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import streamlit as st
import requests
import torch
from transformers import pipeline
from transformers import BartTokenizer, BartForConditionalGeneration
# Replace with your Hugging Face model repository path
model_repo_path = 'Muh113/Text-Summ'
# Load the model and tokenizer
model = BartForConditionalGeneration.from_pretrained(model_repo_path)
tokenizer = BartTokenizer.from_pretrained(model_repo_path)
# Initialize the summarization pipeline
summarizer = pipeline('summarization', model=model,tokenizer=tokenizer)
# Streamlit app layout
st.title("Text Summarization App")
# User input
text_input = st.text_area("Enter text to summarize", height=300)
# Summarize the text
if st.button("Summarize"):
if text_input:
with st.spinner("Generating summary..."):
try:
summary = summarizer(text_input, max_length=150, min_length=30, do_sample=False)
st.subheader("Summary")
st.write(summary[0]['summary_text'])
except Exception as e:
st.error(f"Error during summarization: {e}")
else:
st.warning("Please enter some text to summarize.") |