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
from src.envs import RESULTS_REPO


def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
    """Creates a dataframe from all the individual experiment results"""
    raw_data = get_raw_eval_results(results_path, requests_path)
    # all_data_json = [v.to_dict() for v in raw_data]
    # print(raw_data)
    df = pd.DataFrame.from_records(json.load(open(raw_data[0])))
    print(list(df.columns))
    df['95% CI'] = " "
    # df['model']="nothing"
    # df.columns = cols
    # df.iloc[0]= create dummy
    # print(dir(AutoEvalColumn))
    df = df.sort_values(by=[AutoEvalColumn.task0.name], ascending=False)
    decimal = 1
    for i,row in df.iterrows():
        if 'lower' not in row:
            continue
        interval = '+'+str(round(row['upper'] - row['score'], decimal))+' / '+str(round(row['lower'] - row['score'], decimal))
        df.at[i,'95% CI'] = interval
    df['model'] = df['model'].apply(lambda x: 'SberDevices-GigaChatMaxWithoutFilter' if x=='SberDevices-GigaChatMax' else x) # change naming as per request
    df['model'] = df['model'].apply(lambda x: 'SberDevices-GigaChatMaxDefault' if x=='SberDevices-GigaChatMaxCensored' else x) # change naming as per request
    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 df


def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
    """Creates the different dataframes for the evaluation queues requestes"""
    entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".") and not entry.endswith(".jsonl")]
    all_evals = []

    for entry in entries:
        if ".json" in entry and 'toeval' not in entry:
            file_path = os.path.join(save_path, entry)
            with open(file_path) as fp:
                print(file_path)
                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 and 'toeval' not in entry and 'results' 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:
                if 'toeval' in sub_entry:
                    continue
                file_path = os.path.join(save_path, entry, sub_entry)
                with open(file_path) as fp:
                    # print(file_path)
                    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]