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

# Download the model from Google Drive
@st.cache(allow_output_mutation=True)
def load_model_from_gdrive():
    url = https://drive.google.com/drive/folders/19P3ZcWor8znyaOMJgx_gaHuOyf4alnP3?usp=drive_link  # Replace with your actual Google Drive link
    output = 'model.zip'
    gdown.download(url, output, quiet=False)
    # Unzip the model
    import zipfile
    with zipfile.ZipFile(output, 'r') as zip_ref:
        zip_ref.extractall('model')
    # Load the model and tokenizer
    model = AutoModelForSeq2SeqLM.from_pretrained('model')
    tokenizer = AutoTokenizer.from_pretrained('model')
    return model, tokenizer

model, tokenizer = load_model_from_gdrive()
summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)

# Streamlit app
st.title("Text Summarization App")
st.write("Enter the text you want to summarize:")

# Text input
user_input = st.text_area("Text to summarize", height=200)

# Summarize text
if st.button("Summarize"):
    if user_input:
        summary = summarizer(user_input, max_length=130, min_length=30, do_sample=False)
        st.subheader("Summary:")
        st.write(summary[0]['summary_text'])
    else:
        st.write("Please enter text to summarize.")