import gradio as gr
import pandas as pd
from typing import List, Dict, Union, Optional

class SmartSelectColumns(gr.SelectColumns):
    """
    Enhanced SelectColumns component that supports substring matching and column mapping.
    Inherits from gr.SelectColumns but adds additional filtering capabilities.
    """
    def __init__(
        self,
        *args,
        column_filters: Optional[Dict[str, List[str]]] = None,
        column_mapping: Optional[Dict[str, str]] = None,
        **kwargs
    ):
        """
        Initialize the SmartSelectColumns component.
        
        Args:
            column_filters: Dict mapping filter names to lists of substrings to match
            column_mapping: Dict mapping display names to actual column names
            *args, **kwargs: Arguments passed to parent SelectColumns
        """
        super().__init__(*args, **kwargs)
        self.column_filters = column_filters or {}
        self.column_mapping = column_mapping or {}
        
    def preprocess(self, x: List[str]) -> List[str]:
        """Transform selected display names back to actual column names."""
        if self.column_mapping:
            reverse_mapping = {v: k for k, v in self.column_mapping.items()}
            return [reverse_mapping.get(col, col) for col in x]
        return x
    
    def get_filtered_columns(self, df: pd.DataFrame) -> Dict[str, List[str]]:
        """
        Get columns filtered by substring matches.
        
        Args:
            df: Input DataFrame
            
        Returns:
            Dict mapping filter names to lists of matching columns
        """
        filtered_cols = {}
        
        for filter_name, substrings in self.column_filters.items():
            matching_cols = []
            for col in df.columns:
                if any(substr.lower() in col.lower() for substr in substrings):
                    matching_cols.append(col)
            filtered_cols[filter_name] = matching_cols
            
        return filtered_cols

    def update(
        self, 
        value: Union[pd.DataFrame, Dict[str, List[str]]], 
        interactive: Optional[bool] = None
    ) -> Dict:
        """
        Update the component with new values.
        
        Args:
            value: Either a DataFrame or dict of predefined column groups
            interactive: Whether the component should be interactive
        
        Returns:
            Dict containing the update configuration
        """
        if isinstance(value, pd.DataFrame):
            # Get filtered column groups
            filtered_cols = self.get_filtered_columns(value)
            
            # Create display names for columns if mapping exists
            choices = list(value.columns)
            if self.column_mapping:
                choices = [self.column_mapping.get(col, col) for col in choices]
                
            return {
                "choices": choices,
                "filtered_cols": filtered_cols,
                "interactive": interactive if interactive is not None else self.interactive
            }
        return super().update(value, interactive)

# Example usage
if __name__ == "__main__":
    df = pd.DataFrame({
        "ioi_score_1": [1, 2, 3],
        "ioi_score_2": [4, 5, 6],
        "other_metric": [7, 8, 9],
        "performance_1": [10, 11, 12]
    })
    
    # Define filters and mappings
    column_filters = {
        "IOI": ["ioi"],
        "Performance Metrics": ["performance"]
    }
    
    column_mapping = {
        "ioi_score_1": "IOI Score (Type 1)",
        "ioi_score_2": "IOI Score (Type 2)",
        "other_metric": "Other Metric",
        "performance_1": "Performance Metric 1"
    }
    
    # Create interface
    with gr.Blocks() as demo:
        select_cols = SmartSelectColumns(
            column_filters=column_filters,
            column_mapping=column_mapping,
            multiselect=True
        )
        
        # Update component with DataFrame
        select_cols.update(df)
        
    demo.launch()
































import gradio as gr
import pandas as pd
from typing import List, Dict, Union, Optional, Any
from dataclasses import fields

class SmartSelectColumns(gr.SelectColumns):
    """
    Enhanced SelectColumns component for Gradio Leaderboard with smart filtering and mapping capabilities.
    """
    def __init__(
        self,
        column_filters: Optional[Dict[str, List[str]]] = None,
        column_mapping: Optional[Dict[str, str]] = None,
        initial_selected: Optional[List[str]] = None,
        *args,
        **kwargs
    ):
        """
        Initialize SmartSelectColumns with enhanced functionality.
        
