Update app.py

app.py

@@ -12,16 +12,16 @@ 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')
+kw_model=KeyBERT(model='AI-Growth-Lab/PatentSBERTa')
 import yake
 
-kw_extractor = yake.KeywordExtractor(top=50, stopwords=patseer_stopwords)
-candidates = kw_extractor.extract_keywords(text)
-candidates = [candidate[0] for candidate in candidates]
+kw_extractor=yake.KeywordExtractor(top=50, stopwords=patseer_stopwords)
+candidates=kw_extractor.extract_keywords(text)
+candidates=[candidate[0] for candidate in candidates]
 from keyphrase_vectorizers import KeyphraseCountVectorizer
 keywords=kw_model.extract_keywords(text,candidates, keyphrase_ngram_range=(1, 3), top_n=50,vectorizer=KeyphraseCountVectorizer(stop_words=patseer_stopwords))
 
 if keywords != []:
       st.info("Extracted keywords")
-      keywords = pd.DataFrame(keywords, columns=["Keyword", "Score"])
+      keywords=pd.DataFrame(keywords, columns=["Keyword", "Score"])
       st.table(keywords)