app.pyi

import json
import gzip
import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from io import StringIO
from dataclasses import dataclass, field

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.display.utils import (
    BENCHMARK_COLS,
    BENCHMARK_COLS_MULTIMODAL,
    BENCHMARK_COLS_MIB_SUBGRAPH,
    BENCHMARK_COLS_MIB_CAUSALGRAPH,
    COLS,
    COLS_MIB_SUBGRAPH,
    COLS_MIB_CAUSALGRAPH,
    COLS_MULTIMODAL,
    EVAL_COLS,
    EVAL_TYPES,
    AutoEvalColumn,
    AutoEvalColumn_mib_subgraph,
    AutoEvalColumn_mib_causalgraph,
    fields,
)
from src.envs import API, EVAL_REQUESTS_PATH, QUEUE_REPO, REPO_ID, TOKEN, RESULTS_REPO_MIB_SUBGRAPH, EVAL_RESULTS_MIB_SUBGRAPH_PATH, RESULTS_REPO_MIB_CAUSALGRAPH, EVAL_RESULTS_MIB_CAUSALGRAPH_PATH
from src.populate import get_evaluation_queue_df, get_leaderboard_df, get_leaderboard_df_mib_subgraph, get_leaderboard_df_mib_causalgraph
from src.submission.submit import add_new_eval


from src.about import TasksMib_Subgraph

# class SmartSelectColumns(SelectColumns):
#     """
#     Enhanced SelectColumns component with basic filtering functionality.
#     """
#     def __init__(
#         self,
#         benchmark_keywords: Optional[List[str]] = None,
#         model_keywords: Optional[List[str]] = None,
#         initial_selected: Optional[List[str]] = None,
#         **kwargs
#     ):
#         """
#         Initialize SmartSelectColumns with minimal configuration.
        
#         Args:
#             benchmark_keywords: List of benchmark names to filter by
#             model_keywords: List of model names to filter by
#             initial_selected: List of columns to show initially
#         """
#         super().__init__(**kwargs)
#         self.benchmark_keywords = benchmark_keywords or []
#         self.model_keywords = model_keywords or []
#         self.initial_selected = initial_selected or []

#     def get_filtered_groups(self, df: pd.DataFrame) -> Dict[str, List[str]]:
#         """
#         Create column groups based on simple substring matching.
#         """
#         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] = 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] = 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):
#             choices = list(value.columns)
#             selected = self.initial_selected if self.initial_selected else choices
#             filtered_cols = self.get_filtered_groups(value)
            
#             return {
#                 "choices": choices,
#                 "value": selected,
#                 "filtered_cols": filtered_cols
#             }
        
#         if hasattr(value, '__dataclass_fields__'):
#             field_names = [field.name for field in fields(value)]
#             return {
#                 "choices": field_names,
#                 "value": self.initial_selected if self.initial_selected else field_names
#             }
            
#         return super().update(value)

from gradio.events import Dependency

class ModifiedLeaderboard(Leaderboard):
    """Extends Leaderboard to support substring-based column filtering"""
    
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        
        # Process substring groups if they exist
        if (isinstance(self.select_columns_config, SelectColumns) and 
            self.select_columns_config.substring_groups):
            
            self.process_substring_groups()
    
    def process_substring_groups(self):
        """Processes substring groups to add them to the selectable columns"""
        groups = self.select_columns_config.substring_groups
        if not groups:
            return
            
        # Create a mapping of group name to matching columns
        group_to_columns = {}
        for group_name, patterns in groups.groups.items():
            matching_cols = set()
            for pattern in patterns:
                regex = re.compile(pattern.replace('*', '.*'))
                matching_cols.update(
                    col for col in self.headers 
                    if regex.search(col)
                )
            if matching_cols:
                group_to_columns[group_name] = list(matching_cols)
        
        # Add groups to the headers and update column selection logic
        self.group_to_columns = group_to_columns
        self.original_headers = self.headers.copy()
        
        # Add group names to the start of headers
        self.headers = list(group_to_columns.keys()) + self.original_headers
        
        # Update default selection to include groups
        if self.select_columns_config.default_selection:
            self.select_columns_config.default_selection = (
                list(group_to_columns.keys()) + 
                self.select_columns_config.default_selection
            )

    def preprocess(self, payload):
        """Override preprocess to handle group selection"""
        df = super().preprocess(payload)
        
