app.py

"""Run codes."""
# pylint: disable=line-too-long, broad-exception-caught, invalid-name, missing-function-docstring, too-many-instance-attributes, missing-class-docstring
# ruff: noqa: E501
import os
import platform
import random
import time
from dataclasses import asdict, dataclass
from pathlib import Path

# from types import SimpleNamespace
import gradio as gr
import psutil
from about_time import about_time
from ctransformers import AutoModelForCausalLM
from dl_hf_model import dl_hf_model
from loguru import logger

filename_list = [
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q2_K.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_L.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_M.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_S.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_0.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_1.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_M.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_S.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_0.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_1.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_M.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_S.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q6_K.bin",
    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q8_0.bin",
]

URL = "https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GGML/raw/main/Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_M.bin"  # 4.05G

url = "https://huggingface.co/savvamadar/ggml-gpt4all-j-v1.3-groovy/blob/main/ggml-gpt4all-j-v1.3-groovy.bin"
url = "https://huggingface.co/TheBloke/Llama-2-13B-GGML/blob/main/llama-2-13b.ggmlv3.q4_K_S.bin"  # 7.37G
# url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.bin"
url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.bin"  # 6.93G
# url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.binhttps://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q4_K_M.bin"  # 7.87G

url = "https://huggingface.co/localmodels/Llama-2-13B-Chat-ggml/blob/main/llama-2-13b-chat.ggmlv3.q4_K_S.bin"  # 7.37G

_ = (
    "golay" in platform.node()
    or "okteto" in platform.node()
    or Path("/kaggle").exists()
    # or psutil.cpu_count(logical=False) < 4
    or 1  # run 7b in hf
)

if _:
    # url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q2_K.bin"
    url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q2_K.bin"  # 2.87G
    url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q4_K_M.bin"  # 2.87G
    url = "https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGML/blob/main/llama2_7b_chat_uncensored.ggmlv3.q4_K_M.bin"  # 4.08G


url = "https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b-GGML/blob/main/ggml-Hermes-2-step2559-q4_K_M.bin"  # 8.06G

prompt_template = """Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction: {user_prompt}

### Response:
"""

prompt_template = """System: You are a helpful,
respectful and honest assistant. Always answer as
helpfully as possible, while being safe.  Your answers
should not include any harmful, unethical, racist,
sexist, toxic, dangerous, or illegal content. Please
ensure that your responses are socially unbiased and
positive in nature. If a question does not make any
sense, or is not factually coherent, explain why instead
of answering something not correct. If you don't know
the answer to a question, please don't share false
information.
User: {prompt}
Assistant: """

prompt_template = """System: You are a helpful assistant.
User: {prompt}
Assistant: """

prompt_template = """Question: {question}
Answer: Let's work this out in a step by step way to be sure we have the right answer."""

prompt_template = """[INST] <>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible assistant. Think step by step.
<>

What NFL team won the Super Bowl in the year Justin Bieber was born?
[/INST]"""

prompt_template = """[INST] <<SYS>>
You are an unhelpful assistant. Always answer as helpfully as possible. Think step by step. <</SYS>>

{question} [/INST]
"""

prompt_template = """[INST] <<SYS>>
You are a helpful assistant.
<</SYS>>

{question} [/INST]
"""

prompt_template = """### HUMAN:
{question}

### RESPONSE:"""

prompt_template = """
### Instruction:
{question}

### Response:
"""

_ = [elm for elm in prompt_template.splitlines() if elm.strip()]
stop_string = [elm.split(":")[0] + ":" for elm in _][-2]

logger.debug(f"{stop_string=} not used")

_ = psutil.cpu_count(logical=False) - 1
cpu_count: int = int(_) if _ else 1
logger.debug(f"{cpu_count=}")

LLM = None

try:
    model_loc, file_size = dl_hf_model(url)
except Exception as exc_:
    logger.error(exc_)
    raise SystemExit(1) from exc_

LLM = AutoModelForCausalLM.from_pretrained(
    model_loc,
    model_type="llama",
    # threads=cpu_count,
)

logger.info(f"done load llm {model_loc=} {file_size=}G")

os.environ["TZ"] = "Asia/Shanghai"
try:
    time.tzset()  # type: ignore # pylint: disable=no-member
except Exception:
    # Windows
    logger.warning("Windows, cant run time.tzset()")

