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import os
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import HfApi
from src.about import CITATION_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE
from src.populate import (
MU_MATH_COLUMNS_DICT,
U_MATH_COLUMNS_DICT,
U_MATH_AND_MU_MATH_COLUMNS_DICT,
Field,
get_mu_math_leaderboard_df,
get_u_math_leaderboard_df,
get_joined_leaderboard_df,
)
def restart_space():
TOKEN = os.environ.get("HF_TOKEN") # A read/write token for your org
API = HfApi(token=TOKEN)
REPO_ID = "toloka/u-math-leaderboard"
API.restart_space(repo_id=REPO_ID)
LEADERBOARD_U_MATH_DF = get_u_math_leaderboard_df()
LEADERBOARD_MU_MATH_DF = get_mu_math_leaderboard_df()
LEADERBOARD_U_MATH_MU_MATH_JOINED_DF = get_joined_leaderboard_df()
def init_leaderboard(dataframe: pd.DataFrame, columns_dict: dict[str, Field]) -> gr.components.Component:
if dataframe is None or dataframe.empty:
raise ValueError("Leaderboard DataFrame is empty or None.")
def filter_dataframe_by_selected_columns(full_df: pd.DataFrame, columns: list[str]) -> pd.DataFrame:
always_here_cols = [c.pretty_name for c in columns_dict.values() if c.never_hidden]
selected_columns = [c for c in columns if c in full_df.columns and c not in always_here_cols]
# keep the order of the columns
filtered_df = full_df[[c for c in full_df.columns if c in (always_here_cols + selected_columns)]]
return filtered_df
def filter_dataframe_by_selected_tag_columns(
full_df: pd.DataFrame, current_tag: str
) -> tuple[pd.DataFrame, list[str], str, str]:
always_here_cols = [c.pretty_name for c in columns_dict.values() if c.never_hidden]
selected_columns = [
c.pretty_name for c in columns_dict.values() if current_tag in c.tags and c.pretty_name not in always_here_cols
]
# keep the order of the columns
filtered_df = full_df[[c for c in full_df.columns if c in (always_here_cols + selected_columns)]]
_columns_to_select_visibility = [
c.pretty_name for c in columns_dict.values() if not c.fully_hidden and not c.never_hidden
]
return filtered_df, [c for c in _columns_to_select_visibility if c in filtered_df.columns], "All", "All"
def filter_dataframe_by_search(full_df: pd.DataFrame, current_df: pd.DataFrame, search: str) -> pd.DataFrame:
filtered_df = full_df[
(full_df[columns_dict["model_name"].pretty_name].str.contains(search, case=False, na=False))
]
return filtered_df[current_df.columns]
def filter_dataframe_by_model_type(
full_df: pd.DataFrame, current_df: pd.DataFrame, filter_name: str
) -> pd.DataFrame:
if filter_name == "All":
return full_df[current_df.columns]
else:
query_symbol = filter_name[0]
filtered_df = full_df[full_df[columns_dict["model_type_symbol"].pretty_name] == query_symbol]
return filtered_df[current_df.columns]
def filter_dataframe_by_model_size(
full_df: pd.DataFrame, current_df: pd.DataFrame, filter_name: str
) -> pd.DataFrame:
if filter_name == "All":
return full_df[current_df.columns]
else:
query_symbol = filter_name[0]
filtered_df = full_df[full_df[columns_dict["model_size_symbol"].pretty_name] == query_symbol]
return filtered_df[current_df.columns]
def filter_dataframe_by_model_family(
full_df: pd.DataFrame, current_df: pd.DataFrame, filter_name: str,
) -> pd.DataFrame:
if filter_name == "All":
return full_df[current_df.columns]
else:
filtered_df = full_df[full_df[columns_dict["model_family"].pretty_name] == filter_name]
return filtered_df[current_df.columns]
with gr.Column(scale=1) as col:
with gr.Row():
with gr.Column(scale=8):
with gr.Accordion("β‘οΈ See All Columns", open=False):
columns_to_select_visibility = [
c.pretty_name for c in columns_dict.values() if not c.fully_hidden and not c.never_hidden
]
all_columns_selector = gr.CheckboxGroup(
choices=columns_to_select_visibility,
value=[
c.pretty_name
for c in columns_dict.values()
if c.pretty_name in columns_to_select_visibility and c.displayed_by_default
],
label="Select Columns to Display:",
interactive=True,
container=False,
)
with gr.Column(variant='panel'):
gr.Markdown("Visible Columns:", elem_id="visible-columns-label")
all_tags = {}
with gr.Row():
for c in columns_dict.values():
for tag in c.tags:
if tag not in all_tags:
all_tags[tag] = gr.Button(tag, interactive=True, size="sm", variant="secondary", min_width=50)
with gr.Column(scale=8):
with gr.Row():
search_bar = gr.Textbox(
placeholder="π Search for your model and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
model_type_filter_selector = gr.Dropdown(
label="Filter model types:",
choices=["All", "π Open-Weights", "π₯ Proprietary"],
value="All",
elem_id="model-type-filter",
interactive=True,
multiselect=False,
min_width=120,
)
model_size_filter_selector = gr.Dropdown(
label="Filter model sizes:",
choices=["All", "π΄ Tiny (<5B)", "π Small (5-50B)", "π Medium (50-100B)", "π Large (>100B)"],
value="All",
elem_id="model-size-filter",
