Update app.py

app.py

@@ -43,19 +43,19 @@ audiospeech = pipeline('text-to-speech', model = 'suno/bark-small') #the voice i
 def summarization_n_audiospeech(pdf_file):
     abstract_text = abstract(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
+    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('.')[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)
+    tts_output = audiospeech(summary)
     audio_data = tts_output['audio'][0]
     
     with BytesIO() as buffer:
         sf.write(buffer, audio_data, 16000, format = 'wav')
         audio_bytes = buffer.getvalue()
 
-    return fin_summary, audio_bytes
+    return summary, audio_bytes