"""A gradio app that renders a static leaderboard. This is used for Hugging Face Space.""" import ast import argparse import glob import pickle import plotly import gradio as gr import numpy as np import pandas as pd import gradio as gr import pandas as pd from pathlib import Path import json from constants import * from datetime import datetime, timezone # from datasets import Dataset, load_dataset, concatenate_datasets import os, uuid from utils_display import model_info from constants import column_names, LEADERBOARD_REMARKS, DEFAULT_K, LEADERBOARD_REMARKS_MAIN import pytz from data_utils import post_processing # get the last updated time from the elo_ranks.all.jsonl file LAST_UPDATED = None # with open("_intro.md", "r") as f: # INTRO_MD = f.read() INTRO_MD = "" with open("_about_us.md", "r") as f: ABOUT_MD = f.read() with open("_header.md", "r") as f: HEADER_MD = f.read() with open("_metrics.md", "r") as f: METRICS_MD = f.read() original_df = None # available_models = [] # to be filled in later available_models = list(model_info.keys()) def df_filters(mode_selection_radio, show_open_source_model_only): global original_df # remove the rows when the model contains "❌" original_df = original_df[~original_df["Model"].str.contains("❌")] modes = { "greedy": ["greedy"], "sampling (Temp=0.5)": ["sampling"], "all": ["greedy", "sampling"] } # filter the df by the mode_selection_radio default_main_df = original_df[original_df["Mode"].isin(modes[mode_selection_radio])] default_main_df.insert(0, "", range(1, 1 + len(default_main_df))) return default_main_df.copy() def _gstr(text): return gr.Text(text, visible=False) def _tab_leaderboard(): global original_df, available_models with gr.TabItem("📊 Main", elem_id="od-benchmark-tab-table-ablation", id=0, elem_classes="subtab"): default_main_df = original_df.copy() # default_main_df.insert(0, "", range(1, 1 + len(default_main_df))) # default_main_df_no_task = default_main_df.copy() default_mode = "greedy" default_main_df = df_filters(default_mode, False) with gr.Row(): with gr.Column(scale=5): mode_selection_radio = gr.Radio(["greedy", "sampling (Temp=0.5)", "all"], show_label=False, elem_id="rank-column-radio", value=default_mode) # with gr.Row(): # with gr.Column(scale=2): leaderboard_table = gr.components.Dataframe( value=default_main_df, datatype= ["number", "markdown", "markdown", "number"], # max_rows=None, height=6000, elem_id="leaderboard-table", interactive=False, visible=True, column_widths=[50, 260, 100, 100, 120, 120, 100,100,110,100], wrap=True # min_width=60, ) # checkbox_show_task_categorized.change(fn=length_margin_change, inputs=[length_margin_choices, gr.Text("main", visible=False), checkbox_show_task_categorized, show_open_source_model_only, rank_column_radio], outputs=[leaderboard_table]) # show_open_source_model_only.change(fn=length_margin_change, inputs=[length_margin_choices, gr.Text("main", visible=False), checkbox_show_task_categorized, show_open_source_model_only, rank_column_radio], outputs=[leaderboard_table]) # rank_column_radio.change(fn=length_margin_change, inputs=[length_margin_choices, gr.Text("main", visible=False), checkbox_show_task_categorized, show_open_source_model_only, rank_column_radio], outputs=[leaderboard_table]) mode_selection_radio.change(fn=df_filters, inputs=[mode_selection_radio, _gstr("")], outputs=[leaderboard_table]) def _tab_submit(): pass def build_demo(): global original_df, available_models, gpt4t_dfs, haiku_dfs, llama_dfs with gr.Blocks(theme=gr.themes.Soft(), css=css, js=js_light) as demo: gr.HTML(BANNER, elem_id="banner") # convert LAST_UPDATED to the PDT time LAST_UPDATED = datetime.now(pytz.timezone('US/Pacific')).strftime("%Y-%m-%d %H:%M:%S") # header_md_text = HEADER_MD.replace("{model_num}", str(len(original_df["-1"]))).replace("{LAST_UPDATED}", str(LAST_UPDATED)) # gr.Markdown(header_md_text, elem_classes="markdown-text") with gr.Tabs(elem_classes="tab-buttons") as tabs: with gr.TabItem("🏅 Leaderboard", elem_id="od-benchmark-tab-table", id=0): _tab_leaderboard() with gr.TabItem("🚀 Submit Your Results", elem_id="od-benchmark-tab-table", id=3): _tab_submit() with gr.TabItem("📮 About Us", elem_id="od-benchmark-tab-table", id=4): gr.Markdown(ABOUT_MD, elem_classes="markdown-text") with gr.Row(): with gr.Accordion("📙 Citation", open=False, elem_classes="accordion-label"): gr.Textbox( value=CITATION_TEXT, lines=7, label="Copy the BibTeX snippet to cite this source", elem_id="citation-button", show_copy_button=True) # ).style(show_copy_button=True) return demo def data_load(result_file): global original_df print(f"Loading {result_file}") column_names_main = column_names.copy() # column_names_main.update({}) main_ordered_columns = ORDERED_COLUMN_NAMES click_url = True # read json file from the result_file with open(result_file, "r") as f: data = json.load(f) # floatify the data, if possible for d in data: for k, v in d.items(): try: d[k] = float(v) except: pass original_df = pd.DataFrame(data) original_df = post_processing(original_df, column_names_main, ordered_columns=main_ordered_columns, click_url=click_url, rank_column=RANKING_COLUMN) # print(original_df.columns) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--share", action="store_true") parser.add_argument("--result_file", help="Path to results table", default="ZeroEval-main/result_dirs/zebra-grid.summary.json") args = parser.parse_args() data_load(args.result_file) print(original_df) demo = build_demo() demo.launch(share=args.share, height=3000, width="100%")