import pandas as pd
from pathlib import Path
from ..styles import highlight_color

abs_path = Path(__file__).parent.parent.parent

def replace_models_names(model_name):
    if "gpt" in model_name:
        return model_name
    replaces = {'meta-llama': 'meta_llama',
        'epfl-llm':'epfl_llm',
        '01-ai':'01_ai'}
    new_name = model_name.replace('model-', '')
    for k, v in replaces.items():
        if new_name.startswith(k):
            new_name = new_name.replace(k, v)
    new_name = new_name.replace('-','/',1)
    new_name = new_name.replace('_','-',1)
    new_name = f"[{new_name}](https://huggingface.co/{new_name})"
    return new_name

def generate_ORDER_LIST_LDEK_and_data_types(json_data):
    ORDER_LIST_LDEK = ["model_name", "overall_accuracy"]
    data_types = ["markdown", "number"]

    for key in json_data.keys():
        if key not in ["model_name", "overall_accuracy"]:
            ORDER_LIST_LDEK.append(key)
            data_types.append("number")
    ORDER_LIST_LDEK[2:] = sorted(ORDER_LIST_LDEK[2:])
    return ORDER_LIST_LDEK, data_types

def filter_columns_ldek(column_choices):
    selected_columns = [col for col in ORDER_LIST_LDEK if col in column_choices]
    return LDEK_ACCS[selected_columns]

def load_json_data(file_path, ORDER_LIST_LDEK):
    LDEK_ACCS = pd.read_json(file_path)

    for column in LDEK_ACCS.columns:
        if LDEK_ACCS[column].apply(type).eq(dict).any():
            LDEK_ACCS[column] = LDEK_ACCS[column].apply(str)

    LDEK_ACCS["model_name"] = LDEK_ACCS["model_name"].apply(
        lambda name: replace_models_names(name)
    )

    for column in LDEK_ACCS.select_dtypes(include='number').columns:
        LDEK_ACCS[column] = LDEK_ACCS[column].round(2)

    LDEK_ACCS["overall_accuracy"] = pd.to_numeric(LDEK_ACCS["overall_accuracy"], errors='coerce')

    ordered_columns = [col for col in ORDER_LIST_LDEK if col in LDEK_ACCS.columns]
    LDEK_ACCS = LDEK_ACCS[ordered_columns]

    if "overall_accuracy" in LDEK_ACCS.columns:
        LDEK_ACCS = LDEK_ACCS.sort_values(by="overall_accuracy", ascending=False)

    return LDEK_ACCS



file_path = str(abs_path / "leaderboards/ldek_accs.json")
with open(file_path, 'r', encoding='utf-8') as file:
    sample_data = pd.read_json(file_path).iloc[0].to_dict()

ORDER_LIST_LDEK, DATA_TYPES_LDEK = generate_ORDER_LIST_LDEK_and_data_types(sample_data)
LDEK_ACCS = load_json_data(file_path, ORDER_LIST_LDEK)
LDEK_ACCS = LDEK_ACCS.style.highlight_max(
    color = highlight_color,
    subset=LDEK_ACCS.columns[1:]).format(precision=2)
COLUMN_HEADERS_LDEK = ORDER_LIST_LDEK
print(ORDER_LIST_LDEK)


file_path2 = str(abs_path / "leaderboards/ldek_en_accs.json")
with open(file_path, 'r', encoding='utf-8') as file:
    sample_data2 = pd.read_json(file_path).iloc[0].to_dict()
ORDER_LIST_LDEK_EN, DATA_TYPES_LDEK_EN = generate_ORDER_LIST_LDEK_and_data_types(sample_data2)
LDEK_EN_ACCS = load_json_data(file_path2, ORDER_LIST_LDEK_EN)
LDEK_EN_ACCS = LDEK_EN_ACCS.style.highlight_max(
    color = highlight_color,
    subset=LDEK_EN_ACCS.columns[1:]).format(precision=2)
COLUMN_HEADERS_LDEK_EN = ORDER_LIST_LDEK_EN
print(ORDER_LIST_LDEK_EN)