app.py

# app.py

# app.py (add this at the very top)

import subprocess
import sys
import os
from pathlib import Path

def setup_salt():
    """Clone and setup SALT library like in Colab."""
    try:
        # Check if salt is already available
        import salt.dataset
        print("✅ SALT library already available")
        return True
    except ImportError:
        pass
    
    print("📥 Setting up SALT library...")
    
    try:
        # Clone SALT repo if not exists
        salt_dir = Path("salt")
        if not salt_dir.exists():
            print("🔄 Cloning SALT repository...")
            subprocess.check_call([
                "git", "clone", "https://github.com/sunbirdai/salt.git"
            ])
        else:
            print("📁 SALT repository already exists")
        
        # Install SALT requirements
        salt_requirements = salt_dir / "requirements.txt"
        if salt_requirements.exists():
            print("📦 Installing SALT requirements...")
            subprocess.check_call([
                sys.executable, "-m", "pip", "install", "-q", "-r", str(salt_requirements)
            ])
        
        # Add SALT directory to Python path
        salt_path = str(salt_dir.absolute())
        if salt_path not in sys.path:
            sys.path.insert(0, salt_path)
            print(f"🔗 Added {salt_path} to Python path")
        
        # Test import
        import salt.dataset
        print("✅ SALT library setup completed successfully")
        return True
        
    except Exception as e:
        print(f"❌ Failed to setup SALT: {e}")
        return False

# Setup SALT on startup
print("🚀 Starting SALT Translation Leaderboard...")
if not setup_salt():
    print("❌ Cannot continue without SALT library")
    print("💡 Please check that git is available and GitHub is accessible")
    sys.exit(1)





import gradio as gr
import pandas as pd
import json
import traceback
from datetime import datetime
from typing import Optional, Dict, Tuple

# Import our modules
from src.test_set import get_public_test_set, get_complete_test_set, create_test_set_download, validate_test_set_integrity
from src.validation import validate_submission_complete
from src.evaluation import evaluate_predictions, generate_evaluation_report, get_google_translate_baseline
from src.leaderboard import (
    load_leaderboard, add_model_to_leaderboard, get_leaderboard_stats, 
    filter_leaderboard, export_leaderboard, get_model_comparison
)
from src.plotting import (
    create_leaderboard_ranking_plot, create_metrics_comparison_plot,
    create_language_pair_heatmap, create_coverage_analysis_plot,
    create_model_performance_timeline, create_google_comparison_plot,
    create_detailed_model_analysis, create_submission_summary_plot
)
from src.utils import sanitize_model_name, get_all_language_pairs, get_google_comparable_pairs
from config import *

# Global variables for caching
current_leaderboard = None
public_test_set = None
complete_test_set = None

def initialize_data():
    """Initialize test sets and leaderboard data."""
    global public_test_set, complete_test_set, current_leaderboard
    
    try:
        print("🔄 Initializing SALT Translation Leaderboard...")
        
        # Load test sets
        print("📥 Loading test sets...")
        public_test_set = get_public_test_set()
        complete_test_set = get_complete_test_set()
        
        # Load leaderboard
        print("🏆 Loading leaderboard...")
        current_leaderboard = load_leaderboard()
        
        print(f"✅ Initialization complete!")
        print(f"   - Test set: {len(public_test_set):,} samples")
        print(f"   - Language pairs: {len(get_all_language_pairs())}")
        print(f"   - Current models: {len(current_leaderboard)}")
        
        return True
        
    except Exception as e:
        print(f"❌ Initialization failed: {e}")
        traceback.print_exc()
        return False

def download_test_set() -> Tuple[str, str]:
    """Create downloadable test set and return file path and info."""
    
    try:
        global public_test_set
        if public_test_set is None:
            public_test_set = get_public_test_set()
        
        # Create download file
        download_path, stats = create_test_set_download()
        
        # Create info message
        info_msg = f"""
        📥 **SALT Test Set Downloaded Successfully!**
        
        **Dataset Statistics:**
        - **Total Samples**: {stats['total_samples']:,}
        - **Language Pairs**: {stats['language_pairs']}
        - **Google Comparable**: {stats['google_comparable_samples']:,} samples
        - **Languages**: {', '.join(stats['languages'])}
        
        **File Format:**
        - `sample_id`: Unique identifier for each sample
        - `source_text`: Text to be translated
        - `source_language`: Source language code
        - `target_language`: Target language code  
        - `domain`: Content domain (if available)
        - `google_comparable`: Whether this pair can be compared with Google Translate
        
        **Next Steps:**
        1. Run your model on the source texts
        2. Create a CSV/JSON file with columns: `sample_id`, `prediction`
        3. Upload your predictions using the "Submit Predictions" tab
        """
        
        return download_path, info_msg
        
    except Exception as e:
        error_msg = f"❌ Error creating test set download: {str(e)}"
        return None, error_msg

def validate_submission(file, model_name: str, author: str, description: str) -> Tuple[str, Optional[pd.DataFrame]]:
    """Validate uploaded prediction file."""
    
    try:
        if file is None:
            return "❌ Please upload a predictions file", None
        
        if not model_name.strip():
            return "❌ Please provide a model name", None
        
        # Read file content
        file_content = file.read()
        filename = file.name
        
        # Get test set for validation
        global complete_test_set
        if complete_test_set is None:
            complete_test_set = get_complete_test_set()
        
        # Validate submission
        validation_result = validate_submission_complete(
            file_content, filename, complete_test_set, model_name
        )
        
        if validation_result['valid']:
            # Store validation info for later use
            return validation_result['report'], validation_result['predictions']
        else:
            return validation_result['report'], None
        
    except Exception as e:
        error_msg = f"❌ Validation error: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
        return error_msg, None

def evaluate_submission(
    predictions_df: pd.DataFrame, 
    model_name: str, 
    author: str, 
    description: str,
    validation_info: Dict
) -> Tuple[str, pd.DataFrame, object, object]:
    """Evaluate validated predictions and update leaderboard."""
    
    try:
        if predictions_df is None:
            return "❌ No valid predictions to evaluate", None, None, None
        
        # Get complete test set with targets
        global complete_test_set, current_leaderboard
        if complete_test_set is None:
            complete_test_set = get_complete_test_set()
        
        # Run evaluation
        print(f"🔄 Evaluating {model_name}...")
        evaluation_results = evaluate_predictions(predictions_df, complete_test_set)
        
        if evaluation_results.get('error'):
            return f"❌ Evaluation error: {evaluation_results['error']}", None, None, None
        
        # Add to leaderboard
        print("🏆 Adding to leaderboard...")
        model_type = "user_submission"  # Could be enhanced to detect model type
        
        updated_leaderboard = add_model_to_leaderboard(
            model_name=sanitize_model_name(model_name),
            author=author or "Anonymous", 
            evaluation_results=evaluation_results,
            validation_info=validation_info,
            model_type=model_type,
            description=description or ""
        )
        
        # Update global leaderboard
        current_leaderboard = updated_leaderboard
        
        # Generate evaluation report
        report = generate_evaluation_report(evaluation_results, model_name)
        
        # Create visualization plots
        summary_plot = create_submission_summary_plot(validation_info, evaluation_results)
        ranking_plot = create_leaderboard_ranking_plot(updated_leaderboard)
        
        # Format success message
        rank = updated_leaderboard[updated_leaderboard['model_name'] == sanitize_model_name(model_name)].index[0] + 1
        total_models = len(updated_leaderboard)
        
        success_msg = f"""
        🎉 **Evaluation Complete!**
        
        **Your Results:**
        - **Model**: {model_name}
        - **Rank**: #{rank} out of {total_models} models
        - **Quality Score**: {evaluation_results['averages'].get('quality_score', 0):.4f}
        - **BLEU**: {evaluation_results['averages'].get('bleu', 0):.2f}
        - **ChrF**: {evaluation_results['averages'].get('chrf', 0):.4f}
        
        **Coverage:**
        - **Samples Evaluated**: {evaluation_results['evaluated_samples']:,}
        - **Language Pairs**: {evaluation_results['summary']['language_pairs_covered']}
        - **Google Comparable**: {evaluation_results['summary']['google_comparable_pairs']} pairs
        
        {report}
        """
        
        return success_msg, updated_leaderboard, summary_plot, ranking_plot
        
    except Exception as e:
        error_msg = f"❌ Evaluation failed: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
        return error_msg, None, None, None

def refresh_leaderboard_display(
    search_query: str = "",
    model_type_filter: str = "all",
    min_coverage: float = 0.0,
    google_only: bool = False
) -> Tuple[pd.DataFrame, object, object, str]:
    """Refresh and filter leaderboard display."""
    
    try:
        global current_leaderboard
        if current_leaderboard is None:
            current_leaderboard = load_leaderboard()
        
