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import streamlit as st
from transformers import pipeline
# Initialize the summarization pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
# Streamlit UI setup
st.title("📝 Text Summarization App")
# User input box (allows large input text)
user_input = st.text_area("Enter text to summarize:", "", height=300)
# Custom CSS to position the slider in the top-right corner
st.markdown("""
<style>
.slider-container {
position: fixed;
top: 10px;
right: 10px;
z-index: 1000;
}
</style>
""", unsafe_allow_html=True)
# Slider for adjusting summary length in the top-right corner
with st.container():
st.markdown('<div class="slider-container">', unsafe_allow_html=True)
summary_length = st.slider(
"Adjust Summary Length",
min_value=1,
max_value=3,
value=2,
step=1,
format="Summary Length: %d"
)
st.markdown('</div>', unsafe_allow_html=True)
# Set min and max summary length based on the selected slider value
if summary_length == 1: # Short
min_len = 10
max_len = 50
elif summary_length == 2: # Medium
min_len = 50
max_len = 150
else: # Long
min_len = 150
max_len = 300
# Display the selected range for feedback
st.write(f"Selected Summary Length: {'Short' if summary_length == 1 else 'Medium' if summary_length == 2 else 'Long'}")
# Function to split text into manageable chunks
def chunk_text(text, max_chunk_size=1024):
tokens = text.split()
chunks = []
current_chunk = []
for token in tokens:
current_chunk.append(token)
if len(' '.join(current_chunk)) > max_chunk_size:
chunks.append(' '.join(current_chunk[:-1]))
current_chunk = [token]
chunks.append(' '.join(current_chunk)) # Add the final chunk
return chunks
# Button to trigger summarization
if st.button("Summarize"):
if user_input.strip(): # Ensure there's input before summarizing
try:
# Split the input into chunks
text_chunks = chunk_text(user_input)
# Summarize each chunk separately
summaries = []
for chunk in text_chunks:
summary = summarizer(chunk, max_length=max_len, min_length=min_len, length_penalty=2.0, num_beams=4, early_stopping=True)[0]['summary_text']
summaries.append(summary)
# Combine summaries from all chunks
full_summary = " ".join(summaries)
# Display the generated summary
st.subheader("Summarized Text:")
st.write(full_summary)
except Exception as e:
st.error(f"An error occurred while summarizing: {e}")
else:
st.warning("Please enter some text to summarize.")
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