main.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
docker build -t llm_eval_system:v20250724_1442 .

docker stop llm_eval_system_7862 && docker rm llm_eval_system_7862

docker run -itd \
--name llm_eval_system_7862 \
--restart=always \
--network host \
-e port=7862 \
llm_eval_system:v20250724_1442 \
/bin/bash

docker run -itd \
--name llm_eval_system_7862 \
--restart=always \
--network host \
-v /data/tianxing/PycharmProjects/llm_eval_system:/data/tianxing/PycharmProjects/llm_eval_system \
python:3.12 \
/bin/bash

nohup python3 main.py --server_port 7862 &
"""
import argparse
import json
import logging
from pathlib import Path
import platform
from typing import Tuple, List
import time

import gradio as gr
import numpy as np
import pandas as pd

from project_settings import environment, project_path, log_directory
from toolbox.os.command import Command
import log
from tabs.fs_tab import get_fs_tab
from tabs.shell_tab import get_shell_tab

log.setup_size_rotating(log_directory=log_directory)

logger = logging.getLogger("main")


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--eval_data_dir",
        default=(project_path / "data/eval_data").as_posix(),
        type=str,
    )
    parser.add_argument(
        "--server_port",
        default=7860,
        type=int,
    )
    args = parser.parse_args()
    return args


css = """
#dataset_df th:nth-child(1), #dataset_df td:nth-child(1) {
    max-width: 50px !important; /* 第一列 */
}
#dataset_df th:nth-child(2), #dataset_df td:nth-child(2) {
    max-width: 500px !important; /* 第二列 */
}
#dataset_df th:nth-child(3), #dataset_df td:nth-child(3) {
    max-width: 50px !important; /* 第三列 */
}

#view_chat_df th:nth-child(1), #view_chat_df td:nth-child(1) {
    max-width: 50px !important; /* 第一列 */
}
#view_chat_df th:nth-child(2), #view_chat_df td:nth-child(2) {
    max-width: 500px !important; /* 第二列 */
}
#view_chat_df th:nth-child(3), #view_chat_df td:nth-child(3) {
    max-width: 400px !important; /* 第三列 */
}
#view_chat_df th:nth-child(4), #view_chat_df td:nth-child(4) {
    max-width: 400px !important; /* 第四列 */
}
#view_chat_df th:nth-child(5), #view_chat_df td:nth-child(5) {
    max-width: 400px !important; /* 第五列 */
}
#view_chat_df th:nth-child(6), #view_chat_df td:nth-child(6) {
    max-width: 80px !important; /* 第六列 */
}
"""

eval_data_dir: Path = None
llm_ranking: pd.DataFrame = None
last_update_ts: float = 0
update_interval = 1 * 60 * 60


def load_board():
    result = list()
    for filename in eval_data_dir.glob("**/*.jsonl"):
        name = filename.stem
        dataset = filename.parts[-1]
        date = filename.parts[-2]
        service = filename.parts[-3]
        client = filename.parts[-4]
        model_name = filename.parts[-5]
        company = filename.parts[-6]
        script = filename.parts[-7]

        if date.endswith("-delete"):
            continue
        # if name.endswith("-chat"):
        #     continue

        score_list = list()
        time_cost_list = list()
        total = 0

        with open(filename.as_posix(), "r", encoding="utf-8") as f:
            for row in f:
                try:
                    row = json.loads(row)
                except Exception as e:
                    print(f"json load row failed. error type: {type(e)}, error text: {str(e)}")
                    logger.error(f"json load row failed. error type: {type(e)}, error text: {str(e)}")
                    raise e
                if name.endswith("-choice"):
                    score_ = row["correct"]
                elif name.endswith("-chat"):
                    score_ = row["score"]
                elif name.endswith("-summary"):
                    score_ = row["score"]
                else:
                    raise AssertionError

                time_cost_ = row["time_cost"]

                score_list.append(score_)
                time_cost_list.append(time_cost_)
                total += 1

        if total == 0:
            continue
        score = np.mean(score_list)
        time_cost_mean = np.mean(time_cost_list)
        time_cost_var = np.var(time_cost_list)

        time_cost_p75 = np.percentile(time_cost_list, 95)
        time_cost_p95 = np.percentile(time_cost_list, 95)
        time_cost_p99 = np.percentile(time_cost_list, 99)

        row_ = {
            "company": company,
            "model_name": model_name,
            "dataset": dataset,
            "score": round(score, 4),
            "time_cost(mean)": round(time_cost_mean, 4),
            "time_cost(var)": round(time_cost_var, 4),
            "time_cost(75%)": round(time_cost_p75, 4),
            "time_cost(95%)": round(time_cost_p95, 4),
            "time_cost(99%)": round(time_cost_p99, 4),
            "service": service,
            "client": client,
            "script": f"{script}.py",
            "version": date,
            "count": total,
        }
        result.append(row_)
    result = pd.DataFrame(result)
    return result


def load_board_lazy():
    global llm_ranking
    global last_update_ts

    now = time.time()
    if now - last_update_ts > update_interval:
        llm_ranking = load_board()
        last_update_ts = now

    return llm_ranking


def when_click_board_button(columns: List[str]):
    result = load_board_lazy()

    try:
        result = result[columns]
    except KeyError as e:
        raise gr.Error(f"{str(e)}, columns: {list(result.columns)}")
    return result


