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import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from src.about import (
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
AutoEvalColumn,
fields,
)
from src.envs import API, EVAL_RESULTS_PATH, REPO_ID, RESULTS_REPO, TOKEN
def restart_space():
API.restart_space(repo_id=REPO_ID)
### Space initialisation
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, token=TOKEN
)
except Exception:
restart_space()
LEADERBOARD_DF = pd.read_csv(f"{EVAL_RESULTS_PATH}/results.csv")
# multiply each column by 100, except first column
for col in LEADERBOARD_DF.columns[1:]:
LEADERBOARD_DF[col] = LEADERBOARD_DF[col] * 100
# add average column
LEADERBOARD_DF["Average"] = LEADERBOARD_DF.mean(axis=1)
# # move average to the front
LEADERBOARD_DF = LEADERBOARD_DF[["model", "Average"] + [col for col in LEADERBOARD_DF.columns if col not in ["model", "Average"]]]
# round to nearest tenth
LEADERBOARD_DF = LEADERBOARD_DF.round(1)
# sort by average
LEADERBOARD_DF = LEADERBOARD_DF.sort_values(by="Average", ascending=False)
def init_leaderboard(dataframe):
if dataframe is None or dataframe.empty:
raise ValueError("Leaderboard DataFrame is empty or None.")
return Leaderboard(
value=dataframe,
datatype=[c.type for c in fields(AutoEvalColumn)],
select_columns=SelectColumns(
default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
label="Select Columns to Display:",
),
search_columns=[AutoEvalColumn.model.name],
hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
bool_checkboxgroup_label="Hide models",
interactive=False,
)
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("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
leaderboard = init_leaderboard(LEADERBOARD_DF)
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch() |