src/populate.py

import json
import os
import pandas as pd
from datetime import datetime, timedelta
import dateutil

from src.display.formatting import has_no_nan_values, make_clickable_model
from src.display.utils import AutoEvalColumn, EvalQueueColumn, ModelType, Tasks, Precision, WeightType
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)
    # print(raw_data)
    all_data_json = [v.to_dict() for v in raw_data]
    df = pd.DataFrame.from_records(all_data_json)
    # print(df)
    if df.empty:
        print("No evaluation results found. Returning empty DataFrame with correct columns.")
        return pd.DataFrame(columns=cols)
    df = df.sort_values(by=[AutoEvalColumn().average.name], ascending=False)
    df = df[cols].round(decimals=4)
    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"""
    all_evals = []
    
    # Define a threshold to identify "stuck" jobs
    time_threshold = datetime.now() - timedelta(hours=1)

    # Use os.walk for a robust way to find all files recursively
    for root, _, files in os.walk(save_path):
        for filename in files:
            if filename.endswith(".json"):
                file_path = os.path.join(root, filename)
                try:
                    with open(file_path, "r") as fp:
                        data = json.load(fp)
                    
                    # Check for "stuck" jobs
                    if data.get("status") == "RUNNING":
                        submitted_time_str = data.get("submitted_at")
                        if submitted_time_str:
                            submitted_time = dateutil.parser.isoparse(submitted_time_str)
                            if submitted_time < time_threshold:
                                print(f"Stuck job detected for {data['model']}. Changing status to PENDING.")
                                data["status"] = "PENDING"
                    
                    data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
                    data[EvalQueueColumn.revision.name] = data.get("revision", "main")
                    all_evals.append(data)

                except Exception as e:
                    print(f"Error processing file {file_path}: {e}")
                    continue

    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) if pending_list else pd.DataFrame(columns=cols)
    df_running = pd.DataFrame.from_records(running_list, columns=cols) if running_list else pd.DataFrame(columns=cols)
    df_finished = pd.DataFrame.from_records(finished_list, columns=cols) if finished_list else pd.DataFrame(columns=cols)

    return df_finished[cols], df_running[cols], df_pending[cols]