app.py

from transformers import pipeline, set_seed
from random import randint
import gradio as gr

generator = pipeline('text-generation', model='gpt2')

def gpt2(string, max_length, amount):
    set_seed(randint(randint(1000,10000),randint(50000,300000)))
    return '\n\n'.join([d['generated_text'] for d in generator(string, max_length=max_length, num_return_sequences=amount))
max_length_slider = gr.inputs.Slider(minimum=10, maximum=500, step=1, default=100, label='max_length')
amount_slider = gr.inputs.Slider(minimum=1, maximum=5, step=1, default=1, label='num_return_sequences (Amount)')
iface = gr.Interface(fn=gpt2, inputs=['text', max_length_slider], outputs='text')

iface.launch()