Update app.py

app.py

@@ -236,7 +236,7 @@ for key in text_per_page.keys(): # go through keys in dictionary
       break
 print(abstract_from_pdf)
 
-#from transformers import pipeline
+from transformers import pipeline
 summarizer = pipeline("summarization", model="ainize/bart-base-cnn")
 #summarizer = pipeline("summarization", model="linydub/bart-large-samsum") # various models were tried and the best one was selected
 #summarizer = pipeline("summarization", model="slauw87/bart_summarisation")
@@ -252,7 +252,7 @@ print(summarized_text)
 # the aim of this section of code is to get a summary of just one sentence by summarizing the summary all while the summary is longer than one sentence.
 # unfortunately, I tried many many models and none of them actually summarize the text to as short as one sentence.
 #I had searched for ways to fine tune the summarization model to specify that the summarization should be done in just one sentence but did not find a way to implement it
-#from transformers import pipeline
+from transformers import pipeline
 summarized_text_list_list=summarized_text_list['summary_text']
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 #print(summarizer)