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from joblib import load
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import gradio as gr
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import numpy as np
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encoder_location = load("encoder.pkl")
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regressor_model = load("Yogyakarta_housing_price_prediction_model.pkl")
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output_scaler = load("label_scaler.pkl")
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input_scaler = load("Feature_scaler.pkl")
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def Yogyakarta_Housing_Price_Prediction(bed,bath,carport,surface_are,building_area,location):
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encoded_location = encoder_location.transform([[location]])[0]
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input_features = np.array([[bed,bath,carport,surface_are,building_area,encoded_location]])
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input_features = input_scaler.transform(input_features)
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predicted_price = regressor_model.predict(input_features)
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predicted_price = predicted_price.reshape(-1,1)
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predicted_price = output_scaler.inverse_transform(predicted_price)
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predicted_price = predicted_price[0][0]
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if predicted_price >= 1000000000:
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return f"Rp {np.round((predicted_price/1000000000),4)} Milliar"
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else:
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return f"Rp {np.round((predicted_price/1000000),2)} Juta"
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UI = gr.Interface(fn = Yogyakarta_Housing_Price_Prediction,
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inputs = [
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gr.Number(label="Jumlah Kamar"),
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gr.Number(label="Jumlah Kamar Mandi"),
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gr.Number(label = "Jumlah Parkiran"),
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gr.Slider(1,2000,step=1,label = "Luas lahan (m²)"),
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gr.Slider(1,2000,step = 1, label = "Luas Bangunan (m²)"),
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gr.Dropdown(["Bantul","Sleman","Yogyakarta"], label = "Lokasi")
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],
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outputs = gr.Label(label = "Prediksi Harga Rumah"),
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title = "Prediksi Harga Rumah di DIY")
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UI.launch() |
