Update app.py

app.py

@@ -13,9 +13,9 @@ torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
 tokenizer = T5Tokenizer.from_pretrained('deep-learning-analytics/GrammarCorrector')
 model = T5ForConditionalGeneration.from_pretrained('deep-learning-analytics/GrammarCorrector').to(torch_device)
 
-def correct_grammar(input_text, num_correct_options=num_correct_options):
+def correct_grammar(input_text, num_return_sequences=num_correct_options):
   batch = tokenizer([input_text], truncation=True, padding = 'max_length', max_length = 64, return_tensors = 'pt').to(torch_device)
-  results = model.generate(**batch, max_length = 64, num_beams = 2, num_correct_options = num_correct_options, temperature = 1.5)
+  results = model.generate(**batch, max_length = 64, num_beams = 2, num_return_sequences = num_correct_options, temperature = 1.5)
   
   return results