| 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, | |
| eos_token_id=model.config.eos_token_id, | |
| length_penalty=2.0, | |
| max_length=142, | |
| min_length=56, | |
| num_beams=4, | |
| ) | |
| return tokenizer.decode(summary_text_ids[0], skip_special_tokens=True) | |
| interface = gr.Interface(summ, [gr.Textbox(label = 'original text')], | |
| [gr.Textbokx(label = 'summary')]) | |
| interface.launch(share = True) |