app.py

from transformers import pipeline, AutoTokenizer
import gradio as gr

model_id = "gpt2-large"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
model.to_bettertransformer()

pipe = pipeline("text-generation", model=model, tokenizer= tokenizer)

def gpt(prompt, top_k, penalty_alpha):
    return pipe(prompt, top_k=top_k, penalty_alpha=penalty_alpha)[0]["generated_text"]

gr.Interface(
    gpt,
    ["text",gr.Slider(minimum=0, maximum=50, step=1,label="Top_k"),gr.Slider(minimum=0.1, maximum=1.0,label="penalty_alpha")],
    "text",
    title= "Arun's GPT chatbot",
    description = "This is Arun's experimental GPT interface exposing gpt2-large, feel free to experiment"
).launch()