Update app.py

app.py

@@ -14,14 +14,14 @@ patseer_stopwords=['a' , 'abaft' , 'abafter' , 'abaftest' , 'ability' , 'able' ,
 #from sklearn.feature_extraction.text import CountVectorizer
 from keybert import KeyBERT
 kw_model=KeyBERT(model='AI-Growth-Lab/PatentSBERTa')
-import yake
+#import yake
 #vectorizer = CountVectorizer(ngram_range=(1, 3), stop_words=patseer_stopwords)
-kw_extractor=yake.KeywordExtractor(top=50)
-candidates=kw_extractor.extract_keywords(text)
-candidates=[candidate[0] for candidate in candidates]
+#kw_extractor=yake.KeywordExtractor(top=50)
+#candidates=kw_extractor.extract_keywords(text)
+#candidates=[candidate[0] for candidate in candidates]
 from keyphrase_vectorizers import KeyphraseCountVectorizer
 vectorizer=KeyphraseCountVectorizer(stop_words=patseer_stopwords)
-keywords=kw_model.extract_keywords(text,candidates, keyphrase_ngram_range=(1, 3),top_n=50,vectorizer=KeyphraseCountVectorizer())
+keywords=kw_model.extract_keywords(text,keyphrase_ngram_range=(1, 3),top_n=50,vectorizer=KeyphraseCountVectorizer())
 
 if keywords != []:
  st.info("Extracted keywords")