app.py

import gradio as gr
from transformers import pipeline, AutoModelForSeq2SeqLM, MBart50Tokenizer, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('facebook/mbart-large-50', src_lang="bn_IN", tgt_lang="bn_IN", use_fast=True)
model = AutoModelForSeq2SeqLM.from_pretrained("jafrilalam/bangla_sentence_correction", use_safetensors=True)

def correct_text(given_sentence):
    inputs = tokenizer.encode(
        given_sentence,
        truncation=True,
        return_tensors="pt",
        max_length=len(given_sentence),
    )

    output_ids = model.generate(
        inputs,
        max_new_tokens=len(given_sentence),
        early_stopping=True,
    )
    
    return tokenizer.decode(output_ids[0], skip_special_tokens=True)

iface = gr.Interface(
    fn=correct_text,
    inputs=gr.Textbox(lines=4, label="Incorrect Bangla Sentence"),
    outputs=gr.Textbox(label="Corrected Bengali Sentence")
)

iface.launch()