app.py

import os
os.system('pip install streamlit transformers torch')

import streamlit as st
from transformers import pipeline
import torch

from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM

# Load the model and tokenizer
model_path = '.'  # Path to the current directory where files are located

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
summarizer = pipeline('summarization', model=model, tokenizer=tokenizer)

st.title("Text Summarization with Fine-Tuned Model")
st.write("Enter text to generate a summary using the fine-tuned summarization model.")

text = st.text_area("Input Text", height=200)
if st.button("Summarize"):
    if text:
        with st.spinner("Summarizing..."):
            summary = summarizer(text, max_length=150, min_length=30, do_sample=False)
            st.success("Summary Generated")
            st.write(summary[0]['summary_text'])
    else:
        st.warning("Please enter some text to summarize.")

if __name__ == "__main__":
    st.set_option('deprecation.showfileUploaderEncoding', False)
    st.markdown(
        """
        <style>
        .reportview-container {
            flex-direction: row;
            justify-content: center;
        }
        </style>
        """,
        unsafe_allow_html=True
    )