import json
import os

import pandas as pd

from src.display.formatting import has_no_nan_values, make_clickable_model
from src.display.utils import AutoEvalColumn, EvalQueueColumn
from src.leaderboard.read_evals import get_raw_eval_results


def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
    metadata=json.load(open(f"{requests_path}/metadata.json"))
    raw_data = get_raw_eval_results(results_path, requests_path, metadata)
    all_data_json = [v.to_dict() for v in raw_data]
    # print(all_data_json)
    json.dump(all_data_json, open("all_data.json", "w"), indent=2, ensure_ascii=False)
    df = pd.DataFrame.from_records(all_data_json)
    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
    df = df[cols].round(decimals=2)

    # set column rank to pd.DataFrame df
    df['rank'] = df[AutoEvalColumn.average.name].rank(ascending=False, method="min")
    # df.insert(0, "rank", df[AutoEvalColumn.average.name].rank(ascending=False, method="min"), True)

    # filter out if any of the benchmarks have not been produced
    #df2 = df[has_no_nan_values(df, benchmark_cols)]
    return raw_data, df


def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
    entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
    all_evals = []

    # for entry in entries:
    #     if ".json" in entry:
    #         file_path = os.path.join(save_path, entry)
    #         with open(file_path) as fp:
    #             data = json.load(fp)
    #
    #         data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
    #         data[EvalQueueColumn.revision.name] = data.get("revision", "main")
    #
    #         all_evals.append(data)
    #     elif ".md" not in entry:
    #         # this is a folder
    #         sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if not e.startswith(".")]
    #         for sub_entry in sub_entries:
    #             file_path = os.path.join(save_path, entry, sub_entry)
    #             with open(file_path) as fp:
    #                 data = json.load(fp)
    #
    #             data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
    #             data[EvalQueueColumn.revision.name] = data.get("revision", "main")
    #             all_evals.append(data)

    pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
    running_list = [e for e in all_evals if e["status"] == "RUNNING"]
    finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
    df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
    df_running = pd.DataFrame.from_records(running_list, columns=cols)
    df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
    return df_finished[cols], df_running[cols], df_pending[cols]