        # If we don't have substring groups, return normally
        if not hasattr(self, 'group_to_columns'):
            return df
            
        # Process group selections
        selected_columns = set()
        for column in payload.headers:
            if column in self.group_to_columns:
                # If a group is selected, add all its columns
                selected_columns.update(self.group_to_columns[column])
            elif column in self.original_headers:
                # Add individually selected columns
                selected_columns.add(column)
        
        # Return DataFrame with only selected columns
        return df[list(selected_columns)]
    from typing import Callable, Literal, Sequence, Any, TYPE_CHECKING
    from gradio.blocks import Block
    if TYPE_CHECKING:
        from gradio.components import Timer

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

class SmartSelectColumns(SelectColumns):
    """
    Enhanced SelectColumns component matching exact original parameters.
    """
    def __init__(
        self,
        benchmark_keywords: Optional[List[str]] = None,
        model_keywords: Optional[List[str]] = None,
        initial_selected: Optional[List[str]] = None,
        label: Optional[str] = None,
        show_label: bool = True,
        info: Optional[str] = None,
        allow: bool = True
    ):
        # Match exact parameters from working SelectColumns
        super().__init__(
            default_selection=initial_selected or [],
            cant_deselect=[],
            allow=allow,
            label=label,
            show_label=show_label,
            info=info
        )
        
        self.benchmark_keywords = benchmark_keywords or []
        self.model_keywords = model_keywords or []
        
        # Store groups for later use
        self._groups = {}
    
    def get_filtered_groups(self, columns: List[str]) -> Dict[str, List[str]]:
        """Get column groups based on keywords."""
        filtered_groups = {}
        
        # Add benchmark groups
        for benchmark in self.benchmark_keywords:
            matching_cols = [
                col for col in columns 
                if benchmark in col.lower()
            ]
            if matching_cols:
                filtered_groups[f"Benchmark group for {benchmark}"] = matching_cols
        
        # Add model groups
        for model in self.model_keywords:
            matching_cols = [
                col for col in columns 
                if model in col.lower()
            ]
            if matching_cols:
                filtered_groups[f"Model group for {model}"] = matching_cols
                
        self._groups = filtered_groups
        return filtered_groups


import re
@dataclass
class SubstringSelectColumns(SelectColumns):
    """
    Extends SelectColumns to support filtering columns by predefined substrings.
    When a substring is selected, all columns containing that substring will be selected.
    """
    substring_groups: Dict[str, List[str]] = field(default_factory=dict)
    selected_substrings: List[str] = field(default_factory=list)
    
    def __post_init__(self):
        # Ensure default_selection is a list
        if self.default_selection is None:
            self.default_selection = []
            
        # Build reverse mapping of column to substrings
        self.column_to_substrings = {}
        for substring, patterns in self.substring_groups.items():
            for pattern in patterns:
                # Convert glob-style patterns to regex
                regex = re.compile(pattern.replace('*', '.*'))
                # Find matching columns in default_selection
                for col in self.default_selection:
                    if regex.search(col):
                        if col not in self.column_to_substrings:
                            self.column_to_substrings[col] = []
                        self.column_to_substrings[col].append(substring)
        
        # Apply initial substring selections
        if self.selected_substrings:
            self.update_selection_from_substrings()
    
    def update_selection_from_substrings(self) -> List[str]:
        """
        Updates the column selection based on selected substrings.
        Returns the new list of selected columns.
        """
        selected_columns = self.cant_deselect.copy()
        
        # If no substrings selected, show all columns
        if not self.selected_substrings:
            selected_columns.extend([
                col for col in self.default_selection 
                if col not in self.cant_deselect
            ])
            return selected_columns
        
        # Add columns that match any selected substring
        for col, substrings in self.column_to_substrings.items():
            if any(s in self.selected_substrings for s in substrings):
                if col not in selected_columns:
                    selected_columns.append(col)
                    
        return selected_columns





def restart_space():
    API.restart_space(repo_id=REPO_ID)



### Space initialisation
try:
    # print(EVAL_REQUESTS_PATH)
    snapshot_download(
        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
    )
except Exception:
    restart_space()


try:
    # print(RESULTS_REPO_MIB_SUBGRAPH)
    snapshot_download(
        repo_id=RESULTS_REPO_MIB_SUBGRAPH, local_dir=EVAL_RESULTS_MIB_SUBGRAPH_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
    )
except Exception:
    restart_space()


try:
    # print(RESULTS_REPO_MIB_CAUSALGRAPH)
    snapshot_download(
        repo_id=RESULTS_REPO_MIB_CAUSALGRAPH, local_dir=EVAL_RESULTS_MIB_CAUSALGRAPH_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
    )
except Exception:
    restart_space()