        Args:
            column_filters: Dict mapping filter names to lists of substrings to match
            column_mapping: Dict mapping actual column names to display names
            initial_selected: List of column names to be initially selected
            *args, **kwargs: Additional arguments passed to parent SelectColumns
        """
        super().__init__(*args, **kwargs)
        self.column_filters = column_filters or {}
        self.column_mapping = column_mapping or {}
        self.reverse_mapping = {v: k for k, v in self.column_mapping.items()} if column_mapping else {}
        self.initial_selected = initial_selected or []
        
    def preprocess(self, x: List[str]) -> List[str]:
        """
        Transform selected display names back to actual column names.
        
        Args:
            x: List of selected display names
            
        Returns:
            List of actual column names
        """
        return [self.reverse_mapping.get(col, col) for col in x]
    
    def postprocess(self, y: List[str]) -> List[str]:
        """
        Transform actual column names to display names.
        
        Args:
            y: List of actual column names
            
        Returns:
            List of display names
        """
        return [self.column_mapping.get(col, col) for col in y]
    
    def get_filtered_columns(self, df: pd.DataFrame) -> Dict[str, List[str]]:
        """
        Get columns filtered by substring matches.
        
        Args:
            df: Input DataFrame
            
        Returns:
            Dict mapping filter names to lists of matching display names
        """
        filtered_cols = {}
        
        for filter_name, substrings in self.column_filters.items():
            matching_cols = []
            for col in df.columns:
                if any(substr.lower() in col.lower() for substr in substrings):
                    display_name = self.column_mapping.get(col, col)
                    matching_cols.append(display_name)
            filtered_cols[filter_name] = matching_cols
            
        return filtered_cols

    def update(
        self, 
        value: Union[pd.DataFrame, Dict[str, List[str]], Any], 
        interactive: Optional[bool] = None
    ) -> Dict:
        """
        Update component with new values, supporting DataFrame fields.
        
        Args:
            value: DataFrame, dict of columns, or fields object
            interactive: Whether component should be interactive
            
        Returns:
            Dict containing update configuration
        """
        if isinstance(value, pd.DataFrame):
            filtered_cols = self.get_filtered_columns(value)
            choices = [self.column_mapping.get(col, col) for col in value.columns]
            
            # Set initial selection if provided
            value = self.initial_selected if self.initial_selected else choices
            
            return {
                "choices": choices,
                "value": value,
                "filtered_cols": filtered_cols,
                "interactive": interactive if interactive is not None else self.interactive
            }
        
        # Handle fields object (e.g., from dataclass)
        if hasattr(value, '__dataclass_fields__'):
            field_names = [field.name for field in fields(value)]
            choices = [self.column_mapping.get(name, name) for name in field_names]
            return {
                "choices": choices,
                "value": self.initial_selected if self.initial_selected else choices,
                "interactive": interactive if interactive is not None else self.interactive
            }
            
        return super().update(value, interactive)

def initialize_leaderboard(df: pd.DataFrame, column_class: Any, 
                         filters: Dict[str, List[str]], 
                         mappings: Dict[str, str],
                         initial_columns: Optional[List[str]] = None) -> gr.Leaderboard:
    """
    Initialize a Gradio Leaderboard with SmartSelectColumns.
    
    Args:
        df: Input DataFrame
        column_class: Class containing column definitions (e.g., AutoEvalColumn_mib_subgraph)
        filters: Column filters for substring matching
        mappings: Column name mappings (actual -> display)
        initial_columns: List of columns to show initially
        
    Returns:
        Configured Leaderboard instance
    """

    # Define filters and mappings
    filters = {
        "IOI Metrics": ["ioi"],
        "Performance Metrics": ["performance"]
    }
    
    mappings = {
        "ioi_score_1": "IOI Score (Type 1)",
        "ioi_score_2": "IOI Score (Type 2)",
        "other_metric": "Other Metric",
        "performance_1": "Performance Metric 1"
    }



