fastchat/serve/monitor/monitor.py

"""
Live monitor of the website statistics and leaderboard.

Dependency:
sudo apt install pkg-config libicu-dev
pip install pytz gradio gdown plotly polyglot pyicu pycld2 tabulate
"""

import argparse
import ast
import pickle
import os
import threading
import time

import gradio as gr
import numpy as np

from fastchat.serve.monitor.basic_stats import report_basic_stats, get_log_files
from fastchat.serve.monitor.clean_battle_data import clean_battle_data
from fastchat.serve.monitor.elo_analysis import report_elo_analysis_results
from fastchat.utils import build_logger, get_window_url_params_js


notebook_url = "https://colab.research.google.com/drive/1RAWb22-PFNI-X1gPVzc927SGUdfr6nsR?usp=sharing"


basic_component_values = [None] * 6
leader_component_values = [None] * 5


def make_leaderboard_md(elo_results):
    leaderboard_md = f"""
# 🏆 Chatbot Arena Leaderboard
| [Blog](https://lmsys.org/blog/2023-05-03-arena/) | [GitHub](https://github.com/lm-sys/FastChat) | [Paper](https://arxiv.org/abs/2306.05685) | [Dataset](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) |

This leaderboard is based on the following three benchmarks.
- [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) - a crowdsourced, randomized battle platform. We use 100K+ user votes to compute Elo ratings.
- [MT-Bench](https://arxiv.org/abs/2306.05685) - a set of challenging multi-turn questions. We use GPT-4 to grade the model responses.
- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - a test to measure a model's multitask accuracy on 57 tasks.

💻 Code: The Arena Elo ratings are computed by this [notebook]({notebook_url}). The MT-bench scores (single-answer grading on a scale of 10) are computed by [fastchat.llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge). The MMLU scores are mostly computed by [InstructEval](https://github.com/declare-lab/instruct-eval). Higher values are better for all benchmarks. Empty cells mean not available. Last updated: November, 2023.
"""
    return leaderboard_md


def make_leaderboard_md_live(elo_results):
    leaderboard_md = f"""
# Leaderboard
Last updated: {elo_results["last_updated_datetime"]}
{elo_results["leaderboard_table"]}
"""
    return leaderboard_md


def update_elo_components(max_num_files, elo_results_file):
    log_files = get_log_files(max_num_files)

    # Leaderboard
    if elo_results_file is None:  # Do live update
        battles = clean_battle_data(log_files, [])
        elo_results = report_elo_analysis_results(battles)

        leader_component_values[0] = make_leaderboard_md_live(elo_results)
        leader_component_values[1] = elo_results["win_fraction_heatmap"]
        leader_component_values[2] = elo_results["battle_count_heatmap"]
        leader_component_values[3] = elo_results["bootstrap_elo_rating"]
        leader_component_values[4] = elo_results["average_win_rate_bar"]

    # Basic stats
    basic_stats = report_basic_stats(log_files)
    md0 = f"Last updated: {basic_stats['last_updated_datetime']}"

    md1 = "### Action Histogram\n"
    md1 += basic_stats["action_hist_md"] + "\n"

    md2 = "### Anony. Vote Histogram\n"
    md2 += basic_stats["anony_vote_hist_md"] + "\n"

    md3 = "### Model Call Histogram\n"
    md3 += basic_stats["model_hist_md"] + "\n"

    md4 = "### Model Call (Last 24 Hours)\n"
    md4 += basic_stats["num_chats_last_24_hours"] + "\n"

    basic_component_values[0] = md0
    basic_component_values[1] = basic_stats["chat_dates_bar"]
    basic_component_values[2] = md1
    basic_component_values[3] = md2
    basic_component_values[4] = md3
    basic_component_values[5] = md4


def update_worker(max_num_files, interval, elo_results_file):
    while True:
        tic = time.time()
        update_elo_components(max_num_files, elo_results_file)
        durtaion = time.time() - tic
        print(f"update duration: {durtaion:.2f} s")
        time.sleep(max(interval - durtaion, 0))


def load_demo(url_params, request: gr.Request):
    logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")
    return basic_component_values + leader_component_values


def model_hyperlink(model_name, link):
    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'


def load_leaderboard_table_csv(filename, add_hyperlink=True):
    lines = open(filename).readlines()
    heads = [v.strip() for v in lines[0].split(",")]
    rows = []
    for i in range(1, len(lines)):
        row = [v.strip() for v in lines[i].split(",")]
        for j in range(len(heads)):
            item = {}
            for h, v in zip(heads, row):
                if h == "Arena Elo rating":
                    if v != "-":
                        v = int(ast.literal_eval(v))
                    else:
                        v = np.nan
                elif h == "MMLU":
                    if v != "-":
                        v = round(ast.literal_eval(v) * 100, 1)
                    else:
                        v = np.nan
                elif h == "MT-bench (win rate %)":
                    if v != "-":
                        v = round(ast.literal_eval(v[:-1]), 1)
                    else:
                        v = np.nan
                elif h == "MT-bench (score)":
                    if v != "-":
                        v = round(ast.literal_eval(v), 2)
                    else:
                        v = np.nan
                item[h] = v
            if add_hyperlink:
                item["Model"] = model_hyperlink(item["Model"], item["Link"])
        rows.append(item)

