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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization")
def summarize_text(text):
try:
# Tokenize input with truncation to fit model requirements
inputs = tokenizer([text], max_length=1024, return_tensors='pt', truncation=True)
# Generate summary
summary_ids = model.generate(inputs['input_ids'], max_length=150, num_beams=4, early_stopping=True)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return summary
except Exception as e:
st.error(f"An error occurred: {e}")
return ""
# Initialize session state for input history if it doesn't exist
if 'input_history' not in st.session_state:
st.session_state['input_history'] = []
# Streamlit interface
st.title("Text Summarizer")
# User text inputs
bulk_text = st.text_area("Enter the bulk text (e.g., client calls, meeting transcripts)", height=300)
if st.button("Summarize Text"):
if bulk_text:
with st.spinner("Generating summary..."):
summary = summarize_text(bulk_text)
if summary:
# Save the input and summary to the session state history
st.session_state['input_history'].append({"text": bulk_text, "summary": summary})
st.subheader("Summary:")
st.write(summary)
else:
st.warning("No summary was generated. Please check the input and try again.")
else:
st.warning("Please enter the bulk text.")
# Display the history of inputs and summaries
if st.session_state['input_history']:
st.subheader("History")
for i, entry in enumerate(st.session_state['input_history']):
st.write(f"**Input {i+1} (Text):** {entry['text']}")
st.write(f"**Summary {i+1}:** {entry['summary']}")
st.write("---")
# Instructions for using the app
st.write("Enter your bulk text and click 'Summarize Text' to get a summary of the text.")
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