# Example usage
if __name__ == "__main__":
    # Sample data
    df = pd.DataFrame({
        "ioi_score_1": [1, 2, 3],
        "ioi_score_2": [4, 5, 6],
        "other_metric": [7, 8, 9],
        "performance_1": [10, 11, 12],
        "Method": ["A", "B", "C"]
    })
    
    # Define filters and mappings
    filters = {
        "IOI Metrics": ["ioi"],
        "gemma2.5": ["gemma2_5`"]
    }
    
    mappings = {
        "ioi_score_1": "IOI Score (Type 1)",
        "ioi_score_2": "IOI Score (Type 2)",
        "other_metric": "Other Metric",
        "performance_1": "Performance Metric 1"
    }
    
    # Create demo interface
    with gr.Blocks() as demo:
        # Initialize leaderboard with smart columns
        leaderboard = initialize_leaderboard(
            df=df,
            column_class=None,  # Replace with your actual column class
            filters=filters,
            mappings=mappings,
            initial_columns=["Method", "IOI Score (Type 1)"]
        )
    
    
    # Create renamed DataFrame with display names
    renamed_df = df.rename(columns=mappings)

    initial_columns=["Method", "IOI Score (Type 1)"]
    initial_columns=?
    
    # Initialize SmartSelectColumns
    smart_columns = SmartSelectColumns(
        column_filters=filters,
        column_mapping=mappings,
        initial_selected=initial_columns,
        multiselect=True
    )
    column_class=None

    return gr.Leaderboard(
        value=renamed_df,
        datatype=[c.type for c in fields(column_class)],
        select_columns=smart_columns,
        search_columns=["Method"],
        hide_columns=[],
        interactive=False
    )
        
    demo.launch()





































from gradio_leaderboard import SelectColumns, Leaderboard
import pandas as pd
from typing import List, Dict, Union, Optional, Any
from dataclasses import fields

class SmartSelectColumns(SelectColumns):
    """
    Enhanced SelectColumns component for gradio_leaderboard with explicit column grouping.
    """
    def __init__(
        self,
        column_groups: Optional[Dict[str, List[str]]] = None,
        column_mapping: Optional[Dict[str, str]] = None,
        initial_selected: Optional[List[str]] = None,
        **kwargs
    ):
        """
        Initialize SmartSelectColumns with enhanced functionality.
        
        Args:
            column_groups: Dict mapping group names to lists of columns in that group
            column_mapping: Dict mapping actual column names to display names
            initial_selected: List of columns to show initially
        """
        super().__init__(**kwargs)
        self.column_groups = column_groups or {}
        self.column_mapping = column_mapping or {}
        self.reverse_mapping = {v: k for k, v in self.column_mapping.items()} if column_mapping else {}
        self.initial_selected = initial_selected or []
        
    def preprocess_value(self, x: List[str]) -> List[str]:
        """Transform selected display names back to actual column names."""
        return [self.reverse_mapping.get(col, col) for col in x]
    
    def postprocess_value(self, y: List[str]) -> List[str]:
        """Transform actual column names to display names."""
        return [self.column_mapping.get(col, col) for col in y]

    def update(
        self, 
        value: Union[pd.DataFrame, Dict[str, List[str]], Any]
    ) -> Dict:
        """Update component with new values."""
        if isinstance(value, pd.DataFrame):
            # Get all column names and convert to display names
            choices = [self.column_mapping.get(col, col) for col in value.columns]
            
            # Use initial selection or default columns
            selected = self.initial_selected if self.initial_selected else choices
            
            # Convert column groups to use display names
            filtered_cols = {}
            for group_name, columns in self.column_groups.items():
                filtered_cols[group_name] = [
                    self.column_mapping.get(col, col) 
                    for col in columns 
                    if col in value.columns
                ]
            
            return {
                "choices": choices,
                "value": selected,
                "filtered_cols": filtered_cols
            }
        
        # Handle fields object
        if hasattr(value, '__dataclass_fields__'):
            field_names = [field.name for field in fields(value)]
            choices = [self.column_mapping.get(name, name) for name in field_names]
            return {
                "choices": choices,
                "value": self.initial_selected if self.initial_selected else choices
            }
            
        return super().update(value)


