import subprocess import gradio as gr import pandas as pd from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download from src.about import ( CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_QUEUE_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT_1, EVALUATION_EXAMPLE_IMG, LLM_BENCHMARKS_TEXT_2, ENTITY_DISTRIBUTION_IMG, LLM_BENCHMARKS_TEXT_3, TITLE, LOGO ) from src.display.css_html_js import custom_css from src.display.utils import ( DATASET_BENCHMARK_COLS, TYPES_BENCHMARK_COLS, DATASET_COLS, Clinical_TYPES_COLS, EVAL_COLS, EVAL_TYPES, NUMERIC_INTERVALS, TYPES, AutoEvalColumn, ModelType, ModelArch, PromptTemplateName, Precision, WeightType, fields, ) from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN from src.populate import get_evaluation_queue_df, get_leaderboard_df from src.submission.submit import add_new_eval, PLACEHOLDER_DATASET_WISE_NORMALIZATION_CONFIG def restart_space(): API.restart_space(repo_id=REPO_ID) 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(EVAL_RESULTS_PATH) snapshot_download( repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN ) except Exception: restart_space() # Span based results _, span_based_datasets_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, DATASET_COLS, DATASET_BENCHMARK_COLS, "SpanBasedWithPartialOverlap", "datasets") span_based_datasets_leaderboard_df = span_based_datasets_original_df.copy() _, span_based_types_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, Clinical_TYPES_COLS, TYPES_BENCHMARK_COLS, "SpanBasedWithPartialOverlap", "clinical_types") span_based_types_leaderboard_df = span_based_types_original_df.copy() # Token based results _, token_based_datasets_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, DATASET_COLS, DATASET_BENCHMARK_COLS, "TokenBasedWithMacroAverage", "datasets") token_based_datasets_leaderboard_df = token_based_datasets_original_df.copy() _, token_based_types_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, Clinical_TYPES_COLS, TYPES_BENCHMARK_COLS, "TokenBasedWithMacroAverage", "clinical_types") token_based_types_leaderboard_df = token_based_types_original_df.copy() ( finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df, ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) def update_df(evaluation_metric, shown_columns, subset="datasets"): print(evaluation_metric) if subset == "datasets": match evaluation_metric: case "Span Based": leaderboard_table_df = span_based_datasets_leaderboard_df.copy() hidden_leader_board_df = span_based_datasets_original_df case "Token Based": leaderboard_table_df = token_based_datasets_leaderboard_df.copy() hidden_leader_board_df = token_based_datasets_original_df case _: pass else: match evaluation_metric: case "Span Based": leaderboard_table_df = span_based_types_leaderboard_df.copy() hidden_leader_board_df = span_based_types_original_df case "Token Based": leaderboard_table_df = token_based_types_leaderboard_df.copy() hidden_leader_board_df = token_based_types_original_df case _: pass value_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns return leaderboard_table_df[value_cols], hidden_leader_board_df # Searching and filtering def update_table( hidden_df: pd.DataFrame, columns: list, query: str, type_query: list = None, architecture_query: list = None, size_query: list = None, precision_query: str = None, show_deleted: bool = False, ): filtered_df = filter_models(hidden_df, type_query, architecture_query, size_query, precision_query, show_deleted) filtered_df = filter_queries(query, filtered_df) df = select_columns(filtered_df, columns, list(hidden_df.columns)) return df def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))] def select_columns(df: pd.DataFrame, columns: list, cols:list) -> pd.DataFrame: always_here_cols = [ AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name, ] # We use COLS to maintain sorting filtered_df = df[always_here_cols + [c for c in cols if c in df.columns and c in columns]] return filtered_df def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame: final_df = [] if query != "": queries = [q.strip() for q in query.split(";")] for _q in queries: _q = _q.strip() if _q != "": temp_filtered_df = search_table(filtered_df, _q) if len(temp_filtered_df) > 0: final_df.append(temp_filtered_df) if len(final_df) > 0: filtered_df = pd.concat(final_df) filtered_df = filtered_df.drop_duplicates( subset=[ AutoEvalColumn.model.name, # AutoEvalColumn.precision.name, # AutoEvalColumn.revision.name, ] ) return filtered_df def filter_models( df: pd.DataFrame, type_query: list, architecture_query: list, size_query: list, precision_query: list, show_deleted: bool ) -> pd.DataFrame: # Show all models # if show_deleted: # filtered_df = df # else: # Show only still on the hub models # filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True] filtered_df = df if type_query is not None: type_emoji = [t[0] for t in type_query] filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] if architecture_query is not None: arch_types = [t for t in architecture_query] filtered_df = filtered_df.loc[df[AutoEvalColumn.architecture.name].isin(arch_types)] # filtered_df = filtered_df.loc[df[AutoEvalColumn.architecture.name].isin(architecture_query + ["None"])] if precision_query is not None: if AutoEvalColumn.precision.name in df.columns: filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])] if size_query is not None: numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query])) params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce") mask = params_column.apply(lambda x: any(numeric_interval.contains(x))) filtered_df = filtered_df.loc[mask] return filtered_df def change_submit_request_form(model_architecture): match model_architecture: case "Encoder": return ( gr.Textbox(label="Threshold for gliner models", visible=False), gr.Radio( choices=["True", "False"], label="Load GLiNER Tokenizer", visible=False ), gr.Dropdown( choices=[prompt_template.value for prompt_template in PromptTemplateName], label="Prompt for generation", multiselect=False, # value="HTML Highlighted Spans", interactive=True, visible=False ) ) case "Decoder": return ( gr.Textbox(label="Threshold for gliner models", visible=False), gr.Radio( choices=["True", "False"], label="Load GLiNER Tokenizer", visible=False ), gr.Dropdown( choices=[prompt_template.value for prompt_template in PromptTemplateName], label="Prompt for generation", multiselect=False, # value="HTML Highlighted Spans", interactive=True, visible=True ) ) case "GLiNER Encoder": return ( gr.Textbox(label="Threshold for gliner models", visible=True), gr.Radio( choices=["True", "False"], label="Load GLiNER Tokenizer", visible=True ), gr.Dropdown( choices=[prompt_template.value for prompt_template in PromptTemplateName], label="Prompt for generation", multiselect=False, # value="HTML Highlighted Spans", interactive=True, visible=False ) ) demo = gr.Blocks(css=custom_css) with demo: gr.HTML(TITLE) gr.HTML(LOGO, elem_classes="logo") gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") with gr.Tabs(elem_classes="tab-buttons") as tabs: with gr.TabItem("🏅 NER Datasets", elem_id="llm-benchmark-tab-table", id=0): with gr.Row(): with gr.Column(): with gr.Row(): search_bar = gr.Textbox( placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...", show_label=False, elem_id="search-bar", ) with gr.Row(): shown_columns = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden and not c.clinical_type_col], value=[ c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden and not c.never_hidden and not c.clinical_type_col ], label="Select columns to show", elem_id="column-select", interactive=True, ) # with gr.Row(): # deleted_models_visibility = gr.Checkbox( # value=False, label="Show gated/private/deleted models", interactive=True # ) with gr.Column(min_width=320): # with gr.Box(elem_id="box-filter"): eval_metric = gr.Radio( choices=["Span Based", "Token Based"], value = "Span Based", label="Evaluation Metric", ) filter_columns_type = gr.CheckboxGroup( label="Model Types", choices=[t.to_str() for t in ModelType], value=[t.to_str() for t in ModelType], interactive=True, elem_id="filter-columns-type", ) filter_columns_architecture = gr.CheckboxGroup( label="Architecture Types", choices=[i.value.name for i in ModelArch], value=[i.value.name for i in ModelArch], interactive=True, elem_id="filter-columns-architecture", ) # filter_columns_size = gr.CheckboxGroup( # label="Model sizes (in billions of parameters)", # choices=list(NUMERIC_INTERVALS.keys()), # value=list(NUMERIC_INTERVALS.keys()), # interactive=True, # elem_id="filter-columns-size", # ) datasets_leaderboard_df, datasets_original_df = update_df(eval_metric.value, shown_columns.value, subset="datasets") leaderboard_table = gr.components.Dataframe( value=datasets_leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value], headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value, datatype=TYPES, elem_id="leaderboard-table", interactive=False, visible=True, ) # Dummy leaderboard for handling the case when the user uses backspace key hidden_leaderboard_table_for_search = gr.components.Dataframe( value=datasets_original_df[DATASET_COLS], headers=DATASET_COLS, datatype=TYPES, visible=False, ) eval_metric.change( lambda a, b: update_df(a,b, "datasets") , inputs=[ eval_metric, shown_columns, ], outputs=[ leaderboard_table, hidden_leaderboard_table_for_search, ] ) search_bar.submit( update_table, [ hidden_leaderboard_table_for_search, shown_columns, search_bar, filter_columns_type, filter_columns_architecture ], leaderboard_table, ) for selector in [ shown_columns, filter_columns_type, filter_columns_architecture, # filter_columns_size, # deleted_models_visibility, ]: selector.change( update_table, [ hidden_leaderboard_table_for_search, shown_columns, search_bar, filter_columns_type, filter_columns_architecture, ], leaderboard_table, queue=True, ) with gr.TabItem("🏅 Clinical Types", elem_id="llm-benchmark-tab-table", id=4): with gr.Row(): with gr.Column(): with gr.Row(): search_bar = gr.Textbox( placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...", show_label=False, elem_id="search-bar", ) with gr.Row(): shown_columns = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden and not c.dataset_task_col], value=[ c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden and not c.never_hidden and not c.dataset_task_col ], label="Select columns to show", elem_id="column-select", interactive=True, ) # with gr.Row(): # deleted_models_visibility = gr.Checkbox( # value=False, label="Show gated/private/deleted models", interactive=True # ) with gr.Column(min_width=320): eval_metric = gr.Radio( choices=["Span