Update app.py

app.py

@@ -325,14 +325,16 @@ def get_spacy():
     
 @st.experimental_singleton(suppress_st_warning=True)
 def facebook_model():
-    
-    summarizer = pipeline('summarization',model='facebook/bart-large-cnn')
+    model_name = 'facebook/bart-large-cnn'
+    summarizer = pipeline('summarization',model=model_name,tokenizer=model_name,
+    device=0 if torch.cuda.is_available() else -1)
     return summarizer
     
 @st.experimental_singleton(suppress_st_warning=True)
 def schleifer_model():
-    
-    summarizer = pipeline('summarization',model='sshleifer/distilbart-cnn-12-6')
+    model_name = 'sshleifer/distilbart-cnn-12-6'
+    summarizer = pipeline('summarization',model=model_name, tokenizer=model_name,
+    device=0 if torch.cuda.is_available() else -1)
     return summarizer
 
 #@st.experimental_singleton(suppress_st_warning=True)    
@@ -447,8 +449,8 @@ if summarize:
             text="Loading Facebook-Bart Model and Extracting summary. This might take a few seconds depending on the length of your text..."
         ):
             summarizer_model = facebook_model()
-            summarized_text = summarizer_model(text_to_summarize, max_length=max_len, min_length=min_len)
-            summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text])
+            summarized_text = summarizer_model(text_to_summarize.strip().replace("\n", " "), max_length=max_len, min_length=min_len,clean_up_tokenization_spaces=True)
+            summarized_text = ' '.join([summ['summary_text'].replace("<n>", " ") for summ in summarized_text])
     
     elif model_type == "Sshleifer-DistilBart":
         if url_text: