Delete leaderboard.py

leaderboard.py

@@ -1,381 +0,0 @@
-"""
-Leaderboard module for Dynamic Highscores system.
-
-This module implements the unified leaderboard with tag-based filtering
-for displaying all evaluated models.
-"""
-
-import os
-import json
-import pandas as pd
-import gradio as gr
-import plotly.express as px
-import plotly.graph_objects as go
-
-class Leaderboard:
-    """Manages the unified leaderboard with filtering capabilities."""
-    
-    def __init__(self, db_manager):
-        """Initialize the leaderboard manager.
-        
-        Args:
-            db_manager: Database manager instance
-        """
-        self.db_manager = db_manager
-        self.model_tags = ["All", "Merge", "Agent", "Reasoning", "Coding", "General", "Specialized", "Instruction", "Chat"]
-        
-        # Define color scheme for tags
-        self.tag_colors = {
-            "Merge": "#FF6B6B",
-            "Agent": "#4ECDC4",
-            "Reasoning": "#FFD166",
-            "Coding": "#6B5B95",
-            "General": "#88D8B0",
-            "Specialized": "#FF8C42",
-            "Instruction": "#5D9CEC",
-            "Chat": "#AC92EB"
-        }
-    
-    def get_leaderboard_data(self, tag=None, benchmark_id=None):
-        """Get leaderboard data, optionally filtered by tag or benchmark.
-        
-        Args:
-            tag: Model tag to filter by (None for all)
-            benchmark_id: Benchmark ID to filter by (None for all)
-            
-        Returns:
-            pd.DataFrame: Leaderboard data
-        """
-        # Get evaluation results from database
-        if tag and tag != "All":
-            df = self.db_manager.get_leaderboard_df(tag=tag)
-        else:
-            df = self.db_manager.get_leaderboard_df()
-        
-        # Filter by benchmark if specified
-        if benchmark_id and not df.empty:
-            df = df[df['benchmark_id'] == benchmark_id]
-        
-        return df
-    
-    def format_leaderboard_for_display(self, df):
-        """Format leaderboard data for display.
-        
-        Args:
-            df: Leaderboard DataFrame
-            
-        Returns:
-            pd.DataFrame: Formatted leaderboard for display
-        """
-        if df.empty:
-            return pd.DataFrame()
-        
-        # Select and rename columns for display
-        display_df = df[['model_name', 'benchmark_name', 'tag', 'score', 'completed_at']].copy()
-        display_df.columns = ['Model', 'Benchmark', 'Tag', 'Score', 'Completed']
-        
-        # Round score to 2 decimal places
-        display_df['Score'] = display_df['Score'].round(2)
-        
-        # Sort by score (descending)
-        display_df = display_df.sort_values('Score', ascending=False)
-        
-        return display_df
-    
-    def create_performance_chart(self, df, chart_type="bar"):
-        """Create a performance chart from leaderboard data.
-        
-        Args:
-            df: Leaderboard DataFrame
-            chart_type: Type of chart to create ("bar" or "scatter")
-            
-        Returns:
-            plotly.graph_objects.Figure: Performance chart
-        """
-        if df.empty:
-            # Return empty figure
-            fig = go.Figure()
-            fig.update_layout(
-                title="No data available",
-                xaxis_title="Model",
-                yaxis_title="Score"
-            )
-            return fig
-        
-        # Prepare data for visualization
-        plot_df = df[['model_name', 'benchmark_name', 'tag', 'score']].copy()
-        plot_df.columns = ['Model', 'Benchmark', 'Tag', 'Score']
-        
-        # Create chart based on type
-        if chart_type == "scatter":
-            fig = px.scatter(
-                plot_df,
-                x="Model",
-                y="Score",
-                color="Tag",
-                symbol="Benchmark",
-                size="Score",
-                hover_data=["Model", "Benchmark", "Score"],
-                color_discrete_map=self.tag_colors
-            )
-        else:  # Default to bar chart
-            fig = px.bar(
-                plot_df,
-                x="Model",
-                y="Score",
-                color="Tag",
-                barmode="group",
-                hover_data=["Model", "Benchmark", "Score"],
-                color_discrete_map=self.tag_colors
-            )
-        
-        # Customize layout
-        fig.update_layout(
-            title="Model Performance Comparison",
-            xaxis_title="Model",
-            yaxis_title="Score",
-            legend_title="Tag",
-            font=dict(size=12)
-        )
-        
-        return fig
-    
-    def create_tag_distribution_chart(self, df):
-        """Create a chart showing distribution of models by tag.
