src/populate.py

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:
    """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)
    
    # Ensure all required columns exist before filtering
    for col in benchmark_cols:
        if col not in df.columns:
            df[col] = None
            
    # Filter out if any of the benchmarks have not been produced
    df = df[has_no_nan_values(df, benchmark_cols)]
    df = df.sort_values(by="Security Score ⬆️", ascending=False)
    df = df[cols].round(decimals=2)

    return df


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)

            # Create a new dict with the required column names
            formatted_data = {
                "model": make_clickable_model(data["model"]),
                "revision": data.get("revision", "main"),
                "private": data.get("private", False),
                "precision": data.get("precision", ""),
                "weight_type": data.get("weight_type", ""),
                "status": data.get("status", "")
            }

            all_evals.append(formatted_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)

                # Create a new dict with the required column names
                formatted_data = {
                    "model": make_clickable_model(data["model"]),
                    "revision": data.get("revision", "main"),
                    "private": data.get("private", False),
                    "precision": data.get("precision", ""),
                    "weight_type": data.get("weight_type", ""),
                    "status": data.get("status", "")
                }

                all_evals.append(formatted_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]