import asyncio import gradio as gr import numpy as np import pandas as pd from huggingface_hub import HfFileSystem from src.constants import RESULTS_DATASET_ID, TASKS from src.hub import load_file def fetch_result_paths(): fs = HfFileSystem() paths = fs.glob(f"{RESULTS_DATASET_ID}/**/**/*.json") return paths def sort_result_paths_per_model(paths): from collections import defaultdict d = defaultdict(list) for path in paths: model_id, _ = path[len(RESULTS_DATASET_ID) + 1:].rsplit("/", 1) d[model_id].append(path) return {model_id: sorted(paths) for model_id, paths in d.items()} def update_load_results_component(): return (gr.Button("Load", interactive=True), ) * 2 async def load_results_dataframe(model_id, result_paths_per_model=None): if not model_id or not result_paths_per_model: return result_paths = result_paths_per_model[model_id] results = await asyncio.gather(*[load_file(path) for path in result_paths]) data = {"results": {}, "configs": {}} for result in results: data["results"].update(result["results"]) data["configs"].update(result["configs"]) model_name = result.get("model_name", "Model") df = pd.json_normalize([data]) # df.columns = df.columns.str.split(".") # .split return a list instead of a tuple return df.set_index(pd.Index([model_name])).reset_index() async def load_results_dataframes(*model_ids, result_paths_per_model=None): result = await asyncio.gather(*[load_results_dataframe(model_id, result_paths_per_model) for model_id in model_ids]) return result def display_results(task, *dfs): dfs = [df.set_index("index") for df in dfs if "index" in df.columns] if not dfs: return None, None df = pd.concat(dfs) df = df.T.rename_axis(columns=None) return display_tab("results", df, task), display_tab("configs", df, task) def display_tab(tab, df, task): df = df.style.format(na_rep="") df.hide( [ row for row in df.index if ( not row.startswith(f"{tab}.") or row.startswith(f"{tab}.leaderboard.") or row.endswith(".alias") or (not row.startswith(f"{tab}.{task}") if task != "All" else row.startswith(f"{tab}.leaderboard_arc_challenge")) ) ], axis="index", ) df.apply(highlight_min_max, axis=1) start = len(f"{tab}.leaderboard_") if task == "All" else len(f"{tab}.{task} ") df.format_index(lambda idx: idx[start:].removesuffix(",none"), axis="index") return df.to_html() def update_tasks_component(): return ( gr.Radio( ["All"] + list(TASKS.values()), label="Tasks", info="Evaluation tasks to be displayed", value="All", visible=True, ), ) * 2 def clear_results(): # model_id_1, model_id_2, dataframe_1, dataframe_2, load_results_btn, load_configs_btn, results_task, configs_task return ( None, None, None, None, *(gr.Button("Load", interactive=False), ) * 2, *( gr.Radio( ["All"] + list(TASKS.values()), label="Tasks", info="Evaluation tasks to be displayed", value="All", visible=False, ), ) * 2, ) def highlight_min_max(s): if s.name.endswith("acc,none") or s.name.endswith("acc_norm,none") or s.name.endswith("exact_match,none"): return np.where(s == np.nanmax(s.values), "background-color:green", "background-color:#D81B60") else: return [""] * len(s)