    return rows


def build_basic_stats_tab():
    empty = "Loading ..."
    basic_component_values[:] = [empty, None, empty, empty, empty, empty]

    md0 = gr.Markdown(empty)
    gr.Markdown("#### Figure 1: Number of model calls and votes")
    plot_1 = gr.Plot(show_label=False)
    with gr.Row():
        with gr.Column():
            md1 = gr.Markdown(empty)
        with gr.Column():
            md2 = gr.Markdown(empty)
    with gr.Row():
        with gr.Column():
            md3 = gr.Markdown(empty)
        with gr.Column():
            md4 = gr.Markdown(empty)
    return [md0, plot_1, md1, md2, md3, md4]


def build_leaderboard_tab(elo_results_file, leaderboard_table_file):
    if elo_results_file is None:  # Do live update
        md = "Loading ..."
        p1 = p2 = p3 = p4 = None
    else:
        with open(elo_results_file, "rb") as fin:
            elo_results = pickle.load(fin)

        md = make_leaderboard_md(elo_results)
        p1 = elo_results["win_fraction_heatmap"]
        p2 = elo_results["battle_count_heatmap"]
        p3 = elo_results["bootstrap_elo_rating"]
        p4 = elo_results["average_win_rate_bar"]

    md_1 = gr.Markdown(md, elem_id="leaderboard_markdown")

    if leaderboard_table_file:
        data = load_leaderboard_table_csv(leaderboard_table_file)
        headers = [
            "Model",
            "Arena Elo rating",
            "MT-bench (score)",
            "MMLU",
            "License",
        ]
        values = []
        for item in data:
            row = []
            for key in headers:
                value = item[key]
                row.append(value)
            values.append(row)
        values.sort(key=lambda x: -x[1] if not np.isnan(x[1]) else 1e9)

        headers[1] = "⭐ " + headers[1]
        headers[2] = "📈 " + headers[2]

        gr.Dataframe(
            headers=headers,
            datatype=["markdown", "number", "number", "number", "str"],
            value=values,
            elem_id="leaderboard_dataframe",
        )
        gr.Markdown(
            """ ## Visit our [HF space](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) for more analysis!
            If you want to see more models, please help us [add them](https://github.com/lm-sys/FastChat/blob/main/docs/arena.md#how-to-add-a-new-model).
            """,
            elem_id="leaderboard_markdown",
        )
    else:
        pass

    leader_component_values[:] = [md, p1, p2, p3, p4]

    """
    with gr.Row():
        with gr.Column():
            gr.Markdown(
                "#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles"
            )
            plot_1 = gr.Plot(p1, show_label=False)
        with gr.Column():
            gr.Markdown(
                "#### Figure 2: Battle Count for Each Combination of Models (without Ties)"
            )
            plot_2 = gr.Plot(p2, show_label=False)
    with gr.Row():
        with gr.Column():
            gr.Markdown(
                "#### Figure 3: Bootstrap of Elo Estimates (1000 Rounds of Random Sampling)"
            )
            plot_3 = gr.Plot(p3, show_label=False)
        with gr.Column():
            gr.Markdown(
                "#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)"
            )
            plot_4 = gr.Plot(p4, show_label=False)
    """

    from fastchat.serve.gradio_web_server import acknowledgment_md

    gr.Markdown(acknowledgment_md)

    # return [md_1, plot_1, plot_2, plot_3, plot_4]
    return [md_1]


def build_demo(elo_results_file, leaderboard_table_file):
    from fastchat.serve.gradio_web_server import block_css

    text_size = gr.themes.sizes.text_lg

    with gr.Blocks(
        title="Monitor",
        theme=gr.themes.Base(text_size=text_size),
        css=block_css,
    ) as demo:
        with gr.Tabs() as tabs:
            with gr.Tab("Leaderboard", id=0):
                leader_components = build_leaderboard_tab(
                    elo_results_file, leaderboard_table_file
                )

            with gr.Tab("Basic Stats", id=1):
                basic_components = build_basic_stats_tab()

        url_params = gr.JSON(visible=False)
        demo.load(
            load_demo,
            [url_params],
            basic_components + leader_components,
            _js=get_window_url_params_js,
        )

    return demo


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default="0.0.0.0")
    parser.add_argument("--port", type=int)
    parser.add_argument("--share", action="store_true")
    parser.add_argument("--concurrency-count", type=int, default=10)
    parser.add_argument("--update-interval", type=int, default=300)
    parser.add_argument("--max-num-files", type=int)
    parser.add_argument("--elo-results-file", type=str)
    parser.add_argument("--leaderboard-table-file", type=str)
    args = parser.parse_args()

    logger = build_logger("monitor", "monitor.log")
    logger.info(f"args: {args}")

    if args.elo_results_file is None:  # Do live update
        update_thread = threading.Thread(
            target=update_worker,
            args=(args.max_num_files, args.update_interval, args.elo_results_file),
        )
        update_thread.start()

    demo = build_demo(args.elo_results_file, args.leaderboard_table_file)
    demo.queue(
        concurrency_count=args.concurrency_count, status_update_rate=10, api_open=False
    ).launch(
        server_name=args.host, server_port=args.port, share=args.share, max_threads=200
    )