-        
-        Args:
-            df: Leaderboard DataFrame
-            
-        Returns:
-            plotly.graph_objects.Figure: Tag distribution chart
-        """
-        if df.empty:
-            # Return empty figure
-            fig = go.Figure()
-            fig.update_layout(
-                title="No data available",
-                xaxis_title="Tag",
-                yaxis_title="Count"
-            )
-            return fig
-        
-        # Count models by tag
-        tag_counts = df['tag'].value_counts().reset_index()
-        tag_counts.columns = ['Tag', 'Count']
-        
-        # Create pie chart
-        fig = px.pie(
-            tag_counts,
-            names='Tag',
-            values='Count',
-            title='Model Distribution by Tag',
-            color='Tag',
-            color_discrete_map=self.tag_colors
-        )
-        
-        # Customize layout
-        fig.update_layout(
-            font=dict(size=12)
-        )
-        
-        return fig
-    
-    def create_benchmark_comparison_chart(self, df):
-        """Create a chart comparing performance across benchmarks.
-        
-        Args:
-            df: Leaderboard DataFrame
-            
-        Returns:
-            plotly.graph_objects.Figure: Benchmark comparison chart
-        """
-        if df.empty:
-            # Return empty figure
-            fig = go.Figure()
-            fig.update_layout(
-                title="No data available",
-                xaxis_title="Benchmark",
-                yaxis_title="Average Score"
-            )
-            return fig
-        
-        # Calculate average score by benchmark
-        benchmark_avg = df.groupby('benchmark_name')['score'].mean().reset_index()
-        benchmark_avg.columns = ['Benchmark', 'Average Score']
-        
-        # Create bar chart
-        fig = px.bar(
-            benchmark_avg,
-            x='Benchmark',
-            y='Average Score',
-            title='Average Performance by Benchmark',
-            color='Benchmark'
-        )
-        
-        # Customize layout
-        fig.update_layout(
-            xaxis_title="Benchmark",
-            yaxis_title="Average Score",
-            font=dict(size=12)
-        )
-        
-        return fig
-
-# Leaderboard UI components
-def create_leaderboard_ui(leaderboard, db_manager):
-    """Create the leaderboard UI components.
