Update app.py

app.py

@@ -14,17 +14,13 @@ import streamlit as st
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Load KeyBert Model
-kw_extractor = KeyBERT('all-MiniLM-L6-v2')
-#kw_extractor = KeyBERT('distilbert-base-nli-mean-tokens')
+kw_extractor = KeyBERT('valurank/MiniLM-L6-Keyword-Extraction', use_auth_token=st.secrets["hf-auth-token"])
 
 # Load T5 for Paraphrasing
-t5_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_paraphraser')
+t5_model = T5ForConditionalGeneration.from_pretrained('valurank/t5-paraphraser', use_auth_token=st.secrets["hf-auth-token"])
 t5_tokenizer = T5Tokenizer.from_pretrained('t5-base')
 t5_model = t5_model.to(device)
 
-
-doc = st.text_area("Enter a custom document")
-
 def get_keybert_results_with_vectorizer(text, number_of_results=20):
     keywords = kw_extractor.extract_keywords(text, vectorizer=KeyphraseCountVectorizer(), stop_words=None, top_n=number_of_results)
     return keywords
@@ -83,5 +79,8 @@ def extract_paraphrased_sentences(article):
     return t5_paraphrasing_keywords
     
 
+doc = st.text_area("Enter a custom document")
 
-        
+if doc:
+    keywords = extract_paraphrased_sentences(doc)
+    st.write(keywords)