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from apscheduler.schedulers.background import BackgroundScheduler
from leaderboard.content import (
TITLE,
BANNER,
INTRO,
INTRO2,
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
)
import gradio as gr
import pandas as pd
import json
df = pd.DataFrame()
def update_data():
global df
with open("leaderboard/leaderboard.json", "r") as f:
data = json.load(f)
df = create_dataframe(data)
def filter_columns(df, show_all):
if show_all:
return df
else:
mean_columns = [col for col in df.columns if "Mean" in col or col == "Model"]
return df[mean_columns]
def create_dataframe(data):
rows = []
for model in data["models"]:
name_with_link = f'<a href="{model["url"]}" target="_blank" style="color: blue; text-decoration: underline;">{model["name"]}</a>'
row = {"Model": name_with_link}
row.update(model["scores"])
rows.append(row)
df = pd.DataFrame(rows)
for col in df.columns:
if "Mean" in col:
df[col] = df[col].apply(lambda x: f"<strong>{x}</strong>")
return df
def update_display(show_all, df):
filtered_df = filter_columns(df, show_all)
legend_visibility = gr.update(visible=show_all)
return filtered_df, legend_visibility
update_data()
demo = gr.Blocks()
with demo:
gr.HTML(TITLE)
gr.HTML(BANNER)
gr.Markdown(INTRO, elem_classes="markdown-text")
gr.Markdown(INTRO2, elem_classes="markdown-text")
show_all_columns = gr.Checkbox(label="Show all datasets", value=True)
data_display = gr.Dataframe(df, datatype="markdown")
legend_accordion = gr.Accordion("Legend:", open=False, visible=True)
with legend_accordion:
gr.Markdown(
"""
- Exist.: Existence
- Count: Count
- Posi.: Position
- Col.: Color
- Post.: Poster
- Cel.: Celebrity
- Sce.: Scene
- Lan.: Landmark
- Art.: Artwork
- Com. R.: Commonsense Reasoning
- Code: Code Reasoning
- Num.: Numerical Calculation
- Tran.: Text Translation
- OCR: OCR
"""
)
with gr.Row():
with gr.Accordion("📙 Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
elem_id="citation-button",
lines=10,
show_copy_button=True,
)
show_all_columns.change(
update_display,
inputs=[show_all_columns, gr.State(df)],
outputs=[data_display, legend_accordion],
)
scheduler = BackgroundScheduler()
scheduler.add_job(update_data, "cron", hour=0) # Update data once a day at midnight
scheduler.start()
demo.queue(default_concurrency_limit=40).launch(share=True)
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