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from pathlib import Path
from utils import load_json_results
import gradio as gr
from leaderboard_tab import search_leaderboard, update_columns_to_show, create_leaderboard_tab
# Constants
LLM_IN_CONTEXT_ABOUT_SECTION = """"""
# Global variables
llm_in_context_df = None
def load_reranking_leaderboard():
"""Load and prepare the reranking leaderboard data"""
global llm_in_context_df
dataframe_path = Path(__file__).parent / "results" / "llm_in_context_results.json"
# Prepare dataframe
llm_in_context_df = load_json_results(
dataframe_path,
prepare_for_display=True,
sort_col="Overall Score",
drop_cols=["Revision", "Precision", "Task"]
)
llm_in_context_df.insert(0, "Rank", range(1, 1 + len(llm_in_context_df)))
llm_in_context_df.rename(columns={"nDCG": "nDCG@10", "MRR": "MRR@10"}, inplace=True)
return llm_in_context_df
def reranking_search_leaderboard(model_name, columns_to_show):
"""Search function for reranking leaderboard"""
return search_leaderboard(llm_in_context_df, model_name, columns_to_show)
def update_reranker_columns_to_show(columns_to_show):
"""Update displayed columns for reranking leaderboard"""
return update_columns_to_show(llm_in_context_df, columns_to_show)
def create_llm_in_context_tab():
"""Create the complete reranking leaderboard tab"""
global llm_in_context_df
# Load data if not already loaded
if (llm_in_context_df is None):
llm_in_context_df = load_reranking_leaderboard()
# Define default columns to show
default_columns = ["Rank", "Model", "Overall Score", "Model Parameters (in Millions)",
"Embedding Dimensions", "Downloads Last Month", "MRR@10", "nDCG@10", "MAP"]
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("👑 Context Dependant 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=llm_in_context_df.columns.tolist(),
value=initial_columns_to_show,
scale=4
)
leaderboard = gr.Dataframe(
value=llm_in_context_df.loc[:, initial_columns_to_show],
datatype="markdown",
wrap=False,
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("💎 Context About 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=llm_in_context_df.columns.tolist(),
value=initial_columns_to_show,
scale=4
)
leaderboard = gr.Dataframe(
value=llm_in_context_df.loc[:, initial_columns_to_show],
datatype="markdown",
wrap=False,
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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