import json import os from datetime import datetime, timezone import gradio as gr import pandas as pd from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download from src.display.utils import ( COLS, TYPES, BENCHMARK_COLS, EVAL_COLS, EVAL_TYPES, AutoEvalColumn, ModelType, NUMERIC_INTERVALS, fields, ) from src.display.css_html_js import custom_css, get_window_url_params from src.display.about import ( CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_QUEUE_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE, ) from src.tools.plots import ( create_metric_plot_obj, create_scores_df, create_plot_df, join_model_info_with_results, HUMAN_BASELINES, ) from src.tools.collections import update_collections from src.populate import get_evaluation_queue_df, get_leaderboard_df from src.envs import H4_TOKEN, QUEUE_REPO, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, RESULTS_REPO, API, REPO_ID, IS_PUBLIC from src.submission.submit import add_new_eval def restart_space(): API.restart_space(repo_id=REPO_ID, token=H4_TOKEN) try: snapshot_download( repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30 ) except Exception: restart_space() try: snapshot_download( repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30 ) except Exception: restart_space() original_df = get_leaderboard_df(EVAL_RESULTS_PATH, COLS, BENCHMARK_COLS) update_collections(original_df.copy()) leaderboard_df = original_df.copy() #models = original_df["model_name_for_query"].tolist() # needed for model backlinks in their to the leaderboard # plot_df = create_plot_df(create_scores_df(join_model_info_with_results(original_df))) #to_be_dumped = f"models = {repr(models)}\n" ( finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df, ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) # Basics def change_tab(query_param: str): query_param = query_param.replace("'", '"') query_param = json.loads(query_param) if isinstance(query_param, dict) and "tab" in query_param and query_param["tab"] == "evaluation": return gr.Tabs.update(selected=1) else: return gr.Tabs.update(selected=0) # Searching and filtering def update_table( hidden_df: pd.DataFrame, columns: list, type_query: list, precision_query: str, size_query: list, show_deleted: bool, query: str, ): filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted) filtered_df = filter_queries(query, filtered_df) df = select_columns(filtered_df, columns) 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): """Added by Abishek""" 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: # 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] type_emoji = [t[0] for t in type_query] filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] filtered_df = filtered_df[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 # 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("🏅 LLM Benchmark", 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 and press ENTER...", # show_label=False, # elem_id="search-bar", # ) # with gr.Row(): # shown_columns = gr.CheckboxGroup( # choices=[ # c # for c in COLS # if c # not in [ # AutoEvalColumn.dummy.name, # AutoEvalColumn.model.name, # AutoEvalColumn.model_type_symbol.name, # AutoEvalColumn.still_on_hub.name, # ] # ], # value=[ # c # for c in COLS_LITE # if c # not in [ # AutoEvalColumn.dummy.name, # AutoEvalColumn.model.name, # AutoEvalColumn.model_type_symbol.name, # AutoEvalColumn.still_on_hub.name, # ] # ], # label="Select columns to show", # elem_id="column-select", # interactive=True, # ) # with gr.Row(): # deleted_models_visibility = gr.Checkbox( # value=True, label="Show gated/private/deleted models", interactive=True # ) # with gr.Column(min_width=320): # with gr.Box(elem_id="box-filter"): # filter_columns_type = gr.CheckboxGroup( # label="Model types", # choices=[ # ModelType.PT.to_str(), # ModelType.FT.to_str(), # ModelType.IFT.to_str(), # ModelType.RL.to_str(), # ], # value=[ # ModelType.PT.to_str(), # ModelType.FT.to_str(), # ModelType.IFT.to_str(), # ModelType.RL.to_str(), # ], # interactive=True, # elem_id="filter-columns-type", # ) # filter_columns_precision = gr.CheckboxGroup( # label="Precision", # choices=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"], # value=["torch.float16", "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"], # interactive=True, # elem_id="filter-columns-precision", # ) # filter_columns_size = gr.CheckboxGroup( # label="Model sizes", # 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[ # [AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] # + shown_columns.value # + [AutoEvalColumn.dummy.name] # ], # headers=[ # AutoEvalColumn.model_type_symbol.name, # AutoEvalColumn.model.name, # ] # + shown_columns.value # + [AutoEvalColumn.dummy.name], # datatype=TYPES, # max_rows=None, # 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=original_df, # headers=COLS, # datatype=TYPES, # max_rows=None, # visible=False, # ) # search_bar.submit( # update_table, # [ # hidden_leaderboard_table_for_search, # leaderboard_table, # shown_columns, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, # search_bar, # ], # leaderboard_table, # ) # shown_columns.change( # update_table, # [ # hidden_leaderboard_table_for_search, # leaderboard_table, # shown_columns, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, # search_bar, # ], # leaderboard_table, # queue=True, # ) # filter_columns_type.change( # update_table, # [ # hidden_leaderboard_table_for_search, # leaderboard_table, # shown_columns, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, # search_bar, # ], # leaderboard_table, # queue=True, # ) # filter_columns_precision.change( # update_table, # [ # hidden_leaderboard_table_for_search, # leaderboard_table, # shown_columns, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, # search_bar, # ], # leaderboard_table, # queue=True, # ) # filter_columns_size.change( # update_table, # [ # hidden_leaderboard_table_for_search, # leaderboard_table, # shown_columns, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, # search_bar, # ], # leaderboard_table, # queue=True, # ) # deleted_models_visibility.change( # update_table, # [ # hidden_leaderboard_table_for_search, # leaderboard_table, # shown_columns, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, # search_bar, # ], # leaderboard_table, # queue=True, # ) # with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): # gr.Markdown(LLM_BENCHMARKS_TEXT, 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, # max_rows=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, # max_rows=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, # max_rows=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", placeholder="main") # private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC) # model_type = gr.Dropdown( # choices=[ # ModelType.PT.to_str(" : "), # ModelType.FT.to_str(" : "), # ModelType.IFT.to_str(" : "), # ModelType.RL.to_str(" : "), # ], # label="Model type", # multiselect=False, # value=None, # interactive=True, # ) # # with gr.Column(): # precision = gr.Dropdown( # choices=[ # "float16", # "bfloat16", # "8bit (LLM.int8)", # "4bit (QLoRA / FP4)", # "GPTQ" # ], # label="Precision", # multiselect=False, # value="float16", # interactive=True, # ) # weight_type = gr.Dropdown( # choices=["Original", "Delta", "Adapter"], # label="Weights type", # multiselect=False, # value="Original", # 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() # submit_button.click( # add_new_eval, # [ # model_name_textbox, # base_model_name_textbox, # revision_name_textbox, # precision, # private, # 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, # elem_id="citation-button", # ).style(show_copy_button=True) # # dummy = gr.Textbox(visible=False) # demo.load( # change_tab, # dummy, # tabs, # _js=get_window_url_params, # ) dummy1 = gr.Textbox(visible=False) hidden_leaderboard_table_for_search = gr.components.Dataframe( headers=COLS, datatype=TYPES, max_rows=None, visible=False, ) def display(x, y): return original_df INTRODUCTION_TEXT = """ This is a copied space from Open Source LLM leaderboard. Instead of displaying the results as table the space simply provides a gradio API interface to access the full leaderboard data easily. Example python on how to access the data: ```python from gradio_client import Client import json client = Client("https://felixz-open-llm-leaderboard.hf.space/") json_data = client.predict("","", api_name='/predict') with open(json_data, 'r') as file: file_data = file.read() # Load the JSON data data = json.loads(file_data) # Get the headers and the data headers = data['headers'] data = data['data'] ``` """ interface = gr.Interface( fn=display, inputs=[ gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text"), dummy1 ], outputs=[hidden_leaderboard_table_for_search] ) #scheduler = BackgroundScheduler() #scheduler.add_job(restart_space, "interval", seconds=12000) #scheduler.start() interface.launch() #demo.queue(concurrency_count=40).launch()