"""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


# notebook_url = "https://colab.research.google.com/drive/1RAWb22-PFNI-X1gPVzc927SGUdfr6nsR?usp=sharing"
notebook_url = "https://colab.research.google.com/drive/1KdwokPjirkTmpO_P1WByFNFiqxWQquwH#scrollTo=o_CpbkGEbhrK"


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


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

    leaderboard_md = f"""
# 🏆 LMSYS Chatbot Arena Leaderboard
| [Vote](https://chat.lmsys.org) | [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) |

LMSYS [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) is a crowdsourced open platform for LLM evals.
We've collected over **400,000** human preference votes to rank LLMs with the Elo ranking system.
"""
    return leaderboard_md


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

    leaderboard_md = f"""
Total #models: **{total_models}**. Total #votes: **{total_votes}**. Last updated: March 13, 2024.

Contribute your vote 🗳️ at [chat.lmsys.org](https://chat.lmsys.org)! Find more analysis in the [notebook]({notebook_url}).
"""
    return leaderboard_md


def make_full_leaderboard_md(elo_results):
    leaderboard_md = f"""
Three benchmarks are displayed: **Arena Elo**, **MT-Bench** and **MMLU**.
- [Chatbot Arena](https://chat.lmsys.org/?arena) - a crowdsourced, randomized battle platform. We use 200K+ 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 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.
"""
    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 get_full_table(arena_df, 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)
        if model_key in arena_df.index:
            idx = arena_df.index.get_loc(model_key)
            row.append(round(arena_df.iloc[idx]["rating"]))
        else:
            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 get_arena_table(arena_df, model_table_df):
    # sort by rating
    arena_df = arena_df.sort_values(by=["rating"], ascending=False)
    values = []
    for i in range(len(arena_df)):
        row = []
        model_key = arena_df.index[i]
        print(model_key)
        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)
        # model display name
        row.append(model_name)
        # elo rating
        row.append(round(arena_df.iloc[i]["rating"]))
        upper_diff = round(
            arena_df.iloc[i]["rating_q975"] - arena_df.iloc[i]["rating"]
        )
        lower_diff = round(
            arena_df.iloc[i]["rating"] - arena_df.iloc[i]["rating_q025"]
        )
        row.append(f"+{upper_diff}/-{lower_diff}")
        # num battles
        row.append(round(arena_df.iloc[i]["num_battles"]))
        # 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]
        )

        cutoff_date = model_table_df[model_table_df["key"] == model_key]["Knowledge cutoff date"].values[0]
        if cutoff_date == "-":
            row.append("Unknown")
        else:
            row.append(cutoff_date)
        values.append(row)
    return values

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

        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"]
        arena_df = elo_results["leaderboard_table_df"]
        default_md = make_default_md(arena_df, elo_results)

    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_arena_table(arena_df, model_table_df)
            with gr.Tab("Arena Elo", id=0):
                md = make_arena_leaderboard_md(arena_df)
                gr.Markdown(md, elem_id="leaderboard_markdown")
                gr.Dataframe(
                    headers=[
                        "Rank",
                        "🤖 Model",
                        "⭐ Arena Elo",
                        "📊 95% CI",
                        "🗳️ Votes",
                        "Organization",
                        "License",
                        "Knowledge Cutoff",
                    ],
                    datatype=[
                        "str",
                        "markdown",
                        "number",
                        "str",
                        "number",
                        "str",
                        "str",
                        "str",
                    ],
                    value=arena_table_vals,
                    elem_id="arena_leaderboard_dataframe",
                    height=700,
                    column_widths=[50, 200, 120, 100, 100, 150, 150, 100],
                    wrap=True,
                )
            with gr.Tab("Full Leaderboard", id=1):
                md = make_full_leaderboard_md(elo_results)
                gr.Markdown(md, elem_id="leaderboard_markdown")
                full_table_vals = get_full_table(arena_df, model_table_df)
                gr.Dataframe(
                    headers=[
                        "🤖 Model",
                        "⭐ Arena Elo",
                        "📈 MT-bench",
                        "📚 MMLU",
                        "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,
                )
        if not show_plot:
            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

    gr.Markdown(
        f"""Note: we take the 95% confidence interval into account when determining a model's ranking.
A model is ranked higher only if its lower bound of model score is higher than the upper bound of the other model's score.
""",
        elem_id="leaderboard_markdown"
    )

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

    if show_plot:
        gr.Markdown(
            f"""## More Statistics for Chatbot Arena\n
Below are figures for more statistics. The code for generating them is also included in this [notebook]({notebook_url}).
You can find more discussions in this blog [post](https://lmsys.org/blog/2023-12-07-leaderboard/).
    """,
            elem_id="leaderboard_markdown"
        )
        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)

    gr.Markdown(acknowledgment_md)

    if show_plot:
        return [md_1, plot_1, plot_2, plot_3, plot_4]
    return [md_1]

block_css = """
#notice_markdown {
    font-size: 104%
}
#notice_markdown th {
    display: none;
}
#notice_markdown td {
    padding-top: 6px;
    padding-bottom: 6px;
}
#leaderboard_markdown {
    font-size: 104%
}
#leaderboard_markdown td {
    padding-top: 6px;
    padding-bottom: 6px;
}
#leaderboard_dataframe td {
    line-height: 0.1em;
}
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 [Kaggle](https://www.kaggle.com/), [MBZUAI](https://mbzuai.ac.ae/), [a16z](https://www.a16z.com/), [Together AI](https://www.together.ai/), [Anyscale](https://www.anyscale.com/), [HuggingFace](https://huggingface.co/) for their generous [sponsorship](https://lmsys.org/donations/).

<div class="sponsor-image-about">
    <img src="https://storage.googleapis.com/public-arena-asset/kaggle.png" alt="Kaggle">
    <img src="https://storage.googleapis.com/public-arena-asset/mbzuai.jpeg" alt="MBZUAI">
    <img src="https://storage.googleapis.com/public-arena-asset/a16z.jpeg" alt="a16z">
    <img src="https://storage.googleapis.com/public-arena-asset/together.png" alt="Together AI">
    <img src="https://storage.googleapis.com/public-arena-asset/anyscale.png" alt="AnyScale">
    <img src="https://storage.googleapis.com/public-arena-asset/huggingface.png" alt="HuggingFace">
</div>
"""

def build_demo(elo_results_file, leaderboard_table_file):
    text_size = gr.themes.sizes.text_lg

    with gr.Blocks(
        title="Chatbot Arena Leaderboard",
        theme=gr.themes.Base(text_size=text_size),
        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")
    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)