import gradio as gr from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns,SearchColumns 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, FOOTER_TEXT ) from src.display.css_html_js import custom_css from src.display.utils import ( BENCHMARK_COLS, COLS, EVAL_COLS, EVAL_TYPES, AutoEvalColumn, # 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 from src.submission.submit import add_new_eval 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) ( finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df, ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) def hide_skill_columns(dataframe, exceptions=[]): return dataframe[[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default or c.name in exceptions]] def perform_cell_formatting(dataframe): return dataframe.style.format({'Contamination Score': "{:.2f}",'Benchmark Score': "{:.2f}",'Speed (words/sec)': "{:.2f}"}).apply( lambda rows: [ "background-color: red;" if (value >0) else "background-color: green;" for value in rows ], subset=["Contamination Score"], ) def init_leaderboard(dataframe): dataframe = hide_skill_columns(dataframe) styler = perform_cell_formatting(dataframe) return gr.Dataframe( value=styler, datatype="markdown", wrap=True, show_fullscreen_button=False, interactive=False, column_widths=[30,50,50,150,60,60,60], max_height=420, elem_classes="leaderboard_col_style" ) def init_skill_leaderboard(dataframe): ## create selector for model skills, based on the selector filter the dataframe skills = ['MMLU', 'General Knowledge', 'Reasoning & Math', 'Translation (incl Dialects)', 'Trust & Safety', 'Writing (incl Dialects)', 'RAG QA', 'Reading Comprehension', 'Arabic Language & Grammar', 'Diacritization', 'Dialect Detection', 'Sentiment Analysis', 'Summarization', 'Instruction Following', 'Transliteration', 'Paraphrasing', 'Entity Extraction', 'Long Context', 'Coding', 'Hallucination', 'Function Calling', 'Structuring'] skills_dropdown = gr.Dropdown(choices=skills, label="Select Skill", value=skills[0]) def filter_dataframe(skill): filtered_df = dataframe.sort_values(by=[skill], ascending=False).reset_index(drop=True) filtered_df = hide_skill_columns(filtered_df, exceptions=[skill]) filtered_df["Rank"] = range(1, len(filtered_df) + 1) styler = perform_cell_formatting(filtered_df) return gr.Dataframe( value=styler, datatype="markdown", wrap=True, show_fullscreen_button=False, interactive=False, column_widths=[30,50,50,150,60,60,60,80], max_height=420, elem_classes="leaderboard_col_style" ) leaderboard_by_skill = filter_dataframe(skills[0]) skills_dropdown.change(filter_dataframe, inputs=skills_dropdown, outputs=leaderboard_by_skill) return leaderboard_by_skill def init_size_leaderboard(dataframe): dataframe = hide_skill_columns(dataframe) size_keys = ["Large","Medium","Small","Nano"] size_names = ["Large (More than 30B Parameter)","Medium (~30B)","Small (~10B)","Nano (~3B)"] sizes_dropdown = gr.Dropdown(choices=size_names, label="Select Model Size", value=size_names[0]) def filter_dataframe(size_name): ##map size name to size key size_name_mapped_to_key = size_keys[size_names.index(size_name)] ##slice array from 0 to index of size size_list = size_keys[size_keys.index(size_name_mapped_to_key):] filtered_df = dataframe[dataframe["Category"].isin(size_list)].reset_index(drop=True) filtered_df["Rank"] = range(1, len(filtered_df) + 1) styler = perform_cell_formatting(filtered_df) return gr.Dataframe( value=styler, datatype="markdown", wrap=True, show_fullscreen_button=False, interactive=False, column_widths=[30,50,50,150,60,60,60], max_height=420, elem_classes="leaderboard_col_style" ) leaderboard_by_skill = filter_dataframe(size_names[0]) sizes_dropdown.change(filter_dataframe, inputs=sizes_dropdown, outputs=leaderboard_by_skill) return leaderboard_by_skill demo = gr.Blocks(css=custom_css) with demo: gr.HTML(TITLE, elem_classes="abl_header") gr.HTML(INTRODUCTION_TEXT, elem_classes="abl_desc_text") with gr.Tabs(elem_classes="tab-buttons") as tabs: with gr.TabItem("🏅 Leaderboard - Top Models", elem_id="llm-benchmark-tab-table", id=0): leaderboard = init_leaderboard(LEADERBOARD_DF) with gr.TabItem("🏅 Top by Size", elem_id="llm-benchmark-tab-size", id=1): leaderboard = init_size_leaderboard(LEADERBOARD_DF) with gr.TabItem("🏅 Top by Skill", elem_id="llm-benchmark-tab-skills", id=2): leaderboard = init_skill_leaderboard(LEADERBOARD_DF) with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-about", id=4): gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-submit", id=5): 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") submit_button = gr.Button("Submit Eval", variant="huggingface" ) submission_result = gr.Markdown() submit_button.click( add_new_eval, [ model_name_textbox, ], submission_result, ) with gr.Row(): with gr.Accordion("📙 Citation", open=False): citation_button = gr.Textbox( value=CITATION_BUTTON_TEXT, label=CITATION_BUTTON_LABEL, lines=10, elem_id="citation-button", show_copy_button=True, ) with gr.Row(): gr.HTML(FOOTER_TEXT) scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=900) scheduler.start() demo.queue(default_concurrency_limit=40).launch()