app.py

import gradio as gr
import re
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))

model_name = "csebuetnlp/mT5_multilingual_XLSum"
tokenizer = AutoTokenizer.from_pretrained(model_name,use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def generate_summary(text):

   input_ids = tokenizer(
    [WHITESPACE_HANDLER(text)],
    return_tensors="pt",
    padding="max_length",
    truncation=True,
    max_length=512)["input_ids"]

   output_ids = model.generate(
       input_ids=input_ids,
       max_length=84,
       no_repeat_ngram_size=2,
       num_beams=4
   )[0]

   summary = tokenizer.decode(
       output_ids,
       skip_special_tokens=True,
       clean_up_tokenization_spaces=False
   )

   return summary

demo = gr.Interface(fn=generate_summary,
                    inputs=gr.Textbox(lines=10, placeholder="Matinni kiriting!"),
                    outputs=gr.Textbox(lines=4)
                    )

demo.launch()