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import streamlit as st
from transformers import AutoTokenizer, BartForConditionalGeneration
@st.cache_resource
def load_model():
summarizer = BartForConditionalGeneration.from_pretrained("sshleifer/distilbart-cnn-12-6")
tokenizer_sum = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6")
return summarizer, tokenizer_sum
summarizer, tokenizer_sum = load_model()
def generate_summary(text, length):
inputs = tokenizer_sum([text], max_length=1024, return_tensors="pt")
summary_ids = summarizer.generate(inputs["input_ids"], num_beams=2, min_length=1, max_length=length)
out = tokenizer_sum.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
st.write(out)
st.title('Summarizer')
st.write('Submit a news article in the field below, and the Bart-based model with provide a summary.')
length = st.slider('Maximum length of summary', value = 50, min_value = 15, max_value = 150, step = 1)
user_input = st.text_area("Enter your text:")
if st.button("Send a review for processing"):
if user_input:
generate_summary(user_input, length)
else:
st.warning("Please enter some text before processing.")
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