app.py

import streamlit as st
text = st.text_area('You is here')
### Run Model
from transformers import T5ForConditionalGeneration, T5Tokenizer, AutoTokenizer, AutoModelForSeq2SeqLM
import torch
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = AutoTokenizer.from_pretrained('deep-learning-analytics/GrammarCorrector')
model = AutoModelForSeq2SeqLM.from_pretrained('deep-learning-analytics/GrammarCorrector').to(torch_device)

def correct_grammar(input_text,num_return_sequences=1):
  batch = tokenizer([input_text],truncation=True,padding='max_length',max_length=64, return_tensors="pt").to(torch_device)
  translated = model.generate(**batch,max_length=64,num_beams=2, num_return_sequences=num_return_sequences, temperature=1.5)
  tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
  return tgt_text
  
if text:
  result = correct_grammar(text)
  st.json(result)