-    
-    Args:
-        leaderboard: Leaderboard instance
-        db_manager: Database manager instance
-        
-    Returns:
-        gr.Blocks: Gradio Blocks component with leaderboard UI
-    """
-    with gr.Blocks() as leaderboard_ui:
-        gr.Markdown("# Dynamic Highscores Leaderboard")
-        
-        with gr.Row():
-            with gr.Column(scale=1):
-                tag_filter = gr.Dropdown(
-                    choices=leaderboard.model_tags,
-                    value="All",
-                    label="Filter by Tag"
-                )
-                
-                benchmark_filter = gr.Dropdown(
-                    choices=[("all", "All Benchmarks")],
-                    value="all",
-                    label="Filter by Benchmark"
-                )
-                
-                refresh_button = gr.Button("Refresh Leaderboard")
-            
-            with gr.Column(scale=2):
-                chart_type = gr.Radio(
-                    choices=["bar", "scatter"],
-                    value="bar",
-                    label="Chart Type"
-                )
-                
-                view_type = gr.Radio(
-                    choices=["Table", "Chart", "Dashboard"],
-                    value="Table",
-                    label="View Type"
-                )
-        
-        # Table view
-        leaderboard_table = gr.Dataframe(
-            headers=["Model", "Benchmark", "Tag", "Score", "Completed"],
-            label="Leaderboard",
-            visible=True
-        )
-        
-        # Chart view
-        with gr.Row(visible=False) as chart_view:
-            performance_chart = gr.Plot(label="Performance Chart")
-        
-        # Dashboard view
-        with gr.Row(visible=False) as dashboard_view:
-            with gr.Column(scale=2):
-                dashboard_performance_chart = gr.Plot(label="Performance Comparison")
-            
-            with gr.Column(scale=1):
-                with gr.Row():
-                    tag_distribution_chart = gr.Plot(label="Model Distribution")
-                
-                with gr.Row():
-                    benchmark_comparison_chart = gr.Plot(label="Benchmark Comparison")
-        
-        # Event handlers
-        def refresh_benchmarks():
-            benchmarks = db_manager.get_benchmarks()
-            
-            # Format for dropdown
-            choices = [("all", "All Benchmarks")]
-            choices.extend([(str(b["id"]), b["name"]) for b in benchmarks])
-            
-            return gr.update(choices=choices)
-        
-        def update_leaderboard(tag, benchmark_id, chart_type_val, view_type_val):
-            # Get leaderboard data
-            if benchmark_id == "all":
-                benchmark_id = None
-            else:
-                benchmark_id = int(benchmark_id)
-                
-            df = leaderboard.get_leaderboard_data(tag=tag, benchmark_id=benchmark_id)
-            
-            # Format for display
-            display_df = leaderboard.format_leaderboard_for_display(df)
-            
-            # Create charts
-            perf_chart = leaderboard.create_performance_chart(df, chart_type=chart_type_val)
-            tag_chart = leaderboard.create_tag_distribution_chart(df)
-            benchmark_chart = leaderboard.create_benchmark_comparison_chart(df)
-            
-            # Update visibility based on view type
-            table_visible = view_type_val == "Table"
-            chart_visible = view_type_val == "Chart"
-            dashboard_visible = view_type_val == "Dashboard"
-            
-            return (
-                display_df,
-                perf_chart,
-                perf_chart,  # Same chart for both views
-                tag_chart,
-                benchmark_chart,
-                gr.update(visible=table_visible),
-                gr.update(visible=chart_visible),
-                gr.update(visible=dashboard_visible)
-            )
-        
-        # Connect event handlers
-        refresh_button.click(
-            fn=lambda tag, benchmark, chart_t, view_t: update_leaderboard(tag, benchmark, chart_t, view_t),
-            inputs=[tag_filter, benchmark_filter, chart_type, view_type],
-            outputs=[
-                leaderboard_table,
-                performance_chart,
-                dashboard_performance_chart,
-                tag_distribution_chart,
-                benchmark_comparison_chart,
-                leaderboard_table,
-                chart_view,
-                dashboard_view
-            ]
-        )
-        
-        view_type.change(
-            fn=lambda view_t: (
-                gr.update(visible=view_t == "Table"),
-                gr.update(visible=view_t == "Chart"),
-                gr.update(visible=view_t == "Dashboard")
-            ),
-            inputs=[view_type],
-            outputs=[leaderboard_table, chart_view, dashboard_view]
-        )
-        
-        # Initialize on load
-        leaderboard_ui.load(
-            fn=refresh_benchmarks,
-            inputs=[],
-            outputs=[benchmark_filter]
-        )
-        
-        leaderboard_ui.load(
-            fn=lambda: update_leaderboard("All", "all", "bar", "Table"),
-            inputs=[],
-            outputs=[
-                leaderboard_table,
-                performance_chart,
-                dashboard_performance_chart,
-                tag_distribution_chart,
-                benchmark_comparison_chart,
-                leaderboard_table,
-                chart_view,
-                dashboard_view
-            ]
-        )
-    
-    return leaderboard_ui