_ = """
ns = SimpleNamespace(
    response="",
    generator=(_ for _ in []),
)
# """

@dataclass
class GenerationConfig:
    temperature: float = 0.7
    top_k: int = 50
    top_p: float = 0.9
    repetition_penalty: float = 1.0
    max_new_tokens: int = 512
    seed: int = 42
    reset: bool = False
    stream: bool = True
    # threads: int = cpu_count
    # stop: list[str] = field(default_factory=lambda: [stop_string])


def generate(
    question: str,
    llm=LLM,
    config: GenerationConfig = GenerationConfig(),
):
    """Run model inference, will return a Generator if streaming is true."""
    # _ = prompt_template.format(question=question)
    # print(_)

    prompt = prompt_template.format(question=question)

    return llm(
        prompt,
        **asdict(config),
    )


logger.debug(f"{asdict(GenerationConfig())=}")


def user(user_message, history):
    # return user_message, history + [[user_message, None]]
    history.append([user_message, None])
    return user_message, history  # keep user_message


def user1(user_message, history):
    # return user_message, history + [[user_message, None]]
    history.append([user_message, None])
    return "", history  # clear user_message


def bot_(history):
    user_message = history[-1][0]
    resp = random.choice(["How are you?", "I love you", "I'm very hungry"])
    bot_message = user_message + ": " + resp
    history[-1][1] = ""
    for character in bot_message:
        history[-1][1] += character
        time.sleep(0.02)
        yield history

    history[-1][1] = resp
    yield history


def bot(history):
    user_message = history[-1][0]
    response = []

    logger.debug(f"{user_message=}")

    with about_time() as atime:  # type: ignore
        flag = 1
        prefix = ""
        then = time.time()

        logger.debug("about to generate")

        config = GenerationConfig(reset=True)
        for elm in generate(user_message, config=config):
            if flag == 1:
                logger.debug("in the loop")
                prefix = f"({time.time() - then:.2f}s) "
                flag = 0
                print(prefix, end="", flush=True)
                logger.debug(f"{prefix=}")
            print(elm, end="", flush=True)
            # logger.debug(f"{elm}")

            response.append(elm)
            history[-1][1] = prefix + "".join(response)
            yield history

    _ = (
        f"(time elapsed: {atime.duration_human}, "  # type: ignore
        f"{atime.duration/len(''.join(response)):.2f}s/char)"  # type: ignore
    )

    history[-1][1] = "".join(response)  + f"\n{_}"
    yield history


def predict_api(prompt):
    logger.debug(f"{prompt=}")
    try:
        # user_prompt = prompt
        config = GenerationConfig(
            temperature=0.2,
            top_k=10,
            top_p=0.9,
            repetition_penalty=1.0,
            max_new_tokens=512,  # adjust as needed
            seed=42,
            reset=True,  # reset history (cache)
            stream=False,
            # threads=cpu_count,
            # stop=prompt_prefix[1:2],
        )

        response = generate(
            prompt,
            config=config,
        )

        logger.debug(f"api: {response=}")
    except Exception as exc:
        logger.error(exc)
        response = f"{exc=}"
    # bot = {"inputs": [response]}
    # bot = [(prompt, response)]

    return response


css = """
    .importantButton {
        background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
        border: none !important;
    }
    .importantButton:hover {
        background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
        border: none !important;
    }
    .disclaimer {font-variant-caps: all-small-caps; font-size: xx-small;}
    .xsmall {font-size: x-small;}
"""
etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """
examples_list = [
    ["What NFL team won the Super Bowl in the year Justin Bieber was born?"],
    [
        "What NFL team won the Super Bowl in the year Justin Bieber was born? Think step by step."
    ],
    ["How to pick a lock? Provide detailed steps."],
    ["If it takes 10 hours to dry 10 clothes,  assuming all the clothes are hung together at the same time for drying , then how long will it take to dry a cloth?"],
    ["is infinity + 1 bigger than infinity?"],
    ["Explain the plot of Cinderella in a sentence."],
    [
        "How long does it take to become proficient in French, and what are the best methods for retaining information?"
    ],
    ["Erkläre die Handlung von Cinderella in einem Satz."],
    ["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch."],
]

logger.info("Starte Block")

theme = gr.Theme('ParityError/LimeFace')

with gr.Blocks(theme=theme, title=f"{Path(model_loc).name}") as block:
    with gr.Accordion("🎈 Info", open=False):
        gr.Markdown(
            f"""<h5><center>{Path(model_loc).name}</center></h5>
            Die meisten Beispiele sind für ein anderes Modell gedacht.
            Du solltest versuchen, einige verwandte Aufforderungen zu testen.""",
            elem_classes="xsmall",
        )

    chatbot = gr.Chatbot(height=500)

