import json
import os
from ast import literal_eval
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:
    """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_leaderboard_df(EVAL_REQUESTS_PATH, tasks) -> pd.DataFrame:

    model_result_filepaths = []
    for root,_, files in os.walk(EVAL_REQUESTS_PATH):
        if len(files) == 0 or any([not f.endswith(".json") for f in files]):
            continue
        for file in files:
            model_result_filepaths.append(os.path.join(root, file))
    
    model_res = []
    for model in model_result_filepaths:
        import json
        with open(model) as f:
            model_res.append(json.load(f))

    for model in model_res:
        model["test"] = literal_eval(model["test"])
        model["valid"] = literal_eval(model["valid"])
        model["params"] = int(model["params"])
        model['submitted_time'] = model['submitted_time'].split('T')[0]
        #model['paper_url'] = '[Link](' + model['paper_url'] + ')'
        #model['github_url'] = '[Link](' + model['github_url'] + ')'

    name2short_name = {task.value.benchmark: task.value.col_name for task in tasks}
    for model in model_res:
        model.update({name2short_name[i]: str(model['test'][i][0])[:4] + '±' + str(model['test'][i][1])[:4] if i in model['test'] else '-' for i in name2short_name})

    columns_to_show = ['model', 'author', 'email', 'paper_url', 'github_url', 'submitted_time', 'params'] + list(name2short_name.values())

    # Check if model_res is empty
    if len(model_res) > 0:
        df_res = pd.DataFrame([{col: model[col] for col in columns_to_show} for model in model_res])
    else:
        # Initialize an empty DataFrame with the desired columns
        df_res = pd.DataFrame(columns=columns_to_show)

    #df_res = pd.DataFrame([{col: model[col] for col in columns_to_show} for model in model_res])
    print(df_res)
    ranks = df_res[list(name2short_name.values())].rank(ascending = False)
    df_res.rename(columns={'model': 'Model', 'author': 'Author', 'email': 'Email', 'paper_url': 'Paper URL', 'github_url': 'Github URL', 'submitted_time': 'Time', 'params': '# of Params'}, inplace=True)
    df_res['Average Rank⬆️'] = ranks.mean(axis=1)
    df_res.sort_values(by='Average Rank⬆️', ascending=True, inplace=True)
    return df_res

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]