comparator / app.py
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Make display_details robust
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import io
import json
import gradio as gr
import pandas as pd
from huggingface_hub import HfFileSystem
RESULTS_DATASET_ID = "datasets/open-llm-leaderboard/results"
EXCLUDED_KEYS = {
"pretty_env_info",
"chat_template",
"group_subtasks",
}
# EXCLUDED_RESULTS_KEYS = {
# "leaderboard",
# }
# EXCLUDED_RESULTS_LEADERBOARDS_KEYS = {
# "alias",
# }
DETAILS_DATASET_ID = "datasets/open-llm-leaderboard/{model_name_sanitized}-details"
DETAILS_FILENAME = "samples_{subtask}_*.json"
TASKS = {
"leaderboard_arc_challenge": ("ARC", "leaderboard_arc_challenge"),
"leaderboard_bbh": ("BBH", "leaderboard_bbh"),
"leaderboard_gpqa": ("GPQA", "leaderboard_gpqa"),
"leaderboard_ifeval": ("IFEval", "leaderboard_ifeval"),
"leaderboard_math_hard": ("MATH", "leaderboard_math"),
"leaderboard_mmlu_pro": ("MMLU-Pro", "leaderboard_mmlu_pro"),
"leaderboard_musr": ("MuSR", "leaderboard_musr"),
}
SUBTASKS = {
"leaderboard_arc_challenge": ["leaderboard_arc_challenge"],
"leaderboard_bbh": [
"leaderboard_bbh_boolean_expressions",
"leaderboard_bbh_causal_judgement",
"leaderboard_bbh_date_understanding",
"leaderboard_bbh_disambiguation_qa",
"leaderboard_bbh_formal_fallacies",
"leaderboard_bbh_geometric_shapes",
"leaderboard_bbh_hyperbaton",
"leaderboard_bbh_logical_deduction_five_objects",
"leaderboard_bbh_logical_deduction_seven_objects",
"leaderboard_bbh_logical_deduction_three_objects",
"leaderboard_bbh_movie_recommendation",
"leaderboard_bbh_navigate",
"leaderboard_bbh_object_counting",
"leaderboard_bbh_penguins_in_a_table",
"leaderboard_bbh_reasoning_about_colored_objects",
"leaderboard_bbh_ruin_names",
"leaderboard_bbh_salient_translation_error_detection",
"leaderboard_bbh_snarks", "leaderboard_bbh_sports_understanding",
"leaderboard_bbh_temporal_sequences",
"leaderboard_bbh_tracking_shuffled_objects_five_objects",
"leaderboard_bbh_tracking_shuffled_objects_seven_objects",
"leaderboard_bbh_tracking_shuffled_objects_three_objects",
"leaderboard_bbh_web_of_lies",
],
"leaderboard_gpqa": [
"leaderboard_gpqa_extended",
"leaderboard_gpqa_diamond",
"leaderboard_gpqa_main",
],
"leaderboard_ifeval": ["leaderboard_ifeval"],
# "leaderboard_math_hard": [
"leaderboard_math": [
"leaderboard_math_algebra_hard",
"leaderboard_math_counting_and_prob_hard",
"leaderboard_math_geometry_hard",
"leaderboard_math_intermediate_algebra_hard",
"leaderboard_math_num_theory_hard",
"leaderboard_math_prealgebra_hard",
"leaderboard_math_precalculus_hard",
],
"leaderboard_mmlu_pro": ["leaderboard_mmlu_pro"],
"leaderboard_musr": [
"leaderboard_musr_murder_mysteries",
"leaderboard_musr_object_placements",
"leaderboard_musr_team_allocation",
],
}
fs = HfFileSystem()
def fetch_result_paths():
paths = fs.glob(f"{RESULTS_DATASET_ID}/**/**/*.json")
return paths
def filter_latest_result_path_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: max(paths) for model_id, paths in d.items()}
def get_result_path_from_model(model_id, result_path_per_model):
return result_path_per_model[model_id]
def update_load_results_component():
return gr.Button("Load Results", interactive=True)
def load_data(result_path) -> pd.DataFrame:
with fs.open(result_path, "r") as f:
data = json.load(f)
return data
def load_results_dataframe(model_id):
if not model_id:
return
result_path = get_result_path_from_model(model_id, latest_result_path_per_model)
data = load_data(result_path)
model_name = data.get("model_name", "Model")
df = pd.json_normalize([{key: value for key, value in data.items() if key not in EXCLUDED_KEYS}])
# df.columns = df.columns.str.split(".") # .split return a list instead of a tuple
return df.set_index(pd.Index([model_name])).reset_index()
def load_results_dataframes(*model_ids):
return [load_results_dataframe(model_id) for model_id in model_ids]
def display_results(df_1, df_2, task):
df = pd.concat([df.set_index("index") for df in [df_1, df_2] if "index" in df.columns])
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 False)
)
],
axis="index",
)
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",
interactive=True,
)
def update_subtasks_component(task):