def when_click_view_dataset_button(filename: str):
    filename = (project_path / filename).as_posix()
    result = list()
    with open(filename, "r", encoding="utf-8") as f:
        for row in f:
            row = json.loads(row)
            result.append(row)
    result = pd.DataFrame(result)
    return result


def when_click_view_chat_button(filename: str):
    filename = (project_path / filename).as_posix()
    result = list()
    with open(filename, "r", encoding="utf-8") as f:
        for row in f:
            row = json.loads(row)

            idx = row["idx"]
            prompt: str = row["prompt"]
            conversation = prompt.split("\n\n")[-1].strip()
            response = row["response"]
            prediction = row["prediction"]
            evaluate = row["evaluate"]
            score = row["score"]

            row_ = {
                "idx": idx,
                "conversation": conversation,
                "response": response,
                "prediction": prediction,
                "evaluate": json.dumps(evaluate, ensure_ascii=False, indent=4),
                "score": score,
            }
            result.append(row_)
    result = pd.DataFrame(result)
    return result



board_columns_choices = [
    "company", "model_name", "dataset", "score",
    "time_cost(mean)",
    "time_cost(var)",
    "time_cost(75%)", "time_cost(95%)", "time_cost(99%)",
    "service", "client",
    "script", "version", "count"
]
board_columns_choices_default_value = [
    "company", "model_name", "dataset", "score",
    "time_cost(mean)",
    "time_cost(var)",
    # "time_cost(75%)", "time_cost(95%)", "time_cost(99%)",
]
dataset_examples_list = [
    [
        "arc-easy-1000-choice.jsonl",
        "ARC（AI2 推理挑战赛）\nAI2 的推理挑战赛 (ARC) 数据集是一个多项选择题问答数据集，包含 3 年级至 9 年级的科学考试题目。\n该数据集分为两个部分：简单部分和挑战部分。\n\n从简单部分取前1000条作为 arc-easy-1000-choice.jsonl",
        "data/dataset/arc-easy-1000-choice.jsonl"
    ],
    [
        "agent-lingoace-zh-400-choice.jsonl",
        "lingoace数据集。",
        "data/dataset/agent-lingoace-zh-400-choice.jsonl"
    ],
]


def main():
    args = get_args()

    global eval_data_dir
    global llm_ranking

    eval_data_dir = Path(args.eval_data_dir)

    llm_ranking_board = when_click_board_button(board_columns_choices_default_value)

    # chat
    chat_eval_data_examples = list()
    for filename in eval_data_dir.glob("**/*-chat.jsonl"):
        dataset = filename.parts[-1]
        model_name = filename.parts[-5]
        company = filename.parts[-6]
        chat_eval_data_examples.append([
            company, model_name, dataset, filename.as_posix()
        ])

    # ui
    with gr.Blocks(css=css) as blocks:
        with gr.Tabs():
            with gr.TabItem("board"):
                board_columns = gr.CheckboxGroup(
                    choices=board_columns_choices,
                    value=board_columns_choices_default_value,
                    label="columns"
                )
                board_button = gr.Button(value="View", variant="primary", visible=True)
                board_board = gr.DataFrame(
                    value=llm_ranking_board,
                    max_height=800, min_width=160,
                    label="board",
                    # interactive=True,
                    show_search="search"
                )

                board_button.click(
                    fn=when_click_board_button,
                    inputs=[board_columns],
                    outputs=[board_board],
                )
            with gr.TabItem("view_chat"):
                view_chat_company = gr.Textbox(label="company", visible=False)
                view_chat_model_name = gr.Textbox(label="model_name", visible=False)
                view_chat_dataset = gr.Textbox(label="dataset", visible=False)
                view_chat_filename = gr.Textbox(label="filename", visible=True)
                gr.Examples(
                    examples=chat_eval_data_examples,
                    inputs=[view_chat_company, view_chat_model_name, view_chat_dataset, view_chat_filename],
                    outputs=None,
                )
                with gr.Row():
                    view_chat_button = gr.Button(value="View", variant="primary", visible=True)
                view_chat_df = gr.DataFrame(
                    value=None,
                    max_height = 1000, min_width = 160,
                    label="dataset", interactive=True,
                    show_search="search",
                    elem_id="view_chat_df"
                )
                view_chat_button.click(
                    fn=when_click_view_chat_button,
                    inputs=[view_chat_filename],
                    outputs=[view_chat_df],
                )
            with gr.TabItem("dataset"):
                dataset_name = gr.Textbox(label="name")
                dataset_desc = gr.Textbox(label="desc")
                dataset_filename = gr.Textbox(label="filename")

                gr.Examples(
                    examples=dataset_examples_list,
                    inputs=[dataset_name, dataset_desc, dataset_filename],
                    outputs=None,
                )
                dataset_button = gr.Button(value="View", variant="primary", visible=True)
                dataset_df = gr.DataFrame(
                    value=None, label="dataset", interactive=True,
                    show_search="search",
                    elem_id="dataset_df"
                )
                dataset_button.click(
                    fn=when_click_view_dataset_button,
                    inputs=[dataset_filename],
                    outputs=[dataset_df],
                )
            _ = get_fs_tab()
            _ = get_shell_tab()

    # http://127.0.0.1:7861/
    # http://10.75.27.247:7861/
    blocks.queue().launch(
        share=False if platform.system() == "Windows" else False,
        server_name="127.0.0.1" if platform.system() == "Windows" else "0.0.0.0",
        # server_name="0.0.0.0",
        server_port=environment.get("port", default=args.server_port, dtype=int),
    )
    return


if __name__ == "__main__":
    main()