interactive=True,
multiselect=False,
min_width=120,
)
model_family_filter_selector = gr.Dropdown(
label="Filter model families:",
choices=["All"] + list(dataframe[columns_dict["model_family"].pretty_name].unique()),
value="All",
elem_id="model-family-filter",
interactive=True,
multiselect=False,
min_width=120,
)
# create the hidden and visible dataframes to display
hidden_leaderboard_df = gr.components.Dataframe(
value=dataframe,
datatype=[c.gradio_column_type for c in columns_dict.values()],
visible=False,
interactive=False,
)
leaderboard_df = gr.components.Dataframe(
value=dataframe[[c.pretty_name for c in columns_dict.values() if c.displayed_by_default]],
datatype=[c.gradio_column_type for c in columns_dict.values()],
elem_id="leaderboard-df",
interactive=False,
)
# Add the callbacks
all_columns_selector.change(
fn=filter_dataframe_by_selected_columns,
inputs=[hidden_leaderboard_df, all_columns_selector],
outputs=[leaderboard_df],
)
search_bar.submit(
fn=filter_dataframe_by_search,
inputs=[hidden_leaderboard_df, leaderboard_df, search_bar],
outputs=[leaderboard_df],
)
model_type_filter_selector.change(
fn=filter_dataframe_by_model_type,
inputs=[hidden_leaderboard_df, leaderboard_df, model_type_filter_selector],
outputs=[leaderboard_df],
)
model_size_filter_selector.change(
fn=filter_dataframe_by_model_size,
inputs=[hidden_leaderboard_df, leaderboard_df, model_size_filter_selector],
outputs=[leaderboard_df],
)
model_family_filter_selector.change(
fn=filter_dataframe_by_model_family,
inputs=[hidden_leaderboard_df, leaderboard_df, model_family_filter_selector],
outputs=[leaderboard_df],
)
# Wire up each visible-column button to filter by tag
for tag, button in all_tags.items():
button.click(
fn=filter_dataframe_by_selected_tag_columns,
inputs=[hidden_leaderboard_df, button],
outputs=[leaderboard_df, all_columns_selector, model_type_filter_selector, model_size_filter_selector],
)
# On first load, show the default columns
filter_dataframe_by_selected_columns(dataframe, all_columns_selector.value)
return col
demo = gr.Blocks(css=".scatter-plot {height: 500px;}")
with demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
print(LEADERBOARD_U_MATH_DF)
print(LEADERBOARD_MU_MATH_DF)
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("π U-MATH", elem_id="u-math-benchmark-tab-table", id=0):
leaderboard_umath = init_leaderboard(LEADERBOARD_U_MATH_DF, U_MATH_COLUMNS_DICT)
# gr.ScatterPlot(
# value=LEADERBOARD_U_MATH_DF,
# title="U-MATH: Text vs Visual Accuracy",
# x=U_MATH_COLUMNS_DICT["u_math_text_acc"].pretty_name,
# y=U_MATH_COLUMNS_DICT["u_math_visual_acc"].pretty_name,
# color=U_MATH_COLUMNS_DICT["model_family"].pretty_name,
# tooltip=[U_MATH_COLUMNS_DICT["full_model_name"].pretty_name, U_MATH_COLUMNS_DICT["u_math_acc"].pretty_name],
# elem_classes="scatter-plot",
# height=500,
# )
with gr.TabItem("π
ΞΌ-MATH (Meta-Benchmark)", elem_id="mu-math-benchmark-tab-table", id=1):
leaderboard_mumath = init_leaderboard(LEADERBOARD_MU_MATH_DF, MU_MATH_COLUMNS_DICT)
# gr.ScatterPlot(
# value=LEADERBOARD_MU_MATH_DF,
# title="ΞΌ-MATH: True Positive Rate (Recall) vs True Negative Rate (Specificity)",
# x=MU_MATH_COLUMNS_DICT["mu_math_tpr"].pretty_name,
# y=MU_MATH_COLUMNS_DICT["mu_math_tnr"].pretty_name,
# color=MU_MATH_COLUMNS_DICT["model_family"].pretty_name,
# tooltip=[MU_MATH_COLUMNS_DICT["full_model_name"].pretty_name, MU_MATH_COLUMNS_DICT["mu_math_f1"].pretty_name],
# elem_classes="scatter-plot",
# height=500,
# )
with gr.TabItem("π U-MATH vs ΞΌ-MATH", elem_id="u-math-vs-mu-math-tab-table", id=2):
leaderboard_aggregated = init_leaderboard(LEADERBOARD_U_MATH_MU_MATH_JOINED_DF, U_MATH_AND_MU_MATH_COLUMNS_DICT)
# gr.ScatterPlot(
# value=LEADERBOARD_U_MATH_MU_MATH_JOINED_DF,
# title="U-MATH Accuracy (Solving) vs ΞΌ-MATH F1 Score (Judging)",
# x=U_MATH_AND_MU_MATH_COLUMNS_DICT["u_math_acc"].pretty_name,
# y=U_MATH_AND_MU_MATH_COLUMNS_DICT["mu_math_f1"].pretty_name,
# color=U_MATH_AND_MU_MATH_COLUMNS_DICT["model_family"].pretty_name,
# tooltip=[
# U_MATH_AND_MU_MATH_COLUMNS_DICT["full_model_name"].pretty_name,
# U_MATH_AND_MU_MATH_COLUMNS_DICT["u_math_text_acc"].pretty_name,
# U_MATH_AND_MU_MATH_COLUMNS_DICT["u_math_visual_acc"].pretty_name,
# ],
# elem_classes="scatter-plot",
# height=500,
# )
with gr.TabItem("π About", elem_id="about-tab-table", id=3):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
citation_button = gr.Textbox(
value=CITATION_TEXT,
label="π Citation",
lines=9,
elem_id="citation-button",
show_copy_button=True,
container=True,
)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=60 * 60)
scheduler.start()
# demo.queue(default_concurrency_limit=40).launch(ssr_mode=False)
demo.queue(default_concurrency_limit=40).launch(allowed_paths=[".cache"])
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