# Example usage
if __name__ == "__main__":
    # Sample DataFrame
    # df = pd.DataFrame({
    #     "eval_name": ["test1", "test2", "test3"],
    #     "Method": ["method1", "method2", "method3"],
    #     "ioi_llama3": [0.1, 0.2, 0.3],
    #     "ioi_qwen2_5": [0.4, 0.5, 0.6],
    #     "ioi_gpt2": [0.7, 0.8, 0.9],
    #     "mcqa_llama3": [0.2, 0.3, 0.4],
    #     "Average": [0.35, 0.45, 0.55]
    # })

    # Complete column groups for both benchmarks and models
    column_groups = {
        # Benchmark groups
        "Benchmark group for ioi": ["ioi_gpt2", "ioi_qwen2_5", "ioi_gemma2", "ioi_llama3"],
        "Benchmark group for mcqa": ["mcqa_qwen2_5", "mcqa_gemma2", "mcqa_llama3"],
        "Benchmark group for arithmetic_addition": ["arithmetic_addition_llama3"],
        "Benchmark group for arithmetic_subtraction": ["arithmetic_subtraction_llama3"],
        "Benchmark group for arc_easy": ["arc_easy_gemma2", "arc_easy_llama3"],
        "Benchmark group for arc_challenge": ["arc_challenge_llama3"],
        
        # Model groups
        "Model group for qwen2_5": ["ioi_qwen2_5", "mcqa_qwen2_5"],
        "Model group for gpt2": ["ioi_gpt2"],
        "Model group for gemma2": ["ioi_gemma2", "mcqa_gemma2", "arc_easy_gemma2"],
        "Model group for llama3": [
            "ioi_llama3", 
            "mcqa_llama3", 
            "arithmetic_addition_llama3", 
            "arithmetic_subtraction_llama3", 
            "arc_easy_llama3", 
            "arc_challenge_llama3"
        ]
    }

    # Complete mappings for more readable display names
    mappings = {
        # IOI benchmark mappings
        "ioi_llama3": "IOI (LLaMA-3)",
        "ioi_qwen2_5": "IOI (Qwen-2.5)",
        "ioi_gpt2": "IOI (GPT-2)",
        "ioi_gemma2": "IOI (Gemma-2)",
        
        # MCQA benchmark mappings
        "mcqa_llama3": "MCQA (LLaMA-3)",
        "mcqa_qwen2_5": "MCQA (Qwen-2.5)",
        "mcqa_gemma2": "MCQA (Gemma-2)",
        
        # Arithmetic benchmark mappings
        "arithmetic_addition_llama3": "Arithmetic Addition (LLaMA-3)",
        "arithmetic_subtraction_llama3": "Arithmetic Subtraction (LLaMA-3)",
        
        # ARC benchmark mappings
        "arc_easy_llama3": "ARC Easy (LLaMA-3)",
        "arc_easy_gemma2": "ARC Easy (Gemma-2)",
        "arc_challenge_llama3": "ARC Challenge (LLaMA-3)",
        
        # Other columns
        "eval_name": "Evaluation Name",
        "Method": "Method",
        "Average": "Average Score"
    }

    # Create SmartSelectColumns instance
    smart_columns = SmartSelectColumns(
        column_groups=column_groups,
        column_mapping=mappings,
        initial_selected=["Method", "Average"]
    )

    # Create Leaderboard directly
    leaderboard = Leaderboard(
        value=df,
        datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
        select_columns=smart_columns,
        search_columns=["Method"],
        hide_columns=[],
        interactive=False
    )

























from gradio_leaderboard import SelectColumns, Leaderboard
import pandas as pd
from typing import List, Dict, Union, Optional, Any
from dataclasses import fields

class SmartSelectColumns(SelectColumns):
    """
    Enhanced SelectColumns component for gradio_leaderboard with dynamic column filtering.
    """
    def __init__(
        self,
        benchmark_keywords: Optional[List[str]] = None,
        model_keywords: Optional[List[str]] = None,
        column_mapping: Optional[Dict[str, str]] = None,
        initial_selected: Optional[List[str]] = None,
        **kwargs
    ):
        """
        Initialize SmartSelectColumns with dynamic filtering.
        
