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):
    """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
#     display_mapping = {}
#     for task in TasksMib_Subgraph:
#         for model in task.value.models:
#             field_name = f"{task.value.benchmark}_{model}"
#             display_name = f"{task.value.benchmark}({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)

#     return Leaderboard(
#         value=renamed_df,  # Use DataFrame with display names
#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
#         select_columns=SelectColumns(
#             default_selection=all_columns,  # Now contains display names
#             label="Select Results:"
#         ),
#         search_columns=["Method"],
#         hide_columns=[],
#         interactive=False,
#     )




    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)
    

    # 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"
    }
    
    column_filters = filters
    column_mapping = mappings
    initial_columns = renamed_df


    # Initialize SmartSelectColumns
    smart_columns = SmartSelectColumns(
        column_filters=filters,
        column_mapping=mappings,
        initial_selected=initial_columns,
        multiselect=True
    )

    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
    )


# 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")
    
        # 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)