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)

# Load explanation model
from transformers import T5ForConditionalGeneration, T5Tokenizer

explain_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
explain_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")

def correct_text(text):
    input_text = "gec: " + text
    result = pipe(input_text, max_length=512, clean_up_tokenization_spaces=True)
    return result[0]['generated_text']

def explain_corrections(original, corrected):
    prompt = f"""Original: {original}
Corrected: {corrected}
Explain the changes made, identify grammar or spelling issues, and give writing improvement tips."""
    inputs = explain_tokenizer(prompt, return_tensors="pt", truncation=True)
    outputs = explain_model.generate(**inputs, max_length=512)
    return explain_tokenizer.decode(outputs[0], skip_special_tokens=True)

# Streamlit UI
st.title("✍️ English Writing Assistant")
user_input = st.text_area("Enter your sentence, paragraph, or essay:")

if st.button("Check & Improve"):
    if user_input.strip() == "":
        st.warning("Please enter some text.")
    else:
        corrected = correct_text(user_input)
        explanation = explain_corrections(user_input, corrected)

        st.subheader("✅ Corrected Text:")
        st.write(corrected)

        st.subheader("📘 Explanation & Suggestions:")
        st.write(explanation)