Create HousePricesModel.py

HousePricesModel.py

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+import numpy as np
+import joblib
+import gradio as gr
+
+ # Load the trained model and preprocessing tools
+model = joblib.load("knn_house_model.pkl")
+scaler = joblib.load("scaler.pkl")
+label_encoder = joblib.load("label_encoder.pkl")
+
+ # Function to predict house price
+def predict_price(num_rooms, distance, country, build_quality):
+    country_encoded = label_encoder.transform([country])[0]
+    features = np.array([[num_rooms, distance, country_encoded, build_quality]])
+    features_scaled = scaler.transform(features)
+    predicted_price = model.predict(features_scaled)[0]
+    return f"Predicted House Price: ${predicted_price:,.2f}"
+
+ # Gradio Interface
+inputs = [
+    gr.Number(label="Number of Rooms"),
+    gr.Number(label="Distance to Center (km)"),
+    gr.Dropdown(label="Country", choices=label_encoder.classes_.tolist()),
+    gr.Slider(minimum=1, maximum=10, label="Build Quality")
+]
+
+ outputs = gr.Textbox(label="Prediction Result")
+
+ # Create and launch Gradio app
+app = gr.Interface(fn=predict_price, inputs=inputs, outputs=outputs, title="House Price Prediction")
+app.launch()