import asyncio

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

import src.constants as constants
from src.hub import load_details_file


def update_task_description_component(task):
    base_description = constants.TASK_DESCRIPTIONS.get(task, "")
    additional_info = "A higher score is a better score."
    description = f"{base_description}\n\n{additional_info}" if base_description else additional_info
    return gr.Textbox(
        description,
        label="Task Description",
        lines=5,
        visible=True,
    )


def update_subtasks_component(task, profile: gr.OAuthProfile | None):
    visible_login_btn = True if task == "leaderboard_gpqa" else False
    subtasks = None if task == "leaderboard_gpqa" and not profile else constants.SUBTASKS.get(task)
    return (
        gr.LoginButton(size="sm", visible=visible_login_btn),
        gr.Radio(
            choices=subtasks,
            info="Evaluation subtasks to be loaded",
            value=None,
        ),
    )


def update_load_details_component(model_id_1, model_id_2, subtask):
    if (model_id_1 or model_id_2) and subtask:
        return gr.Button("Load Details", interactive=True)
    else:
        return gr.Button("Load Details", interactive=False)


async def load_details_dataframe(model_id, subtask):
    fs = HfFileSystem()
    if not model_id or not subtask:
        return
    model_name_sanitized = model_id.replace("/", "__")
    paths = fs.glob(
        f"{constants.DETAILS_DATASET_ID}/**/{constants.DETAILS_FILENAME}".format(
            model_name_sanitized=model_name_sanitized, subtask=subtask
        )
    )
    if not paths:
        return
    path = max(paths)
    data = await load_details_file(path)
    df = pd.json_normalize(data)
    # df = df.rename_axis("Parameters", axis="columns")
    df["model_name"] = model_id  # Keep model_name
    return df
    # return df.set_index(pd.Index([model_id])).reset_index()


async def load_details_dataframes(subtask, *model_ids):
    result = await asyncio.gather(*[load_details_dataframe(model_id, subtask) for model_id in model_ids])
    return result


def display_details(sample_idx, show_only_differences, *dfs):
    rows = [df.iloc[sample_idx] for df in dfs if "model_name" in df.columns and sample_idx < len(df)]
    if not rows:
        return
    # Pop model_name and add it to the column name
    df = pd.concat([row.rename(row.pop("model_name")) for row in rows], axis="columns")

    # Wrap long strings to avoid overflow; e.g. URLs in "doc.Websites visited_NEV_2"
    def wrap(row):
        try:
            result = row.str.wrap(140)
            return result if result.notna().all() else row  # NaN when data is a list
        except AttributeError:  # when data is number
            return row

    df = df.apply(wrap, axis=1)
    if show_only_differences:
        any_difference = df.ne(df.iloc[:, 0], axis=0).any(axis=1)
    # Style
    return (
        df.style.format(escape="html", na_rep="")
        # .hide(axis="index")
        # Hide non-different rows
        .hide([row for row in df.index if show_only_differences and not any_difference[row]])
        .to_html()
    )


def update_sample_idx_component(*dfs):
    maximum = max([len(df) - 1 for df in dfs])
    return gr.Number(
        label="Sample Index",
        info="Index of the sample to be displayed",
        value=0,
        minimum=0,
        maximum=maximum,
        visible=True,
    )


def clear_details():
    # model_id_1, model_id_2, details_dataframe_1, details_dataframe_2, details_task, subtask, load_details_btn, sample_idx
    return (
        None,
        None,
        None,
        None,
        None,
        None,
        gr.Button("Load Details", interactive=False),
        gr.Number(label="Sample Index", info="Index of the sample to be displayed", value=0, minimum=0, visible=False),
    )


def display_loading_message_for_details():
    return "<h3 style='text-align: center;'>Loading...</h3>"