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import torch
import streamlit as st
from transformers import PegasusForConditionalGeneration, AutoTokenizer
@st.cache(allow_output_mutation=True)
def do_summary(model_name):
model = PegasusForConditionalGeneration.from_pretrained(model_name)
return model
@st.cache(allow_output_mutation=True)
def do_tokenize(model_name):
tokenizer = AutoTokenizer.from_pretrained(model_name)
return tokenizer
model = do_summary("google/pegasus-cnn_dailymail")
tokenizer = do_tokenize("google/pegasus-cnn_dailymail")
def summarize(passage):
txt = " ".join(passage)
#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)
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)
print("summ end")
return summy
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