.

utilities_cookies/encrypted_cookie_manager.py

@@ -1,10 +1,10 @@
 import os
 import base64
 import streamlit as st
-from typing import Tuple, Optional, MutableMapping
 from cryptography import fernet
 from cryptography.fernet import Fernet
 from cryptography.hazmat.primitives import hashes
+from typing import Tuple, Optional, MutableMapping
 from utilities_cookies.cookie_manager import CookieManager
 from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
 

utilities_database/user_database_widgets.py

@@ -91,8 +91,8 @@ class LogIn:
 
                 if login_submit_button:
                     authenticate_user_check = db_utils.check_usr_pass(user_log_in_database=user_login_table,
-                                                             user_name=user_name,
-                                                             password=password)
+                                                                      user_name=user_name,
+                                                                      password=password)
 
                     if not authenticate_user_check:
                         st.error("Неверное имя пользователя или пароль!")

utilities_language_general/esp_utils.py

@@ -113,7 +113,6 @@ def get_distractors_from_model(doc, model, scaler, classifier, pos_dict:dict, ta
     
     distractors = []
     query = lemma if '_' in lemma else f'{lemma}_{pos}'
-    raw_lemma = query
     lemma = '_'.join(lemma.split('_')[::2])
     if model.has_index_for(query):
         candidates = model.most_similar(query, topn=max_num_distractors + 100)
@@ -128,7 +127,7 @@ def get_distractors_from_model(doc, model, scaler, classifier, pos_dict:dict, ta
         if candidate[0].count('_') == 1 and pos != 'phrase':
             distractor_lemma, distractor_pos = candidate[0].split('_')
             decision = make_decision(doc, model_type='w2v', model=model, scaler=scaler, classifier=classifier, pos_dict=pos_dict, 
-                                     level=level_name, target_lemma=raw_lemma, target_text=target_text, target_pos=pos, target_position=lemma_index, 
+                                     level=level_name, target_lemma=query, target_text=target_text, target_pos=pos, target_position=lemma_index, 
                                      substitute_lemma=distractor_lemma, substitute_pos=distractor_pos)
             distractor_similarity = candidate[1]
             candidate_gender = get_tags(distractor_lemma).get('Gender')
@@ -161,7 +160,7 @@ def get_distractors_from_model(doc, model, scaler, classifier, pos_dict:dict, ta
             distractor_lemma = f'{d1_lemma}_{d2_lemma}'
             distractor_similarity = candidate[1]
             decision = make_decision(doc, model_type='w2v', model=model, scaler=scaler, classifier=classifier, pos_dict=pos_dict, 
-                                     level=level_name, target_lemma=raw_lemma, target_text=target_text, target_pos=pos, target_position=lemma_index, 
+                                     level=level_name, target_lemma=query, target_text=target_text, target_pos=pos, target_position=lemma_index, 
                                      substitute_lemma=candidate[0], substitute_pos=d_pos)
             condition = (((d1_pos == pos or d2_pos == pos)
                           or (COMBINE_POS['phrase'][level_name].get(d_pos) is not None and COMBINE_POS['phrase'][level_name].get(pos) is not None