src/populate.py

# src/populate.py
import json
import os 
import pandas as pd

# 외부에서 정의된 함수를 import 해옵니다.
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: 
    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) 
    
    # "model" 컬럼에 make_clickable_model 적용
    # 반드시 원본 모델명이 보존되도록 합니다
    if "model" in df.columns:
        # 원본 모델명 임시 저장
        df["original_model_name"] = df["model"].copy()
        # 하이퍼링크 적용
        df["model"] = df["model"].apply(make_clickable_model)
    
    # 모든 벤치마크가 생산되지 않은 행을 필터링
    df = df[has_no_nan_values(df, benchmark_cols)]
    return df

def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
    """평가 대기열에 대한 각 DataFrame을 생성합니다."""
    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)
                # 원본 모델명 저장
                original_model = data.get("model", "")
                data[EvalQueueColumn.model.name] = make_clickable_model(original_model)
                data[EvalQueueColumn.revision.name] = data.get("revision", "main")
                all_evals.append(data)
        elif ".md" not in entry:
            # 폴더인 경우: 파일 여부를 확인할 때 전체 경로를 사용
            sub_entries = [
                e for e in os.listdir(os.path.join(save_path, entry)) 
                if os.path.isfile(os.path.join(save_path, entry, e)) and 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)
                    original_model = data.get("model", "")
                    data[EvalQueueColumn.model.name] = make_clickable_model(original_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]