xgboost model update

app.py

@@ -5,20 +5,6 @@ from xgboost import XGBRegressor
 import pickle
 import pandas as pd
 
-# filename = 'simple_rf_model.pickle'
-# rf = pickle.load(open(filename, 'rb'))
-#
-# def predict_price(area, year, if_center, if_senamiestis, rooms):
-#     x = pd.DataFrame([{'details__Plotas': area, 'details__Metai': year, 'seniunija__Centras': int(if_center),
-#                            'seniunija__Senamiestis': int(if_senamiestis), 'details__Kambarių sk.': rooms}])
-#     print(x)
-#     return float(rf.predict(x))
-#
-#
-# iface = gr.Interface(fn=predict_price, inputs=[gr.inputs.Slider(10, 200, 1, 80), gr.inputs.Slider(1950, 2022, 1, 2010),
-#                                                "checkbox", "checkbox", gr.inputs.Slider(1, 5, 1, 2)], outputs="text")
-# iface.launch()
-
 filename = 'xgboost_model_all.pickle'
 xgb = pickle.load(open(filename, 'rb'))
 
@@ -61,3 +47,17 @@ iface = gr.Interface(fn=predict_price, inputs=[gr.inputs.Slider(10, 180, 1, 80),
                                                "checkbox",
                                                ], outputs="text")
 iface.launch()
+
+# filename = 'simple_rf_model.pickle'
+# rf = pickle.load(open(filename, 'rb'))
+#
+# def predict_price(area, year, if_center, if_senamiestis, rooms):
+#     x = pd.DataFrame([{'details__Plotas': area, 'details__Metai': year, 'seniunija__Centras': int(if_center),
+#                            'seniunija__Senamiestis': int(if_senamiestis), 'details__Kambarių sk.': rooms}])
+#     print(x)
+#     return float(rf.predict(x))
+#
+#
+# iface = gr.Interface(fn=predict_price, inputs=[gr.inputs.Slider(10, 200, 1, 80), gr.inputs.Slider(1950, 2022, 1, 2010),
+#                                                "checkbox", "checkbox", gr.inputs.Slider(1, 5, 1, 2)], outputs="text")
+# iface.launch()