return gr.Radio(
SUBTASKS.get(task),
info="Evaluation subtasks to be displayed",
value=None,
)
def update_load_details_component(model_id_1, model_id_2, subtask):
if (model_id_1 or model_id_2) and subtask:
return gr.Button("Load Details", interactive=True)
else:
return gr.Button("Load Details", interactive=False)
def load_details_dataframe(model_id, subtask):
if not model_id or not subtask:
return
model_name_sanitized = model_id.replace("/", "__")
paths = fs.glob(
f"{DETAILS_DATASET_ID}/**/{DETAILS_FILENAME}".format(
model_name_sanitized=model_name_sanitized, subtask=subtask
)
)
if not paths:
return
path = max(paths)
with fs.open(path, "r") as f:
data = [json.loads(line) for line in f]
df = pd.json_normalize(data)
# df = df.rename_axis("Parameters", axis="columns")
df["model_name"] = model_id # Keep model_name
return df
# return df.set_index(pd.Index([model_id])).reset_index()
def load_details_dataframes(subtask, *model_ids):
return [load_details_dataframe(model_id, subtask) for model_id in model_ids]
def display_details(sample_idx, *dfs):
rows = [df.iloc[sample_idx] for df in dfs if "model_name" in df.columns and sample_idx < len(df)]
if not rows:
return
# Pop model_name and add it to the column name
df = pd.concat([row.rename(row.pop("model_name")) for row in rows], axis="columns")
return (
df.style
.format(na_rep="")
# .hide(axis="index")
.to_html()
)
# if __name__ == "__main__":
latest_result_path_per_model = filter_latest_result_path_per_model(fetch_result_paths())
with gr.Blocks(fill_height=True) as demo:
gr.HTML("<h1 style='text-align: center;'>Compare Results of the 🤗 Open LLM Leaderboard</h1>")
gr.HTML("<h3 style='text-align: center;'>Select 2 models to load and compare their results</h3>")
with gr.Row():
with gr.Column():
model_id_1 = gr.Dropdown(choices=list(latest_result_path_per_model.keys()), label="Models")
dataframe_1 = gr.Dataframe(visible=False)
with gr.Column():
model_id_2 = gr.Dropdown(choices=list(latest_result_path_per_model.keys()), label="Models")
dataframe_2 = gr.Dataframe(visible=False)
with gr.Row():
# with gr.Tab("All"):
# pass
with gr.Tab("Results"):
task = gr.Radio(
["All"] + list(TASKS.values()),
label="Tasks",
info="Evaluation tasks to be displayed",
value="All",
interactive=False,
)
load_results_btn = gr.Button("Load Results", interactive=False)
with gr.Tab("Results"):
results = gr.HTML()
with gr.Tab("Configs"):
configs = gr.HTML()
with gr.Tab("Details"):
details_task = gr.Radio(
["All"] + list(TASKS.values()),
label="Tasks",
info="Evaluation tasks to be displayed",
value="All",
interactive=True,
)
subtask = gr.Radio(
SUBTASKS.get(details_task.value),
label="Subtasks",
info="Evaluation subtasks to be displayed (choose one of the Tasks above)",
)
sample_idx = gr.Number(value=0, label="Sample Index", info="Index of the sample to be displayed", minimum=0)
load_details_btn = gr.Button("Load Details", interactive=False)
details = gr.HTML()
details_dataframe_1 = gr.Dataframe(visible=False)
details_dataframe_2 = gr.Dataframe(visible=False)
details_dataframe = gr.DataFrame(visible=False)
model_id_1.change(
fn=update_load_results_component,
outputs=load_results_btn,
)
load_results_btn.click(
fn=load_results_dataframes,
inputs=[model_id_1, model_id_2],
outputs=[dataframe_1, dataframe_2],
).then(
fn=display_results,
inputs=[dataframe_1, dataframe_2, task],
outputs=[results, configs],
).then(
fn=update_tasks_component,
outputs=task,
)
task.change(
fn=display_results,
inputs=[dataframe_1, dataframe_2, task],
outputs=[results, configs],
)
details_task.change(
fn=update_subtasks_component,
inputs=details_task,
outputs=subtask,
)
gr.on(
triggers=[model_id_1.change, model_id_2.change, subtask.change, details_task.change],
fn=update_load_details_component,
inputs=[model_id_1, model_id_2, subtask],
outputs=load_details_btn,
)
load_details_btn.click(
fn=load_details_dataframes,
inputs=[subtask, model_id_1, model_id_2],
outputs=[details_dataframe_1, details_dataframe_2],
).then(
fn=display_details,
inputs=[sample_idx, details_dataframe_1, details_dataframe_2],
outputs=details,
)
sample_idx.change(
fn=display_details,
inputs=[sample_idx, details_dataframe_1, details_dataframe_2],
outputs=details,
)
demo.launch()