        # Apply filters
        filtered_df = filter_leaderboard(
            current_leaderboard,
            search_query=search_query,
            model_type=model_type_filter,
            min_coverage=min_coverage,
            google_comparable_only=google_only
        )
        
        # Create plots
        ranking_plot = create_leaderboard_ranking_plot(filtered_df)
        comparison_plot = create_metrics_comparison_plot(filtered_df)
        
        # Get stats
        stats = get_leaderboard_stats(filtered_df)
        stats_text = f"""
        📊 **Leaderboard Statistics**
        
        - **Total Models**: {stats['total_models']}
        - **Average Quality Score**: {stats['avg_quality_score']:.4f}
        - **Google Comparable Models**: {stats['google_comparable_models']}
        
        **Best Model**: {stats['best_model']['name'] if stats['best_model'] else 'None'}
        **Latest Submission**: {stats['latest_submission'][:10] if stats['latest_submission'] else 'None'}
        """
        
        return filtered_df, ranking_plot, comparison_plot, stats_text
        
    except Exception as e:
        error_msg = f"Error loading leaderboard: {str(e)}"
        empty_df = pd.DataFrame()
        return empty_df, None, None, error_msg

def get_model_details(model_name: str) -> Tuple[str, object]:
    """Get detailed analysis for a specific model."""
    
    try:
        global current_leaderboard
        if current_leaderboard is None:
            return "Leaderboard not loaded", None
        
        # Find model
        model_row = current_leaderboard[current_leaderboard['model_name'] == model_name]
        
        if model_row.empty:
            return f"Model '{model_name}' not found", None
        
        model_info = model_row.iloc[0]
        
        # Parse detailed metrics
        try:
            detailed_results = json.loads(model_info['detailed_metrics'])
        except:
            detailed_results = {}
        
        # Create detailed plot
        detail_plot = create_detailed_model_analysis(detailed_results, model_name)
        
        # Format model details
        details_text = f"""
        # 🔍 Model Details: {model_name}
        
        **Basic Information:**
        - **Author**: {model_info['author']}
        - **Submission Date**: {model_info['submission_date'][:10]}
        - **Model Type**: {model_info['model_type']}
        - **Description**: {model_info['description'] or 'No description provided'}
        
        **Performance Metrics:**
        - **Quality Score**: {model_info['quality_score']:.4f}
        - **BLEU**: {model_info['bleu']:.2f}
        - **ChrF**: {model_info['chrf']:.4f}
        - **ROUGE-1**: {model_info['rouge1']:.4f}
        - **ROUGE-L**: {model_info['rougeL']:.4f}
        
        **Coverage Information:**
        - **Total Samples**: {model_info['total_samples']:,}
        - **Language Pairs Covered**: {model_info['language_pairs_covered']}
        - **Google Comparable Pairs**: {model_info['google_pairs_covered']}
        - **Coverage Rate**: {model_info['coverage_rate']:.1%}
        
        **Google Translate Comparison:**
        - **Google Quality Score**: {model_info['google_quality_score']:.4f}
        - **Google BLEU**: {model_info['google_bleu']:.2f}
        - **Google ChrF**: {model_info['google_chrf']:.4f}
        """
        
        return details_text, detail_plot
        
    except Exception as e:
        error_msg = f"Error getting model details: {str(e)}"
        return error_msg, None

# Initialize data on startup
print("🚀 Starting SALT Translation Leaderboard...")
initialization_success = initialize_data()

# Create Gradio interface
with gr.Blocks(
    title=TITLE,
    theme=gr.themes.Soft(),
    css="""
    .gradio-container {
        max-width: 1400px !important;
        margin: 0 auto;
    }
    .main-header {
        text-align: center;
        margin-bottom: 2rem;
        padding: 2rem;
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white;
        border-radius: 10px;
    }
    .metric-box {
        background: #f8f9fa;
        padding: 1rem;
        border-radius: 8px;
        margin: 0.5rem 0;
        border-left: 4px solid #007bff;
    }
    .error-box {
        background: #f8d7da;
        color: #721c24;
        padding: 1rem;
        border-radius: 8px;
        border-left: 4px solid #dc3545;
    }
    .success-box {
        background: #d4edda;
        color: #155724;
        padding: 1rem;
        border-radius: 8px;
        border-left: 4px solid #28a745;
    }
    """
) as demo:
    
    # Header
    gr.Markdown(f"""
    <div class="main-header">
    
    # {TITLE}
    
    {DESCRIPTION}
    
    **Supported Languages**: {len(ALL_UG40_LANGUAGES)} Ugandan languages | **Google Comparable**: {len(GOOGLE_SUPPORTED_LANGUAGES)} languages
    
    </div>
    """)
    
    # Status indicator
    if initialization_success:
        status_msg = "✅ System initialized successfully"
    else:
        status_msg = "❌ System initialization failed - some features may not work"
    
    gr.Markdown(f"**Status**: {status_msg}")
    
    with gr.Tabs():
        
        # Tab 1: Get Test Set
        with gr.Tab("📥 Download Test Set", id="download"):
            gr.Markdown("""
            ## 📋 Get the SALT Translation Test Set
            
            Download the standardized test set to evaluate your translation model. 
            The test set contains source texts in multiple Ugandan languages that you need to translate.
            """)
            
            with gr.Row():
                download_btn = gr.Button("📥 Download Test Set", variant="primary", size="lg")
            
            with gr.Row():
                with gr.Column():
                    download_file = gr.File(label="📂 Test Set File", interactive=False)
                with gr.Column():
                    download_info = gr.Markdown(label="ℹ️ Test Set Information")
            
            gr.Markdown("""
            ### 📖 Instructions
            
            1. **Download** the test set using the button above
            2. **Run your model** on the source texts to generate translations
            3. **Create a predictions file** with your model's outputs
            4. **Submit** your predictions using the "Submit Predictions" tab
            
            ### 📋 Required Prediction Format
            
            Your predictions file must be a CSV/TSV/JSON with these columns:
            - `sample_id`: The unique identifier from the test set
            - `prediction`: Your model's translation for that sample
            
            **Example CSV:**
            ```
            sample_id,prediction
            salt_000001,Oli otya mukwano gwange?
            salt_000002,Webale nyo olukya
            ...
            ```
            """)
        
        # Tab 2: Submit Predictions
        with gr.Tab("🚀 Submit Predictions", id="submit"):
            gr.Markdown("""
            ## 🎯 Submit Your Model's Predictions
            
            Upload your model's predictions on the SALT test set for evaluation.
            """)
            
            with gr.Row():
                with gr.Column(scale=1):
                    # Model information
                    gr.Markdown("### 📝 Model Information")
                    
                    model_name_input = gr.Textbox(
                        label="🤖 Model Name",
                        placeholder="e.g., MyTranslator-v1.0",
                        info="Unique name for your model"
                    )
                    
                    author_input = gr.Textbox(
                        label="👤 Author/Organization", 
                        placeholder="Your name or organization",
                        value="Anonymous"
                    )
                    
                    description_input = gr.Textbox(
                        label="📄 Description (Optional)",
                        placeholder="Brief description of your model",
                        lines=3
                    )
                    
                    # File upload
                    gr.Markdown("### 📤 Upload Predictions")
                    
                    predictions_file = gr.File(
                        label="📂 Predictions File",
                        file_types=[".csv", ".tsv", ".json"],
                        info="CSV/TSV/JSON file with your model's predictions"
                    )
                    
                    validate_btn = gr.Button("✅ Validate Submission", variant="secondary")
                    submit_btn = gr.Button("🚀 Submit for Evaluation", variant="primary", interactive=False)
                
                with gr.Column(scale=1):
                    gr.Markdown("### 📊 Validation Results")
                    validation_output = gr.Markdown()
            
            # Results section
            gr.Markdown("### 🏆 Evaluation Results")
            
            with gr.Row():
                evaluation_output = gr.Markdown()
            
            with gr.Row():
                with gr.Column():
                    submission_plot = gr.Plot(label="📈 Your Submission Analysis")
                with gr.Column():
                    updated_leaderboard_plot = gr.Plot(label="🏆 Updated Leaderboard")
            
            with gr.Row():
                results_table = gr.Dataframe(label="📊 Updated Leaderboard", interactive=False)
        
        # Tab 3: Leaderboard
        with gr.Tab("🏆 Leaderboard", id="leaderboard"):
            with gr.Row():
                with gr.Column(scale=3):
                    search_input = gr.Textbox(
                        label="🔍 Search Models",
                        placeholder="Search by model name, author...",
                    )
                with gr.Column(scale=1):
                    model_type_dropdown = gr.Dropdown(
                        label="🔧 Model Type",
                        choices=["all", "user_submission", "baseline"],
                        value="all"
                    )
                with gr.Column(scale=1):
                    min_coverage_slider = gr.Slider(
                        label="📊 Min Coverage",
                        minimum=0.0,
                        maximum=1.0,
                        value=0.0,
                        step=0.1
                    )
                with gr.Column(scale=1):
                    google_only_checkbox = gr.Checkbox(
                        label="🤖 Google Comparable Only",
                        value=False
                    )
            
            with gr.Row():
                refresh_btn = gr.Button("🔄 Refresh", variant="secondary")
            
            with gr.Row():
                leaderboard_stats = gr.Markdown()
            
            with gr.Row():
                with gr.Column():
                    leaderboard_plot = gr.Plot(label="🏆 Rankings")
                with gr.Column():
                    comparison_plot = gr.Plot(label="📊 Multi-Metric Comparison")
            
            with gr.Row():
                leaderboard_table = gr.Dataframe(
                    label="📈 Full Leaderboard",
                    interactive=False,
                    wrap=True
                )
        
        # Tab 4: Model Analysis
        with gr.Tab("🔍 Model Analysis", id="analysis"):
            with gr.Row():
                model_select = gr.Dropdown(
                    label="🤖 Select Model",
                    choices=[],
                    value=None,
                    info="Choose a model for detailed analysis"
                )
                analyze_btn = gr.Button("🔍 Analyze", variant="primary")
            
            with gr.Row():
                model_details = gr.Markdown()
            
            with gr.Row():
                model_analysis_plot = gr.Plot(label="📊 Detailed Performance Analysis")
        
        # Tab 5: Documentation
        with gr.Tab("📚 Documentation", id="docs"):
            gr.Markdown(f"""
            # 📖 SALT Translation Leaderboard Documentation
            
            ## 🎯 Overview
            
            The SALT Translation Leaderboard is a scientific evaluation platform for translation models on Ugandan languages. 
            Submit your model's predictions on our standardized test set to see how it compares with other models.
            
            ## 🗣️ Supported Languages
            
            **All UG40 Languages ({len(ALL_UG40_LANGUAGES)} total):**
            {', '.join([f"{code} ({LANGUAGE_NAMES.get(code, code)})" for code in ALL_UG40_LANGUAGES])}
            
            **Google Translate Comparable ({len(GOOGLE_SUPPORTED_LANGUAGES)} languages):**
            {', '.join([f"{code} ({LANGUAGE_NAMES.get(code, code)})" for code in GOOGLE_SUPPORTED_LANGUAGES])}
            
            ## 📊 Evaluation Metrics
            
            ### Primary Metrics
            - **Quality Score**: Composite metric (0-1, higher better) combining multiple metrics
            - **BLEU**: Translation quality score (0-100, higher better)
            - **ChrF**: Character-level F-score (0-1, higher better)
            
            ### Secondary Metrics  
            - **ROUGE-1/ROUGE-L**: Recall-oriented metrics (0-1, higher better)
            - **CER/WER**: Character/Word Error Rate (0-1, lower better)
            - **Length Ratio**: Prediction/reference length ratio
            
            ## 🔄 Submission Process
            
            ### Step 1: Download Test Set
            1. Go to "Download Test Set" tab
            2. Click "Download Test Set" button
            3. Save the `salt_test_set.csv` file
            
            ### Step 2: Generate Predictions
            1. Load the test set in your code
            2. For each row, translate `source_text` from `source_language` to `target_language`
            3. Save results as CSV with columns: `sample_id`, `prediction`
            
            ### Step 3: Submit & Evaluate
            1. Go to "Submit Predictions" tab
            2. Fill in model information
            3. Upload your predictions file
            4. Validate and submit for evaluation
            
            ## 📋 File Formats
            
            ### Test Set Format
            ```csv
            sample_id,source_text,source_language,target_language,domain,google_comparable
            salt_000001,"Hello world",eng,lug,general,true
            salt_000002,"How are you?",eng,ach,conversation,true
            ```
            
            ### Predictions Format
            ```csv
            sample_id,prediction
            salt_000001,"Amakuru ensi"
            salt_000002,"Ibino nining?"
            ```
            
            ## 🏆 Leaderboard Types
            
            ### 1. Full UG40 Leaderboard
            - Includes all {len(get_all_language_pairs())} language pairs
            - Complete evaluation across all Ugandan languages
            - Primary ranking system
            
            ### 2. Google Translate Comparable  
            - Limited to {len(get_google_comparable_pairs())} pairs
            - Only languages supported by Google Translate
            - Allows direct comparison with Google Translate baseline
            
            ## 🔬 Scientific Rigor
            
            - **Standardized Evaluation**: Same test set for all models
            - **Multiple Metrics**: Comprehensive evaluation beyond just BLEU
            - **Coverage Tracking**: Transparency about what each model covers
            - **Reproducible**: All evaluation code and data available
            
            ## 🤝 Contributing
            
            This leaderboard is maintained by [Sunbird AI](https://sunbird.ai). 
            
