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import os
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from src.about import (
BOTTOM_LOGO,
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_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,
AddSpecialTokens,
AutoEvalColumn,
ModelType,
NumFewShots,
Precision,
WeightType,
fields,
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
def restart_space():
API.restart_space(repo_id=REPO_ID)
# Space initialization
try:
print(EVAL_REQUESTS_PATH)
snapshot_download(
repo_id=QUEUE_REPO,
local_dir=EVAL_REQUESTS_PATH,
repo_type="dataset",
tqdm_class=None,
etag_timeout=30,
)
except Exception:
restart_space()
try:
print(EVAL_RESULTS_PATH)
snapshot_download(
repo_id=RESULTS_REPO,
local_dir=EVAL_RESULTS_PATH,
repo_type="dataset",
tqdm_class=None,
etag_timeout=30,
)
except Exception:
restart_space()
# Searching and filtering
def filter_models(
df: pd.DataFrame,
type_query: list,
size_query: list,
precision_query: list,
add_special_tokens_query: list,
num_few_shots_query: list,
show_deleted: bool,
show_merges: bool,
show_flagged: bool,
) -> pd.DataFrame:
print(f"Initial df shape: {df.shape}")
print(f"Initial df content:\n{df}")
filtered_df = df
# Model Type フィルタリング
type_column = "T" if "T" in df.columns else "Type_"
type_emoji = [t.split()[0] for t in type_query]
filtered_df = df[df[type_column].isin(type_emoji)]
print(f"After type filter: {filtered_df.shape}")
# Precision フィルタリング
filtered_df = filtered_df[filtered_df["Precision"].isin(precision_query + ["Unknown", "?"])]
print(f"After precision filter: {filtered_df.shape}")
# Model Size フィルタリング
if "Unknown" in size_query:
size_mask = filtered_df["#Params (B)"].isna() | (filtered_df["#Params (B)"] == 0)
else:
size_mask = filtered_df["#Params (B)"].apply(
lambda x: any(x in NUMERIC_INTERVALS[s] for s in size_query if s != "Unknown")
)
filtered_df = filtered_df[size_mask]
print(f"After size filter: {filtered_df.shape}")
# Add Special Tokens フィルタリング
filtered_df = filtered_df[filtered_df["Add Special Tokens"].isin(add_special_tokens_query + ["Unknown", "?"])]
print(f"After add_special_tokens filter: {filtered_df.shape}")
# Num Few Shots フィルタリング
filtered_df = filtered_df[
filtered_df["Few-shot"].astype(str).isin([str(x) for x in num_few_shots_query] + ["Unknown", "?"])
]
print(f"After num_few_shots filter: {filtered_df.shape}")
# Show deleted models フィルタリング
if not show_deleted:
filtered_df = filtered_df[filtered_df["Available on the hub"]]
print(f"After show_deleted filter: {filtered_df.shape}")
print("Filtered dataframe head:")
print(filtered_df.head())
return filtered_df
def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
return df[df[AutoEvalColumn.dummy.name].str.contains(query, case=False)]
def filter_queries(query: str, filtered_df: pd.DataFrame):
"""Added by Abishek"""
final_df = []
if query != "":
queries = [q.strip() for q in query.split(";")]
for _q in queries:
_q = _q.strip()
if _q != "":
temp_filtered_df = search_table(filtered_df, _q)
if len(temp_filtered_df) > 0:
final_df.append(temp_filtered_df)
if len(final_df) > 0:
filtered_df = pd.concat(final_df)
filtered_df = filtered_df.drop_duplicates(
subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
)
return filtered_df
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
always_here_cols = [
AutoEvalColumn.model_type_symbol.name, # 'T'
AutoEvalColumn.model.name, # 'Model'
]
# 'always_here_cols' を 'columns' から除外して重複を避ける
columns = [c for c in columns if c not in always_here_cols]
new_columns = (
always_here_cols + [c for c in COLS if c in df.columns and c in columns] + [AutoEvalColumn.dummy.name]
)
# 重複を排除しつつ順序を維持
seen = set()
unique_columns = []
for c in new_columns:
if c not in seen:
unique_columns.append(c)
seen.add(c)
# 'Model' カラムにリンクを含む形式で再構築
if "Model" in df.columns:
df["Model"] = df["Model"].apply(
lambda x: (
f'[{x.split(">")[-2].split("<")[0]}]({x.split("href=")[1].split(chr(34))[1]})'
if isinstance(x, str) and "href=" in x
else x
)
)
# フィルタリングされたカラムでデータフレームを作成
filtered_df = df[unique_columns]
return filtered_df
def update_table(
hidden_df: pd.DataFrame,
columns: list,
type_query: list,
precision_query: str,
size_query: list,
add_special_tokens_query: list,
num_few_shots_query: list,
show_deleted: bool,
show_merges: bool,
show_flagged: bool,
query: str,
):
print(
f"Update table called with: type_query={type_query}, precision_query={precision_query}, size_query={size_query}"
)
print(f"hidden_df shape before filtering: {hidden_df.shape}")
filtered_df = filter_models(
hidden_df,
type_query,
size_query,
precision_query,
add_special_tokens_query,
num_few_shots_query,
show_deleted,
show_merges,
show_flagged,
)
print(f"filtered_df shape after filter_models: {filtered_df.shape}")
filtered_df = filter_queries(query, filtered_df)
print(f"filtered_df shape after filter_queries: {filtered_df.shape}")
print(
f"Filter applied: query={query}, columns={columns}, type_query={type_query}, precision_query={precision_query}"
)
print("Filtered dataframe head:")
print(filtered_df.head())
df = select_columns(filtered_df, columns)
print(f"Final df shape: {df.shape}")
print("Final dataframe head:")
print(df.head())
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
# Prepare the dataframes
original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
leaderboard_df = original_df.copy()
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
failed_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
leaderboard_df = filter_models(
leaderboard_df,
[t.to_str(" : ") for t in ModelType],
list(NUMERIC_INTERVALS.keys()),
[i.value.name for i in Precision],
