import streamlit as st from transformers import pipeline # Load the summarization pipeline with a different model summarizer = pipeline("summarization", model="t5-small") def summarize_text(text): """Summarize the input text using Hugging Face's pipeline.""" summary = summarizer(text, max_length=150, min_length=50, do_sample=False) return summary[0]['summary_text'] # Streamlit UI st.title("Text Summarization with Hugging Face") st.write("Enter the text you want to summarize:") # Initialize history in session state if not already done if 'history' not in st.session_state: st.session_state.history = [] # Text input from the user user_input = st.text_area("Input Text", height=200) if st.button("Summarize"): if user_input: # Generate summary summary = summarize_text(user_input) # Save to history st.session_state.history.append({"input": user_input, "summary": summary}) st.subheader("Summary:") st.write(summary) else: st.error("Please enter some text to summarize.") # Display history st.subheader("Summary History:") if st.session_state.history: for entry in st.session_state.history: st.write(f"**Input Text:** {entry['input']}") st.write(f"**Summary:** {entry['summary']}") st.write("---") else: st.write("No summaries available yet.")