app.py

import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from datetime import datetime

# --- 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,
        INTRODUCTION_TEXT,
        LLM_BENCHMARKS_TEXT,
        TITLE,
    )
    from src.display.css_html_js import custom_css
    from src.envs import REPO_ID
    from src.submission.submit import add_new_eval
    print("Successfully imported from src module.")
# Option 2: If you don't have these files, define placeholders
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
    
    This leaderboard tracks the performance of various AI models across multiple machine learning engineering domains.
    Our comprehensive evaluation system uses ELO ratings to provide a fair comparison between different models.
    
    ## How to read this leaderboard
    - Select a domain category to view specialized rankings
    - Higher ELO scores indicate better performance
    - Click on any model name to learn more about it
    """
    LLM_BENCHMARKS_TEXT = """
    # About the MLE-Dojo Benchmark
    
    ## Evaluation Methodology
    The MLE-Dojo benchmark evaluates models across various domains including:
    
    - **MLE-Lite**: Basic machine learning engineering tasks
    - **Tabular**: Data manipulation, analysis, and modeling with structured data
    - **NLP**: Natural language processing tasks including classification, generation, and understanding
    - **CV**: Computer vision tasks including image classification, object detection, and generation
    
    Our evaluation uses a sophisticated ELO rating system that considers the relative performance of models against each other.
    
    ## Contact
    For more information or to submit your model, please contact us at [email protected]
    """
    TITLE = "<h1>🏆 MLE-Dojo Benchmark Leaderboard</h1>"
    custom_css = ""
    REPO_ID = "your/space-id"
    def add_new_eval(*args): return "Submission placeholder."

# --- Elo Leaderboard Configuration ---
# Enhanced data with Rank (placeholder), Organizer, License, and URL
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},
    {'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},
]

# Add organization logos (for visual enhancement)
org_logos = {
    'OpenAI': '📱',  # You can replace these with actual icon URLs in production
    'DeepSeek': '🔍',
    'Google': '🌐',
    'Default': '🤖'
}

# Create a master DataFrame
master_df = pd.DataFrame(data)

# Add last updated timestamp
last_updated = datetime.now().strftime("%B %d, %Y at %H:%M:%S")

# Define categories with fancy icons
CATEGORIES = [
    ("🏆 Overall", "Overall"), 
    ("💡 MLE-Lite", "MLE-Lite"), 
    ("📊 Tabular", "Tabular"), 
    ("📝 NLP", "NLP"), 
    ("👁️ CV", "CV")
]
DEFAULT_CATEGORY = "Overall"

# 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_label):
    """
    Enhanced function to update the leaderboard with visual improvements
    """
    # Extract the category value from the label if it's a tuple (icon, value)
    if isinstance(category_label, tuple):
        category = category_label[1]
    else:
        # For backward compatibility or direct values
        category = category_label.split(" ")[-1] if " " in category_label else category_label
    
    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]
        if score_column not in master_df.columns:
            print(f"Error: Default column '{score_column}' also not found.")
            return pd.DataFrame({
                "Rank": [],
                "Model": [],
                "Organizer": [],
                "License": [],
                "Elo Score": []
            })

    # Select base columns + the score column for sorting
    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 with medal emojis for top 3
    df.reset_index(drop=True, inplace=True)
    
    # Create fancy rank with medals for top positions
    def get_rank_display(idx):
        if idx == 0:
            return "🥇 1"
        elif idx == 1:
            return "🥈 2"
        elif idx == 2:
            return "🥉 3"
        else:
            return f"{idx + 1}"
    
    df.insert(0, 'Rank', df.index.map(get_rank_display))
    
    # Add organization icons to model names
    df['Model'] = df.apply(
        lambda row: f"""<div style="display: flex; align-items: center;">
            <span style="font-size: 1.5em; margin-right: 10px;">{org_logos.get(row['organizer'], org_logos['Default'])}</span>
            <a href='{row['url'] if pd.notna(row['url']) else '#'}' target='_blank' 
               style='color: #0066cc; text-decoration: none; font-weight: 500; font-size: 1.05em;'>
               {row['model_name']}
            </a>
        </div>""",
        axis=1
    )

    # Format Elo scores with visual indicators
    df['Elo Display'] = df[score_column].apply(
        lambda score: f"""<div style="display: flex; align-items: center;">
            <span style="font-weight: bold; color: {'#1a5fb4' if score >= 1000 else '#2ec27e' if score >= 900 else '#e5a50a' if score >= 800 else '#ff7800'}">
                {score}
            </span>
            <div style="margin-left: 10px; height: 12px; width: 60px; background-color: #eaeaea; border-radius: 6px; overflow: hidden;">
                <div style="height: 100%; width: {min(100, max(5, (score-700)/7))}%; background-color: {'#1a5fb4' if score >= 1000 else '#2ec27e' if score >= 900 else '#e5a50a' if score >= 800 else '#ff7800'};"></div>
            </div>
        </div>"""
    )

    # Rename columns for display
    df.rename(columns={score_column: 'Elo Score'}, inplace=True)
    df.rename(columns={'organizer': 'Organizer', 'license': 'License'}, inplace=True)

    # Select and reorder columns for final display
    final_columns = ["Rank", "Model", "Organizer", "License", "Elo Display"]
    df = df[final_columns]

    # Rename for display
    df.columns = ["Rank", "Model", "Organization", "License", f"Elo Score ({category})"]
    
    return df

# --- Mock/Placeholder functions/data for other tabs ---
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"]
EVAL_TYPES = ["str", "str", "str", "str"]

# --- Keep restart function if relevant ---
def restart_space():
    print(f"Attempting to restart space: {REPO_ID}")
    # Replace with your actual space restart mechanism if needed

# --- Enhanced CSS for beauty and readability ---
enhanced_css = """
/* Base styling */
:root {
    --primary-color: #1a5fb4;
    --secondary-color: #2ec27e;
    --accent-color: #e5a50a;
    --warning-color: #ff7800;
    --text-color: #333333;
    --background-color: #ffffff;
    --card-background: #f9f9f9;
    --border-color: #e0e0e0;
    --shadow-color: rgba(0, 0, 0, 0.1);
}

/* Typography */
body, .gradio-container {
    font-family: 'Inter', 'Segoe UI', Roboto, -apple-system, BlinkMacSystemFont, system-ui, sans-serif !important;
    font-size: 16px !important;
    line-height: 1.6 !important;
    color: var(--text-color) !important;
    background-color: var(--background-color) !important;
}

/* Headings */
h1 {
    font-size: 2.5rem !important;
    font-weight: 700 !important;
    margin-bottom: 1.5rem !important;
    color: var(--primary-color) !important;
    text-align: center !important;
    letter-spacing: -0.02em !important;
    line-height: 1.2 !important;
}

h2 {
    font-size: 1.8rem !important;
    font-weight: 600 !important;
    margin-top: 1.5rem !important;
    margin-bottom: 1rem !important;
    color: var(--primary-color) !important;
    letter-spacing: -0.01em !important;
}

h3 {
    font-size: 1.4rem !important;
    font-weight: 600 !important;
    margin-top: 1.2rem !important;
    margin-bottom: 0.8rem !important;
    color: var(--text-color) !important;
}

/* Tabs styling */
.tabs {
    margin-top: 1rem !important;
    border-radius: 12px !important;
    overflow: hidden !important;
    box-shadow: 0 4px 12px var(--shadow-color) !important;
}

.tab-nav button {
    font-size: 1.1rem !important;
    font-weight: 500 !important;
    padding: 0.8rem 1.5rem !important;
    border-radius: 0 !important;
    transition: all 0.2s ease !important;
}

.tab-nav button.selected {
    background-color: var(--primary-color) !important;
    color: white !important;
    font-weight: 600 !important;
}

/* Card styling */
.gradio-container .gr-box, .gradio-container .gr-panel {
    border-radius: 12px !important;
    border: 1px solid var(--border-color) !important;
    box-shadow: 0 4px 12px var(--shadow-color) !important;
    overflow: hidden !important;
}

