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: metadata=json.load(open(f"{requests_path}/metadata.json")) raw_data = get_raw_eval_results(results_path, requests_path, metadata) all_data_json = [v.to_dict() for v in raw_data] # print(all_data_json) json.dump(all_data_json, open("all_data.json", "w"), indent=2, ensure_ascii=False) df = pd.DataFrame.from_records(all_data_json) df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) df = df[cols].round(decimals=2) # set column rank to pd.DataFrame df df['rank'] = df[AutoEvalColumn.average.name].rank(ascending=False, method="min") # df.insert(0, "rank", df[AutoEvalColumn.average.name].rank(ascending=False, method="min"), True) # filter out if any of the benchmarks have not been produced #df2 = 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]: 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]