Update app.py

app.py

@@ -35,7 +35,7 @@ def abstract(pdf_file):
 
 
 
-summarization = pipeline('summarization', model = 'pszemraj/long-t5-tglobal-base-16384-book-summary') #best summarization model i tested regarding this assessment
+summarization = pipeline('summarization', model = 'facebook/bart-large-cnn') #best summarization model i tested regarding this assessment
 audiospeech = pipeline('text-to-speech', model = 'suno/bark-small') #the voice is a bit distorted but gives a good output & takes less time
 
 
@@ -45,7 +45,7 @@ def summarization_n_audiospeech(pdf_file):
 
     summary = summarization(abstract_text, max_length=50, min_length=10)[0]['summary_text'] #didn't know exactly what would give one sentence, so i checked multiple times the min & max lengths regarding the 11th article. for a dif article, those parameters would probably have to be different as well
           
-    fin_summary = summary.split('.', 1)[0] + '.' #extract and print only the first sentence of the summary
+    fin_summary = summary.split('.')[0] + '.' #extract and print only the first sentence of the summary
 
     #converting the summarization into an audio output
     tts_output = audiospeech(fin_summary)