/* Table styling */
table {
    width: 100% !important;
    border-collapse: separate !important;
    border-spacing: 0 !important;
    margin: 1.5rem 0 !important;
    border-radius: 8px !important;
    overflow: hidden !important;
    box-shadow: 0 4px 12px var(--shadow-color) !important;
}

th {
    background-color: #f0f5ff !important;
    color: var(--primary-color) !important;
    font-weight: 600 !important;
    padding: 1rem !important;
    font-size: 1.1rem !important;
    text-align: left !important;
    border-bottom: 2px solid var(--primary-color) !important;
}

td {
    padding: 1rem !important;
    border-bottom: 1px solid var(--border-color) !important;
    font-size: 1rem !important;
    vertical-align: middle !important;
}

tr:nth-child(even) {
    background-color: #f8fafd !important;
}

tr:hover {
    background-color: #edf2fb !important;
}

tr:first-child td {
    border-top: none !important;
}

/* Button styling */
button.primary, .gr-button.primary {
    background-color: var(--primary-color) !important;
    color: white !important;
    font-weight: 500 !important;
    padding: 0.8rem 1.5rem !important;
    border-radius: 8px !important;
    border: none !important;
    cursor: pointer !important;
    transition: all 0.2s ease !important;
    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1) !important;
}

button.primary:hover, .gr-button.primary:hover {
    background-color: #0b4a9e !important;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15) !important;
    transform: translateY(-1px) !important;
}

/* Radio buttons */
.gr-radio {
    display: flex !important;
    flex-wrap: wrap !important;
    gap: 10px !important;
    margin: 1rem 0 !important;
}

.gr-radio label {
    background-color: #f5f7fa !important;
    border: 1px solid var(--border-color) !important;
    border-radius: 8px !important;
    padding: 0.7rem 1.2rem !important;
    font-size: 1rem !important;
    font-weight: 500 !important;
    cursor: pointer !important;
    transition: all 0.2s ease !important;
    display: flex !important;
    align-items: center !important;
    gap: 8px !important;
}

.gr-radio label:hover {
    background-color: #eaeef3 !important;
    border-color: #c0c9d6 !important;
}

.gr-radio label.selected {
    background-color: #e0e9f7 !important;
    border-color: var(--primary-color) !important;
    color: var(--primary-color) !important;
    font-weight: 600 !important;
}

/* Input fields */
input, textarea, select {
    font-size: 1rem !important;
    padding: 0.8rem !important;
    border-radius: 8px !important;
    border: 1px solid var(--border-color) !important;
    transition: all 0.2s ease !important;
}

input:focus, textarea:focus, select:focus {
    border-color: var(--primary-color) !important;
    box-shadow: 0 0 0 2px rgba(26, 95, 180, 0.2) !important;
    outline: none !important;
}

/* Accordion styling */
.gr-accordion {
    border-radius: 8px !important;
    overflow: hidden !important;
    margin: 1rem 0 !important;
    border: 1px solid var(--border-color) !important;
}

.gr-accordion-header {
    padding: 1rem !important;
    background-color: #f5f7fa !important;
    font-weight: 600 !important;
    font-size: 1.1rem !important;
    color: var(--text-color) !important;
}

.gr-accordion-content {
    padding: 1rem !important;
    background-color: white !important;
}

/* Markdown text improvements */
.markdown-text {
    font-size: 1.05rem !important;
    line-height: 1.7 !important;
}

.markdown-text p {
    margin-bottom: 1rem !important;
}

.markdown-text ul, .markdown-text ol {
    margin-left: 1.5rem !important;
    margin-bottom: 1rem !important;
}

.markdown-text li {
    margin-bottom: 0.5rem !important;
}

.markdown-text strong {
    font-weight: 600 !important;
    color: #333 !important;
}

/* Status indicators */
.status-badge {
    display: inline-block;
    padding: 0.3rem 0.7rem;
    border-radius: 99px;
    font-size: 0.85rem;
    font-weight: 500;
    text-align: center;
}

.status-pending {
    background-color: #fff8e0;
    color: #b58a00;
    border: 1px solid #ffd74d;
}

.status-running {
    background-color: #e0f2ff;
    color: #0066cc;
    border: 1px solid #66b3ff;
}

.status-completed {
    background-color: #e6f7ef;
    color: #00875a;
    border: 1px solid #57d9a3;
}