[i.value.name for i in AddSpecialTokens],
[i.value.name for i in NumFewShots],
False,
False,
False,
)
leaderboard_df_filtered = filter_models(
leaderboard_df,
[t.to_str(" : ") for t in ModelType],
list(NUMERIC_INTERVALS.keys()),
[i.value.name for i in Precision],
[i.value.name for i in AddSpecialTokens],
[i.value.name for i in NumFewShots],
False,
False,
False,
)
# DataFrameの初期化部分のみを修正
initial_columns = ["T"] + [
c.name for c in fields(AutoEvalColumn) if (c.never_hidden or c.displayed_by_default) and c.name != "T"
]
leaderboard_df_filtered = select_columns(leaderboard_df, initial_columns)
# Model列のリンク形式を修正
leaderboard_df_filtered["Model"] = leaderboard_df_filtered["Model"].apply(
lambda x: (
f'[{x.split(">")[-2].split("<")[0]}]({x.split("href=")[1].split(chr(34))[1]})'
if isinstance(x, str) and "href=" in x
else x
)
)
# 数値データを文字列に変換
for col in leaderboard_df_filtered.columns:
if col not in ["T", "Model"]:
leaderboard_df_filtered[col] = leaderboard_df_filtered[col].astype(str)
# Leaderboard demo
with gr.Blocks() as demo_leaderboard:
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
label="Select columns to show",
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
],
elem_id="column-select",
)
with gr.Row():
deleted_models_visibility = gr.Checkbox(label="Show private/deleted models", value=False)
merged_models_visibility = gr.Checkbox(label="Show merges", value=False)
flagged_models_visibility = gr.Checkbox(label="Show flagged models", value=False)
with gr.Column(min_width=320):
filter_columns_type = gr.CheckboxGroup(
label="Model types",
choices=[t.to_str() for t in ModelType],
value=[t.to_str() for t in ModelType],
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],
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()),
elem_id="filter-columns-size",
)
filter_columns_add_special_tokens = gr.CheckboxGroup(
label="Add Special Tokens",
choices=[i.value.name for i in AddSpecialTokens],
value=[i.value.name for i in AddSpecialTokens],
elem_id="filter-columns-add-special-tokens",
)
filter_columns_num_few_shots = gr.CheckboxGroup(
label="Num Few Shots",
choices=[i.value.name for i in NumFewShots],
value=[i.value.name for i in NumFewShots],
elem_id="filter-columns-num-few-shots",
)
# DataFrameコンポーネントの初期化
leaderboard_table = gr.Dataframe(
value=leaderboard_df_filtered,
headers=initial_columns,
datatype=TYPES,
elem_id="leaderboard-table",
interactive=False,
visible=True,
)
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.Dataframe(
value=original_df[COLS],
headers=COLS,
datatype=TYPES,
visible=False,
)
# 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)
gr.on(
triggers=[
hidden_search_bar.change,
shown_columns.change,
filter_columns_type.change,
filter_columns_precision.change,
filter_columns_size.change,
filter_columns_add_special_tokens.change,
filter_columns_num_few_shots.change,
deleted_models_visibility.change,
merged_models_visibility.change,
flagged_models_visibility.change,
search_bar.submit,
],
fn=update_table,
inputs=[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
filter_columns_add_special_tokens,
filter_columns_num_few_shots,
deleted_models_visibility,
merged_models_visibility,
flagged_models_visibility,
search_bar,
],
outputs=leaderboard_table,
)
# Check query parameter once at startup and update search bar + hidden component
demo_leaderboard.load(fn=load_query, outputs=[search_bar, hidden_search_bar])
# Submission demo
with gr.Blocks() as demo_submission:
with gr.Column():
with gr.Row():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Column():
with gr.Accordion(
f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
open=False,
):
with gr.Row():
finished_eval_table = gr.Dataframe(
value=finished_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Accordion(
f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
open=False,
):
with gr.Row():
running_eval_table = gr.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.Dataframe(
value=pending_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Accordion(
f"❎ Failed Evaluation Queue ({len(failed_eval_queue_df)})",
open=False,
):
with gr.Row():
failed_eval_table = gr.Dataframe(
value=failed_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Row():
gr.Markdown("# ✉️✨ 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")
model_type = gr.Dropdown(
label="Model type",
choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
multiselect=False,
value=None,
)
with gr.Column():
precision = gr.Dropdown(
label="Precision",
choices=[i.value.name for i in Precision if i != Precision.Unknown],
multiselect=False,
value="float16",
)
weight_type = gr.Dropdown(
label="Weights type",
choices=[i.value.name for i in WeightType],
multiselect=False,
value="Original",
)
base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
add_special_tokens = gr.Dropdown(
label="AddSpecialTokens",
choices=[i.value.name for i in AddSpecialTokens if i != AddSpecialTokens.Unknown],
multiselect=False,
value="False",
)
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,
weight_type,
model_type,
add_special_tokens,
],
submission_result,
)
# Main demo
with gr.Blocks(css=custom_css) as demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
demo_leaderboard.render()
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
demo_submission.render()
with gr.Row():
with gr.Accordion("📙 Citation", open=False):
citation_button = gr.Textbox(
label=CITATION_BUTTON_LABEL,
value=CITATION_BUTTON_TEXT,
lines=20,
elem_id="citation-button",
show_copy_button=True,
)
gr.HTML(BOTTOM_LOGO)
if __name__ == "__main__":
if os.getenv("SPACE_ID"):
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()