app.py

"""A gradio app that renders a static leaderboard. This is used for Hugging Face Space."""
import ast
import argparse
import glob
import pickle

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


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

def make_default_md(arena_df, elo_results):
    total_votes = sum(arena_df["num_battles"]) // 2
    total_models = len(arena_df)

    leaderboard_md = f"""
# NeurIPS LLM Merging Competition Leaderboard
[Website](https://llm-merging.github.io/index) | [Starter Kit (Github)](https://github.com/llm-merging/LLM-Merging) | [Discord](https://discord.com/invite/dPBHEVnV) |

"""
    return leaderboard_md


def make_arena_leaderboard_md(arena_df):
    total_votes = sum(arena_df["num_battles"]) // 2
    total_models = len(arena_df)
    space = "   "
    leaderboard_md = f"""
Total #models: **{total_models}**.{space} Total #votes: **{"{:,}".format(total_votes)}**.{space} Last updated: June 1, 2024.

"""
    return leaderboard_md

def make_category_arena_leaderboard_md(arena_df, arena_subset_df, name="Overall"):
    total_votes = sum(arena_df["num_battles"]) // 2
    total_models = len(arena_df)
    space = "   "
    total_subset_votes = sum(arena_subset_df["num_battles"]) // 2
    total_subset_models = len(arena_subset_df)
    leaderboard_md = f"""### {cat_name_to_explanation[name]}
#### [Coverage] {space} #models: **{total_subset_models} ({round(total_subset_models/total_models *100)}%)** {space} #votes: **{"{:,}".format(total_subset_votes)} ({round(total_subset_votes/total_votes * 100)}%)**{space}
"""
    return leaderboard_md

def make_full_leaderboard_md(elo_results):
    leaderboard_md = f"""
Three benchmarks are displayed: **Test Task 1**, **Test Task 2**, **Test Task 3**.

Higher values are better for all benchmarks.
"""
    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)

    # 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 get_full_table(model_table_df):
    values = []
    for i in range(len(model_table_df)):
        row = []
        model_key = model_table_df.iloc[i]["key"]
        model_name = model_table_df.iloc[i]["Model"]
        # model display name
        row.append(model_name)
        row.append(np.nan)
        row.append(np.nan)
        row.append(np.nan)
        # row.append(model_table_df.iloc[i]["MT-bench (score)"])
        # row.append(model_table_df.iloc[i]["MMLU"])
        # Organization
        row.append(model_table_df.iloc[i]["Organization"])
        # license
        row.append(model_table_df.iloc[i]["License"])

        values.append(row)
    values.sort(key=lambda x: -x[1] if not np.isnan(x[1]) else 1e9)
    return values

def create_ranking_str(ranking, ranking_difference):
    if ranking_difference > 0:
        # return f"{int(ranking)} (\u2191{int(ranking_difference)})"
        return f"{int(ranking)} \u2191"
    elif ranking_difference < 0:
        # return f"{int(ranking)} (\u2193{int(-ranking_difference)})"
        return f"{int(ranking)} \u2193"
    else:
        return f"{int(ranking)}"
    
def recompute_final_ranking(arena_df):
    # compute ranking based on CI
    ranking = {}
    for i, model_a in enumerate(arena_df.index):
        ranking[model_a] = 1
        for j, model_b in enumerate(arena_df.index):
            if i == j:
                continue
            if arena_df.loc[model_b]["rating_q025"] > arena_df.loc[model_a]["rating_q975"]:
                ranking[model_a] += 1
    return list(ranking.values())
    
def get_arena_table(arena_df, model_table_df, arena_subset_df=None):
    arena_df = arena_df.sort_values(by=["final_ranking", "rating"], ascending=[True, False])
    arena_df["final_ranking"] = recompute_final_ranking(arena_df)
    arena_df = arena_df.sort_values(by=["final_ranking"], ascending=True)

