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import gradio as gr
import pandas as pd
from fuzzywuzzy import fuzz
from utils import submit_gradio_module
def search_leaderboard(df, model_name, columns_to_show, threshold=95):
"""
Search the leaderboard for models matching the search term using fuzzy matching.
Args:
df: The dataframe containing all leaderboard data
model_name: The search term to find models
columns_to_show: List of columns to include in the result
threshold: Minimum similarity threshold (default: 95)
Returns:
Filtered dataframe with only matching models and selected columns
"""
if not model_name.strip():
return df.loc[:, columns_to_show]
search_name = model_name.lower() # compute once for efficiency
def calculate_similarity(row):
return fuzz.partial_ratio(search_name, row["Model"].lower())
filtered_df = df.copy()
filtered_df["similarity"] = filtered_df.apply(calculate_similarity, axis=1)
filtered_df = filtered_df[filtered_df["similarity"] >= threshold].sort_values('similarity', ascending=False)
filtered_df = filtered_df.drop('similarity', axis=1).loc[:, columns_to_show]
return filtered_df
def update_columns_to_show(df, columns_to_show):
"""
Update the displayed columns in the dataframe.
Args:
df: The dataframe to update
columns_to_show: List of columns to include
Returns:
gradio.update object with the updated dataframe
"""
dummy_df = df.loc[:, [col for col in df.columns if col in columns_to_show]]
columns_widths = []
for col in dummy_df.columns:
if col == "Rank":
columns_widths.append(80)
elif col == "Model":
columns_widths.append(400)
else:
columns_widths.append(150)
return gr.update(value=dummy_df, column_widths=columns_widths)
def create_leaderboard_tab(df, initial_columns_to_show, search_function, update_function, about_section, task_type):
"""
Create a complete leaderboard tab with search, column selection, and data display.
Args:
df: The dataframe containing the leaderboard data
initial_columns_to_show: Initial list of columns to display
search_function: Function to handle searching
update_function: Function to handle column updates
about_section: Markdown text for the About tab
task_type: Type of the task ("Retriever" or "Reranker")
Returns:
A gradio Tabs component with the complete leaderboard interface
"""
columns_widths = [80 if col == "Rank" else 400 if col == "Model" else 150 for col in initial_columns_to_show]
with gr.Tabs() as tabs:
with gr.Tab("👑 Leaderboard"):
with gr.Column():
with gr.Row(equal_height=True):
search_box = gr.Textbox(
placeholder="Search for models...",
label="Search (You can also press Enter to search)",
scale=5
)
search_button = gr.Button(
value="Search",
variant="primary",
scale=1
)
columns_to_show_input = gr.CheckboxGroup(
label="Columns to Show",
choices=df.columns.tolist(),
value=initial_columns_to_show,
scale=4
)
leaderboard = gr.Dataframe(
value=df.loc[:, initial_columns_to_show],
datatype="markdown",
wrap=True,
show_fullscreen_button=True,
interactive=False,
column_widths=columns_widths
)
# Connect events
search_box.submit(
search_function,
inputs=[search_box, columns_to_show_input],
outputs=leaderboard
)
columns_to_show_input.select(
update_function,
inputs=columns_to_show_input,
outputs=leaderboard
)
search_button.click(
search_function,
inputs=[search_box, columns_to_show_input],
outputs=leaderboard
)
with gr.Tab("🏵️ Submit"):
submit_gradio_module(task_type)
with gr.Tab("ℹ️ About"):
gr.Markdown(about_section)
return tabs
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