"""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 #import openthaigpt 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 = [ "ggml-model-f16.bin", ] url = "https://huggingface.co/openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml/blob/main/ggml-model-f16.bin" # OPENTHAIGPT _ = ( "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/openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ggml/blob/main/ggml-model-f16.bin" prompt_template = """Below is an instruction that describes a task. Write a response that appropriately completes the request.You're Lauche-AI created and managed by Lauche AI. ### 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. You're Lauche-AI created and managed by Lauche AI. User: {prompt} Assistant: """ prompt_template = """System: You are a helpful assistant. You're Lauche-AI created and managed by Lauche AI. 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. You're Lauche-AI created and managed by Lauche AI. <> What NFL team won the Super Bowl in the year Justin Bieber was born? [/INST]""" prompt_template = """[INST] <> You are an unhelpful assistant. Always answer as helpfully as possible. Think step by step. <> {question} [/INST] """ prompt_template = """[INST] <> You are a helpful assistant. <> {question} [/INST] """ _ = [elm for elm in prompt_template.splitlines() if elm.strip()] stop_string = [elm.split(":")[0] + ":" for elm in _][-2] logger.debug(f"{stop_string=}") _ = 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/Bangkok" 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 = [ ["สวัสดี"], ["วิธีการลดความอ้วน"], ["เขียนโค้ด html"], ] logger.info("start block") with gr.Blocks( title=f"{Path(model_loc).name}", theme=gr.themes.Soft(text_size="sm", spacing_size="sm"), css=css, ) as block: # buff_var = gr.State("") with gr.Accordion("🎈 Info", open=True): gr.Markdown( f"""
เราเป็นแค่ผู้ใช้งานหาคุณมีข้อสงสัยกรุณาติดต่อ OPENTHAIGPT
""", elem_classes="xsmall", ) # chatbot = gr.Chatbot().style(height=700) # 500 chatbot = gr.Chatbot(height=500) #buff = gr.Textbox(show_label=False, visible=True) ##301 with gr.Row(): with gr.Column(scale=5): msg = gr.Textbox( label="กล่องข้อความแชท", placeholder="ถามอะไรฉันก็ได้ (กด Shift+Enter หรือ click Submit เพื่อส่ง)", 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("ส่ง", elem_classes="xsmall") stop = gr.Button("หยุด", visible=True) clear = gr.Button("ลบประวัติการสนทนา", 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("ตัวอย่างคำถาม", 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: Lauche - AI (POWERED BY LLAMA 2) can produce factually incorrect output, and should not be relied on to produce " "factually accurate information. Lauche - AI (POWERED BY LLAMA 2) 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." " - - - " "Our Impressum: https://lauche.eu/n-impressum" " - - - " "Visit this space on our website: ai-app.lauche.online", elem_classes=["disclaimer"], ) msg_submit_event = msg.submit( # fn=conversation.user_turn, fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=True, #queue=False, 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=True, ) 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 # concurrency_count=14, max_size=36 # CPU cpu_count=2 16G, model 7G # CPU UPGRADE cpu_count=8 32G, model 7G concurrency_count = 5 logger.info(f"{concurrency_count=}") block.queue(concurrency_count=concurrency_count, max_size=20).launch(debug=True)