|
import gradio as gr |
|
from transformers import AutoTokenizer, T5ForConditionalGeneration |
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-xxl") |
|
model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-xxl") |
|
|
|
def edit_text(input_text): |
|
|
|
input_ids = tokenizer(input_text, return_tensors="pt").input_ids |
|
|
|
outputs = model.generate(input_ids, max_length=1005) |
|
edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
|
return edited_text |
|
|
|
|
|
iface = gr.Interface( |
|
fn=edit_text, |
|
inputs=gr.Textbox(label="Enter a sentence to edit:"), |
|
outputs=gr.Textbox(label="Edited sentence:"), |
|
title="CoEdIT Text Editor", |
|
description="Edit text using the CoEdIT-xl model.", |
|
) |
|
|
|
if __name__ == "__main__": |
|
iface.launch(share=False) |
|
|
