app.py

import os

import gradio as gr
from text_generation import Client

# HF-hosted endpoint for testing purposes (requires an HF API token)
API_TOKEN = os.environ.get("API_TOKEN", None)

CURRENT_CLIENT = Client("https://afrts4trc759c6eq.us-east-1.aws.endpoints.huggingface.cloud/generate_stream",
                        timeout=120,
                        headers={
                            "Accept": "application/json",
                            "Authorization": f"Bearer {API_TOKEN}",
                            "Content-Type": "application/json"}
                        )

DEFAULT_HEADER = os.environ.get("HEADER", "")
DEFAULT_USER_NAME = os.environ.get("USER_NAME", "user")
DEFAULT_ASSISTANT_NAME = os.environ.get("ASSISTANT_NAME", "assistant")
DEFAULT_SEPARATOR = os.environ.get("SEPARATOR", "<|im_end|>")
PROMPT_TEMPLATE = "<|im_start|>{user_name}\n{query}{separator}\n<|im_start|>{assistant_name}\n{response}"
repo = None


def get_total_inputs(inputs, chatbot, preprompt, user_name, assistant_name, sep):
    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()

    return total_inputs


def has_no_history(chatbot, history):
    return not chatbot and not history


def generate(
        user_message,
        chatbot,
        history,
        temperature,
        top_p,
        max_new_tokens,
        repetition_penalty,
        header,
        user_name,
        assistant_name,
        separator
):
    # Don't return meaningless message when the input is empty
    if not user_message:
        print("Empty input")

    history.append(user_message)

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

        past_messages.extend(
            [{"role": "user", "content": user_data}, {"role": "assistant", "content": model_data.rstrip()}]
        )

    print(past_messages)
    if len(past_messages) < 1:
        prompt = header + PROMPT_TEMPLATE.format(user_name=user_name,
                                                 query=user_message,
                                                 assistant_name=assistant_name,
                                                 response="",
                                                 separator=separator)
    else:
        prompt = header
        for i in range(0, len(past_messages), 2):
            intermediate_prompt = PROMPT_TEMPLATE.format(user_name=user_name,
                                                         query=past_messages[i]["content"],
                                                         assistant_name=assistant_name,
                                                         response=past_messages[i + 1]["content"],
                                                         separator=separator)
            # print(prompt, separator, intermediate_prompt)
            prompt = prompt + intermediate_prompt + separator + "\n"

        # print(prompt)
        prompt = prompt + PROMPT_TEMPLATE.format(user_name=user_name,
                                                 query=user_message,
                                                 assistant_name=assistant_name,
                                                 response="",
                                                 separator=separator)

    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        top_k=40,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        truncate=1024,
        # seed=42,
        # stop_sequences=[user_name, DEFAULT_SEPARATOR]
        stop_sequences=[DEFAULT_SEPARATOR]
    )

    # print(prompt)
    stream = CURRENT_CLIENT.generate_stream(
        prompt,
        **generate_kwargs,
    )

    output = ""
    for idx, response in enumerate(stream):
        # print(response.token)
        if response.token.text == '':
            pass
            # print(response.token.text)
            # break

        if response.token.special:
            continue
        output += response.token.text
        if idx == 0:
            history.append(" " + output)
        else:
            history[-1] = output

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

        yield chat, history, user_message, ""

    return chat, history, user_message, ""


def clear_chat():
    return [], []


title = """<h1 align="center">CroissantLLMChat Playground 🥐</h1>"""
custom_css = """
#banner-image {
    display: block;
    margin-left: auto;
    margin-right: auto;
}
#chat-message {
    font-size: 14px;
    min-height: 300px;
}
"""

with gr.Blocks(analytics_enabled=False, css=custom_css) as demo:
    gr.HTML(title)

    with gr.Row():
        with gr.Column():
            gr.Markdown(
                """
                ## Demo platform for 🥐 CroissantLLMChat
                
                
                ### Usage recommendations
                
                We recommend testing the chat model for open-ended writing tasks, tips, translations, etc...
                We find direct instructions to work best, and performance to drop after the first round of interactions.
                We limit the length of each message to 256 tokens by default (can be changed in the settings below), and of the entire conversation so clear the Chat between tests !

