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

generator = pipeline('text-generation', model='gpt2-large')
set_seed(randint(randint(1000,10000),randint(50000,300000)))

def gpt2(string, max_length):
    return generator(string, max_length=max_length, num_return_sequences=1)[0]['generated_text']

max_length_slider = gr.inputs.Slider(minimum=50, maximum=500, step=10, default=100, label="Maximum Length")

iface = gr.Interface(fn=gpt2, inputs=["text", max_length_slider], outputs="text")

iface.launch()