/* Footer */
.footer {
    margin-top: 2rem;
    padding: 1rem;
    text-align: center;
    font-size: 0.9rem;
    color: #666;
    border-top: 1px solid var(--border-color);
}

/* Enhanced leaderboard title */
.leaderboard-header {
    display: flex;
    align-items: center;
    justify-content: space-between;
    margin-bottom: 1.5rem;
    padding-bottom: 1rem;
    border-bottom: 2px solid var(--border-color);
}

.leaderboard-title {
    font-size: 2.2rem;
    font-weight: 700;
    color: var(--primary-color);
    margin: 0;
    display: flex;
    align-items: center;
    gap: 0.5rem;
}

.leaderboard-subtitle {
    font-size: 1.1rem;
    color: #666;
    margin-top: 0.5rem;
}

.timestamp {
    font-size: 0.85rem;
    color: #666;
    font-style: italic;
}

/* Category selector buttons */
.category-buttons {
    display: flex;
    flex-wrap: wrap;
    gap: 10px;
    margin-bottom: 1.5rem;
}

.category-button {
    padding: 0.7rem 1.2rem;
    background-color: #f0f5ff;
    border: 1px solid #d0e0ff;
    border-radius: 8px;
    font-weight: 500;
    cursor: pointer;
    transition: all 0.2s ease;
    display: flex;
    align-items: center;
    gap: 8px;
}

.category-button:hover {
    background-color: #e0ebff;
    border-color: #b0d0ff;
}

.category-button.active {
    background-color: var(--primary-color);
    color: white;
    border-color: var(--primary-color);
}

/* Logo and brand styling */
.logo {
    font-size: 2.5em;
    margin-right: 0.5rem;
}

/* Medal styling for top ranks */
.rank-1 {
    color: #ffd700;
    font-weight: bold;
}

.rank-2 {
    color: #c0c0c0;
    font-weight: bold;
}

.rank-3 {
    color: #cd7f32;
    font-weight: bold;
}
"""

# Combine with any existing CSS
custom_css = enhanced_css + custom_css

# --- Gradio App Definition ---
demo = gr.Blocks(css=custom_css, theme=gr.themes.Soft())

with demo:
    # Enhanced header with timestamp
    gr.HTML(f"""
    <div class="leaderboard-header">
        <div>
            <div class="leaderboard-title">
                <span class="logo">🏆</span> MLE-Dojo Benchmark Leaderboard
            </div>
            <div class="leaderboard-subtitle">
                Comprehensive evaluation of AI models across multiple domains
            </div>
        </div>
        <div class="timestamp">
            Last updated: {last_updated}
        </div>
    </div>
    """)

    # Introduction with enhanced styling
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("📊 Leaderboard", elem_id="llm-benchmark-tab-table", id=0):
            with gr.Column():
                gr.HTML("""
                <h2 style="display: flex; align-items: center; gap: 10px;">
                    <span style="font-size: 1.3em;">📈</span> Model Performance Rankings
                </h2>
                <p class="leaderboard-subtitle">Select a category to view specialized performance metrics</p>
                """)
                
                # Enhanced category selector
                category_selector = gr.Radio(
                    choices=[x[0] for x in CATEGORIES],
                    label="Select Performance Domain:",
                    value="🏆 Overall",
                    interactive=True,
                    elem_classes="fancy-radio"
                )
                
                # Visual separator
                gr.HTML('<div style="height: 1px; background-color: #e0e0e0; margin: 20px 0;"></div>')
                
                # Enhanced leaderboard table
                leaderboard_df_component = gr.Dataframe(
                    value=update_leaderboard(DEFAULT_CATEGORY),
                    headers=["Rank", "Model", "Organization", "License", f"Elo Score ({DEFAULT_CATEGORY})"],
                    datatype=["html", "html", "str", "str", "html"],
                    interactive=False,
                    row_count=(len(master_df), "fixed"),
                    col_count=(5, "fixed"),
                    wrap=True,
                    elem_id="leaderboard-table",
                )
                
