Update app.py

app.py

@@ -51,8 +51,8 @@ model_pegasus = PegasusForConditionalGeneration.from_pretrained(model_name).to(t
 def get_max_str(lst):
     return max(lst, key=len)
 def get_response(input_text,num_return_sequences=10,num_beams=10):
-    batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=30,return_tensors='pt').to(torch_device)
-    translated = model_pegasus.generate(**batch,max_length=30,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5)
+    batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=10,return_tensors='pt').to(torch_device)
+    translated = model_pegasus.generate(**batch,max_length=10,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5)
     tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
     try:
         adequacy_filtered_phrases = adequacy_score.filter(input_text,tgt_text, adequacy_threshold, device)