diff --git a/data.xlsx b/data.xlsx
index aede1fd..735da43 100644
Binary files a/data.xlsx and b/data.xlsx differ
diff --git a/restaurant.xlsx b/restaurant.xlsx
index d84a44d..4cb72c9 100644
Binary files a/restaurant.xlsx and b/restaurant.xlsx differ
diff --git a/tiles.py b/tiles.py
index ce31ae0..69ccb7f 100644
--- a/tiles.py
+++ b/tiles.py
@@ -9,6 +9,7 @@ import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
 import json
+import openpyxl
 
 pygame.init()
 
@@ -484,8 +485,54 @@ def choose_pizza(prefernce):
 
     clf = DecisionTreeClassifier(random_state=400)
     clf = clf.fit(x_train, y_train)
+    ans = clf.predict([prefernce])
+
+    append_choice(ans, prefernce, d, df)
+
+    return ans
+
+def append_df_to_excel(df, excel_path):
+    df_excel = pd.read_excel(excel_path)
+    result = pd.concat([df_excel, df], ignore_index=True)
+    result.to_excel(excel_path, index=False)
+
+def append_choice(ans, pre, d, df):
+    new_row = pre
+    new_row.append(list(d.keys())[list(d.values()).index(int(ans))])
+    if new_row[3] == 30:
+        new_row[3] = 'low'
+    if new_row[3] == 50:
+        new_row[3] = 'high'
+
+    if new_row[4] == 0:
+        new_row[4] = 'none'
+    if new_row[4] == 1:
+        new_row[4] = 'tomato'
+    if new_row[4] == 2:
+        new_row[4] = 'feta'
+    if new_row[4] == 3:
+        new_row[4] = 'olives'
+
+    if new_row[5] == 0:
+        new_row[5] = 'none'
+    if new_row[5] == 1:
+        new_row[5] = 'salami'
+    if new_row[5] == 2:
+        new_row[5] = 'mushrooms'
+    if new_row[5] == 3:
+        new_row[5] = 'pineapple'
+    if new_row[5] == 4:
+        new_row[5] = 'shrimps'
+    if new_row[5] == 5:
+        new_row[5] = 'sausage'
+
+    data = {"budget": new_row[0], "spiciness": new_row[1], "vege": new_row[2], "level of hunger": new_row[3],
+            "allergy": new_row[4], "favorite ingridient": new_row[5], "drink in": 1, "pizza": new_row[7]}
+
+    n_df = pd.DataFrame(data, index=[len(df) + 1])
+    append_df_to_excel(n_df, "restaurant.xlsx")
+
 
-    return clf.predict([prefernce])
 
 def get_pizza(number):
     with open("dishes.json") as f: