app.py

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 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



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,
#     )



from src.about import TasksMib_Subgraph


# def init_leaderboard_mib_subgraph(dataframe, track):
#     if dataframe is None or dataframe.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")
    
#     # Get unique tasks and models for filters
#     tasks = list(set(task.value.benchmark for task in TasksMib_Subgraph))
#     models = list(set(
#         model 
#         for task in TasksMib_Subgraph 
#         for model in task.value.models
#     ))
    
#     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:",
#         ),
#         column_filters=[
#             ColumnFilter(
#                 column="task_filter",
#                 choices=tasks,
#                 label="Filter by Task:",
#                 default=None
#             ),
#             ColumnFilter(
#                 column="model_filter",
#                 choices=models,
#                 label="Filter by Model:",
#                 default=None
#             )
#         ],
#         search_columns=["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):
#     if dataframe is None or dataframe.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")
    
#     # Add filter columns to display
#     dataframe['Task'] = dataframe.apply(
#         lambda row: [task.value.benchmark for task in TasksMib_Subgraph 
#                     if any(f"{task.value.benchmark}_{model}" in row.index 
#                           for model in task.value.models)][0], 
#         axis=1
#     )
    
#     dataframe['Model'] = dataframe.apply(
#         lambda row: [model for task in TasksMib_Subgraph 
#                     for model in task.value.models 
#                     if f"{task.value.benchmark}_{model}" in row.index][0],
#         axis=1
#     )
    
#     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", "Task", "Model"],  # Add Task and Model to searchable columns
#         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."""
#     if dataframe is None or dataframe.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")

#     # Get tasks and models using the new class methods
#     tasks = TasksMib_Subgraph.get_all_tasks()
#     models = TasksMib_Subgraph.get_all_models()

#     # Create a mapping from selection to actual column names
#     selection_map = {}
    
#     # Add task mappings - when a task is selected, show all its columns
#     for task in tasks:
#         # For each task, find all valid task_model combinations
#         valid_combos = []
#         for model in models:
#             col_name = f"{task}_{model}"
#             if col_name in dataframe.columns:
#                 valid_combos.append(col_name)
#         if valid_combos:
#             selection_map[task] = valid_combos
    
#     # Add model mappings - when a model is selected, show all its columns
#     for model in models:
#         # For each model, find all valid task_model combinations
#         valid_combos = []
#         for task in tasks:
#             col_name = f"{task}_{model}"
#             if col_name in dataframe.columns:
#                 valid_combos.append(col_name)
#         if valid_combos:
#             selection_map[model] = valid_combos

#     return Leaderboard(
#         value=dataframe,
#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
#         select_columns=SelectColumns(
#             choices=[tasks, models],  # Two groups of choices
#             labels=["Tasks", "Models"],  # Labels for each group
#             default_selection=[*tasks, *models],  # Show everything by default
#             cant_deselect=["Method"],  # Method column always visible
#             label="Filter by Tasks or Models:",
#             selection_map=selection_map  # Map selections to actual columns
#         ),
#         search_columns=["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 for gradio-leaderboard 0.0.13"""
#     if dataframe is None or dataframe.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")

#     # Get all unique tasks and models
#     tasks = [task.value.benchmark for task in TasksMib_Subgraph]
#     models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
    
#     # Create two selection groups: one for tasks and one for models
#     # In 0.0.13, we can only have one SelectColumns, so we'll combine them
#     selection_choices = [
#         *[f"Task: {task}" for task in tasks],  # Prefix with 'Task:' for clarity
#         *[f"Model: {model}" for model in models]  # Prefix with 'Model:' for clarity
#     ]

#     return Leaderboard(
#         value=dataframe,
#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
#         select_columns=SelectColumns(
#             default_selection=selection_choices,  # Show all by default
#             choices=selection_choices,
#             cant_deselect=["Method"],  # Method column always visible
#             label="Select Tasks or Models:",
#         ),
#         search_columns=["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 focusing only on task and model filtering.
    
