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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the grammar correction model
model_name = "microsoft/deberta-v3-base"
# Disable fast tokenization by setting `use_fast=False`
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Function to correct grammar
def correct_grammar(text):
# Encode input text
inputs = tokenizer.encode(text, return_tensors="pt")
# Generate the corrected text
with torch.no_grad():
outputs = model.generate(inputs, max_length=512, num_beams=5, early_stopping=True)
# Decode the corrected text
corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return corrected_text
# Gradio Interface
interface = gr.Interface(
fn=correct_grammar,
inputs="text",
outputs="text",
title="Grammar Correction",
description="Enter a sentence or paragraph to receive grammar corrections using DeBERTa."
)
if __name__ == "__main__":
interface.launch()
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