app.py

import streamlit as st
from transformers import pipeline

# Load Hugging Face summarization model
@st.cache_resource
def load_summarization_model():
    summarizer = pipeline("summarization", model="Ramji/bart-cn-large-medical-summary")  # You can replace with another model
    return summarizer

summarizer = load_summarization_model()

# Streamlit UI
st.title("Text Summarization with Hugging Face")

st.write("This app uses a Finetuned Bart model to summarize long text inputs.")

# Input text area for the user
user_input = st.text_area("Enter text to summarize:", height=300)

# Summarize when the button is clicked
if st.button("Summarize"):
    if user_input:
        # Get the summary
        summary = summarizer(user_input)
        print("output", summary)
        # Display the result
        st.subheader("Summary")
        st.write(summary[0]['summary_text'])
    else:
        st.write("Please enter some text to summarize.")