#     This implementation creates a focused view where users can select which task-model
#     combinations they want to see, making the analysis of results more straightforward.
#     """
#     if dataframe is None or dataframe.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")
    
#     # Get all task-model combinations that actually exist in our data
#     task_model_columns = []
#     for task in TasksMib_Subgraph:
#         for model in task.value.models:
#             col_name = f"{task.value.benchmark}_{model}"
#             if col_name in dataframe.columns:
#                 task_model_columns.append(col_name)

#     return Leaderboard(
#         value=dataframe,
#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
#         select_columns=SelectColumns(
#             default_selection=task_model_columns,
#             label="Select Task-Model Combinations:",
#         ),
#         search_columns=["Method"],  # Keep Method searchable but not in column selection
#         hide_columns=[],  # We don't need to hide any columns
#         bool_checkboxgroup_label="Hide models",
#         interactive=False,
#     )








# def init_leaderboard_mib_subgraph(dataframe, track):
#     """Initialize the subgraph leaderboard with verified task/model column selection"""
#     if dataframe is None or dataframe.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")
    
#     # First, let's identify which columns actually exist in our dataframe
#     print("Available columns in dataframe:", dataframe.columns.tolist())
    
#     # Create task selections based on TasksMib_Subgraph definition
#     task_selections = []
#     for task in TasksMib_Subgraph:
#         task_cols = []
#         for model in task.value.models:
#             col_name = f"{task.value.benchmark}_{model}"
#             if col_name in dataframe.columns:
#                 task_cols.append(col_name)
        
#         if task_cols:  # Only add tasks that have data
#             print(f"Task {task.value.benchmark} has columns:", task_cols)
#             task_selections.append(f"Task: {task.value.benchmark}")
    
#     # Create model selections by checking which models appear in columns
#     model_selections = []
#     all_models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
    
#     for model in all_models:
#         model_cols = []
#         for task in TasksMib_Subgraph:
#             if model in task.value.models:
#                 col_name = f"{task.value.benchmark}_{model}"
#                 if col_name in dataframe.columns:
#                     model_cols.append(col_name)
        
#         if model_cols:  # Only add models that have data
#             print(f"Model {model} has columns:", model_cols)
#             model_selections.append(f"Model: {model}")
    
#     # Combine all selections
#     selections = task_selections + model_selections
#     print("Final selection options:", selections)

#     # Print DataFrame information
#     print("\nDebugging DataFrame:")
#     print("DataFrame columns:", dataframe.columns.tolist())
#     print("DataFrame shape:", dataframe.shape)
#     print("DataFrame head:\n", dataframe.head())
    
#     return Leaderboard(
#         value=dataframe,
#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
#         select_columns=SelectColumns(
#             default_selection=selections,
#             label="Select Tasks or Models:"
#         ),
#         search_columns=["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 benchmark and model filtering capabilities."""
#     if dataframe is None or dataframe.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")
    
#     # Print DataFrame information for debugging
#     # print("\nDebugging DataFrame columns:", dataframe.columns.tolist())
    
#     # Get result columns (excluding Method and Average)
#     result_columns = [col for col in dataframe.columns 
#                      if col not in ['Method', 'Average', 'eval_name'] and '_' in col]
    
#     # Create benchmark and model selections
#     benchmarks = set()
#     models = set()
    
#     print(f"\nDebugging Result Columns: {result_columns}")
#     # Extract unique benchmarks and models from column names
#     for col in result_columns:
#         print(f"col is {col}")
#         benchmark, model = col.split('_', maxsplit=1)
#         benchmarks.add(benchmark)
#         models.add(model)
#         print(f"benchmark is {benchmark} and model is {model}")
    
#     # Create selection groups
#     benchmark_selections = {
#         # For each benchmark, store which columns should be shown
#         benchmark: [col for col in result_columns if col.startswith(f"{benchmark}_")]
#         for benchmark in benchmarks
#     }
    
#     model_selections = {
#         # For each model, store which columns should be shown
#         model: [col for col in result_columns if col.startswith(f"_{model}")]
#         for model in models
#     }
    
#     # Combine the selection mappings
#     selection_groups = {
#         **benchmark_selections,
#         **model_selections
#     }
    
#     print("\nDebugging Selection Groups:")
#     print("Benchmarks:", benchmark_selections.keys())
#     print("Models:", model_selections.keys())
    
#     # Convert keys to list for selection options
#     selection_options = list(selection_groups.keys())
    
#     return Leaderboard(
#         value=dataframe,
#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
#         select_columns=SelectColumns(
#             default_selection=selection_options,  # Show all options by default
#             label="Filter by Benchmark or Model:"
#         ),
#         search_columns=["Method"],
#         hide_columns=[],
#         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_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")
    
        # 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)