return log price

app.py

@@ -4,6 +4,7 @@ import xgboost
 from xgboost import XGBRegressor
 import pickle
 import pandas as pd
+import numpy as np
 
 # filename = 'xgboost_model_all.pickle'
 # xgb = pickle.load(open(filename, 'rb'))
@@ -34,7 +35,7 @@ def predict_price(area, year, rooms, floor_number, distance_from_center, alarm_s
                       'details__Ypatybės__Virtuvė sujungta su kambariu': kitchen_plus_livingroom
     }])
     print(x)
-    return int(xgb.predict(x))
+    return np.log(int(xgb.predict(x)))
 
 
 iface = gr.Interface(fn=predict_price, inputs=[gr.inputs.Slider(10, 180, 1, 80),