app.py

import torch
import gradio as gr
from transformers import pipeline

# Use the mT5 model for multilingual summarization, including Hebrew
text_summary = pipeline("summarization", model="csebuetnlp/mT5_multilingual_XLSum", torch_dtype=torch.bfloat16)

def summary(input):
    # Increase max_length and set max_new_tokens to avoid input length issues
    output = text_summary(input, max_length=512, min_length=30, do_sample=False)
    return output[0]['summary_text']

gr.close_all()

demo = gr.Interface(
    fn=summary,
    inputs=[gr.Textbox(label="Input text to summarize", lines=6)],
    outputs=[gr.Textbox(label="Summarized text", lines=4)],
    title="Hebrew Text Summarizer",
    description="This application will summarize Hebrew text."
)

demo.launch()