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_crm(
    crm_results_path: str, accuracy_cols: list, cost_cols: list
) -> tuple[pd.DataFrame, pd.DataFrame]:
    """Creates a dataframe from all the individual experiment results"""
    sf_finetuned_models = ["SF-TextBase 70B", "SF-TextBase 7B", "SF-TextSum"]

    leaderboard_accuracy_df = pd.read_csv(os.path.join(crm_results_path, "hf_leaderboard_accuracy.csv"))
    leaderboard_accuracy_df = leaderboard_accuracy_df[~leaderboard_accuracy_df["Model Name"].isin(sf_finetuned_models)]
    # leaderboard_accuracy_df = leaderboard_accuracy_df.sort_values(
    #     by=[AutoEvalColumn.accuracy_metric_average.name], ascending=False
    # )
    # print(leaderboard_accuracy_df)
    # print(leaderboard_accuracy_df.columns)
    # print(leaderboard_accuracy_df["Model Name"].nunique())
    leaderboard_accuracy_df = leaderboard_accuracy_df[accuracy_cols].round(decimals=2)

    ref_df = leaderboard_accuracy_df[["Model Name", "LLM Provider"]].drop_duplicates()

    leaderboard_cost_df = pd.read_csv(os.path.join(crm_results_path, "hf_leaderboard_latency_cost.csv"))
    leaderboard_cost_df = leaderboard_cost_df[~leaderboard_cost_df["Model Name"].isin(sf_finetuned_models)]
    leaderboard_cost_df = leaderboard_cost_df.join(ref_df.set_index("Model Name"), on="Model Name")
    leaderboard_cost_df["LLM Provider"] = leaderboard_cost_df["LLM Provider"].fillna("Google")
    leaderboard_cost_df = leaderboard_cost_df[cost_cols].round(decimals=2)

    leaderboard_ts_df = pd.read_csv(os.path.join(crm_results_path, "hf_leaderboard_ts.csv"))
    leaderboard_ts_df = leaderboard_ts_df[~leaderboard_ts_df["Model Name"].isin(sf_finetuned_models)]
    leaderboard_ts_df = leaderboard_ts_df.join(ref_df.set_index("Model Name"), on="Model Name")

    return leaderboard_accuracy_df, leaderboard_cost_df, leaderboard_ts_df


# 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]

#     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]:
#     """Creates the different dataframes for the evaluation queues requestes"""
#     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]