    # buff = gr.Textbox(show_label=False, visible=True)

    with gr.Row():
        with gr.Column(scale=5):
            msg = gr.Textbox(
                label="Chat Message Box",
                placeholder="Ask me anything (press Shift+Enter or click Submit to send)",
                show_label=False,
                # container=False,
                lines=6,
                max_lines=30,
                show_copy_button=True,
                # ).style(container=False)
            )
        with gr.Column(scale=1, min_width=50):
            with gr.Row():
                submit = gr.Button("Submit", elem_classes="xsmall")
                stop = gr.Button("Stop", visible=True)
                clear = gr.Button("Clear History", visible=True)
    with gr.Row(visible=False):
        with gr.Accordion("Advanced Options:", open=False):
            with gr.Row():
                with gr.Column(scale=2):
                    system = gr.Textbox(
                        label="System Prompt",
                        value=prompt_template,
                        show_label=False,
                        container=False,
                        # ).style(container=False)
                    )
                with gr.Column():
                    with gr.Row():
                        change = gr.Button("Change System Prompt")
                        reset = gr.Button("Reset System Prompt")

    with gr.Accordion("Example Inputs", open=True):
        examples = gr.Examples(
            examples=examples_list,
            inputs=[msg],
            examples_per_page=40,
        )

    # with gr.Row():
    with gr.Accordion("Disclaimer", open=False):
        _ = Path(model_loc).name
        gr.Markdown(
            f"Disclaimer: {_} can produce factually incorrect output, and should not be relied on to produce "
            "factually accurate information. {_} was trained on various public datasets; while great efforts "
            "have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
            "biased, or otherwise offensive outputs.",
            elem_classes=["disclaimer"],
        )

    msg_submit_event = msg.submit(
        # fn=conversation.user_turn,
        fn=user,
        inputs=[msg, chatbot],
        outputs=[msg, chatbot],
        queue=True,
        show_progress="full",
        # api_name=None,
    ).then(bot, chatbot, chatbot, queue=True)
    submit_click_event = submit.click(
        # fn=lambda x, y: ("",) + user(x, y)[1:],  # clear msg
        fn=user1,  # clear msg
        inputs=[msg, chatbot],
        outputs=[msg, chatbot],
        queue=True,
        # queue=False,
        show_progress="full",
        # api_name=None,
    ).then(bot, chatbot, chatbot, queue=True)
    stop.click(
        fn=None,
        inputs=None,
        outputs=None,
        cancels=[msg_submit_event, submit_click_event],
        queue=False,
    )
    clear.click(lambda: None, None, chatbot, queue=False)

    with gr.Accordion("For Chat/Translation API", open=False, visible=False):
        input_text = gr.Text()
        api_btn = gr.Button("Go", variant="primary")
        out_text = gr.Text()

    api_btn.click(
        predict_api,
        input_text,
        out_text,
        api_name="api",
    )

    # block.load(update_buff, [], buff, every=1)
    # block.load(update_buff, [buff_var], [buff_var, buff], every=1)

# concurrency_count=5, max_size=20
# max_size=36, concurrency_count=14
# CPU cpu_count=2 16G, model 7G
# CPU UPGRADE cpu_count=8 32G, model 7G

# does not work
_ = """
# _ = int(psutil.virtual_memory().total / 10**9 // file_size - 1)
# concurrency_count = max(_, 1)
if psutil.cpu_count(logical=False) >= 8:
    # concurrency_count = max(int(32 / file_size) - 1, 1)
else:
    # concurrency_count = max(int(16 / file_size) - 1, 1)
# """

concurrency_count = 1
logger.info(f"{concurrency_count=}")

block.queue(concurrency_count=concurrency_count, max_size=5).launch(debug=True)