        Args:
            benchmark_keywords: List of benchmark names to filter by (e.g., ["ioi", "mcqa"])
            model_keywords: List of model names to filter by (e.g., ["llama3", "qwen2_5"])
            column_mapping: Dict mapping actual column names to display names
            initial_selected: List of columns to show initially
        """
        super().__init__(**kwargs)
        self.benchmark_keywords = benchmark_keywords or []
        self.model_keywords = model_keywords or []
        self.column_mapping = column_mapping or {}
        self.reverse_mapping = {v: k for k, v in self.column_mapping.items()} if column_mapping else {}
        self.initial_selected = initial_selected or []
        
    def preprocess_value(self, x: List[str]) -> List[str]:
        """Transform selected display names back to actual column names."""
        return [self.reverse_mapping.get(col, col) for col in x]
    
    def postprocess_value(self, y: List[str]) -> List[str]:
        """Transform actual column names to display names."""
        return [self.column_mapping.get(col, col) for col in y]

    def get_filtered_groups(self, df: pd.DataFrame) -> Dict[str, List[str]]:
        """
        Dynamically create column groups based on keywords.
        """
        filtered_groups = {}
        
        # Create benchmark groups
        for benchmark in self.benchmark_keywords:
            matching_cols = [
                col for col in df.columns 
                if benchmark in col.lower()
            ]
            if matching_cols:
                group_name = f"Benchmark group for {benchmark}"
                filtered_groups[group_name] = [
                    self.column_mapping.get(col, col) 
                    for col in matching_cols
                ]
        
        # Create model groups
        for model in self.model_keywords:
            matching_cols = [
                col for col in df.columns 
                if model in col.lower()
            ]
            if matching_cols:
                group_name = f"Model group for {model}"
                filtered_groups[group_name] = [
                    self.column_mapping.get(col, col) 
                    for col in matching_cols
                ]
        
        return filtered_groups

    def update(
        self, 
        value: Union[pd.DataFrame, Dict[str, List[str]], Any]
    ) -> Dict:
        """Update component with new values."""
        if isinstance(value, pd.DataFrame):
            # Get all column names and convert to display names
            choices = [self.column_mapping.get(col, col) for col in value.columns]
            
            # Use initial selection or default columns
            selected = self.initial_selected if self.initial_selected else choices
            
            # Get dynamically filtered groups
            filtered_cols = self.get_filtered_groups(value)
            
            return {
                "choices": choices,
                "value": selected,
                "filtered_cols": filtered_cols
            }
        
        # Handle fields object
        if hasattr(value, '__dataclass_fields__'):
            field_names = [field.name for field in fields(value)]
            choices = [self.column_mapping.get(name, name) for name in field_names]
            return {
                "choices": choices,
                "value": self.initial_selected if self.initial_selected else choices
            }
            
        return super().update(value)


# Example usage
if __name__ == "__main__":
    # Sample DataFrame
    df = pd.DataFrame({
        "eval_name": ["test1", "test2", "test3"],
        "Method": ["method1", "method2", "method3"],
        "ioi_llama3": [0.1, 0.2, 0.3],
        "ioi_qwen2_5": [0.4, 0.5, 0.6],
        "ioi_gpt2": [0.7, 0.8, 0.9],
        "mcqa_llama3": [0.2, 0.3, 0.4],
        "Average": [0.35, 0.45, 0.55]
    })

    # Define keywords for filtering
    benchmark_keywords = ["ioi", "mcqa", "arithmetic_addition", "arithmetic_subtraction", "arc_easy", "arc_challenge"]
    model_keywords = ["qwen2_5", "gpt2", "gemma2", "llama3"]

