app.py

import gradio as gr
from huggingface_hub import InferenceClient

from datasets import load_dataset

"""
For more information on `huggingface_hub` Inference API support, 
please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, 
peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)
"""

def load_new_dataset():
    gr.Info(message="Loading dataset...")
    ds = load_dataset("fka/awesome-chatgpt-prompts", split="train")

#----------------------

with gr.Blocks() as demo:
    text_input = gr.Textbox(visible=True, label="Query")
    btn_run = gr.Button(visible=True, value="Search")
    results_output = gr.Dataframe(label="Results", visible=True, wrap=True)

    btn_run.click(
        #fn=run_query,
        #inputs=[text_input, query_input, split_dropdown, column_dropdown],
        #outputs=results_output,
        print("button pressed!")
    )

#----------------------

if __name__ == "__main__":
    load_new_dataset()
    demo.launch()