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.filter_models import filter_models
from src.leaderboard.read_evals import get_raw_eval_results, EvalResult

'''
This function, get_leaderboard_df, is designed to read and process evaluation results from a specified results path and requests path, 
ultimately producing a leaderboard in the form of a pandas DataFrame. The process involves several steps, including filtering, sorting, 
and cleaning the data based on specific criteria. Let's break down the function step by step:

'''

## TO-DO: if raw_data is [], return dummy df with correct columns so that the UI shows the right columns
def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> tuple[list[EvalResult], pd.DataFrame]:

    print(f"results_path = {results_path}")

    raw_data = get_raw_eval_results(results_path, requests_path)

    all_data_json = [v.to_dict() for v in raw_data] # if v.is_complete()]
    # all_data_json.append(baseline_row)
    filter_models(all_data_json)

    print(f"all_data_json  = {all_data_json}")

    df = pd.DataFrame.from_records(all_data_json)

    task_attributes = []

    # Iterate over all attributes of AutoEvalColumn class
    for attr_name in dir(AutoEvalColumn):
        # Retrieve the attribute object
        attr = getattr(AutoEvalColumn, attr_name)
        # Check if the attribute has 'is_task' attribute and it is True
        if hasattr(attr, 'is_task') and getattr(attr, 'is_task'):
            task_attributes.append(attr)

    # Now task_attributes contains all attributes where is_task=True
    # print(task_attributes)
    task_col_names_all = [str(item.name) for item in task_attributes]

    # import pdb; pdb.set_trace()

    # Add empty columns with specified names
    for col_name in task_col_names_all:
        if col_name not in df.columns:
            df[col_name] = None
            
    return raw_data, df


def get_evaluation_queue_df(save_path: str, cols: list) -> tuple[pd.DataFrame, pd.DataFrame, 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]