| from transformers import PegasusForConditionalGeneration, PegasusTokenizer |
| import gradio as grad |
| mdl_name = "google/pegasus-xsum" |
| pegasus_tkn = PegasusTokenizer.from_pretrained(mdl_name) |
| mdl = PegasusForConditionalGeneration.from_pretrained(mdl_name) |
|
|
| def summarize(text): |
| tokens = pegasus_tkn(text, truncation=True, padding="longest", return_tensors="pt") |
| translated_txt = mdl.generate(**tokens,num_return_sequences=5,max_length=200,temperature=1.5,num_beams=10) |
| response = pegasus_tkn.batch_decode(translated_txt, skip_special_tokens=True) |
| return response |
| txt=grad.Textbox(lines=10, label="English", placeholder="English Text here") |
| out=grad.Textbox(lines=10, label="Summary") |
| grad.Interface(summarize, inputs=txt, outputs=out).launch() |
|
|
