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

# --- Make sure these imports work relative to your file structure ---
# Option 1: If src is a directory in the same folder as your script:
try:
    from src.about import (
        CITATION_BUTTON_LABEL,
        CITATION_BUTTON_TEXT,
        EVALUATION_QUEUE_TEXT, # Keep if used by commented-out submit tab
        INTRODUCTION_TEXT,
        LLM_BENCHMARKS_TEXT,
        TITLE,
    )
    from src.display.css_html_js import custom_css # Assuming this exists but might be empty
    from src.envs import REPO_ID # Keep if needed for restart_space or other functions
    from src.submission.submit import add_new_eval # Keep if using the submit tab
    print("Successfully imported from src module.")
# Option 2: If you don't have these files, define placeholders (REMOVE THIS if using Option 1)
except ImportError:
    print("Warning: Using placeholder values because src module imports failed.")
    CITATION_BUTTON_LABEL="Citation"
    CITATION_BUTTON_TEXT="Please cite us if you use this benchmark..."
    EVALUATION_QUEUE_TEXT="Current evaluation queue:"
    INTRODUCTION_TEXT="Welcome to the MLE-Dojo Benchmark Leaderboard."
    LLM_BENCHMARKS_TEXT="Information about the benchmarks..."
    TITLE="<h1>🏆 MLE-Dojo Benchmark Leaderboard</h1>"
    custom_css="" # Start with empty CSS if not imported
    REPO_ID="your/space-id" # Replace with actual ID if needed
    def add_new_eval(*args): return "Submission placeholder."
# --- End Placeholder Definitions ---


# --- Elo Leaderboard Configuration ---
# Enhanced data with Rank (placeholder), Organizer, License, and URL
# !!! IMPORTANT: Replace placeholder URLs with actual model/project pages. !!!
# Verify organizer and license information for accuracy.
data = [
{'model_name': 'gpt-4o-mini', 'url': 'https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/', 'organizer': 'OpenAI', 'license': 'Proprietary', 'MLE-Lite_Elo': 753, 'Tabular_Elo': 839, 'NLP_Elo': 758, 'CV_Elo': 754, 'Overall': 778},
{'model_name': 'gpt-4o', 'url': 'https://openai.com/index/hello-gpt-4o/', 'organizer': 'OpenAI', 'license': 'Proprietary', 'MLE-Lite_Elo': 830, 'Tabular_Elo': 861, 'NLP_Elo': 903, 'CV_Elo': 761, 'Overall': 841},
{'model_name': 'o3-mini', 'url': 'https://openai.com/index/openai-o3-mini/', 'organizer': 'OpenAI', 'license': 'Proprietary', 'MLE-Lite_Elo': 1108, 'Tabular_Elo': 1019, 'NLP_Elo': 1056, 'CV_Elo': 1207, 'Overall': 1096}, # Fill details later
{'model_name': 'deepseek-v3', 'url': 'https://api-docs.deepseek.com/news/news1226', 'organizer': 'DeepSeek', 'license': 'DeepSeek', 'MLE-Lite_Elo': 1004, 'Tabular_Elo': 1015, 'NLP_Elo': 1028, 'CV_Elo': 1067, 'Overall': 1023},
{'model_name': 'deepseek-r1', 'url': 'https://api-docs.deepseek.com/news/news250120', 'organizer': 'DeepSeek', 'license': 'DeepSeek', 'MLE-Lite_Elo': 1137, 'Tabular_Elo': 1053, 'NLP_Elo': 1103, 'CV_Elo': 1083, 'Overall': 1100},
{'model_name': 'gemini-2.0-flash', 'url': 'https://ai.google.dev/gemini-api/docs/models#gemini-2.0-flash', 'organizer': 'Google', 'license': 'Proprietary', 'MLE-Lite_Elo': 847, 'Tabular_Elo': 923, 'NLP_Elo': 860, 'CV_Elo': 978, 'Overall': 895},
{'model_name': 'gemini-2.0-pro', 'url': 'https://blog.google/technology/google-deepmind/gemini-model-updates-february-2025/', 'organizer': 'Google', 'license': 'Proprietary', 'MLE-Lite_Elo': 1064, 'Tabular_Elo': 1139, 'NLP_Elo': 1028, 'CV_Elo': 973, 'Overall': 1054},
{'model_name': 'gemini-2.5-pro', 'url': 'https://deepmind.google/technologies/gemini/pro/', 'organizer': 'Google', 'license': 'Proprietary', 'MLE-Lite_Elo': 1257, 'Tabular_Elo': 1150, 'NLP_Elo': 1266, 'CV_Elo': 1177, 'Overall': 1214},
]

# Create a master DataFrame
# Note: Columns 'organizer' and 'license' are created in lowercase here.
master_df = pd.DataFrame(data)

# Define categories for selection (user-facing)
CATEGORIES = ["Overall", "MLE-Lite", "Tabular", "NLP", "CV"] # Overall first
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 relevant columns, sorts by the chosen category's Elo score,
    adds Rank, formats model name as a link, and returns the DataFrame.
    """
    score_column = category_to_column.get(category)
    if score_column is None or score_column not in master_df.columns:
        print(f"Warning: Invalid category '{category}' or column '{score_column}'. Falling back to default.")
        score_column = category_to_column[DEFAULT_CATEGORY]
        # Check fallback column too
        if score_column not in master_df.columns:
            # Return empty df with correct columns if still invalid
            # Use lowercase keys here consistent with master_df for the empty case
            print(f"Error: Default column '{score_column}' also not found.")
            return pd.DataFrame({
                "Rank": [],
                "Model": [],
                "Elo Score": [],
                "Organizer": [], # Changed 'organizer' -> 'Organizer' for consistency in empty case
                "License": []   # Changed 'license' -> 'License' for consistency in empty case
            })

    # Select base columns + the score column for sorting
    # Ensure 'organizer' and 'license' are selected correctly (lowercase)
    cols_to_select = ['model_name', 'url', 'organizer', 'license', score_column]
    df = master_df[cols_to_select].copy()

    # Sort by the selected 'Elo Score' descending
    df.sort_values(by=score_column, ascending=False, inplace=True)

    # Add Rank based on the sorted order
    df.reset_index(drop=True, inplace=True)
    df.insert(0, 'Rank', df.index + 1)

    # Format Model Name as HTML Hyperlink
    # The resulting column name will be 'Model' (capitalized)
    df['Model'] = df.apply(
        lambda row: f"<a href='{row['url'] if pd.notna(row['url']) else '#'}' target='_blank' style='color: #007bff; text-decoration: none;'>{row['model_name']}</a>",
        axis=1
    )

    # Rename the score column to 'Elo Score' for consistent display
    df.rename(columns={score_column: 'Elo Score'}, inplace=True)

    # Rename 'organizer' and 'license' to match desired display headers
    df.rename(columns={'organizer': 'Organizer', 'license': 'License'}, inplace=True)

    # Select and reorder columns for final display using the ACTUAL column names in df
    # Use capitalized 'Organizer' and 'License' here because they have been renamed.
    final_columns = ["Rank", "Model", "Organizer", "License", "Elo Score"]
    df = df[final_columns]

    # Note: The DataFrame returned now has columns:
    # 'Rank', 'Model', 'Organizer', 'License', 'Elo Score'
    return df

# --- Mock/Placeholder functions/data for other tabs ---
# (If the Submit tab is used, ensure these variables are appropriately populated or handled)
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 ---
def restart_space():
    # Make sure REPO_ID is correctly defined/imported if this function is used
    print(f"Attempting to restart space: {REPO_ID}")
    # Replace with your actual space restart mechanism if needed (e.g., HfApi().restart_space(REPO_ID))


# --- Gradio App Definition ---

# ***** FONT SIZE INCREASED HERE *****
# Add CSS rules to make the base font size larger.
# Adjust the '1.2em' value (e.g., to '1.4em', '16px') to change the size.
# The !important flag helps override theme defaults.
# If the imported custom_css already has content, append to it.
font_size_css = """
body {
    font-size: 1.5em !important; /* Increase base font size */
}
/* Optional: Target specific elements if needed */
/*
#leaderboard-table th, #leaderboard-table td {
    font-size: 1em !important; /* Adjust table font size relative to new body size */
    padding: 5px 7px !important; /* Increase padding for better spacing */
}
h1, .markdown-text h1 { font-size: 2.2em !important; } /* Make main title larger */
h2, .markdown-text h2 { font-size: 1.8em !important; } /* Make section titles larger */
button { font-size: 1.1em !important; padding: 8px 16px !important; } /* Slightly larger buttons */
.gr-input, .gr-dropdown, .gr-textbox textarea { font-size: 1em !important; } /* Ensure inputs scale too */
*/
"""
# Append the new CSS to any existing custom_css
custom_css += font_size_css

# Add basic table styling if not already present
if "table {" not in custom_css:
    custom_css += """
table { width: 100%; border-collapse: collapse; margin-top: 10px; margin-bottom: 10px; }
th, td { padding: 8px 12px; border: 1px solid #ddd; text-align: left; white-space: normal; vertical-align: top; } /* Allow wrapping, top align */
th { background-color: #f2f2f2; font-weight: bold; }
tr:nth-child(even) { background-color: #f9f9f9; }
tr:hover { background-color: #e9e9e9; }
td a { color: #007bff; text-decoration: none; }
td a:hover { text-decoration: underline; }
"""

# Use a theme for better default styling
demo = gr.Blocks(css=custom_css, theme=gr.themes.Soft())

with demo:
    # Use the TITLE variable imported or defined above
    gr.HTML(TITLE)

    # Use the INTRODUCTION_TEXT variable imported or defined above
    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 by Category")
                category_selector = gr.Radio(
                    choices=CATEGORIES,
                    label="Select Category:",
                    value=DEFAULT_CATEGORY,
                    interactive=True,
                )
                leaderboard_df_component = gr.Dataframe(
                    # Initialize with sorted data for the default category
                    value=update_leaderboard(DEFAULT_CATEGORY),
                    # Headers for DISPLAY should match the *renamed* columns from update_leaderboard
                    headers=["Rank", "Model", "Organizer", "License", "Elo Score"],
                    # Datatype maps to the final df columns: Rank, Model, Organizer, License, Elo Score
                    datatype=["number", "html", "str", "str", "number"],
                    interactive=False,
                    # --- FIX APPLIED: Removed unsupported 'height' argument ---
                    # row_count determines the number of rows to display
                    row_count=(len(master_df), "fixed"), # Display all rows
                    col_count=(5, "fixed"),
                    wrap=True, # Allow text wrapping in cells
                    elem_id="leaderboard-table" # CSS hook for custom styling
                )
                # 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-about", id=1):
            # Use the LLM_BENCHMARKS_TEXT variable imported or defined above
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

        # --- Submit Tab (Commented out as in original request) ---
        # Make sure EVALUATION_QUEUE_TEXT and add_new_eval are imported/defined if uncommented
        # with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-submit", id=2):
        #     with gr.Column():
        #          with gr.Row():
        #              gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") # Requires import/definition
        #          with gr.Column():
        #              with gr.Accordion(f"✅ Finished Evaluations ({len(finished_eval_queue_df)})", open=False):
        #                   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):
        #                   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):
        #                  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():
        #          with gr.Column():
        #              model_name_textbox = gr.Textbox(label="Model name (on Hugging Face Hub)")
        #              revision_name_textbox = gr.Textbox(label="Revision / Commit Hash", placeholder="main")
        #              model_type = gr.Dropdown(choices=["Type A", "Type B", "Type C"], label="Model type", multiselect=False, value=None, interactive=True) # Example choices
        #          with gr.Column():
        #              precision = gr.Dropdown(choices=["float16", "bfloat16", "float32", "int8", "auto"], label="Precision", multiselect=False, value="auto", interactive=True)
        #              weight_type = gr.Dropdown(choices=["Original", "Adapter", "Delta"], 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()
        #     # Ensure add_new_eval is correctly imported/defined and handles these inputs
        #     submit_button.click(
        #          add_new_eval, # Requires import/definition
        #          [ model_name_textbox, base_model_name_textbox, revision_name_textbox, precision, weight_type, model_type, ],
        #          submission_result,
        #      )

    # --- Citation Row (at the bottom, outside Tabs) ---
    with gr.Accordion("📙 Citation", open=False):
        # Use the CITATION_BUTTON_TEXT and CITATION_BUTTON_LABEL variables imported or defined above
        citation_button = gr.Textbox(
            value=CITATION_BUTTON_TEXT,
            label=CITATION_BUTTON_LABEL,
            lines=10, # Adjust lines if needed for new font size
            elem_id="citation-button",
            show_copy_button=True,
        )

# IGNORE_WHEN_COPYING_START
# content_copy  download
# Use code with caution.
# IGNORE_WHEN_COPYING_END

# --- Keep scheduler if relevant ---
# Only start scheduler if the script is run directly
if __name__ == "__main__":
    try:
        scheduler = BackgroundScheduler()
        # Add job only if restart_space is callable (i.e., not a placeholder or failed import)
        if callable(restart_space):
             # Check if REPO_ID seems valid before scheduling
             if REPO_ID and REPO_ID != "your/space-id":
                 scheduler.add_job(restart_space, "interval", seconds=1800) # Restart every 30 mins
                 scheduler.start()
             else:
                 print("Warning: REPO_ID not set or is placeholder; space restart job not scheduled.")
        else:
             print("Warning: restart_space function not available; space restart job not scheduled.")
    except Exception as e:
        print(f"Failed to initialize or start scheduler: {e}")


# --- Launch the app ---
# Ensures the app launches only when the script is run directly
if __name__ == "__main__":
    # Ensure you have installed necessary libraries: pip install gradio pandas apscheduler
    # Make sure your src module files (about.py etc.) are accessible OR use the placeholder definitions above.
    print("Launching Gradio App...")
    demo.launch()