Update app.py

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 
-st.title("Correct Grammar with Transformers ")
+st.title("Correct Grammar with Transformers 🦄")
 st.write("")
 st.write("Input your text here!")
 
@@ -16,7 +16,7 @@ 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_return_sequences=num_return_sequences):
+def correct_grammar(input_text, num_return_sequences):
     batch = tokenizer([input_text], truncation=True, padding='max_length', max_length=len(input_text), return_tensors="pt").to(torch_device)
     results = model.generate(**batch, max_length=len(input_text), num_beams=2, num_return_sequences=num_return_sequences, temperature=1.5)
     return results