import gradio as gr import pandas as pd from apscheduler.schedulers.background import BackgroundScheduler from src.display.css_html_js import custom_css from src.display.about import ( CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_QUEUE_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, LLM_BENCHMARKS_DETAILS, FAQ_TEXT, TITLE, ) from src.display.utils import ( BENCHMARK_COLS, COLS, EVAL_COLS, EVAL_TYPES, NUMERIC_INTERVALS, TYPES, AutoEvalColumn, ModelType, fields, WeightType, Precision ) from src.populate import get_evaluation_queue_df, get_leaderboard_df from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO from src.submission.submit import add_new_eval from src.display.utils import Tasks from huggingface_hub import snapshot_download ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- def restart_space(): API.restart_space(repo_id=REPO_ID, token=H4_TOKEN) def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout): try: print(f"local_dir for snapshot download = {local_dir}") snapshot_download(repo_id=repo_id, local_dir=local_dir, repo_type=repo_type, tqdm_class=tqdm_class, etag_timeout=etag_timeout) except Exception: print(f"ui_snapshot_download failed. restarting space...") restart_space() # Searching and filtering def update_table(hidden_df: pd.DataFrame, columns: list, type_query: list, precision_query: list, size_query: list, query: str): print(f"hidden_df = {hidden_df}") show_deleted = True filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted) print(f"filtered_df = {filtered_df}") filtered_df = filter_queries(query, filtered_df) df = select_columns(filtered_df, columns) print(f"df = {df}") return df def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))] def select_columns(df: pd.DataFrame, columns: 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] + [AutoEvalColumn.dummy.name] ] return filtered_df def filter_queries(query: str, filtered_df: 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, size_query: list, precision_query: list, show_deleted: bool) -> pd.DataFrame: print(f"filter_models()'s df: {df}\n") # 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] is True] type_emoji = [t[0] for t in type_query] filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["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 ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ui_snapshot_download(repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30) ui_snapshot_download(repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30) print(f"COLS = {COLS}") raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) # k the problem is that the results are only saved in _bk dirs. leaderboard_df = original_df.copy() print(f"leaderboard_df = {leaderboard_df}") ################################################################################################################################ 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: # toggle break 1: this tab just RENDERS existing result files on remote repo. with gr.TabItem("Benchmarks", elem_id="llm-benchmark-tab-table", id=0): with gr.Row(): with gr.Column(): with gr.Row(): search_bar = gr.Textbox(placeholder=" 🔍 Model search (separate multiple queries with `;`)", 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.dummy ], value=[ c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden and not c.never_hidden ], label="Select columns to show", elem_id="column-select", interactive=True, ) with gr.Column(min_width=320): 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_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", ) # leaderboard_table = gr.components.Dataframe( # value=leaderboard_df[ # [c.name for c in fields(AutoEvalColumn) if c.never_hidden] # + shown_columns.value # + [AutoEvalColumn.dummy.name] # ] if leaderboard_df.empty is False else leaderboard_df, # 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, # column_widths=["2%", "20%"] # ) leaderboard_table = gr.components.Dataframe( # value=leaderboard_df, value=leaderboard_df[ [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value + [AutoEvalColumn.dummy.name] ] if leaderboard_df.empty is False else leaderboard_df, 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, # column_widths=["2%", "20%"] ) # Dummy leaderboard for handling the case when the user uses backspace key hidden_leaderboard_table_for_search = gr.components.Dataframe( value=original_df[COLS] if original_df.empty is False else original_df, headers=COLS, datatype=TYPES, visible=False ) for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size]: selector.change( update_table, [ hidden_leaderboard_table_for_search, shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, search_bar, ], leaderboard_table, queue=True, ) # toggle break 2: Submission -> runs add_new_eval() (actual evaluation is done on backend when backend-cli.py is run.) 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") # You can use the revision parameter to point to the specific commit hash when downloading. revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main") private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC) 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(): precision = gr.Dropdown( choices=[i.value.name for i in Precision if i != Precision.Unknown], label="Precision", multiselect=False, value="float32", interactive=True, ) weight_type = gr.Dropdown( choices=[i.value.name for i in WeightType], label="Weights type", multiselect=False, value="Original", interactive=True, ) requested_tasks = gr.CheckboxGroup( choices=[ (i.value.col_name, i.value) for i in Tasks], label="Select tasks", elem_id="task-select", interactive=True, ) base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)") submit_button = gr.Button("Submit Eval") submission_result = gr.Markdown() # we need to add task specification argument here as well. submit_button.click( add_new_eval, [ model_name_textbox, requested_tasks, # is this a list of str or class Task? i think it's Task. base_model_name_textbox, revision_name_textbox, precision, private, weight_type, model_type, ], submission_result) # demo.launch() #### scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=6 * 60 * 60) scheduler.start() # demo.queue(default_concurrency_limit=40).launch() # demo.launch(show_api=False, enable_queue=False) demo.launch() # TypeError: Blocks.launch() got an unexpected keyword argument 'enable_queue'