app.py

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

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

def generate_text(text, max_length, amount):
    """
    Generates text using the GPT-2 model.
    :param text: Input text to generate from.
    :param max_length: Maximum length of generated text.
    :param amount: Number of texts to generate.
    :return: List of generated texts.
    """
    # Set the seed for reproducibility
    set_seed(randint(randint(1000,10000),randint(50000,300000)))

    # Generate the text
    generated_texts = [d['generated_text'] for d in generator(text, max_length=max_length, num_return_sequences=amount)]

    # Return the generated text
    return '\nend of text\n'.join(generated_texts)

# Define the inputs
text_input = gr.inputs.Textbox(lines=5, label='Input Text')
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_equences (Amount)')

# Define the output
output_textbox = gr.outputs.Textbox(label='Output Text')

# Create the interface
interface = gr.Interface(fn=generate_text, 
                         inputs=[text_input, max_length_slider, amount_slider], 
                         outputs=output_textbox,
                         title='Minimal GPT-2 Demo',
                         description='Generate text using GPT-2')

# Set the page layout
interface.layout = 'vertical'

# Run the interface
interface.launch(share=True)