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:
    raw_data = get_raw_eval_results(results_path, requests_path)
    all_data_json = [v.to_dict() for v in raw_data]

    df = pd.DataFrame.from_records(all_data_json)
    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
    df = df[cols].round(decimals=2)

    # filter out if any of the benchmarks have not been produced
    df = 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]:
    try:
        entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
        all_evals = []

        for entry in entries:
            try:
                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
                    try:
                        sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if not e.startswith(".")]
                        for sub_entry in sub_entries:
                            try:
                                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)
                            except Exception as e:
                                print(f"Warning: Could not process file {sub_entry}: {e}")
                    except Exception as e:
                        print(f"Warning: Could not process directory {entry}: {e}")
            except Exception as e:
                print(f"Warning: Could not process entry {entry}: {e}")

        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]
    except Exception as e:
        print(f"Warning: Could not process evaluation queue: {e}")
        # Boş veri çerçeveleri döndür
        df_empty = pd.DataFrame(columns=cols)
        return df_empty, df_empty, df_empty