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import streamlit as st
from transformers import pipeline
import time
# Load selected model's pipeline
@st.cache_resource
def load_pipeline(model_name):
if model_name == 'BART':
return pipeline("summarization", model="facebook/bart-large-cnn")
elif model_name == 'T5':
return pipeline("summarization", model="t5-large")
elif model_name == 'Pegasus':
return pipeline("summarization", model="google/pegasus-cnn_dailymail")
# Streamlit app layout
st.title("Text Summarization with Pre-trained Models (BART, T5, Pegasus)")
text_input = st.text_area("Enter text to summarize:")
if text_input:
# Display word count of input text
word_count = len(text_input.split())
st.write(f"**Word Count:** {word_count}")
model_choice = st.selectbox("Choose a model:", ['BART', 'T5', 'Pegasus'])
if st.button("Generate Summary"):
with st.spinner(f"Generating summary using {model_choice}..."):
start_time = time.time()
summarizer = load_pipeline(model_choice)
summary = summarizer(text_input, max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)[0]['summary_text']
end_time = time.time()
summary_word_count = len(summary.split())
st.subheader(f"Summary using {model_choice}")
st.write(summary.replace('<n>', ''))
st.write(f"**Summary Word Count:** {summary_word_count}")
st.write(f"**Time Taken:** {end_time - start_time:.2f} seconds")
else:
st.error("Please enter text to summarize.")
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