    # arena_df["final_ranking"] = range(1, len(arena_df) + 1)
    # sort by rating
    if arena_subset_df is not None:
        # filter out models not in the arena_df
        arena_subset_df = arena_subset_df[arena_subset_df.index.isin(arena_df.index)]
        arena_subset_df = arena_subset_df.sort_values(by=["rating"], ascending=False)
        # arena_subset_df = arena_subset_df.sort_values(by=["final_ranking"], ascending=True)
        arena_subset_df["final_ranking"] = recompute_final_ranking(arena_subset_df)
        # keep only the models in the subset in arena_df and recompute final_ranking
        arena_df = arena_df[arena_df.index.isin(arena_subset_df.index)]
        # recompute final ranking
        arena_df["final_ranking"] = recompute_final_ranking(arena_df)

        # assign ranking by the order
        arena_subset_df["final_ranking_no_tie"] = range(1, len(arena_subset_df) + 1)
        arena_df["final_ranking_no_tie"] = range(1, len(arena_df) + 1)
        # join arena_df and arena_subset_df on index
        arena_df = arena_subset_df.join(arena_df["final_ranking"], rsuffix="_global", how="inner")
        arena_df["ranking_difference"] =  arena_df["final_ranking_global"] - arena_df["final_ranking"]

        arena_df = arena_df.sort_values(by=["final_ranking", "rating"], ascending=[True, False])
        arena_df["final_ranking"] = arena_df.apply(lambda x: create_ranking_str(x["final_ranking"], x["ranking_difference"]), axis=1)

    values = []
    for i in range(len(arena_df)):
        row = []
        model_key = arena_df.index[i]
        try: # this is a janky fix for where the model key is not in the model table (model table and arena table dont contain all the same models)
            model_name = model_table_df[model_table_df["key"] == model_key]["Model"].values[
                0
            ]
            # rank
            ranking = arena_df.iloc[i].get("final_ranking") or i+1
            row.append(ranking)
            if arena_subset_df is not None:
                row.append(arena_df.iloc[i].get("ranking_difference") or 0)
            # model display name
            row.append(model_name)
            # elo rating
            row.append(round(arena_df.iloc[i]["rating"]))
            # Organization
            row.append(
                model_table_df[model_table_df["key"] == model_key]["Organization"].values[0]
            )
            # license
            row.append(
                model_table_df[model_table_df["key"] == model_key]["License"].values[0]
            )
            values.append(row)
        except Exception as e:
            print(f"{model_key} - {e}")
    return values

key_to_category_name = {
    "full": "Overall",
}
cat_name_to_explanation = {
    "Overall": "Overall Questions",
}

def build_leaderboard_tab(results_file, leaderboard_table_file, show_plot=False):
    arena_dfs = {}
    category_elo_results = {}
    if results_file is None:  # Do live update
        default_md = "Loading ..."
    else:
        with open(results_file, "rb") as fin:
            elo_results = pickle.load(fin)
            if "full" in elo_results:
                print("KEYS ", elo_results.keys())
                for k in elo_results.keys():
                    if k not in key_to_category_name:
                        continue
                    arena_dfs[key_to_category_name[k]] = elo_results[k]["leaderboard_table_df"]
                    category_elo_results[key_to_category_name[k]] = elo_results[k]

        arena_df = arena_dfs["Overall"]
        default_md = make_default_md(arena_df, category_elo_results["Overall"])

    md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown")
    if leaderboard_table_file:
        data = load_leaderboard_table_csv(leaderboard_table_file)
        model_table_df = pd.DataFrame(data)

        with gr.Tabs() as tabs:
            # arena table
            arena_table_vals = get_full_table(model_table_df) 
            with gr.Tab("Arena Elo", id=0):
                md = make_arena_leaderboard_md(arena_df)
                leaderboard_markdown = gr.Markdown(md, elem_id="leaderboard_markdown")
                with gr.Row():
                    with gr.Column(scale=2):
                        category_dropdown = gr.Dropdown(choices=list(arena_dfs.keys()), label="Category", value="Overall")
                    default_category_details = make_category_arena_leaderboard_md(arena_df, arena_df, name="Overall")
                    with gr.Column(scale=4, variant="panel"):
                        category_deets = gr.Markdown(default_category_details, elem_id="category_deets")

