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import os
import logging
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from gradio_space_ci import enable_space_ci
from src.display.about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
FAQ_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
BENCHMARK_COLS,
COLS,
EVAL_COLS,
EVAL_TYPES,
NUMERIC_INTERVALS,
TYPES,
AutoEvalColumn,
ModelType,
Precision,
WeightType,
fields,
)
from src.envs import (
API,
DYNAMIC_INFO_FILE_PATH,
DYNAMIC_INFO_PATH,
DYNAMIC_INFO_REPO,
EVAL_REQUESTS_PATH,
EVAL_RESULTS_PATH,
H4_TOKEN,
IS_PUBLIC,
QUEUE_REPO,
REPO_ID,
RESULTS_REPO,
)
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.scripts.update_all_request_files import update_dynamic_files
from src.submission.submit import add_new_eval
from src.tools.collections import update_collections
from src.tools.plots import create_metric_plot_obj, create_plot_df, create_scores_df
# Start ephemeral Spaces on PRs (see config in README.md)
enable_space_ci()
def restart_space():
API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
def download_dataset(repo_id, local_dir, repo_type="dataset", max_attempts=3):
"""Attempt to download dataset with retries."""
attempt = 0
while attempt < max_attempts:
try:
print(f"Downloading {repo_id} to {local_dir}")
snapshot_download(
repo_id=repo_id,
local_dir=local_dir,
repo_type=repo_type,
tqdm_class=None,
etag_timeout=30,
max_workers=8,
)
return
except Exception as e:
logging.error(f"Error downloading {repo_id}: {e}")
attempt += 1
if attempt == max_attempts:
restart_space()
def init_space(full_init: bool = True):
"""Initializes the application space, loading only necessary data."""
if full_init:
download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH)
download_dataset(DYNAMIC_INFO_REPO, DYNAMIC_INFO_PATH)
download_dataset(RESULTS_REPO, EVAL_RESULTS_PATH)
raw_data, original_df = get_leaderboard_df(
results_path=EVAL_RESULTS_PATH,
requests_path=EVAL_REQUESTS_PATH,
dynamic_path=DYNAMIC_INFO_FILE_PATH,
cols=COLS,
benchmark_cols=BENCHMARK_COLS,
)
update_collections(original_df)
leaderboard_df = original_df.copy()
eval_queue_dfs = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
return leaderboard_df, raw_data, original_df, eval_queue_dfs
# Convert the environment variable "LEADERBOARD_FULL_INIT" to a boolean value, defaulting to True if the variable is not set.
# This controls whether a full initialization should be performed.
do_full_init = os.getenv("LEADERBOARD_FULL_INIT", "True") == "True"
# Calls the init_space function with the `full_init` parameter determined by the `do_full_init` variable.
# This initializes various DataFrames used throughout the application, with the level of initialization detail controlled by the `do_full_init` flag.
leaderboard_df, raw_data, original_df, eval_queue_dfs = init_space(full_init=do_full_init)
finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = eval_queue_dfs
# Data processing for plots now only on demand in the respective Gradio tab
def load_and_create_plots():
plot_df = create_plot_df(create_scores_df(raw_data))
return plot_df
# Searching and filtering
def update_table(
hidden_df: pd.DataFrame,
columns: list,
type_query: list,
precision_query: str,
size_query: list,
hide_models: list,
query: str,
):
filtered_df = filter_models(
df=hidden_df,
type_query=type_query,
size_query=size_query,
precision_query=precision_query,
hide_models=hide_models,
)
filtered_df = filter_queries(query, filtered_df)
df = select_columns(filtered_df, columns)
return df
def load_query(request: gr.Request): # triggered only once at startup => read query parameter if it exists
query = request.query_params.get("query") or ""
return (
query,
query,
) # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed
def search_model(df: pd.DataFrame, query: str) -> pd.DataFrame:
return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False, na=False))]
def search_license(df: pd.DataFrame, query: str) -> pd.DataFrame:
return df[df[AutoEvalColumn.license.name].str.contains(query, case=False, na=False)]
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
dummy_col = [AutoEvalColumn.dummy.name]
# AutoEvalColumn.model_type_symbol.name,
# AutoEvalColumn.model.name,
# We use COLS to maintain sorting
filtered_df = df[always_here_cols + [c for c in COLS if c in df.columns and c in columns] + dummy_col]
return filtered_df
def filter_queries(query: str, df: pd.DataFrame):
tmp_result_df = []
# Empty query return the same df
if query == "":
return df
# all_queries = [q.strip() for q in query.split(";")]
# license_queries = []
all_queries = [q.strip() for q in query.split(";") if q.strip() != ""]
model_queries = [q for q in all_queries if not q.startswith("licence")]
license_queries_raw = [q for q in all_queries if q.startswith("license")]
license_queries = [
q.replace("license:", "").strip() for q in license_queries_raw if q.replace("license:", "").strip() != ""
]
# Handling model name search
for query in model_queries:
tmp_df = search_model(df, query)
if len(tmp_df) > 0:
tmp_result_df.append(tmp_df)
if not tmp_result_df and not license_queries:
# Nothing is found, no license_queries -> return empty df
return pd.DataFrame(columns=df.columns)
if tmp_result_df:
df = pd.concat(tmp_result_df)
df = df.drop_duplicates(
subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
)
if not license_queries:
return df
# Handling license search
tmp_result_df = []
for query in license_queries:
tmp_df = search_license(df, query)
if len(tmp_df) > 0:
tmp_result_df.append(tmp_df)
if not tmp_result_df:
# Nothing is found, return empty df
return pd.DataFrame(columns=df.columns)
df = pd.concat(tmp_result_df)
df = df.drop_duplicates(
subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
)
return df
def filter_models(
df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, hide_models: list
) -> pd.DataFrame:
# Show all models
if "Private or deleted" in hide_models:
filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
else:
filtered_df = df
