app.py

import streamlit as st
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline

# Load grammar correction model
model_name = "vennify/t5-base-grammar-correction"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer)

st.title("✍️ English Grammar & Writing Assistant")

text = st.text_area("Enter your sentence or paragraph:")

if st.button("Correct Text"):
    if not text.strip():
        st.warning("Please enter some text.")
    else:
        input_text = "gec: " + text
        result = pipe(input_text, max_length=512)
        corrected = result[0]['generated_text']
        st.subheader("✅ Corrected Text:")
        st.write(corrected)