app.py

# app.py
!pip install transformers
!pip install streamlit

import streamlit as st
from transformers import pipeline
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
import gdown

import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM

# Load the model and tokenizer
model_path = '.' 

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
    )