import io
import json

import gradio as gr
import pandas as pd
from huggingface_hub import HfFileSystem


RESULTS_DATASET_ID = "datasets/open-llm-leaderboard/results"
EXCLUDED_KEYS =  {
    "pretty_env_info",
    "chat_template",
    "group_subtasks",
}
# EXCLUDED_RESULTS_KEYS = {
#     "leaderboard",
# }
# EXCLUDED_RESULTS_LEADERBOARDS_KEYS = {
#     "alias",
# }


TASKS = {
    "leaderboard_arc_challenge": ("ARC", "leaderboard_arc_challenge"),
    "leaderboard_bbh": ("BBH", "leaderboard_bbh"),
    "leaderboard_gpqa": ("GPQA", "leaderboard_gpqa"),
    "leaderboard_ifeval": ("IFEval", "leaderboard_ifeval"),
    "leaderboard_math_hard": ("MATH", "leaderboard_math"),
    "leaderboard_mmlu_pro": ("MMLU-Pro", "leaderboard_mmlu_pro"),
    "leaderboard_musr": ("MuSR", "leaderboard_musr"),
}

fs = HfFileSystem()


def fetch_result_paths():
    paths = fs.glob(f"{RESULTS_DATASET_ID}/**/**/*.json")
    return paths


def filter_latest_result_path_per_model(paths):
    from collections import defaultdict

    d = defaultdict(list)
    for path in paths:
        model_id, _ = path[len(RESULTS_DATASET_ID) +1:].rsplit("/", 1)
        d[model_id].append(path)
    return {model_id: max(paths) for model_id, paths in d.items()}


def get_result_path_from_model(model_id, result_path_per_model):
    return result_path_per_model[model_id]


def load_data(result_path) -> pd.DataFrame:
    with fs.open(result_path, "r") as f:
        data = json.load(f)
    return data


def load_result_dataframe(model_id):
    result_path = get_result_path_from_model(model_id, latest_result_path_per_model)
    data = load_data(result_path)
    model_name = data.get("model_name", "Model")
    df = pd.json_normalize([{key: value for key, value in data.items() if key not in EXCLUDED_KEYS}])
    # df.columns = df.columns.str.split(".")  # .split return a list instead of a tuple
    return df.set_index(pd.Index([model_name])).reset_index()


def display_results(df_1, df_2, task):
    df = pd.concat([df.set_index("index") for df in [df_1, df_2] if "index" in df.columns])
    df = df.T.rename_axis(columns=None)  # index="Parameters",  # .reset_index()
    # return display_dataframe(df)
    # d = df.set_index(df.index.str.split("."))  # .split return a list instead of a tuple
    # results = d.loc[d.index.str[0] == "results"]
    # results.index = results.index.str.join(".")
    # configs = d.loc[d.index.str[0] == "configs"]
    # configs.index = configs.index.str.join(".")
    # return display_dataframe(results), display_dataframe(configs)
    return display_results_tab(df, task), display_configs_tab(df, task)


def display_results_tab(df, task):
    df = df.style.format(na_rep="")
    df.hide(
        [
            row
            for row in df.index
            if (
                not row.startswith("results.")
                or row.startswith("results.leaderboard.")
                or row.endswith(".alias")
                or (not row.startswith(f"results.{task}") if task != "All" else False)
            )
        ],
        axis="index",
    )
    df.format_index(lambda idx: idx[len("results.leaderboard_"):].removesuffix(",none"), axis="index")
    return df.to_html()


def display_configs_tab(df, task):
    df = df.style.format(na_rep="")
    df.hide(
        [
            row
            for row in df.index
            if (
                not row.startswith("configs.")
                or row.startswith("configs.leaderboard.")
                or row.endswith(".alias")
                or (not row.startswith(f"configs.{task}") if task != "All" else False)
            )
        ],
        axis="index",
    )
    df.format_index(lambda idx: idx[len("configs.leaderboard_"):], axis="index")
    return df.to_html()


# if __name__ == "__main__":
latest_result_path_per_model = filter_latest_result_path_per_model(fetch_result_paths())

with gr.Blocks(fill_height=True) as demo:
    gr.HTML("<h1 style='text-align: center;'>Compare Results of the 🤗 Open LLM Leaderboard</h1>")
    gr.HTML("<h3 style='text-align: center;'>Select 2 results to load and compare</h3>")

    with gr.Row():
        with gr.Column():
            model_id_1 = gr.Dropdown(choices=list(latest_result_path_per_model.keys()), label="Results")
            load_btn_1 = gr.Button("Load")
            dataframe_1 = gr.Dataframe(visible=False)
        with gr.Column():
            model_id_2 = gr.Dropdown(choices=list(latest_result_path_per_model.keys()), label="Results")
            load_btn_2 = gr.Button("Load")
            dataframe_2 = gr.Dataframe(visible=False)
    with gr.Row():
        task = gr.Radio(
            ["All"] + list(TASKS.values()),
            label="Tasks",
            info="Evaluation tasks to be displayed",
            value="All",
        )

    with gr.Row():
        # with gr.Tab("All"):
        #     pass
        with gr.Tab("Results"):
            results = gr.HTML()
        with gr.Tab("Configs"):
            configs = gr.HTML()

    load_btn_1.click(
        fn=load_result_dataframe,
        inputs=model_id_1,
        outputs=dataframe_1,
    ).then(
        fn=display_results,
        inputs=[dataframe_1, dataframe_2, task],
        outputs=[results, configs],
    )
    load_btn_2.click(
        fn=load_result_dataframe,
        inputs=model_id_2,
        outputs=dataframe_2,
    ).then(
        fn=display_results,
        inputs=[dataframe_1, dataframe_2, task],
        outputs=[results, configs],
    )
    task.change(
        fn=display_results,
        inputs=[dataframe_1, dataframe_2, task],
        outputs=[results, configs],
    )

demo.launch()