import torch
import streamlit as st
from transformers import PegasusForConditionalGeneration, AutoTokenizer

@st.cache(allow_output_mutation=True)

model_name = 'google/pegasus-cnn_dailymail'
device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name).to(device)

def summarize(passage):
    txt = " ".join(passage)
    batch = tokenizer(txt, truncation=True, padding='longest', return_tensors="pt").to(device)
    translated = model.generate(**batch)
    summy = tokenizer.batch_decode(translated, skip_special_tokens=True)
    return summy