Update rus_constants.py

utilities_language_general/rus_constants.py

@@ -5,21 +5,25 @@ import pymorphy2
 import streamlit as st
 from transformers import pipeline
 
+
 @st.cache_resource
 def load_morph():
     _morph = pymorphy2.MorphAnalyzer(lang='ru')
     return _morph
 
+
 @st.cache_resource
 def load_w2v(model_path):
     _w2v_model = gensim.models.KeyedVectors.load_word2vec_format(model_path, binary=True)
     return _w2v_model
 
-@st.cache_resource    
+
+@st.cache_resource
 def load_spacy():
     _nlp = spacy.load('ru_core_news_lg')
     return _nlp
 
+
 @st.cache_resource
 def load_bert():
     return pipeline("fill-mask", model="a-v-white/ruBert-base-finetuned-russian-moshkov-child-corpus-pro")
@@ -59,7 +63,8 @@ c2_distractor_set = c2_target_set.union(c1_target_set)
 with open('language_data/phrases.json', 'r', encoding='utf-8') as f:
     PHRASES = set(json.load(f)['PHRASES'])
 
-SIMILARITY_VALUES_w2v = {'A1': 1.0, 'A2': 1.0, 'B1': 1.0, 'B2': 1.0, 'C1': 1.0, 'C2': 1.0, 'Без уровня': 1.0}
-SIMILARITY_VALUES_bert = {'A1': 1.0, 'A2': 1.0, 'B1': 1.0, 'B2': 1.0, 'C1': 1.0, 'C2': 1.0, 'Без уровня': 1.0}
-
+SIMILARITY_VALUES_w2v = st.secrets['SIM_VAL_w2v']
+SIMILARITY_VALUES_w2v = {SIMILARITY_VALUES_w2v[i]: SIMILARITY_VALUES_w2v[i + 1] for i in range(6)}
+SIMILARITY_VALUES_bert = st.secrets['SIM_VAL_bert']
+SIMILARITY_VALUES_bert = {SIMILARITY_VALUES_bert[i]: SIMILARITY_VALUES_bert[i + 1] for i in range(6)}
 BAD_USER_TARGET_WORDS = []