diff --git a/model.py b/model.py
index 5bfca2f..714f4eb 100644
--- a/model.py
+++ b/model.py
@@ -18,13 +18,27 @@ model_cnn_positive= tf.keras.models.load_model('.//neural_network/models/model/b
 
 # Загрузка токенизатора
 with open('.//neural_network/tokenization/tokenizer_negative.pickle', 'rb') as handle:
-    tokenizer = pickle.load(handle)
+    tokenizer_negative = pickle.load(handle)
 
 # Загрузка названий классов
 with open('.//neural_network/classification/class_names_negative.txt', 'r', encoding='utf-8') as file:
-    class_names = [line.strip() for line in file.readlines()]
+    class_names_negative = [line.strip() for line in file.readlines()]
 
-def preprocess_text(text: str):
+# Загрузка токенизатора
+with open('.//neural_network/tokenization/tokenizer_positive.pickle', 'rb') as handle:
+    tokenizer_positive = pickle.load(handle)
+
+# Загрузка названий классов
+with open('.//neural_network/classification/class_names_positive.txt', 'r', encoding='utf-8') as file:
+    class_names_positive = [line.strip() for line in file.readlines()]
+
+def preprocess_text(text: str, type_mood: TypeMood):
+    if type_mood == TypeMood.NEGATIVE:
+        tokenizer = tokenizer_negative
+    elif type_mood == TypeMood.POSITIVE:
+        tokenizer = tokenizer_positive
+    else:
+        raise ValueError("Unsupported model type")
     # Токенизация текста
     sequences = tokenizer.texts_to_sequences([text])
     # Преобразование последовательностей в фиксированной длине
@@ -34,21 +48,27 @@ def preprocess_text(text: str):
 def predict_answer(question: str, type_mood: TypeMood, type_model: TypeModel) -> str:
     if type_model == TypeModel.LSTM and type_mood == TypeMood.NEGATIVE:
         model = model_lstm_negative
+        class_names = class_names_negative
     elif type_model == TypeModel.LSTM and type_mood == TypeMood.POSITIVE:
         model = model_lstm_positive
+        class_names = class_names_positive
     elif type_model == TypeModel.GRU and type_mood == TypeMood.NEGATIVE:
         model = model_gru_negative
+        class_names = class_names_negative
     elif type_model == TypeModel.GRU and type_mood == TypeMood.POSITIVE:
         model = model_gru_positive
+        class_names = class_names_positive
     elif type_model == TypeModel.CNN and type_mood == TypeMood.NEGATIVE:
         model = model_cnn_negative
+        class_names = class_names_negative
     elif type_model == TypeModel.CNN and type_mood == TypeMood.POSITIVE:
         model = model_cnn_positive
+        class_names = class_names_positive
     else:
         raise ValueError("Unsupported model type")
 
     # Предобработка вопроса
-    input_data = preprocess_text(question)
+    input_data = preprocess_text(question, type_mood)
     # Предсказание
     prediction = model.predict(input_data)[0]
     # Получение имени класса