    # Make prediction
+    predicted_price = model.predict(input_data[features])[0]
+
+    # Format the result
+    result = f"Predicted Monthly Rent: CHF {predicted_price:.0f}"
+    result += f"\n\nProperty Details:"
+    result += f"\n- {rooms} rooms, {area} m²"
+    result += f"\n- {'Has balcony' if has_balcony else 'No balcony'}"
+    result += f"\n- {'Renovated' if is_renovated else 'Not renovated'}"
+    
+    return result
+
+def reset_inputs():
+    return [3.5, 75, True, False, ""]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Zurich Apartment Rent Prediction")
+
+    with gr.Row():
+        with gr.Column():
+            rooms = gr.Number(label="Number of Rooms", value=3.5)
+            area = gr.Number(label="Area (m²)", value=75)
+            has_balcony = gr.Checkbox(label="Has Balcony", value=True)
+            is_renovated = gr.Checkbox(label="Is Renovated", value=False)
+            
+            with gr.Row():
+                submit_button = gr.Button("Submit")
+                clear_button = gr.Button("Clear")
+
+        with gr.Column():
+            output = gr.Textbox(label="Output")
+
+    submit_button.click(
+        fn=predict_price,
+        inputs=[None, rooms, area, has_balcony, is_renovated],
+        outputs=output
+    )
+
+    clear_button.click(
+        fn=reset_inputs,
+        inputs=None,
+        outputs=[rooms, area, has_balcony, is_renovated, output]
+    )
+
+demo.launch()

requirements.txt

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+scikit-learn