app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("nsi319/legal-pegasus")
model = AutoModelForSeq2SeqLM.from_pretrained("nsi319/legal-pegasus")

def summarise(text):
  input_tokenized = tokenizer.encode(text, return_tensors='pt',max_length=1024,truncation=True)
  summary_ids = model.generate(input_tokenized,
                                    num_beams=9,
                                    no_repeat_ngram_size=3,
                                    length_penalty=2.0,
                                    min_length=150,
                                    max_length=250,
                                    early_stopping=True)
  return [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids][0]



iface = gr.Interface(fn=summarise, inputs="text", outputs="text")
iface.launch()