                ### Errors

                The model is very small in size (1.3B), about 130 times smaller than GPT3. As such, it's generalist Chat version logically exhibits reduced understanding, reasoning and knowledge capacities, and may still exhibit undesired behavior such as hallucinations, or toxicity (rarely)... 
                For industrial applications, we recommend finetuning the model, but trained this Chat version to allow for experimenting and to showcase the capabilities for it's size.

                ### More info
                🗞️ The blogpost: https://huggingface.co/blog/manu/croissant-llm-blog
                📖 The 45 page report with lots of gems: https://arxiv.org/abs/2402.00786
                🤖 Models, Data, Demo: https://huggingface.co/croissantllm
                ### 
                
                """
            )

    with gr.Row():
        with gr.Group():
            output = gr.Markdown()
            chatbot = gr.Chatbot(elem_id="chat-message", label="Chat")

    with gr.Row():
        with gr.Column(scale=3):
            user_message = gr.Textbox(placeholder="Enter your message here", show_label=False, elem_id="q-input")
            with gr.Row():
                send_button = gr.Button("Send", elem_id="send-btn", visible=True)

                clear_chat_button = gr.Button("Clear chat", elem_id="clear-btn", visible=True)

            with gr.Accordion(label="Parameters", open=False, elem_id="parameters-accordion"):
                temperature = gr.Slider(
                    label="Temperature",
                    value=0.3,
                    minimum=0.1,
                    maximum=1.0,
                    step=0.1,
                    interactive=True,
                    info="Higher values produce more diverse outputs",
                )
                top_p = gr.Slider(
                    label="Top-p (nucleus sampling)",
                    value=0.9,
                    minimum=0.0,
                    maximum=1,
                    step=0.05,
                    interactive=True,
                    info="Higher values sample more low-probability tokens",
                )
                max_new_tokens = gr.Slider(
                    label="Max new tokens",
                    value=256,
                    minimum=0,
                    maximum=512,
                    step=8,
                    interactive=True,
                    info="The maximum numbers of new tokens",
                )
                repetition_penalty = gr.Slider(
                    label="Repetition Penalty",
                    value=1.05,
                    minimum=0.0,
                    maximum=2,
                    step=0.05,
                    interactive=True,
                    info="The parameter for repetition penalty. 1.0 means no penalty.",
                )
            with gr.Accordion(label="Prompt", open=False, elem_id="prompt-accordion"):
                header = gr.Textbox(
                    label="Header instructions",
                    value=DEFAULT_HEADER,
                    interactive=True,
                    info="Instructions given to the assistant at the beginning of the prompt",
                )
                user_name = gr.Textbox(
                    label="User name",
                    value=DEFAULT_USER_NAME,
                    interactive=True,
                    info="Name to be given to the user in the prompt",
                )
                assistant_name = gr.Textbox(
                    label="Assistant name",
                    value=DEFAULT_ASSISTANT_NAME,
                    interactive=True,
                    info="Name to be given to the assistant in the prompt",
                )
                separator = gr.Textbox(
                    label="Separator",
                    value=DEFAULT_SEPARATOR,
                    interactive=True,
                    info="Character to be used when the speaker changes in the prompt",
                )

    history = gr.State([])
    last_user_message = gr.State("")

    user_message.submit(
        generate,
        inputs=[
            user_message,
            chatbot,
            history,
            temperature,
            top_p,
            max_new_tokens,
            repetition_penalty,
            header,
            user_name,
            assistant_name,
            separator
        ],
        outputs=[chatbot, history, last_user_message, user_message],
    )

    send_button.click(
        generate,
        inputs=[
            user_message,
            chatbot,
            history,
            temperature,
            top_p,
            max_new_tokens,
            repetition_penalty,
            header,
            user_name,
            assistant_name,
            separator
        ],
        outputs=[chatbot, history, last_user_message, user_message],
    )

    clear_chat_button.click(clear_chat, outputs=[chatbot, history])

demo.queue().launch()