LEADERBOARD_DF_MIB_SUBGRAPH = get_leaderboard_df_mib_subgraph(EVAL_RESULTS_MIB_SUBGRAPH_PATH, EVAL_REQUESTS_PATH, COLS_MIB_SUBGRAPH, BENCHMARK_COLS_MIB_SUBGRAPH)

# LEADERBOARD_DF_MIB_CAUSALGRAPH = get_leaderboard_df_mib_causalgraph(EVAL_RESULTS_MIB_CAUSALGRAPH_PATH, EVAL_REQUESTS_PATH, COLS_MIB_CAUSALGRAPH, BENCHMARK_COLS_MIB_CAUSALGRAPH)
# In app.py, modify the LEADERBOARD initialization
LEADERBOARD_DF_MIB_CAUSALGRAPH_DETAILED, LEADERBOARD_DF_MIB_CAUSALGRAPH_AGGREGATED, LEADERBOARD_DF_MIB_CAUSALGRAPH_AVERAGED = get_leaderboard_df_mib_causalgraph(
    EVAL_RESULTS_MIB_CAUSALGRAPH_PATH, 
    EVAL_REQUESTS_PATH, 
    COLS_MIB_CAUSALGRAPH, 
    BENCHMARK_COLS_MIB_CAUSALGRAPH
)


# LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
# LEADERBOARD_DF_MULTIMODAL = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS_MULTIMODAL, BENCHMARK_COLS_MULTIMODAL)

(
    finished_eval_queue_df,
    running_eval_queue_df,
    pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)





# def init_leaderboard_mib_subgraph(dataframe, track):
#     # print(f"init_leaderboard_mib: dataframe head before loc is {dataframe.head()}\n")
    
#     if dataframe is None or dataframe.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")

#     # filter for correct track
#     # dataframe = dataframe.loc[dataframe["Track"] == track]

#     # print(f"init_leaderboard_mib: dataframe head after loc is {dataframe.head()}\n")
    
#     return Leaderboard(
#         value=dataframe,
#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
#         select_columns=SelectColumns(
#             default_selection=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.displayed_by_default],
#             cant_deselect=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.never_hidden],
#             label="Select Columns to Display:",
#         ),
#         search_columns=["Method"],  # Changed from AutoEvalColumn_mib_subgraph.model.name to "Method"
#         hide_columns=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.hidden],
#         bool_checkboxgroup_label="Hide models",
#         interactive=False,
#     )





# def init_leaderboard_mib_subgraph(dataframe, track):
#     """Initialize the subgraph leaderboard with grouped column selection by benchmark."""
#     if dataframe is None or dataframe.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")

#     print("\nDebugging DataFrame columns:", dataframe.columns.tolist())
    
#     # Create groups of columns by benchmark
#     benchmark_groups = []
    
#     # For each benchmark in our TasksMib_Subgraph enum...
#     for task in TasksMib_Subgraph:
#         benchmark = task.value.benchmark
#         # Get all valid columns for this benchmark's models
#         benchmark_cols = [
#             f"{benchmark}_{model}"
#             for model in task.value.models
#             if f"{benchmark}_{model}" in dataframe.columns
#         ]
#         if benchmark_cols:  # Only add if we have valid columns
#             benchmark_groups.append(benchmark_cols)
#             print(f"\nBenchmark group for {benchmark}:", benchmark_cols)

#     # Create model groups as well
#     model_groups = []
#     all_models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
    
#     # For each unique model...
#     for model in all_models:
#         # Get all valid columns for this model across benchmarks
#         model_cols = [
#             f"{task.value.benchmark}_{model}"
#             for task in TasksMib_Subgraph
#             if model in task.value.models
#             and f"{task.value.benchmark}_{model}" in dataframe.columns
#         ]
#         if model_cols:  # Only add if we have valid columns
#             model_groups.append(model_cols)
#             print(f"\nModel group for {model}:", model_cols)