    # Optional: Define display names
    mappings = {
        "ioi_llama3": "IOI (LLaMA-3)",
        "ioi_qwen2_5": "IOI (Qwen-2.5)",
        "ioi_gpt2": "IOI (GPT-2)",
        "ioi_gemma2": "IOI (Gemma-2)",
        "mcqa_llama3": "MCQA (LLaMA-3)",
        "mcqa_qwen2_5": "MCQA (Qwen-2.5)",
        "mcqa_gemma2": "MCQA (Gemma-2)",
        "arithmetic_addition_llama3": "Arithmetic Addition (LLaMA-3)",
        "arithmetic_subtraction_llama3": "Arithmetic Subtraction (LLaMA-3)",
        "arc_easy_llama3": "ARC Easy (LLaMA-3)",
        "arc_easy_gemma2": "ARC Easy (Gemma-2)",
        "arc_challenge_llama3": "ARC Challenge (LLaMA-3)",
        "eval_name": "Evaluation Name",
        "Method": "Method",
        "Average": "Average Score"
    }

    # Create SmartSelectColumns instance
    smart_columns = SmartSelectColumns(
        benchmark_keywords=benchmark_keywords,
        model_keywords=model_keywords,
        column_mapping=mappings,
        initial_selected=["Method", "Average"]
    )

    # Create Leaderboard
    leaderboard = Leaderboard(
        value=df,
        datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
        select_columns=smart_columns,
        search_columns=["Method"],
        hide_columns=[],
        interactive=False
    )































Debugging DataFrame columns: ['eval_name', 'Method', 'ioi_llama3', 'ioi_qwen2_5', 'ioi_gpt2', 'ioi_gemma2', 'mcqa_llama3', 'mcqa_qwen2_5', 'mcqa_gemma2', 'arithmetic_addition_llama3', 'arithmetic_subtraction_llama3', 'arc_easy_llama3', 'arc_easy_gemma2', 'arc_challenge_llama3', 'Average']

Benchmark group for ioi: ['ioi_gpt2', 'ioi_qwen2_5', 'ioi_gemma2', 'ioi_llama3']

Benchmark group for mcqa: ['mcqa_qwen2_5', 'mcqa_gemma2', 'mcqa_llama3']

Benchmark group for arithmetic_addition: ['arithmetic_addition_llama3']

Benchmark group for arithmetic_subtraction: ['arithmetic_subtraction_llama3']

Benchmark group for arc_easy: ['arc_easy_gemma2', 'arc_easy_llama3']

Benchmark group for arc_challenge: ['arc_challenge_llama3']

Model group for qwen2_5: ['ioi_qwen2_5', 'mcqa_qwen2_5']

Model group for gpt2: ['ioi_gpt2']

Model group for gemma2: ['ioi_gemma2', 'mcqa_gemma2', 'arc_easy_gemma2']

Model group for llama3: ['ioi_llama3', 'mcqa_llama3', 'arithmetic_addition_llama3', 'arithmetic_subtraction_llama3', 'arc_easy_llama3', 'arc_challenge_llama3']

All available columns: ['ioi_gpt2', 'ioi_qwen2_5', 'ioi_gemma2', 'ioi_llama3', 'mcqa_qwen2_5', 'mcqa_gemma2', 'mcqa_llama3', 'arithmetic_addition_llama3', 'arithmetic_subtraction_llama3', 'arc_easy_gemma2', 'arc_easy_llama3', 'arc_challenge_llama3', 'ioi_qwen2_5', 'mcqa_qwen2_5', 'ioi_gpt2', 'ioi_gemma2', 'mcqa_gemma2', 'arc_easy_gemma2', 'ioi_llama3', 'mcqa_llama3', 'arithmetic_addition_llama3', 'arithmetic_subtraction_llama3', 'arc_easy_llama3', 'arc_challenge_llama3']
* Running on local URL:  http://0.0.0.0:7860
/usr/local/lib/python3.10/site-packages/gradio/blocks.py:2634: UserWarning: Setting share=True is not supported on Hugging Face Spaces
  warnings.warn(

To create a public link, set `share=True` in `launch()`.



model_id: llama3, gemma2, gpt2, qwen2.5,