Create app.py

app.py

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+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)