Update app.py

app.py

@@ -6,8 +6,8 @@ torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
 tokenizer = AutoTokenizer.from_pretrained("prithivida/parrot_paraphraser_on_T5")
 model = AutoModelForSeq2SeqLM.from_pretrained("prithivida/parrot_paraphraser_on_T5")
 def get_response(input_text,num_return_sequences):
-  batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=False,padding='longest',max_length=100, return_tensors="pt").to(torch_device)
-  translated = model.generate(**batch,max_length=100,num_beams=10, num_return_sequences=num_return_sequences, temperature=1.9)
+  batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=100, return_tensors="pt").to(torch_device)
+  translated = model.generate(**batch,max_length=100,num_beams=10, num_return_sequences=num_return_sequences, temperature=0.9)
   tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
   return tgt_text