app.py

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.")