if "Contains a merge/moerge" in hide_models:
filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False]
if "MoE" in hide_models:
filtered_df = filtered_df[filtered_df[AutoEvalColumn.moe.name] == False]
if "Flagged" in hide_models:
filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False]
type_emoji = [t[0] for t in type_query]
filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
filtered_df = filtered_df.loc[mask]
return filtered_df
leaderboard_df = filter_models(
df=leaderboard_df,
type_query=[t.to_str(" : ") for t in ModelType],
size_query=list(NUMERIC_INTERVALS.keys()),
precision_query=[i.value.name for i in Precision],
hide_models=["Private or deleted", "Contains a merge/moerge", "Flagged"], # Deleted, merges, flagged, MoEs
)
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("πŸ… LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder="πŸ” Search models or licenses (e.g., 'model_name; license: MIT') and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[
c.name
for c in fields(AutoEvalColumn)
if not c.hidden and not c.never_hidden and not c.dummy
],
value=[
c.name
for c in fields(AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
with gr.Row():
hide_models = gr.CheckboxGroup(
label="Hide models",
choices=["Private or deleted", "Contains a merge/moerge", "Flagged", "MoE"],
value=["Private or deleted", "Contains a merge/moerge", "Flagged"],
interactive=True,
)
with gr.Column(min_width=320):
# with gr.Box(elem_id="box-filter"):
filter_columns_type = gr.CheckboxGroup(
label="Model types",
choices=[t.to_str() for t in ModelType],
value=[t.to_str() for t in ModelType],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_precision = gr.CheckboxGroup(
label="Precision",
choices=[i.value.name for i in Precision],
value=[i.value.name for i in Precision],
interactive=True,
elem_id="filter-columns-precision",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df[
[c.name for c in fields(AutoEvalColumn) if c.never_hidden]
+ shown_columns.value
+ [AutoEvalColumn.dummy.name]
],
headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
datatype=TYPES,
elem_id="leaderboard-table",
interactive=False,
visible=True,
# column_widths=["2%", "33%"]
)
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.components.Dataframe(
value=original_df[COLS],
headers=COLS,
datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
hide_models,
search_bar,
],
leaderboard_table,
)
# Define a hidden component that will trigger a reload only if a query parameter has been set
hidden_search_bar = gr.Textbox(value="", visible=False)
hidden_search_bar.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
hide_models,
search_bar,
],
leaderboard_table,
)
# Check query parameter once at startup and update search bar + hidden component
demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar])
for selector in [
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
hide_models,
]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
hide_models,
search_bar,
],
leaderboard_table,
queue=True,
)
with gr.TabItem("πŸ“ˆ Metrics through time", elem_id="llm-benchmark-tab-table", id=2):
with gr.Row():
with gr.Column():
plot_df = load_and_create_plots()
chart = create_metric_plot_obj(
plot_df,
[AutoEvalColumn.average.name],
title="Average of Top Scores and Human Baseline Over Time (from last update)",
)
gr.Plot(value=chart, min_width=500)
with gr.Column():
plot_df = load_and_create_plots()
chart = create_metric_plot_obj(
plot_df,
BENCHMARK_COLS,
title="Top Scores and Human Baseline Over Time (from last update)",
)
gr.Plot(value=chart, min_width=500)
with gr.TabItem("πŸ“ About", elem_id="llm-benchmark-tab-table", id=3):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("❗FAQ", elem_id="llm-benchmark-tab-table", id=4):
gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")
with gr.TabItem("πŸš€ Submit ", elem_id="llm-benchmark-tab-table", id=5):
with gr.Column():
with gr.Row():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Row():
gr.Markdown("# βœ‰οΈβœ¨ Submit your model here!", elem_classes="markdown-text")
with gr.Row():
with gr.Column():
model_name_textbox = gr.Textbox(label="Model name")
revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
model_type = gr.Dropdown(
choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
label="Model type",
multiselect=False,
value=ModelType.FT.to_str(" : "),
interactive=True,
)
with gr.Column():
precision = gr.Dropdown(
choices=[i.value.name for i in Precision if i != Precision.Unknown],
label="Precision",
multiselect=False,
value="float16",
interactive=True,
)
weight_type = gr.Dropdown(
choices=[i.value.name for i in WeightType],
label="Weights type",
multiselect=False,
value="Original",
interactive=True,
)
base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
with gr.Column():
with gr.Accordion(
f"βœ… Finished Evaluations ({len(finished_eval_queue_df)})",
open=False,
):
with gr.Row():
finished_eval_table = gr.components.Dataframe(
value=finished_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Accordion(
f"πŸ”„ Running Evaluation Queue ({len(running_eval_queue_df)})",
open=False,
):
with gr.Row():
running_eval_table = gr.components.Dataframe(
value=running_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Accordion(
f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
open=False,
):
with gr.Row():
pending_eval_table = gr.components.Dataframe(
value=pending_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
submit_button.click(
add_new_eval,
[
model_name_textbox,
base_model_name_textbox,
revision_name_textbox,
precision,
private,
weight_type,
model_type,
],
submission_result,
)
with gr.Row():
with gr.Accordion("πŸ“™ Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
lines=20,
elem_id="citation-button",
show_copy_button=True,
)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", hours=3) # restarted every 3h
scheduler.add_job(update_dynamic_files, "interval", hours=2) # launched every 2 hour
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()