                elo_display_df = gr.Dataframe(
                    headers=[
                        "Rank",
                        "🤖 Model",
                        "⭐ Task 1",
                        "📈 Task 2",
                        "📚 Task 3",
                        "Organization",
                        "License",
                    ],
                    datatype=[
                        "number",
                        "markdown",
                        "number",
                        "number",
                        "number",
                        "str",
                        "str",
                    ],
                    value=arena_table_vals,
                    elem_id="arena_leaderboard_dataframe",
                    height=700,
                    column_widths=[70, 190, 110, 160, 150, 140],
                    wrap=True,
                )

                gr.Markdown(
                    f"""Note: .
            """,
                    elem_id="leaderboard_markdown"
                )

                leader_component_values[:] = [default_md]

            # with gr.Tab("Full Leaderboard", id=0):
            #     md = make_full_leaderboard_md(elo_results)
            #     gr.Markdown(md, elem_id="leaderboard_markdown")
            #     with gr.Row():
            #         with gr.Column(scale=2):
            #             category_dropdown = gr.Dropdown(choices=list(arena_dfs.keys()), label="Category", value="Overall")
            #         default_category_details = make_category_arena_leaderboard_md(arena_df, arena_df, name="Overall")
            #         with gr.Column(scale=4, variant="panel"):
            #             category_deets = gr.Markdown(default_category_details, elem_id="category_deets")

            #     full_table_vals = get_full_table(model_table_df)
            #     display_df = gr.Dataframe(
            #         headers=[
            #             "🤖 Model",
            #             "⭐ Task 1",
            #             "📈 Task 2",
            #             "📚 Task 3",
            #             "Organization",
            #             "License",
            #         ],
            #         datatype=["markdown", "number", "number", "number", "str", "str"],
            #         value=full_table_vals,
            #         elem_id="full_leaderboard_dataframe",
            #         column_widths=[200, 100, 100, 100, 150, 150],
            #         height=700,
            #         wrap=True,
            #     )
            #     gr.Markdown(
            #         f"""Note: .
            #         """,
            #         elem_id="leaderboard_markdown"
            #     )

            #     leader_component_values[:] = [default_md]
        if not show_plot:
            gr.Markdown(
                """ ## Submit your model [here]().
                """,
                elem_id="leaderboard_markdown",
            )
    else:
        pass

    def update_leaderboard_df(arena_table_vals):
        elo_datarame = pd.DataFrame(arena_table_vals, columns=[ "Rank", "🤖 Model", "⭐ Arena Elo", "Organization", "License"])

        # goal: color the rows based on the rank with styler
        def highlight_max(s):
            # all items in S which contain up arrow should be green, down arrow should be red, otherwise black
            return ["color: green; font-weight: bold" if "\u2191" in v else "color: red; font-weight: bold" if "\u2193" in v else "" for v in s]
            
        def highlight_rank_max(s):
            return ["color: green; font-weight: bold" if v > 0 else "color: red; font-weight: bold" if v < 0 else "" for v in s]
        
        return elo_datarame.style.apply(highlight_max, subset=["Rank"])

    def update_leaderboard_and_plots(category):
        arena_subset_df = arena_dfs[category]
        arena_subset_df = arena_subset_df[arena_subset_df["num_battles"] > 500]
        elo_subset_results = category_elo_results[category]
        arena_df = arena_dfs["Overall"]
        arena_values = get_arena_table(arena_df, model_table_df, arena_subset_df = arena_subset_df if category != "Overall" else None)
        if category != "Overall":
            arena_values = update_leaderboard_df(arena_values)
            arena_values = gr.Dataframe(
                    headers=[
                        "Rank",
                        "🤖 Model",
                        "⭐ Arena Elo",
                        "Organization",
                        "License",
                    ],
                    datatype=[
                        "number",
                        "markdown",
                        "number",
                        "str",
                        "str",
                    ],
                    value=arena_values,
                    elem_id="arena_leaderboard_dataframe",
                    height=700,
                    column_widths=[60, 190, 110, 160, 150, 140],
                    wrap=True,
                )
        else:
            arena_values = gr.Dataframe(
                    headers=[
                        "Rank",
                        "🤖 Model",
                        "⭐ Arena Elo",
                        "Organization",
                        "License",
                    ],
                    datatype=[
                        "number",
                        "markdown",
                        "number",
                        "str",
                        "str",
                    ],
                    value=arena_values,
                    elem_id="arena_leaderboard_dataframe",
                    height=700,
                    column_widths=[70, 190, 110, 160, 150, 140],
                    wrap=True,
                )


