app.py

import torch
import gradio as gr
from transformers import pipeline

#text_summary = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", torch_dtype=torch.bfloat16)
de_text_summary = pipeline("summarization", model="Shahm/bart-german")
#de_text_summary= pipeline("summarization", model="Joemgu/mlong-t5-large-sumstew")

def summary (input):
    max_length = 1024  # adjust this value as needed
    if len(input) > max_length:
        input = input[:max_length]
    output = de_text_summary(input)
    return output[0]['summary_text']

gr.close_all()

# demo = gr.Interface(fn=summary, inputs="text",outputs="text")
demo = gr.Interface(fn=summary,
                    inputs=[gr.Textbox(label="Text eingeben, der zusammengefasst werden soll",lines=6)],
                    outputs=[gr.Textbox(label="Zusammengefasster Text",lines=4)],
                    title="Projekt 1: Text-Zusammenfassung",
                    description="DIESE ANWENDUNG WIRD ZUR ZUSAMMENFASSUNG DES TEXTES VERWENDET",
                    theme="default",
                    allow_flagging="never",
                    clear_btn="Bereinigen",
                    submit_btn="Übermitteln"
                    )
demo.launch()