            **Contact**: [[email protected]](mailto:[email protected])
            **GitHub**: [Sunbird AI GitHub](https://github.com/sunbirdai)
            
            ## 📄 Citation
            
            If you use this leaderboard in your research, please cite:
            
            ```bibtex
            @misc{{salt_leaderboard_2024,
              title={{SALT Translation Leaderboard: Evaluation of Translation Models on Ugandan Languages}},
              author={{Sunbird AI}},
              year={{2024}},
              url={{https://huggingface.co/spaces/Sunbird/salt-translation-leaderboard}}
            }}
            ```
            
            ## 🔗 Related Resources
            
            - **SALT Dataset**: [sunbird/salt](https://huggingface.co/datasets/sunbird/salt)
            - **Sunbird AI Models**: [Sunbird Organization](https://huggingface.co/Sunbird)
            - **Research Papers**: [Sunbird AI Publications](https://sunbird.ai/research)
            """)
    
    # Event handlers with state management
    predictions_validated = gr.State(value=None)
    validation_info_state = gr.State(value=None)
    
    # Download test set
    download_btn.click(
        fn=download_test_set,
        outputs=[download_file, download_info]
    )
    
    # Validate predictions
    def handle_validation(file, model_name, author, description):
        report, predictions = validate_submission(file, model_name, author, description)
        is_valid = predictions is not None
        return report, predictions, predictions, is_valid
    
    validate_btn.click(
        fn=handle_validation,
        inputs=[predictions_file, model_name_input, author_input, description_input],
        outputs=[validation_output, predictions_validated, validation_info_state, submit_btn]
    )
    
    # Submit for evaluation
    def handle_submission(predictions, model_name, author, description, validation_info):
        if predictions is None:
            return "❌ Please validate your submission first", None, None, None
        
        # Extract validation info dict
        validation_dict = {
            'coverage': getattr(validation_info, 'coverage', 0.8) if hasattr(validation_info, 'coverage') else 0.8,
            'report': 'Validation passed'
        }
        
        return evaluate_submission(predictions, model_name, author, description, validation_dict)
    
    submit_btn.click(
        fn=handle_submission,
        inputs=[predictions_validated, model_name_input, author_input, description_input, validation_info_state],
        outputs=[evaluation_output, results_table, submission_plot, updated_leaderboard_plot]
    )
    
    # Refresh leaderboard
    def update_leaderboard_and_dropdown(*args):
        table, plot1, plot2, stats = refresh_leaderboard_display(*args)
        
        # Update model dropdown choices
        model_choices = table['model_name'].tolist() if not table.empty else []
        
        return table, plot1, plot2, stats, gr.Dropdown(choices=model_choices)
    
    refresh_btn.click(
        fn=update_leaderboard_and_dropdown,
        inputs=[search_input, model_type_dropdown, min_coverage_slider, google_only_checkbox],
        outputs=[leaderboard_table, leaderboard_plot, comparison_plot, leaderboard_stats, model_select]
    )
    
    # Auto-refresh on filter changes
    for input_component in [search_input, model_type_dropdown, min_coverage_slider, google_only_checkbox]:
        input_component.change(
            fn=update_leaderboard_and_dropdown,
            inputs=[search_input, model_type_dropdown, min_coverage_slider, google_only_checkbox],
            outputs=[leaderboard_table, leaderboard_plot, comparison_plot, leaderboard_stats, model_select]
        )
    
    # Model analysis
    analyze_btn.click(
        fn=get_model_details,
        inputs=[model_select],
        outputs=[model_details, model_analysis_plot]
    )
    
    # Load initial data
    demo.load(
        fn=update_leaderboard_and_dropdown,
        inputs=[search_input, model_type_dropdown, min_coverage_slider, google_only_checkbox],
        outputs=[leaderboard_table, leaderboard_plot, comparison_plot, leaderboard_stats, model_select]
    )

# Launch the application
if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True,
        enable_queue=True
    )