import gradio as gr import ipdb from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns 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, TITLE, ) from src.display.css_html_js import custom_css from src.display.utils import ( BENCHMARK_COLS, EVAL_COLS, EVAL_TYPES, ModelInfoColumn, ModelType, fields, WeightType, Precision ) 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, get_model_info_df, get_merged_df from src.submission.submit import add_new_eval from src.utils import norm_sNavie, pivot_df, get_grouped_dfs, pivot_existed_df, rename_metrics, format_df # import ipdb 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(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() # # LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) # df = pd.read_csv('LOTSAv2_EvalBenchmark(Long).csv') # # Step 2: Pivot the DataFrame # LEADERBOARD_DF = df.pivot_table(index='model', # columns='dataset', # values='eval_metrics/MAE[0.5]', # aggfunc='first') # LEADERBOARD_DF.drop(columns=['ALL'], inplace=True) # # # Reset the index if you want the model column to be part of the DataFrame # LEADERBOARD_DF.reset_index(inplace=True) # # Step 3: noramlize the values # # ipdb.set_trace() # LEADERBOARD_DF = norm_sNavie(LEADERBOARD_DF) # # # LEADERBOARD_DF['Average'] = LEADERBOARD_DF.mean(axis=1) # # LEADERBOARD_DF.insert(1, 'Average', LEADERBOARD_DF.pop('Average')) # # LEADERBOARD_DF = LEADERBOARD_DF.sort_values(by=['Average'], ascending=True) # print(f"The leaderboard is {LEADERBOARD_DF}") # print(f'Columns: ', LEADERBOARD_DF.columns) # LEADERBOARD_DF = pd.read_csv('pivoted_df.csv') # domain_df = pivot_df('results/grouped_results_by_domain.csv', tab_name='domain') # print(f'Domain dataframe is {domain_df}') # freq_df = pivot_df('results/grouped_results_by_frequency.csv', tab_name='frequency') # print(f'Freq dataframe is {freq_df}') # term_length_df = pivot_df('results/grouped_results_by_term_length.csv', tab_name='term_length') # print(f'Term length dataframe is {term_length_df}') # variate_type_df = pivot_df('results/grouped_results_by_univariate.csv', tab_name='univariate') # print(f'Variate type dataframe is {variate_type_df}') # model_info_df = get_model_info_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH) grouped_dfs = get_grouped_dfs() domain_df, freq_df, term_length_df, variate_type_df, overall_df = grouped_dfs['domain'], grouped_dfs['frequency'], grouped_dfs['term_length'], grouped_dfs['univariate'], grouped_dfs['overall'] overall_df = rename_metrics(overall_df) overall_df = format_df(overall_df) overall_df = overall_df.sort_values(by=['Rank']) domain_df = pivot_existed_df(domain_df, tab_name='domain') print(f'Domain dataframe is {domain_df}') freq_df = pivot_existed_df(freq_df, tab_name='frequency') print(f'Freq dataframe is {freq_df}') term_length_df = pivot_existed_df(term_length_df, tab_name='term_length') print(f'Term length dataframe is {term_length_df}') variate_type_df = pivot_existed_df(variate_type_df, tab_name='univariate') print(f'Variate type dataframe is {variate_type_df}') model_info_df = get_model_info_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH) # ( # finished_eval_queue_df, # running_eval_queue_df, # pending_eval_queue_df, # ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) def init_leaderboard(ori_dataframe, model_info_df, sort_val: str|None = None): if ori_dataframe is None or ori_dataframe.empty: raise ValueError("Leaderboard DataFrame is empty or None.") model_info_col_list = [c.name for c in fields(ModelInfoColumn) if c.displayed_by_default if c.name not in ['#Params (B)', 'available_on_hub', 'hub', 'Model sha','Hub License']] col2type_dict = {c.name: c.type for c in fields(ModelInfoColumn)} default_selection_list = list(ori_dataframe.columns) + model_info_col_list # print('default_selection_list: ', default_selection_list) # ipdb.set_trace() # default_selection_list = [col for col in default_selection_list if col not in ['#Params (B)', 'available_on_hub', 'hub', 'Model sha','Hub License']] merged_df = get_merged_df(ori_dataframe, model_info_df) new_cols = ['T'] + [col for col in merged_df.columns if col != 'T'] merged_df = merged_df[new_cols] print('Merged df: ', merged_df) if sort_val: if sort_val in merged_df.columns: merged_df = merged_df.sort_values(by=[sort_val]) else: print(f'Warning: cannot sort by {sort_val}') # get the data type datatype_list = [col2type_dict[col] if col in col2type_dict else 'number' for col in merged_df.columns] print('datatype_list: ', datatype_list) # print('merged_df.column: ', merged_df.columns) # ipdb.set_trace() return Leaderboard( value=merged_df, datatype=datatype_list, select_columns=SelectColumns( default_selection=default_selection_list, # default_selection=[c.name for c in fields(ModelInfoColumn) if # c.displayed_by_default and c.name not in ['params', 'available_on_hub', 'hub', # 'Model sha', 'Hub License']], # default_selection=list(dataframe.columns), cant_deselect=[c.name for c in fields(ModelInfoColumn) if c.never_hidden], label="Select Columns to Display:", # How to uncheck?? ), hide_columns=[c.name for c in fields(ModelInfoColumn) if c.hidden], search_columns=['model'], # hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], # filter_columns=[ # ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"), # ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"), # ColumnFilter( # AutoEvalColumn.params.name, # type="slider", # min=0.01, # max=500, # label="Select the number of parameters (B)", # ), # ColumnFilter( # AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=False # ), # ], filter_columns=[ ColumnFilter(ModelInfoColumn.model_type.name, type="checkboxgroup", label="Model types"), ], # bool_checkboxgroup_label="", column_widths=[40, 150] + [150 for _ in range(len(merged_df.columns)-2)], interactive=False, ) 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('🏅 Overall', elem_id="llm-benchmark-tab-table", id=5): leaderboard = init_leaderboard(overall_df, model_info_df, sort_val='MAPE') print(f'FINAL Overall LEADERBOARD {overall_df}') with gr.TabItem("🏅 By Domain", elem_id="llm-benchmark-tab-table", id=0): leaderboard = init_leaderboard(domain_df, model_info_df) print(f"FINAL Domain LEADERBOARD 1 {domain_df}") with gr.TabItem("🏅 By Frequency", elem_id="llm-benchmark-tab-table", id=1): leaderboard = init_leaderboard(freq_df, model_info_df) print(f"FINAL Frequency LEADERBOARD 1 {freq_df}") with gr.TabItem("🏅 By Term Length", elem_id="llm-benchmark-tab-table", id=2): leaderboard = init_leaderboard(term_length_df, model_info_df) print(f"FINAL term length LEADERBOARD 1 {term_length_df}") with gr.TabItem("🏅 By Variate Type", elem_id="llm-benchmark-tab-table", id=3): leaderboard = init_leaderboard(variate_type_df, model_info_df) print(f"FINAL LEADERBOARD 1 {variate_type_df}") with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=4): gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") 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()