                # Stats cards (visual enhancement)
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.HTML(f"""
                        <div style="background-color: #f0f5ff; padding: 20px; border-radius: 12px; text-align: center;">
                            <div style="font-size: 2em;">🔍</div>
                            <div style="font-size: 2em; font-weight: bold; color: #1a5fb4;">{len(master_df)}</div>
                            <div style="font-size: 1.1em; color: #666;">Models Evaluated</div>
                        </div>
                        """)
                    with gr.Column(scale=1):
                        gr.HTML(f"""
                        <div style="background-color: #e6f7ef; padding: 20px; border-radius: 12px; text-align: center;">
                            <div style="font-size: 2em;">🌐</div>
                            <div style="font-size: 2em; font-weight: bold; color: #00875a;">{master_df['organizer'].nunique()}</div>
                            <div style="font-size: 1.1em; color: #666;">Organizations</div>
                        </div>
                        """)
                    with gr.Column(scale=1):
                        gr.HTML(f"""
                        <div style="background-color: #fff8e0; padding: 20px; border-radius: 12px; text-align: center;">
                            <div style="font-size: 2em;">🏅</div>
                            <div style="font-size: 2em; font-weight: bold; color: #b58a00;">{len(CATEGORIES)}</div>
                            <div style="font-size: 1.1em; color: #666;">Performance Domains</div>
                        </div>
                        """)
                
                # 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):
            # Enhanced about section
            gr.HTML("""
            <div class="about-header" style="display: flex; align-items: center; gap: 20px; margin-bottom: 20px;">
                <div style="font-size: 4em;">🧪</div>
                <div>
                    <h2 style="margin: 0;">About the MLE-Dojo Benchmark</h2>
                    <p style="margin: 5px 0 0 0; color: #666;">A comprehensive evaluation framework for AI models</p>
                </div>
            </div>
            """)
            
            # Use the LLM_BENCHMARKS_TEXT variable
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
            
            # Add methodology cards for visual enhancement
            with gr.Row():
                with gr.Column():
                    gr.HTML("""
                    <div style="background-color: #f5f7fa; padding: 20px; border-radius: 12px; height: 100%;">
                        <div style="font-size: 2em; text-align: center; margin-bottom: 15px;">💡</div>
                        <h3 style="text-align: center; margin-top: 0;">MLE-Lite</h3>
                        <p>Evaluates a model's ability to handle basic machine learning engineering tasks including 
                        data preprocessing, feature engineering, model selection, and basic deployment.</p>
                    </div>
                    """)
                with gr.Column():
                    gr.HTML("""
                    <div style="background-color: #f5f7fa; padding: 20px; border-radius: 12px; height: 100%;">
                        <div style="font-size: 2em; text-align: center; margin-bottom: 15px;">📊</div>
                        <h3 style="text-align: center; margin-top: 0;">Tabular</h3>
                        <p>Tests a model's ability to process, analyze and model structured data, including 
                        statistical analysis,statistical analysis, predictive modeling, and data visualization with tabular datasets.</p>
                    </div>
                    """)
            
            with gr.Row():
                with gr.Column():
                    gr.HTML("""
                    <div style="background-color: #f5f7fa; padding: 20px; border-radius: 12px; height: 100%;">
                        <div style="font-size: 2em; text-align: center; margin-bottom: 15px;">📝</div>
                        <h3 style="text-align: center; margin-top: 0;">NLP</h3>
                        <p>Evaluates natural language processing capabilities including text classification, 
                        sentiment analysis, entity recognition, text generation, and language understanding.</p>
                    </div>
                    """)
                with gr.Column():
                    gr.HTML("""
                    <div style="background-color: #f5f7fa; padding: 20px; border-radius: 12px; height: 100%;">
                        <div style="font-size: 2em; text-align: center; margin-bottom: 15px;">👁️</div>
                        <h3 style="text-align: center; margin-top: 0;">CV</h3>
                        <p>Tests computer vision capabilities including image classification, object detection, 
                        image generation, and visual understanding tasks across various domains.</p>
                    </div>
                    """)

