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import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
# Removed Hugging Face Hub imports as they are not needed for the simplified leaderboard
# from huggingface_hub import snapshot_download, HfApi
from src.about import ( # Assuming these still exist and are relevant for other tabs
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css # Keep custom CSS
# Removed utils imports related to the old leaderboard
# from src.display.utils import (...)
from src.envs import REPO_ID # Keep if needed for restart_space or other functions
# Removed constants related to old data paths and repos if not needed elsewhere
# from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
# Removed old data processing functions
# from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval # Keep submission logic
# --- Elo Leaderboard Configuration ---
# Data from the table provided by the user
data = [
{'model': 'gpt-4o-mini', 'MLE-Lite_Elo': 753, 'Tabular_Elo': 839, 'NLP_Elo': 758, 'CV_Elo': 754, 'Overall': 778},
{'model': 'gpt-4o', 'MLE-Lite_Elo': 830, 'Tabular_Elo': 861, 'NLP_Elo': 903, 'CV_Elo': 761, 'Overall': 841},
{'model': 'o3-mini', 'MLE-Lite_Elo': 1108, 'Tabular_Elo': 1019, 'NLP_Elo': 1056, 'CV_Elo': 1207, 'Overall': 1096},
# Renamed 'DeepSeek-v3' to match previous list - adjust if needed
{'model': 'deepseek-v3', 'MLE-Lite_Elo': 1004, 'Tabular_Elo': 1015, 'NLP_Elo': 1028, 'CV_Elo': 1067, 'Overall': 1023},
# Renamed 'DeepSeek-r1' to match previous list - adjust if needed
{'model': 'deepseek-r1', 'MLE-Lite_Elo': 1137, 'Tabular_Elo': 1053, 'NLP_Elo': 1103, 'CV_Elo': 1083, 'Overall': 1100},
# Renamed 'Gemini-2.0-Flash' to match previous list - adjust if needed
{'model': 'gemini-2.0-flash', 'MLE-Lite_Elo': 847, 'Tabular_Elo': 923, 'NLP_Elo': 860, 'CV_Elo': 978, 'Overall': 895},
# Renamed 'Gemini-2.0-Pro' to match previous list - adjust if needed
{'model': 'gemini-2.0-pro', 'MLE-Lite_Elo': 1064, 'Tabular_Elo': 1139, 'NLP_Elo': 1028, 'CV_Elo': 973, 'Overall': 1054},
# Renamed 'Gemini-2.5-Pro' to match previous list - adjust if needed
{'model': 'gemini-2.5-pro', 'MLE-Lite_Elo': 1257, 'Tabular_Elo': 1150, 'NLP_Elo': 1266, 'CV_Elo': 1177, 'Overall': 1214},
]
# Create a master DataFrame
master_df = pd.DataFrame(data)
# Define categories for selection (user-facing)
CATEGORIES = ["MLE-Lite", "Tabular", "NLP", "CV", "Overall"]
DEFAULT_CATEGORY = "Overall" # Set a default category
# Map user-facing categories to DataFrame column names
category_to_column = {
"MLE-Lite": "MLE-Lite_Elo",
"Tabular": "Tabular_Elo",
"NLP": "NLP_Elo",
"CV": "CV_Elo",
"Overall": "Overall"
}
# --- Helper function to update leaderboard ---
def update_leaderboard(category):
"""
Selects the relevant columns for the category, renames the score column
to 'Elo Score', sorts by score descending, and returns the DataFrame.
"""
score_column = category_to_column.get(category)
if score_column is None or score_column not in master_df.columns:
# Fallback if category or column is invalid
print(f"Warning: Invalid category '{category}' or column '{score_column}'. Falling back to default.")
score_column = category_to_column[DEFAULT_CATEGORY]
if score_column not in master_df.columns: # Check fallback column too
return pd.DataFrame({"Model": [], "Elo Score": []}) # Return empty if still invalid
# Select model and the specific score column
df = master_df[['model', score_column]].copy()
# Rename the score column to 'Elo Score' for consistent display
df.rename(columns={score_column: 'Elo Score'}, inplace=True)
# Sort by 'Elo Score' descending
df.sort_values(by='Elo Score', ascending=False, inplace=True)
# Reset index for cleaner display (optional)
df.reset_index(drop=True, inplace=True)
return df
# --- Mock/Placeholder functions/data for other tabs ---
# (Same as previous version - providing empty data)
print("Warning: Evaluation queue data fetching is disabled/mocked due to leaderboard changes.")
finished_eval_queue_df = pd.DataFrame(columns=["Model", "Status", "Requested", "Started"])
running_eval_queue_df = pd.DataFrame(columns=["Model", "Status", "Requested", "Started"])
pending_eval_queue_df = pd.DataFrame(columns=["Model", "Status", "Requested", "Started"])
EVAL_COLS = ["Model", "Status", "Requested", "Started"] # Define for the dataframe headers
EVAL_TYPES = ["str", "str", "str", "str"] # Define for the dataframe types
# --- Keep restart function if relevant ---
# (Same as previous version)
def restart_space():
print(f"Attempting to restart space: {REPO_ID}")
# Replace with your actual space restart mechanism if needed
# --- Gradio App Definition ---
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("🏅 MLE-Dojo Benchmark", elem_id="llm-benchmark-tab-table", id=0):
with gr.Column():
gr.Markdown("## Model Elo Rankings") # New title for the section
category_selector = gr.Radio(
choices=CATEGORIES,
label="Select Category to Sort By", # Updated label
value=DEFAULT_CATEGORY, # Default selection
interactive=True,
container=False,
)
leaderboard_df_component = gr.Dataframe(
# Initialize with sorted data for the default category
value=update_leaderboard(DEFAULT_CATEGORY),
headers=["Model", "Elo Score"],
datatype=["str", "number"],
interactive=False,
# Adjust row count based on the number of models
row_count=(len(master_df), "fixed"),
col_count=(2, "fixed"),
)
# Link the radio button change to the update function
category_selector.change(
fn=update_leaderboard,
inputs=category_selector,
outputs=leaderboard_df_component
)
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
# (Content unchanged)
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
# with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
# # (Content unchanged, still uses potentially empty/mock queue data)
# 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.components.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.components.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.components.Dataframe(
# value=pending_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():
# # Submission form - kept as is
# 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(
# choices=["Type A", "Type B", "Type C"], # Example choices
# label="Model type",
# multiselect=False,
# value=None,
# interactive=True,
# )
# with gr.Column():
# precision = gr.Dropdown(
# choices=["float16", "bfloat16", "float32", "int8"], # Example choices
# label="Precision",
# multiselect=False,
# value="float16",
# interactive=True,
# )
# weight_type = gr.Dropdown(
# choices=["Original", "Adapter", "Delta"], # Example choices
# label="Weights type",
# multiselect=False,
# value="Original",
# interactive=True,
# )
# base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
# 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,
# ],
# submission_result,
# )
with gr.Row():
with gr.Accordion("📙 Citation", open=False):
# (Content unchanged)
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
lines=20,
elem_id="citation-button",
show_copy_button=True,
)
# --- Keep scheduler if relevant ---
# scheduler = BackgroundScheduler()
# scheduler.add_job(restart_space, "interval", seconds=1800) # Restart every 30 mins
# scheduler.start()
# --- Launch the app ---
demo.launch() |