app.py

import gradio as gr
from transformers import pipeline
from huggingface-hub import login

login("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")

def generate_text(
    model,
    text,
    min_length,
    max_length,
    do_not_truncate,
):
    pipe = pipeline(
        'text-generation',
        model='MesonWarrior/gpt2-bugro',
        tokenizer='MesonWarrior/gpt2-bugro',
        min_length=min_length,
        max_length=max_length,
        use_auth_token=True
    )

    return pipe(text)[0]['generated_text']

def interface():
    with gr.Row():
        with gr.Column():
            with gr.Row():
                model = gr.Dropdown(
                    ["Бугро", "Юморески", "Калик"], label="Model", value="Бугро",
                )
            text = gr.Textbox(lines=7, label="Input text")
        output = gr.Textbox(lines=12, label="Output text")
    with gr.Row():
        with gr.Column():
            min_length = gr.Slider(
                minimum=0, maximum=128, value=32, step=1,
                label="Min Length",
            )

            max_length = gr.Slider(
                minimum=0, maximum=512, value=96, step=1,
                label="Max Length",
            )

            do_not_truncate = gr.Checkbox(
                True,
                label="Do not truncate"
            )
        with gr.Column():
            with gr.Row():
                generate_btn = gr.Button(
                    "Generate", variant="primary", label="Generate",
                )

        generate_btn.click(
            fn=generate_text,
            inputs=[
                model,
                text,
                min_length,
                max_length,
                do_not_truncate
            ],
            outputs=output,
        )

with gr.Blocks(
    title="GPT2 VK") as demo:
        gr.Markdown("""
        ## GPT2 VK
        Файнтюны модели [ai-forever/rugpt3medium_based_on_gpt2](https://huggingface.co/ai-forever/rugpt3medium_based_on_gpt2) по вашим любимым пабликам ВКонтакте.
        """)
        interface()

demo.queue().launch()