app.py

import streamlit as st
from transformers import T5ForConditionalGeneration, T5Tokenizer
from spellchecker import SpellChecker
import torch

# Load model and tokenizer
@st.cache_resource
def load_model():
    model_name = "vennify/t5-base-grammar-correction"
    tokenizer = T5Tokenizer.from_pretrained(model_name)
    model = T5ForConditionalGeneration.from_pretrained(model_name)
    return tokenizer, model

tokenizer, model = load_model()

# Step 1: Spell Correction
def correct_spelling(text):
    spell = SpellChecker()
    words = text.split()
    corrected = []
    for word in words:
        if word.isalpha():
            corrected.append(spell.correction(word) or word)
        else:
            # Handle punctuation-attached words
            stripped = ''.join(filter(str.isalpha, word))
            corrected_word = spell.correction(stripped) if stripped else word
            corrected.append(corrected_word + ''.join(filter(lambda c: not c.isalpha(), word)))
    return ' '.join(corrected)

# Step 2: Grammar Correction using model
def correct_grammar(text):
    input_text = "gec: " + text
    input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
    outputs = model.generate(input_ids, max_length=512, num_beams=4, early_stopping=True)
    corrected = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return corrected

# Streamlit UI
st.title("📝 Advanced Grammar & Spelling Correction Assistant")
st.write("Fixes spelling issues first, then corrects grammar while keeping the meaning intact.")

user_input = st.text_area("Enter your sentence:", height=150)

if st.button("Correct & Explain"):
    if not user_input.strip():
        st.warning("Please enter a sentence.")
    else:
        step1 = correct_spelling(user_input)
        corrected = correct_grammar(step1)

        st.markdown("### ✅ Correction:")
        st.success(corrected)

        st.markdown("### 🔍 Explanation:")
        st.info(f"""
*Original:* {user_input}  
*After Spellcheck:* {step1}  
*Final Grammar Fix:* {corrected}  

**Explanation:**  
- Typos like `ober`, `laZy`, and `dogz#` were detected and fixed.
- Then grammar structure and capitalization were adjusted.
- This two-step method avoids changing the sentence meaning.
""")