import json
import os

import pandas as pd

import src.display.formatting as formatting
import src.display.utils as utils
import src.leaderboard.read_evals as read_evals


def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
    print(results_path, requests_path)
    raw_data = read_evals.get_raw_eval_results(results_path, requests_path)
    print("raw_data：",raw_data)
    all_data_json = [v.to_dict() for v in raw_data]
    
    print(all_data_json)
    df = pd.DataFrame.from_records(all_data_json)
    print(df)
    # exit()
    df = df.sort_values(by=[utils.AutoEvalColumn.hallucination_rate.name], ascending=True)
    df = df[cols].round(decimals=2)

    # filter out if any of the benchmarks have not been produced
    df = df[formatting.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[utils.EvalQueueColumn.model.name] = formatting.make_clickable_model(data["model"])
            data[utils.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[utils.EvalQueueColumn.model.name] = formatting.make_clickable_model(data["model"])
                data[utils.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]