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# app.py ์ด๋ฆ„์„ ๊ฐ€์ง„ ํŒŒ์ผ์ด ๊ผญ ํ•„์š”ํ•˜๋‹ค
import gradio as gr
from transformers import PreTrainedTokenizerFast, BartForConditionalGeneration

model_name = "ainize/kobart-news"
tokenizer = PreTrainedTokenizerFast.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)

def summ(txt):
  input_ids = tokenizer.encode(txt, return_tensors="pt")
  summary_text_ids = model.generate(
    input_ids=input_ids,
    bos_token_id=model.config.bos_token_id, # BOS๋Š” Beginning Of Sentence
    eos_token_id=model.config.eos_token_id, # EOS๋Š” End Of Sentence
    length_penalty=2.0, # ์š”์•ฝ์„ ์–ผ๋งˆ๋‚˜ ์งง๊ฒŒ ํ• ์ง€
    max_length=142,
    min_length=56,
    num_beams=4) # beam search
  return tokenizer.decode(summary_text_ids[0], skip_special_tokens=True)

interface = gr.Interface(summ,
                         [gr.Textbox(label="original text")],
                         [gr.Textbox(label="summary")])
interface.launch()