app.py

import gradio as gr
from transformers import pipeline, GenerationConfig



def greet(prompt, temperature,max_length, top_p,):
    return "Hello, World!"

def language_model(prompt, temperature, max_length, top_p, model):

    generator = pipeline("text-generation", model=model)
    config = GenerationConfig.from_pretrained(model)

    # add params to config
    config.top_p = top_p
    config.temperature = temperature
    config.do_sample = True

    if temperature == 0: 
        config.do_sample = False


    # generate text
    data = generator(prompt, max_new_tokens=max_length, generation_config=config)
    return data[0]["generated_text"]

    

demo = gr.Interface(
    fn=language_model,
    inputs=[gr.Textbox(placeholder="Write a nice poem ! ", lines=5, label="Input Text"),
            gr.Slider(minimum=0, maximum=2, value=1, label="Temperature", info="Controls randomness: Lowering results in less random completions."),
            gr.Slider(minimum=1, maximum=256, value=16, label="Maximum Tokens",info="The maximum number of tokens to generate."),
            gr.Slider(minimum=0, maximum=1, value=1, label="Top-p", info="Controls diversity via nucleus sampling."),
            gr.Dropdown(choices=["distilgpt2","gpt2", "gpt2-large"], value="gpt2",label="Model", info="Model to choose from Huggingface")],
    outputs=[gr.Textbox(placeholder="Hello, World!")],
)
demo.launch(share=True)