app.py

from transformers import pipeline
import streamlit as st

# Load grammar correction model
@st.cache_resource
def load_model():
    return pipeline("text2text-generation", model="vennify/t5-base-grammar-correction")

corrector = load_model()

# Streamlit UI
st.title("Grammar Correction Assistant")

user_input = st.text_area("Enter a sentence to correct:", "She don't like going to the gym because it make her tired.")

if st.button("Correct Sentence"):
    with st.spinner("Correcting..."):
        result = corrector(user_input, max_length=100, clean_up_tokenization_spaces=True)
        corrected_sentence = result[0]['generated_text']
        st.markdown(f"**Corrected Sentence:** {corrected_sentence}")