#     # Combine all groups
#     all_groups = benchmark_groups + model_groups
    
#     # Flatten groups for default selection (show everything initially)
#     all_columns = [col for group in all_groups for col in group]
#     print("\nAll available columns:", all_columns)

#     return Leaderboard(
#         value=dataframe,
#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
#         select_columns=SelectColumns(
#             default_selection=all_columns,  # Show all columns initially
#             label="Select Results:"
#         ),
#         search_columns=["Method"],
#         hide_columns=[],
#         interactive=False,
#     )


def init_leaderboard_mib_subgraph(dataframe, track):
    """Initialize the subgraph leaderboard with display names for better readability."""
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")

    print("\nDebugging DataFrame columns:", dataframe.columns.tolist())
    
    # First, create our display name mapping
    # This is like creating a translation dictionary between internal names and display names
    model_name_mapping = {
        "qwen2_5": "Qwen-2.5",
        "gpt2": "GPT-2",
        "gemma2": "Gemma-2",
        "llama3": "Llama-3.1"
    }

    benchmark_mapping = {
        "ioi": "IOI",
        "mcqa": "MCQA",
        "arithmetic_addition": "Arithmetic (+)",
        "arithmetic_subtraction": "Arithmetic (-)",
        "arc_easy": "ARC (Easy)",
        "arc_challenge": "ARC (Challenge)"
    }

    display_mapping = {}
    for task in TasksMib_Subgraph:
        for model in task.value.models:
            field_name = f"{task.value.benchmark}_{model}"
            display_name = f"{benchmark_mapping[task.value.benchmark]} - {model_name_mapping[model]}"
            display_mapping[field_name] = display_name
    

    # Now when creating benchmark groups, we'll use display names
    benchmark_groups = []
    for task in TasksMib_Subgraph:
        benchmark = task.value.benchmark
        benchmark_cols = [
            display_mapping[f"{benchmark}_{model}"]  # Use display name from our mapping
            for model in task.value.models
            if f"{benchmark}_{model}" in dataframe.columns
        ]
        if benchmark_cols:
            benchmark_groups.append(benchmark_cols)
            print(f"\nBenchmark group for {benchmark}:", benchmark_cols)

    # Similarly for model groups
    model_groups = []
    all_models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
    
    for model in all_models:
        model_cols = [
            display_mapping[f"{task.value.benchmark}_{model}"]  # Use display name
            for task in TasksMib_Subgraph
            if model in task.value.models
            and f"{task.value.benchmark}_{model}" in dataframe.columns
        ]
        if model_cols:
            model_groups.append(model_cols)
            print(f"\nModel group for {model}:", model_cols)

    # Combine all groups using display names
    all_groups = benchmark_groups + model_groups
    all_columns = [col for group in all_groups for col in group]

    # Important: We need to rename our DataFrame columns to match display names
    
    renamed_df = dataframe.rename(columns=display_mapping)
    # all_columns = [c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.displayed_by_default]
    # all_columns = [c.name for c in fields(AutoEvalColumn_mib_subgraph)]
    all_columns = renamed_df.columns.tolist()

    print(benchmark_groups)
    print(model_groups)
    filter_groups = {"ioi": "*IOI*",
                     "llama": "*Llama*"}


    # Original code 
    return ModifiedLeaderboard(
        value=renamed_df,  # Use DataFrame with display names
        datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
        select_columns=SubstringSelectColumns(
            substring_groups=filter_groups,
            default_selection=all_columns,  # Now contains display names
            label="Filter Results:",
            allow=True
        ),
        search_columns=["Method"],
        hide_columns=[],
        interactive=False,
    )
    



    # # Complete column groups for both benchmarks and models
    # # 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"
    # }
    # # mappings = {}

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

    # print("\nDebugging DataFrame columns:", renamed_df.columns.tolist())

    # # Create Leaderboard
    # leaderboard = Leaderboard(
    #     value=renamed_df,
    #     datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
    #     select_columns=smart_columns,
    #     search_columns=["Method"],
    #     hide_columns=[],
    #     interactive=False
    # )
    # print(f"Successfully created leaderboard.")
    # return leaderboard

    # print("\nDebugging DataFrame columns:", dataframe.columns.tolist())

    # # Define simple keywords for filtering
    # benchmark_keywords = ["ioi", "mcqa", "arithmetic", "arc"]
    # model_keywords = ["qwen2_5", "gpt2", "gemma2", "llama3"]

