app.py

from transformers import pipeline
import gradio as gr
import spaces
import os
from threading import Thread
from typing import Iterator
import torch

from transformers import (
    AutoTokenizer,
    AutoModelForSeq2SeqLM,
    TextIteratorStreamer
)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained("dascim/greekbart-news24-abstract")
model = AutoModelForSeq2SeqLM.from_pretrained("dascim/greekbart-news24-abstract")
model.eval()
@spaces.GPU(duration=90)
def get_input(text) -> Iterator[str]:
  streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_special_tokens=True)
  input_ids = tokenizer.encode(text, add_special_tokens=True, return_tensors='pt')
  
  generate_kwargs = dict(
        input_ids=input_ids,
        tokenizer=tokenizer,
        device=device,
        streamer=streamer,
        max_new_tokens=120,
        do_sample=False,
        num_beams=1,
        early_stopping=None
    )

  t = Thread(target=model.generate, kwargs=generate_kwargs)
  t.start()

  outputs = []
  for text in streamer:
      outputs.append(text)
      yield "".join(outputs)
  

iface = gr.Interface(fn=get_input,inputs="text",outputs="text",title = "Greek News Summarizer",description="Enter your text (maximum of 512 tokens news article) to get a summary")
iface.launch()