app.py

import streamlit as st
from transformers import T5ForConditionalGeneration, AutoTokenizer

st.title("SpellCorrectorT5")
st.markdown('SpellCorrectorT5 is a fine-tuned version of **pre-trained t5-small model** modelled on randomly selected 50000 sentences modified by imputing random noises/errors and trained using transformers. It not only looks for _spelling errors but also looks for the semantics_ in the sentence and suggest other possible words for the incorrect word.')
ttokenizer = AutoTokenizer.from_pretrained("./")
tmodel = T5ForConditionalGeneration.from_pretrained('./')

form = st.form("T5-form")

examples =["They're house is on fire",
           "Look if their is fire on the top",
           "Where os you're car?",
           "Iu is going to rain",
           "Feel free to raach out to me",
           "Life is shoetest so live freely",
           "We do the boy actually stole the books",
           "I am doing fine. How is you?"]

input_text = form.selectbox(label="Choose an example",
        options=examples)        
form.write("(or)")
input_text = form.text_input(label='Enter your own sentence', value=input_text)
submit = form.form_submit_button("Submit")

if submit:
  input_ids = ttokenizer.encode('seq: '+ input_text, return_tensors='pt')
  
  # generate text until the output length (which includes the context length) reaches 50
  outputs = tmodel.generate(
    input_ids,
    do_sample=True, 
    max_length=50,
    top_p=0.98, 
    num_return_sequences=2
  )
  
  st.subheader("Suggested sentences: ")
  
  i = 0
  for x in outputs:
      out_text = ttokenizer.decode(x, skip_special_tokens=True)
      i = i + 1
      st.success(str(i) + '. ' + out_text)