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import os
import streamlit as st
from transformers import BartTokenizer, TFBartForConditionalGeneration
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
model_name = 'facebook-bart-large-cnn'
tokenizer = BartTokenizer.from_pretrained(model_name)
model = TFBartForConditionalGeneration.from_pretrained(model_name)
def summarize(text, style):
input_length = len(tokenizer.encode(text, return_tensors='tf', max_length=1024, truncation=True)[0])
if style == 'Normal':
max_length = int(input_length * 0.6)
min_length = int(input_length * 0.5)
length_penalty = 1.5
elif style == 'Precise':
max_length = int(input_length * 0.45)
min_length = int(input_length * 0.35)
length_penalty = 1.2
else:
max_length = int(input_length * 0.4)
min_length = int(input_length * 0.3)
length_penalty = 1.0
inputs = tokenizer.encode(text, return_tensors='tf', max_length=1024, truncation=True)
summary_ids = model.generate(
inputs,
max_length=max_length,
min_length=min_length,
length_penalty=length_penalty,
num_beams=4,
no_repeat_ngram_size=3,
early_stopping=True
)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
if not summary.endswith(('.', '!', '?')):
summary += '.'
return summary
st.title('Text Summarizer')
user_input = st.text_area("Enter text to summarize:", "")
summary_style = st.selectbox(
'Choose summarization style:',
('Normal', 'Precise', 'Accurate')
)
if st.button('Summarize'):
if user_input:
summary = summarize(user_input, summary_style)
st.write("Summary:")
st.write(summary)
else:
st.write("Please enter some text to summarize.")
# End of program 2.0 |