        # Optional: Uncomment if you want to re-enable the Submit tab
        # with gr.TabItem("🚀 Submit Model", elem_id="llm-benchmark-tab-submit", id=2):
        #     with gr.Column():
        #         gr.HTML("""
        #         <div class="about-header" style="display: flex; align-items: center; gap: 20px; margin-bottom: 20px;">
        #             <div style="font-size: 4em;">🚀</div>
        #             <div>
        #                 <h2 style="margin: 0;">Submit Your Model for Evaluation</h2>
        #                 <p style="margin: 5px 0 0 0; color: #666;">Add your model to the MLE-Dojo leaderboard</p>
        #             </div>
        #         </div>
        #         """)
        #
        #         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):
        #                  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,
        #                 )
        #         
        #         gr.HTML('<div style="height: 1px; background-color: #e0e0e0; margin: 20px 0;"></div>')
        #         
        #         gr.HTML("""
        #         <h2 style="display: flex; align-items: center; gap: 10px;">
        #             <span style="font-size: 1.3em;">📝</span> Model Submission Form
        #         </h2>
        #         """)
        #         
        #         with gr.Row():
        #             with gr.Column():
        #                 model_name_textbox = gr.Textbox(
        #                     label="Model Name (on Hugging Face Hub)",
        #                     placeholder="Enter your model name...",
        #                     elem_classes="enhanced-input"
        #                 )
        #                 revision_name_textbox = gr.Textbox(
        #                     label="Revision / Commit Hash",
        #                     placeholder="main",
        #                     elem_classes="enhanced-input"
        #                 )
        #                 model_type = gr.Dropdown(
        #                     choices=["Type A", "Type B", "Type C"],
        #                     label="Model Type",
        #                     multiselect=False,
        #                     value=None,
        #                     interactive=True,
        #                     elem_classes="enhanced-dropdown"
        #                 )
        #             with gr.Column():
        #                 precision = gr.Dropdown(
        #                     choices=["float16", "bfloat16", "float32", "int8", "auto"],
        #                     label="Precision",
        #                     multiselect=False,
        #                     value="auto",
        #                     interactive=True,
        #                     elem_classes="enhanced-dropdown"
        #                 )
        #                 weight_type = gr.Dropdown(
        #                     choices=["Original", "Adapter", "Delta"],
        #                     label="Weights Type",
        #                     multiselect=False,
        #                     value="Original",
        #                     interactive=True,
        #                     elem_classes="enhanced-dropdown"
        #                 )
        #                 base_model_name_textbox = gr.Textbox(
        #                     label="Base Model (for delta or adapter weights)",
        #                     placeholder="Only needed for adapter/delta weights",
        #                     elem_classes="enhanced-input"
        #                 )
        #         
        #         submit_button = gr.Button(
        #             "Submit for Evaluation",
        #             elem_classes="primary-button"
        #         )
        #         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,
        #         )

    # Enhanced citation section
    with gr.Accordion("📄 Citation", open=False, elem_classes="citation-accordion"):
        gr.HTML("""
        <div style="display: flex; align-items: center; gap: 20px; margin-bottom: 15px;">
            <div style="font-size: 2.5em;">📄</div>
            <div>
                <h3 style="margin: 0;">How to Cite This Benchmark</h3>
                <p style="margin: 5px 0 0 0; color: #666;">Please use the following citation if you use this benchmark in your research</p>
            </div>
        </div>
        """)
        
        citation_button = gr.Textbox(
            value=CITATION_BUTTON_TEXT,
            label=CITATION_BUTTON_LABEL,
            lines=10,
            elem_id="citation-button",
            show_copy_button=True,
        )
    
    # Footer
    gr.HTML("""
    <div class="footer">
        <p>© 2025 MLE-Dojo Benchmark. All rights reserved.</p>
        <p style="margin-top: 5px; display: flex; justify-content: center; gap: 20px;">
            <a href="#" style="color: #1a5fb4; text-decoration: none;">Privacy Policy</a>
            <a href="#" style="color: #1a5fb4; text-decoration: none;">Terms of Service</a>
            <a href="#" style="color: #1a5fb4; text-decoration: none;">Contact Us</a>
        </p>
    </div>
    """)

# --- Keep scheduler if relevant ---
if __name__ == "__main__":
    try:
        scheduler = BackgroundScheduler()
        if callable(restart_space):
             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 ---
if __name__ == "__main__":
    print("Launching Enhanced Gradio App...")
    demo.launch()