app.py

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("🏅 LLM 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.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()