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)