import json import gzip import gradio as gr from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns import pandas as pd import numpy as np from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download from io import StringIO from typing import Dict, List, Optional from dataclasses import dataclass, field from copy import deepcopy 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, COLS, COLS_MIB_SUBGRAPH, 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, TasksMib_Causalgraph # 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_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_FPL = get_leaderboard_df_mib_subgraph(EVAL_RESULTS_MIB_SUBGRAPH_PATH, EVAL_REQUESTS_PATH, COLS_MIB_SUBGRAPH, BENCHMARK_COLS_MIB_SUBGRAPH) LEADERBOARD_DF_MIB_SUBGRAPH_FEQ = get_leaderboard_df_mib_subgraph(EVAL_RESULTS_MIB_SUBGRAPH_PATH, EVAL_REQUESTS_PATH, COLS_MIB_SUBGRAPH, BENCHMARK_COLS_MIB_SUBGRAPH, metric_type="F=") # 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 ) # 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): """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() # Original code 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="Filter Results:", # ), search_columns=["Method"], hide_columns=["eval_name"], interactive=False, ), renamed_df def init_leaderboard_mib_causalgraph(dataframe, track): # print("Debugging column issues:") # print("\nActual DataFrame columns:") # print(dataframe.columns.tolist()) model_name_mapping = { "Qwen2ForCausalLM": "Qwen-2.5", "GPT2ForCausalLM": "GPT-2", "Gemma2ForCausalLM": "Gemma-2", "LlamaForCausalLM": "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_Causalgraph: for model in task.value.models: field_name = f"{task.value.col_name}_{model}" display_name = f"{benchmark_mapping[task.value.col_name]} - {model_name_mapping[model]}" display_mapping[field_name] = display_name # print(dataframe) renamed_df = dataframe.rename(columns=display_mapping) # idx_to_method = {0: "Full Vector", 1: "DAS", 2: "DBM", 3: "PCA", 4: "SAE"} # idx_to_scores = {0: [0.38, 0.36, 0.38, 0.42], # 1: [0.56, 0.62, 0.54, 0.51], # 2: [0.43, 0.41, 0.53, 0.49], # 3: [0.26, 0.20, 0.32, 0.40], # 4: ["-", "-", 0.33, "-"]} # renamed_df.loc[0]["Method"] = "Full Vector" # for i in range(5): # renamed_df.loc[i] = [idx_to_method[i]] + idx_to_scores[i] print(renamed_df) # Create only necessary columns return Leaderboard( value=renamed_df, 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=["eval_name"], bool_checkboxgroup_label="Hide models", interactive=False, ), renamed_df 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 # Define the preset substrings for filtering PRESET_SUBSTRINGS = ["IOI", "MCQA", "Arithmetic", "ARC", "GPT-2", "Qwen-2.5", "Gemma-2", "Llama-3.1"] TASK_SUBSTRINGS = ["IOI", "MCQA", "Arithmetic", "ARC"] MODEL_SUBSTRINGS = ["GPT-2", "Qwen-2.5", "Gemma-2", "Llama-3.1"] def filter_columns_by_substrings(dataframe: pd.DataFrame, selected_task_substrings: List[str], selected_model_substrings: List[str]) -> pd.DataFrame: """ Filter columns based on the selected substrings. """ original_dataframe = deepcopy(dataframe) if not selected_task_substrings and not selected_model_substrings: return dataframe # No filtering if no substrings are selected if not selected_task_substrings: # Filter columns that contain any of the selected model substrings filtered_columns = [ col for col in dataframe.columns if any(sub.lower() in col.lower() for sub in selected_model_substrings) or col == "Method" ] return dataframe[filtered_columns] elif not selected_model_substrings: # Filter columns that contain any of the selected task substrings filtered_columns = [ col for col in dataframe.columns if any(sub.lower() in col.lower() for sub in selected_task_substrings) or col == "Method" ] return dataframe[filtered_columns] # Filter columns by task first. Use AND logic to combine with model filtering filtered_columns = [ col for col in dataframe.columns if any(sub.lower() in col.lower() for sub in selected_task_substrings) or col == "Method" ] filtered_columns = [ col for col in dataframe[filtered_columns].columns if any(sub.lower() in col.lower() for sub in selected_model_substrings) or col == "Method" ] return dataframe[filtered_columns] def update_leaderboard(dataframe: pd.DataFrame, selected_task_substrings: List[str], selected_model_substrings: List[str]): """ Update the leaderboard based on the selected substrings. """ filtered_dataframe = filter_columns_by_substrings(dataframe, selected_task_substrings, selected_model_substrings) if len(selected_task_substrings) >= 2 or len(selected_task_substrings) == 0: if len(selected_model_substrings) >= 2 or len(selected_model_substrings) == 0: show_average = True else: show_average = False else: show_average = False if show_average: means = filtered_dataframe.replace("-", float("nan")).mean(axis=1, skipna=False) filtered_dataframe["Average"] = means.round(2) filtered_dataframe = filtered_dataframe.sort_values(by=["Average"], ascending=False, na_position='last') filtered_dataframe = filtered_dataframe.replace(float("nan"), "-") # if show_average: # print([row for index, row in filtered_dataframe.iterrows()]) # filtered_dataframe["Average"] = [round(np.mean(row.values()), 2) if "-" not in row.values() else "-" for index, row in filtered_dataframe.iterrows()] # # Sort by Average score descending # if 'Average' in dataframe.columns: # # Convert '-' to NaN for sorting purposes # df['Average'] = pd.to_numeric(['Average'], errors='coerce') # df = df.sort_values(by=['Average'], ascending=True, na_position='last') # # Convert NaN back to '-' # df['Average'] = df['Average'].fillna('-') return filtered_dataframe 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("Circuit Localization", elem_id="subgraph", id=0): with gr.Tabs() as subgraph_tabs: with gr.TabItem("F+", 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. """) # CheckboxGroup for selecting substrings # substring_checkbox = gr.CheckboxGroup( # choices=PRESET_SUBSTRINGS, # label="Filter results:", # value=PRESET_SUBSTRINGS, # Default to all substrings selected # ) task_substring_checkbox = gr.CheckboxGroup( choices=TASK_SUBSTRINGS, label="View tasks:", value=TASK_SUBSTRINGS, # Default to all substrings selected ) model_substring_checkbox = gr.CheckboxGroup( choices = MODEL_SUBSTRINGS, label = "View models:", value = MODEL_SUBSTRINGS ) leaderboard, data = init_leaderboard_mib_subgraph(LEADERBOARD_DF_MIB_SUBGRAPH_FPL, "Subgraph") original_leaderboard = gr.State(value=data) # Update the leaderboard when the user selects/deselects substrings task_substring_checkbox.change( fn=update_leaderboard, inputs=[original_leaderboard, task_substring_checkbox, model_substring_checkbox], outputs=leaderboard ) model_substring_checkbox.change( fn=update_leaderboard, inputs=[original_leaderboard, task_substring_checkbox, model_substring_checkbox], outputs=leaderboard ) print(f"Leaderboard is {leaderboard}") with gr.TabItem("F=", id=1): # 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. """) # CheckboxGroup for selecting substrings # substring_checkbox = gr.CheckboxGroup( # choices=PRESET_SUBSTRINGS, # label="Filter results:", # value=PRESET_SUBSTRINGS, # Default to all substrings selected # ) task_substring_checkbox = gr.CheckboxGroup( choices=TASK_SUBSTRINGS, label="View tasks:", value=TASK_SUBSTRINGS, # Default to all substrings selected ) model_substring_checkbox = gr.CheckboxGroup( choices = MODEL_SUBSTRINGS, label = "View models:", value = MODEL_SUBSTRINGS ) leaderboard, data = init_leaderboard_mib_subgraph(LEADERBOARD_DF_MIB_SUBGRAPH_FEQ, "Subgraph") original_leaderboard = gr.State(value=data) # Update the leaderboard when the user selects/deselects substrings task_substring_checkbox.change( fn=update_leaderboard, inputs=[original_leaderboard, task_substring_checkbox, model_substring_checkbox], outputs=leaderboard ) model_substring_checkbox.change( fn=update_leaderboard, inputs=[original_leaderboard, task_substring_checkbox, model_substring_checkbox], outputs=leaderboard ) print(f"Leaderboard is {leaderboard}") # Then modify the Causal Graph tab section with gr.TabItem("Causal Variable Localization", elem_id="causalgraph", id=1): with gr.Tabs() as causalgraph_tabs: with gr.TabItem("Detailed View", id=0): leaderboard_detailed, data = init_leaderboard_mib_causalgraph( LEADERBOARD_DF_MIB_CAUSALGRAPH_DETAILED, "Causal Graph" ) with gr.TabItem("Aggregated View", id=1): 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. """) # substring_checkbox = gr.CheckboxGroup( # choices=PRESET_SUBSTRINGS, # label="Filter results:", # value=PRESET_SUBSTRINGS, # Default to all substrings selected # ) task_substring_checkbox = gr.CheckboxGroup( choices=TASK_SUBSTRINGS, label="View tasks:", value=TASK_SUBSTRINGS, # Default to all substrings selected ) model_substring_checkbox = gr.CheckboxGroup( choices = MODEL_SUBSTRINGS, label = "View models:", value = MODEL_SUBSTRINGS ) leaderboard_aggregated, data = init_leaderboard_mib_causalgraph( LEADERBOARD_DF_MIB_CAUSALGRAPH_AGGREGATED, "Causal Graph" ) original_leaderboard = gr.State(value=data) task_substring_checkbox.change( fn=update_leaderboard, inputs=[original_leaderboard, task_substring_checkbox, model_substring_checkbox], outputs=leaderboard_aggregated ) model_substring_checkbox.change( fn=update_leaderboard, inputs=[original_leaderboard, task_substring_checkbox, model_substring_checkbox], outputs=leaderboard_aggregated ) with gr.TabItem("Intervention Averaged", id=2): leaderboard_averaged, data = 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)