app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelWithLMHead
import json

# Define tokenizer and model before using them
tokenizer = AutoTokenizer.from_pretrained("t5-base")
model = AutoModelWithLMHead.from_pretrained("t5-base", return_dict=True)
texte = st.text_area("Texte à résumer", height=200)
bouton_ok = st.button("Résumé")
if bouton_ok:
    inputs = tokenizer.encode("summarize: " + texte, return_tensors='pt', max_length=512, truncation=True)
    outputs = model.generate(inputs, max_length=150, min_length=80, length_penalty=5, num_beams=2)
    summary = tokenizer.decode(outputs[0])
    st.text("Résumé :")
    st.text(summary)