    # # Create SmartSelectColumns instance with exact same parameters as working version
    # smart_columns = SmartSelectColumns(
    #     benchmark_keywords=benchmark_keywords,
    #     model_keywords=model_keywords,
    #     initial_selected=["Method", "Average"],
    #     allow=True,
    #     label=None,
    #     show_label=True,
    #     info=None
    # )

    # try:
    #     print("\nCreating leaderboard...")
    #     # Get groups before creating leaderboard
    #     smart_columns.get_filtered_groups(dataframe.columns)
        
    #     leaderboard = Leaderboard(
    #         value=dataframe,
    #         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
    #         select_columns=smart_columns,
    #         search_columns=["Method"],
    #         hide_columns=[],
    #         interactive=False
    #     )
    #     print("Leaderboard created successfully")
    #     return leaderboard
        
    # except Exception as e:
    #     print("Error creating leaderboard:", str(e))
    #     raise






# def init_leaderboard_mib_subgraph(dataframe, track):
#     """Initialize the subgraph leaderboard with group-based column selection."""
#     if dataframe is None or dataframe.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")

#     print("\nDebugging DataFrame columns:", dataframe.columns.tolist())
    
#     # Create selection mapping for benchmark groups
#     selection_mapping = {}
    
#     # Create benchmark groups with descriptive names
#     for task in TasksMib_Subgraph:
#         benchmark = task.value.benchmark
#         # Get all columns for this benchmark's models
#         benchmark_cols = [
#             f"{benchmark}_{model}"
#             for model in task.value.models
#             if f"{benchmark}_{model}" in dataframe.columns
#         ]
#         if benchmark_cols:
#             # Use a descriptive group name as the key
#             group_name = f"Benchmark: {benchmark.upper()}"
#             selection_mapping[group_name] = benchmark_cols
#             print(f"\n{group_name} maps to:", benchmark_cols)

#     # Create model groups with descriptive names
#     all_models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
#     for model in all_models:
#         # Get all columns for this model across benchmarks
#         model_cols = [
#             f"{task.value.benchmark}_{model}"
#             for task in TasksMib_Subgraph
#             if model in task.value.models
#             and f"{task.value.benchmark}_{model}" in dataframe.columns
#         ]
#         if model_cols:
#             # Use a descriptive group name as the key
#             group_name = f"Model: {model}"
#             selection_mapping[group_name] = model_cols
#             print(f"\n{group_name} maps to:", model_cols)

#     # The selection options are the group names
#     selection_options = list(selection_mapping.keys())
#     print("\nSelection options:", selection_options)

#     return Leaderboard(
#         value=dataframe,
#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
#         select_columns=SelectColumns(
#             default_selection=selection_options,  # Show all groups by default
#             label="Select Benchmark or Model Groups:"
#         ),
#         search_columns=["Method"],
#         hide_columns=[],
#         interactive=False,
#     )








# def init_leaderboard_mib_causalgraph(dataframe, track):
#     # print("Debugging column issues:")
#     # print("\nActual DataFrame columns:")
#     # print(dataframe.columns.tolist())
    
#     # print("\nExpected columns for Leaderboard:")
#     expected_cols = [c.name for c in fields(AutoEvalColumn_mib_causalgraph)]
#     # print(expected_cols)
    
#     # print("\nMissing columns:")
#     missing_cols = [col for col in expected_cols if col not in dataframe.columns]
#     # print(missing_cols)
    
#     # print("\nSample of DataFrame content:")
#     # print(dataframe.head().to_string())

#     return Leaderboard(
#         value=dataframe,
#         datatype=[c.type for c in fields(AutoEvalColumn_mib_causalgraph)],
#         select_columns=SelectColumns(
#             default_selection=[c.name for c in fields(AutoEvalColumn_mib_causalgraph) if c.displayed_by_default],
#             cant_deselect=[c.name for c in fields(AutoEvalColumn_mib_causalgraph) if c.never_hidden],
#             label="Select Columns to Display:",
#         ),
#         search_columns=["Method"],
#         hide_columns=[c.name for c in fields(AutoEvalColumn_mib_causalgraph) if c.hidden],
#         bool_checkboxgroup_label="Hide models",
#         interactive=False,
#     )

def init_leaderboard_mib_causalgraph(dataframe, track):
    # print("Debugging column issues:")
    # print("\nActual DataFrame columns:")
    # print(dataframe.columns.tolist())
    