Based", "Token Based"], value = "Span Based", label="Evaluation Metric", ) # with gr.Box(elem_id="box-filter"): filter_columns_type = gr.CheckboxGroup( label="Model Types", choices=[t.to_str() for t in ModelType], value=[t.to_str() for t in ModelType], interactive=True, elem_id="filter-columns-type", ) filter_columns_architecture = gr.CheckboxGroup( label="Architecture Types", choices=[i.value.name for i in ModelArch], value=[i.value.name for i in ModelArch], interactive=True, elem_id="filter-columns-architecture", ) # filter_columns_precision = gr.CheckboxGroup( # label="Precision", # choices=[i.value.name for i in Precision], # value=[i.value.name for i in Precision], # interactive=True, # elem_id="filter-columns-precision", # ) # filter_columns_size = gr.CheckboxGroup( # label="Model sizes (in billions of parameters)", # choices=list(NUMERIC_INTERVALS.keys()), # value=list(NUMERIC_INTERVALS.keys()), # interactive=True, # elem_id="filter-columns-size", # ) types_leaderboard_df, types_original_df = update_df(eval_metric.value, shown_columns.value, subset="clinical_types") leaderboard_table = gr.components.Dataframe( value=types_leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value], headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value, datatype=TYPES, elem_id="leaderboard-table", interactive=False, visible=True, ) # Dummy leaderboard for handling the case when the user uses backspace key hidden_leaderboard_table_for_search = gr.components.Dataframe( value=types_original_df[Clinical_TYPES_COLS], headers=Clinical_TYPES_COLS, datatype=TYPES, visible=False, ) eval_metric.change( fn=lambda a, b: update_df(a,b, "clinical_types"), inputs=[ eval_metric, shown_columns, ], outputs=[ leaderboard_table, hidden_leaderboard_table_for_search ] ) search_bar.submit( update_table, [ hidden_leaderboard_table_for_search, shown_columns, search_bar, filter_columns_type, filter_columns_architecture, ], leaderboard_table, ) for selector in [ shown_columns, filter_columns_type, filter_columns_architecture, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, ]: selector.change( update_table, [ hidden_leaderboard_table_for_search, shown_columns, search_bar, filter_columns_type, filter_columns_architecture, ], leaderboard_table, queue=True, ) with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): gr.Markdown(LLM_BENCHMARKS_TEXT_1, elem_classes="markdown-text") gr.HTML(EVALUATION_EXAMPLE_IMG, elem_classes="logo") gr.Markdown(LLM_BENCHMARKS_TEXT_2, elem_classes="markdown-text") gr.HTML(ENTITY_DISTRIBUTION_IMG, elem_classes="logo") gr.Markdown(LLM_BENCHMARKS_TEXT_3, elem_classes="markdown-text") with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3): with gr.Column(): with gr.Row(): gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") with gr.Column(): with gr.Accordion( f"✅ Finished Evaluations ({len(finished_eval_queue_df)})", open=False, ): with gr.Row(): finished_eval_table = gr.components.Dataframe( value=finished_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5, ) with gr.Accordion( f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})", open=False, ): with gr.Row(): running_eval_table = gr.components.Dataframe( value=running_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5, ) with gr.Accordion( f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})", open=False, ): with gr.Row(): pending_eval_table = gr.components.Dataframe( value=pending_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5, ) with gr.Row(): gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text") with gr.Row(): with gr.Column(): model_name_textbox = gr.Textbox(label="Model name") revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main") model_arch = gr.Radio( choices=[t.to_str(" : ") for t in ModelArch if t != ModelArch.Unknown], label="Model Architecture", ) model_type = gr.Dropdown( choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown], label="Model type", multiselect=False, value=None, interactive=True, ) with gr.Column(): label_normalization_map = gr.Textbox(lines=6, label="Label Normalization Map", placeholder=PLACEHOLDER_DATASET_WISE_NORMALIZATION_CONFIG) gliner_threshold = gr.Textbox(label="Threshold for GLiNER models", visible=False) gliner_tokenizer_bool = gr.Radio( choices=["True", "False"], label="Load GLiNER Tokenizer", visible=False ) prompt_name = gr.Dropdown( choices=[prompt_template.value for prompt_template in PromptTemplateName], label="Prompt for generation", multiselect=False, value="HTML Highlighted Spans", interactive=True, visible=False )# should be a dropdown # parsing_function - this is tied to the prompt & therefore does not need to be specified # generation_parameters = gr.Textbox(label="Generation params in json format") just default for now model_arch.change(fn=change_submit_request_form, inputs=model_arch, outputs=[ gliner_threshold, gliner_tokenizer_bool, prompt_name]) submit_button = gr.Button("Submit Eval") submission_result = gr.Markdown() submit_button.click( add_new_eval, [ model_name_textbox, # base_model_name_textbox, revision_name_textbox, model_arch, label_normalization_map, gliner_threshold, gliner_tokenizer_bool, prompt_name, # weight_type, model_type, ], submission_result, ) 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.queue(default_concurrency_limit=40).launch(allowed_paths=['./assets/'])