src/populate.py

import json
import os

import pandas as pd
from loguru import logger

from display.formatting import make_clickable_model
from display.utils_old import EvalQueueColumn


def fetch_model_results(repo_dir: str, competition_type: str, eval_split: str) -> list[dict]:
    model_results = []
    dirpath = os.path.join(repo_dir, competition_type, eval_split)
    for root, _, files in os.walk(dirpath):
        if len(files) == 0 or not all(f.endswith(".json") for f in files):
            continue
        for file in files:
            filepath = os.path.join(root, file)
            try:
                with open(filepath, "r") as fp:
                    result = json.load(fp)
                model_results.append(result)
            except Exception as e:
                logger.error(f"Error loading model result from {filepath}: {e}")
                continue

    return model_results


def get_tossups_leaderboard_df(repo_dir: str, eval_split: str) -> pd.DataFrame:
    model_results = fetch_model_results(repo_dir, "tossup", eval_split)

    eval_results = []
    for result in model_results:
        try:
            metrics = result["metrics"]
            username = result["username"]
            model_name = result["model_name"]
            buzz_accuracy = metrics["buzz_accuracy"]

            row = {
                "Submission": f"{username}/{model_name}",
                "Avg Score ⬆️": metrics["tossup_score"],
                "Buzz Accuracy": buzz_accuracy,
                "Buzz Position": metrics["buzz_position"],
            }
            if "human_win_rate" in metrics:
                row["Win Rate w/ Humans"] = metrics["human_win_rate"]
                row["Win Rate w/ Humans (Aggressive)"] = metrics["human_win_rate_strict"]
            else:
                row["Win Rate w/ Humans"] = None
                row["Win Rate w/ Humans (Aggressive)"] = None
            eval_results.append(row)
        except Exception as e:
            logger.error(f"Error processing model result '{username}/{model_name}': {e}")
            continue

    df = pd.DataFrame(
        eval_results,
        columns=[
            "Submission",
            "Avg Score ⬆️",
            "Buzz Accuracy",
            "Buzz Position",
            "Win Rate w/ Humans",
            "Win Rate w/ Humans (Aggressive)",
        ],
    )
    df.sort_values(by="Avg Score ⬆️", ascending=False, inplace=True)
    return df


def get_bonuses_leaderboard_df(repo_dir: str, eval_split: str) -> pd.DataFrame:
    model_results = fetch_model_results(repo_dir, "bonus", eval_split)

    eval_results = []
    for result in model_results:
        try:
            metrics = result["metrics"]
            username = result["username"]
            model_name = result["model_name"]

            row = {
                "Submission": f"{username}/{model_name}",
                "Question Accuracy": metrics["question_accuracy"],
                "Part Accuracy": metrics["part_accuracy"],
            }
            eval_results.append(row)
        except Exception as e:
            logger.error(f"Error processing model result '{username}/{model_name}': {e}")
            continue

    df = pd.DataFrame(
        eval_results,
        columns=["Submission", "Question Accuracy", "Part Accuracy"],
    )
    df.sort_values(by="Question Accuracy", ascending=False, inplace=True)
    return df


def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
    # TODO: This function is stale, but might be a good reference point for new implementation
    """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 os.path.isfile(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)

                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]