app.py

import gradio as gr
from transformers import pipeline, set_seed

chef = pipeline("text-generation", "nschenone/rap-distil")

def generate(text):
    max_length: int = 100
    num_beams: int = 5
    num_return_sequences: int = 1
    no_repeat_ngram_size: int = 3
    early_stopping: bool = True
    skip_special_tokens: bool = True
    temperature: float = 1.5

    set_seed(0)

    generated = chef(
        text_inputs=text,
        max_length=max_length,
        num_beams=num_beams,
        num_return_sequences=num_return_sequences,
        no_repeat_ngram_size=no_repeat_ngram_size,
        early_stopping=early_stopping,
        skip_special_tokens=skip_special_tokens,
        temperature=temperature
    )

    return [i["generated_text"] for i in generated]


iface = gr.Interface(fn=generate, inputs="text", outputs="text")
iface.launch()