    # Create only necessary columns
    return Leaderboard(
        value=dataframe,
        datatype=[c.type for c in fields(AutoEvalColumn_mib_causalgraph)],
        select_columns=SelectColumns(
            default_selection=["Method"],  # Start with just Method column
            cant_deselect=["Method"],      # Method column should always be visible
            label="Select Columns to Display:",
        ),
        search_columns=["Method"],
        hide_columns=[],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )


def init_leaderboard(dataframe, track):
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")
    # filter for correct track
    dataframe = dataframe.loc[dataframe["Track"] == track]

    # print(f"\n\n\n dataframe is {dataframe}\n\n\n")
    
    return Leaderboard(
        value=dataframe,
        datatype=[c.type for c in fields(AutoEvalColumn)],
        select_columns=SelectColumns(
            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
            label="Select Columns to Display:",
        ),
        search_columns=[AutoEvalColumn.model.name],
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )

def process_json(temp_file):
    if temp_file is None:
        return {}

    # Handle file upload
    try:
        file_path = temp_file.name
        if file_path.endswith('.gz'):
            with gzip.open(file_path, 'rt') as f:
                data = json.load(f)
        else:
            with open(file_path, 'r') as f:
                data = json.load(f)
    except Exception as e:
        raise gr.Error(f"Error processing file: {str(e)}")

    gr.Markdown("Upload successful!")
    return data


demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        # with gr.TabItem("Strict", elem_id="strict-benchmark-tab-table", id=0):
        #     leaderboard = init_leaderboard(LEADERBOARD_DF, "strict")
        # with gr.TabItem("Strict-small", elem_id="strict-small-benchmark-tab-table", id=1):
        #     leaderboard = init_leaderboard(LEADERBOARD_DF, "strict-small")
        # with gr.TabItem("Multimodal", elem_id="multimodal-benchmark-tab-table", id=2):
        #     leaderboard = init_leaderboard(LEADERBOARD_DF_MULTIMODAL, "multimodal")

        # with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=4):
        #     gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
 
        # with gr.TabItem("👶 Submit", elem_id="llm-benchmark-tab-table", id=5):
        #     with gr.Column():
        #         with gr.Row():                                                                                                           
        #             gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
        
        # with gr.TabItem("Subgraph", elem_id="subgraph", id=0):
        #     leaderboard = init_leaderboard_mib_subgraph(LEADERBOARD_DF_MIB_SUBGRAPH, "Subgraph")
        with gr.TabItem("Subgraph", elem_id="subgraph", id=0):
            # Add description for filters
            gr.Markdown("""
            ### Filtering Options
            Use the dropdown menus below to filter results by specific tasks or models. 
            You can combine filters to see specific task-model combinations.
            """)
            leaderboard = init_leaderboard_mib_subgraph(LEADERBOARD_DF_MIB_SUBGRAPH, "Subgraph")
            print(f"Leaderboard is {leaderboard}")
    
        # Then modify the Causal Graph tab section
        with gr.TabItem("Causal Graph", elem_id="causalgraph", id=1):
            with gr.Tabs() as causalgraph_tabs:
                with gr.TabItem("Detailed View", id=0):
                    leaderboard_detailed = init_leaderboard_mib_causalgraph(
                        LEADERBOARD_DF_MIB_CAUSALGRAPH_DETAILED, 
                        "Causal Graph"
                    )
                with gr.TabItem("Aggregated View", id=1):
                    leaderboard_aggregated = init_leaderboard_mib_causalgraph(
                        LEADERBOARD_DF_MIB_CAUSALGRAPH_AGGREGATED, 
                        "Causal Graph"
                    )
                with gr.TabItem("Intervention Averaged", id=2):
                    leaderboard_averaged = init_leaderboard_mib_causalgraph(
                        LEADERBOARD_DF_MIB_CAUSALGRAPH_AVERAGED, 
                        "Causal Graph"
                    )
        
    # with gr.Row():
    #     with gr.Accordion("📙 Citation", open=False):
    #         citation_button = gr.Textbox(
    #             value=CITATION_BUTTON_TEXT,
    #             label=CITATION_BUTTON_LABEL,
    #             lines=20,
    #             elem_id="citation-button",
    #             show_copy_button=True,
    #         )

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.launch(share=True, ssr_mode=False)