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"""Run.""" |
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from __future__ import annotations |
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import os |
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import time |
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from typing import Iterable |
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import gradio as gr |
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import pynvml |
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from ctransformers import AutoModelForCausalLM |
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from gradio.themes.base import Base |
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from gradio.themes.utils import colors, fonts, sizes |
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from huggingface_hub import hf_hub_download, hf_hub_url |
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from loguru import logger |
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from python_run_cmd import run_cmd |
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ret = run_cmd("which aria2c", mute_stdout=False) |
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logger.debug(ret) |
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os.environ["TZ"] = "Asia/Shanghai" |
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try: |
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time.tzset() |
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logger.debug(f"Timezone set to {os.environ['TZ']=}") |
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except AttributeError: |
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... |
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repo_id = "TheBloke/openbuddy-mistral-7B-v13-GGUF" |
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filename = "openbuddy-mistral-7b-v13.Q4_K_S.gguf" |
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filename = "openbuddy-mistral-7b-v13.Q4_K_M.gguf" |
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model_ready = True |
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logger.debug("Start dl") |
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model_path = f"./{filename}" |
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for idx in range(5): |
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logger.debug(f"attempt {idx + 1}") |
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try: |
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model_path = hf_hub_download( |
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repo_id=repo_id, filename=filename, revision="main" |
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) |
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break |
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except Exception as exc: |
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logger.error(f"failed to download {filename}: {exc}") |
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time.sleep(3) |
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else: |
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logger.warning("Tried 5 times to no vain") |
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model_ready = False |
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logger.debug(f"Done dl, {model_ready=}") |
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if not model_ready: |
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logger.debug("Try wget...") |
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url = hf_hub_url( |
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repo_id, |
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filename, |
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) |
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logger.debug(f"{url=}") |
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ret = run_cmd(f"wget -c {url}", mute_stdout=False) |
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logger.debug(ret) |
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model_path = f"./{filename}" |
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if not ret.returncode: |
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model_ready = True |
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has_cuda = False |
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try: |
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pynvml.nvmlInit() |
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has_cuda = True |
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logger.debug("has cuda") |
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except pynvml.nvml.NVMLError_LibraryNotFound: |
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logger.debug("no cuda") |
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if has_cuda: |
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gpu_layers = 50 |
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else: |
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gpu_layers = 0 |
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logger.debug("Start loading the model") |
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try: |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, model_type="mistral", gpu_layers=gpu_layers |
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) |
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except Exception as exc: |
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logger.error(exc) |
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model_ready = False |
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model = None |
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logger.debug(f"Done loading the model, {model_ready=}") |
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ins = """[INST] <<FRIDAY>> |
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Remember that your English name is "openbuddy" and your name in Chinese is "开友". 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. |
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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. |
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<</FRIDAY>> |
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{} [/INST] |
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""" |
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theme = gr.themes.Monochrome( |
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primary_hue="indigo", |
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secondary_hue="blue", |
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neutral_hue="slate", |
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radius_size=gr.themes.sizes.radius_sm, |
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font=[ |
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gr.themes.GoogleFont("Open Sans"), |
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"ui-sans-serif", |
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"system-ui", |
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"sans-serif", |
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], |
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) |
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def response(question): |
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if model is None: |
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res = "model not ready (got a problem with downloading the file {filename=} from hf.co)" |
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else: |
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res = model(ins.format(question)) |
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yield res |
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examples = ["Hello!"] |
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def process_example(args): |
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x = None |
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for x in response(args): |
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pass |
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return x |
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css = ".generating {visibility: hidden}" |
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class SeafoamCustom(Base): |
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"""Define.""" |
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def __init__( |
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self, |
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*, |
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primary_hue: colors.Color | str = colors.emerald, |
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secondary_hue: colors.Color | str = colors.blue, |
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neutral_hue: colors.Color | str = colors.blue, |
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spacing_size: sizes.Size | str = sizes.spacing_md, |
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radius_size: sizes.Size | str = sizes.radius_md, |
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font: fonts.Font |
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| str |
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| Iterable[fonts.Font | str] = ( |
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fonts.GoogleFont("Quicksand"), |
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"ui-sans-serif", |
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"sans-serif", |
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), |
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font_mono: fonts.Font |
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| str |
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| Iterable[fonts.Font | str] = ( |
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fonts.GoogleFont("IBM Plex Mono"), |
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"ui-monospace", |
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"monospace", |
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), |
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): |
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"""Init.""" |
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super().__init__( |
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primary_hue=primary_hue, |
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secondary_hue=secondary_hue, |
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neutral_hue=neutral_hue, |
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spacing_size=spacing_size, |
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radius_size=radius_size, |
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font=font, |
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font_mono=font_mono, |
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) |
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super().set( |
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button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)", |
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button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)", |
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button_primary_text_color="white", |
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button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)", |
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block_shadow="*shadow_drop_lg", |
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button_shadow="*shadow_drop_lg", |
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input_background_fill="zinc", |
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input_border_color="*secondary_300", |
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input_shadow="*shadow_drop", |
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input_shadow_focus="*shadow_drop_lg", |
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) |
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seafoam = SeafoamCustom() |
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with gr.Blocks(theme=seafoam, analytics_enabled=False, css=css) as demo: |
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with gr.Column(): |
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gr.Markdown( |
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""" ## Testrun |
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Type in the box below and click the button to generate answers to your most pressing questions! |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(scale=3): |
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instruction = gr.Textbox( |
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placeholder="Enter your question here", |
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label="Question", |
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elem_id="q-input", |
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) |
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with gr.Box(): |
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gr.Markdown("**Answer**") |
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output = gr.Markdown(elem_id="q-output") |
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submit = gr.Button("Generate", variant="primary") |
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gr.Examples( |
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examples=examples, |
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inputs=[instruction], |
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cache_examples=False, |
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fn=process_example, |
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outputs=[output], |
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) |
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submit.click(response, inputs=[instruction], outputs=[output]) |
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instruction.submit(response, inputs=[instruction], outputs=[output]) |
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demo.queue(concurrency_count=1, max_size=5).launch(debug=False) |
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