app.py

import os
import gradio as gr
from text_generation import Client, InferenceAPIClient
import time




def get_client(model: str):
    if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
        return Client(os.getenv("OPENCHAT_API_URL"))
    return InferenceAPIClient(model, token=os.getenv("HF_TOKEN", None))


def get_usernames(model: str):
    if model in ("OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"):
        return "", "<|prompter|>", "<|assistant|>", "<|endoftext|>"


def predict(
    model: str,
    inputs: str,
    typical_p: float,
    top_p: float,
    temperature: float,
    top_k: int,
    repetition_penalty: float,
    watermark: bool,
    chatbot,
    history,
):
    client = get_client(model)
    preprompt, user_name, assistant_name, sep = get_usernames(model)

    history.append(inputs)

    past = []
    for data in chatbot:
        user_data, model_data = data

        if not user_data.startswith(user_name):
            user_data = user_name + user_data
        if not model_data.startswith(sep + assistant_name):
            model_data = sep + assistant_name + model_data

        past.append(user_data + model_data.rstrip() + sep)

    if not inputs.startswith(user_name):
        inputs = user_name + inputs

    total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip()

    partial_words = ""

    if model in ("OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"):
        iterator = client.generate_stream(
            total_inputs,
            typical_p=typical_p,
            truncate=1000,
            watermark=watermark,
            max_new_tokens=500,
        )
    else:
        iterator = client.generate_stream(
            total_inputs,
            top_p=top_p if top_p < 1.0 else None,
            top_k=top_k,
            truncate=1000,
            repetition_penalty=repetition_penalty,
            watermark=watermark,
            temperature=temperature,
            max_new_tokens=500,
            stop_sequences=[user_name.rstrip(), assistant_name.rstrip()],
        )

    for i, response in enumerate(iterator):
        if response.token.special:
            continue

        partial_words = partial_words + response.token.text
        if partial_words.endswith(user_name.rstrip()):
            partial_words = partial_words.rstrip(user_name.rstrip())
        if partial_words.endswith(assistant_name.rstrip()):
            partial_words = partial_words.rstrip(assistant_name.rstrip())

        if i == 0:
            history.append(" " + partial_words)
        elif response.token.text not in user_name:
            history[-1] = partial_words

        chat = [
            (history[i].strip(), history[i + 1].strip())
            for i in range(0, len(history) - 1, 2)
        ]
        yield chat, history



def reset_textbox():
    return gr.update(value="")


def radio_on_change(
    value: str,
    typical_p,
    top_p,
    top_k,
    temperature,
    repetition_penalty,
    watermark,
):
    if value in ("OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"):
        typical_p = typical_p.update(value=0.2, visible=True)
        top_p = top_p.update(visible=False)
        top_k = top_k.update(visible=False)
        temperature = temperature.update(visible=False)
        repetition_penalty = repetition_penalty.update(visible=False)
        watermark = watermark.update(False)
    else:
        typical_p = typical_p.update(visible=False)
        top_p = top_p.update(value=0.95, visible=True)
        top_k = top_k.update(value=4, visible=True)
        temperature = temperature.update(value=0.5, visible=True)
        repetition_penalty = repetition_penalty.update(value=1.03, visible=True)
        watermark = watermark.update(True)
    return (
        typical_p,
        top_p,
        top_k,
        temperature,
        repetition_penalty,
        watermark,
    )


title = """<h1 align="center">Assistant API</h1>"""

with gr.Blocks(
    css="""#col_container {margin-left: auto; margin-right: auto;}
                #chatbot {height: 400px; overflow: auto;}"""
) as demo:
    gr.HTML(title)
    gr.Markdown(visible=True)
    with gr.Column(elem_id="col_container"):
        model = gr.Radio(
            value="OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
            choices=[
                "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
            ],
            label="Model",
            interactive=True,
        )

        chatbot = gr.Chatbot(elem_id="chatbot")
        inputs = gr.Textbox(
            placeholder="Hi there!", label="Type and press Enter"
        )
        state = gr.State([])
        
        with gr.Row():
            b0 = gr.Button("send")
            
        # b1 = gr.Button()

        with gr.Accordion("Parameters", open=False):
            typical_p = gr.Slider(
                minimum=-0,
                maximum=1.0,
                value=0.2,
                step=0.05,
                interactive=True,
                label="Typical P mass",
            )
            top_p = gr.Slider(
                minimum=-0,
                maximum=1.0,
                value=0.25,
                step=0.05,
                interactive=True,
                label="Top-p (nucleus sampling)",
                visible=False,
            )
            temperature = gr.Slider(
                minimum=-0,
                maximum=5.0,
                value=0.6,
                step=0.1,
                interactive=True,
                label="Temperature",
                visible=False,
            )
            top_k = gr.Slider(
                minimum=1,
                maximum=50,
                value=50,
                step=1,
                interactive=True,
                label="Top-k",
                visible=False,
            )
            repetition_penalty = gr.Slider(
                minimum=0.1,
                maximum=3.0,
                value=1.03,
                step=0.01,
                interactive=True,
                label="Repetition Penalty",
                visible=False,
            )
            watermark = gr.Checkbox(value=False, label="Text watermarking")

    model.change(
        lambda value: radio_on_change(
            value,
            typical_p,
            top_p,
            top_k,
            temperature,
            repetition_penalty,
            watermark,
        ),
        inputs=model,
        outputs=[
            typical_p,
            top_p,
            top_k,
            temperature,
            repetition_penalty,
            watermark,
        ],
    )

    inputs.submit(
        predict,
        [
            model,
            inputs,
            typical_p,
            top_p,
            temperature,
            top_k,
            repetition_penalty,
            watermark,
            chatbot,
            state,
        ],
        [chatbot, state],
    )
    b0.click(
        predict,
        [
            model,
            inputs,
            typical_p,
            top_p,
            temperature,
            top_k,
            repetition_penalty,
            watermark,
            chatbot,
            state,
        ],
        [chatbot, state],
        api_name="output"
    )
    b0.click(reset_textbox, [], [inputs])
    inputs.submit(reset_textbox, [], [inputs])

    demo.queue(concurrency_count=16).launch(debug=True)