        leaderboard_md = make_category_arena_leaderboard_md(arena_df, arena_subset_df, name=category)
        return arena_values, leaderboard_md
                
    category_dropdown.change(update_leaderboard_and_plots, inputs=[category_dropdown], outputs=[display_df, category_deets])

    with gr.Accordion(
        "📝 Citation",
        open=True,
    ):
        citation_md = """
        ### Citation
        Please cite the following paper 
    
        """
        gr.Markdown(citation_md, elem_id="leaderboard_markdown")
        gr.Markdown(acknowledgment_md)

    if show_plot:
        return [md_1]
    return [md_1]


block_css = """
#notice_markdown {
    font-size: 104%
}
#notice_markdown th {
    display: none;
}
#notice_markdown td {
    padding-top: 6px;
    padding-bottom: 6px;
}

#category_deets {
    text-align: center;
    padding: 0px;
    padding-left: 5px;
}

#leaderboard_markdown {
    font-size: 104%
}
#leaderboard_markdown td {
    padding-top: 6px;
    padding-bottom: 6px;
}

#leaderboard_header_markdown {
    font-size: 104%;
    text-align: center;
    display:block;
}

#leaderboard_dataframe td {
    line-height: 0.1em;
}

#plot-title {
    text-align: center;
    display:block;
}

#non-interactive-button {
  display: inline-block;
  padding: 10px 10px;
  background-color: #f7f7f7; /* Super light grey background */
  text-align: center;
  font-size: 26px; /* Larger text */
  border-radius: 0; /* Straight edges, no border radius */
  border: 0px solid #dcdcdc; /* A light grey border to match the background */
  user-select: none; /* The text inside the button is not selectable */
  pointer-events: none; /* The button is non-interactive */
}

footer {
    display:none !important
}
.sponsor-image-about img {
    margin: 0 20px;
    margin-top: 20px;
    height: 40px;
    max-height: 100%;
    width: auto;
    float: left;
}
"""

acknowledgment_md = """
### Acknowledgment
We thank []() for their generous [sponsorship]().

<div class="sponsor-image-about">
</div>
"""

def build_demo(elo_results_file, leaderboard_table_file):
    text_size = gr.themes.sizes.text_lg
    theme = gr.themes.Base(text_size=text_size)
    theme.set(button_secondary_background_fill_hover="*primary_300", 
              button_secondary_background_fill_hover_dark="*primary_700")
    with gr.Blocks(
        title="LLM Merging Leaderboard",
        theme=theme,
        # theme = gr.themes.Base.load("theme.json"), # uncomment to use new cool theme
        css=block_css,
    ) as demo:
        leader_components = build_leaderboard_tab(
            elo_results_file, leaderboard_table_file, show_plot=True
        )
    return demo


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--share", action="store_true")
    parser.add_argument("--host", default="0.0.0.0")
    parser.add_argument("--port", type=int, default=7860)
    args = parser.parse_args()

    elo_result_files = glob.glob("elo_results_*.pkl")
    elo_result_files.sort(key=lambda x: int(x[12:-4]))
    elo_result_file = elo_result_files[-1]

    leaderboard_table_files = glob.glob("leaderboard_table_*.csv")
    leaderboard_table_files.sort(key=lambda x: int(x[18:-4]))
    leaderboard_table_file = leaderboard_table_files[-1]

    demo = build_demo(elo_result_file, leaderboard_table_file)
    demo.launch(share=args.share, server_name=args.host, server_port=args.port)