Update Yogyakarta_Housing_Price_Prediction.py

Yogyakarta_Housing_Price_Prediction.py

@@ -1,33 +1,33 @@
-#Make gradio
-from joblib import load
-import gradio as gr
-import numpy as np
-encoder_location = load("encoder.pkl")
-regressor_model = load("Yogyakarta_housing_price_prediction_model.pkl")
-# features ['bed', 'bath', 'carport', 'surface_area(m2)', 'building_area(m2)', 'location']
-output_scaler = load("label_scaler.pkl")
-input_scaler = load("Feature_scaler.pkl")
-def Yogyakarta_Housing_Price_Prediction(bed,bath,carport,surface_are,building_area,location):
-    encoded_location = encoder_location.transform([[location]])[0]
-    input_features = np.array([[bed,bath,carport,surface_are,building_area,encoded_location]])
-    input_features = input_scaler.transform(input_features)
-    predicted_price = regressor_model.predict(input_features)
-    predicted_price = predicted_price.reshape(-1,1)
-    predicted_price = output_scaler.inverse_transform(predicted_price)
-    predicted_price = predicted_price[0][0]
-    if predicted_price >= 1000000000:
-        return f"Rp {np.round((predicted_price/1000000000),4)} Milliar"
-    else:
-        return f"Rp {np.round((predicted_price/1000000),2)} Juta"
-UI = gr.Interface(fn = Yogyakarta_Housing_Price_Prediction,
-                  inputs = [
-                      gr.Number(label="Jumlah Kamar"),
-                      gr.Number(label="Jumlah Kamar Mandi"),
-                      gr.Number(label = "Jumlah Parkiran"),
-                      gr.Slider(1,2000,step=1,label = "Luas lahan (m²)"),
-                      gr.Slider(1,2000,step = 1, label = "Luas Bangunan (m²)"),
-                      gr.Dropdown(["Bantul","Sleman","Yogyakarta"], label = "Lokasi")
-                  ],
-                  outputs = gr.Label(label = "Prediksi Harga Rumah"),
-                  title = "Prediksi Harga Rumah di DIY")
+#Make gradio
+from joblib import load
+import gradio as gr
+import numpy as np
+encoder_location = load("encoder.pkl")
+regressor_model = load("Yogyakarta_housing_price_prediction_model.pkl")
+# features ['bed', 'bath', 'carport', 'surface_area(m2)', 'building_area(m2)', 'location']
+output_scaler = load("label_scaler.pkl")
+input_scaler = load("Feature_scaler.pkl")
+def Yogyakarta_Housing_Price_Prediction(bed,bath,carport,surface_are,building_area,location):
+    encoded_location = encoder_location.transform([[location]])[0]
+    input_features = np.array([[bed,bath,carport,surface_are,building_area,encoded_location]])
+    input_features = input_scaler.transform(input_features)
+    predicted_price = regressor_model.predict(input_features)
+    predicted_price = predicted_price.reshape(-1,1)
+    predicted_price = output_scaler.inverse_transform(predicted_price)
+    predicted_price = predicted_price[0][0]
+    if predicted_price >= 1000000000:
+        return f"Rp {np.round((predicted_price/1000000000),2)} Milliar"
+    else:
+        return f"Rp {np.round((predicted_price/1000000),2)} Juta"
+UI = gr.Interface(fn = Yogyakarta_Housing_Price_Prediction,
+                  inputs = [
+                      gr.Number(label="Jumlah Kamar"),
+                      gr.Number(label="Jumlah Kamar Mandi"),
+                      gr.Number(label = "Jumlah Parkiran"),
+                      gr.Slider(1,2000,step=1,label = "Luas lahan (m²)"),
+                      gr.Slider(1,2000,step = 1, label = "Luas Bangunan (m²)"),
+                      gr.Dropdown(["Bantul","Sleman","Yogyakarta"], label = "Lokasi")
+                  ],
+                  outputs = gr.Label(label = "Prediksi Harga Rumah"),
+                  title = "Prediksi Harga Rumah di DIY")
 UI.launch()