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| from functools import partial | |
| import gradio as gr | |
| 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.datamodel.data import F1Data | |
| 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, REPO_ID, TOKEN, CODE_PROBLEMS_REPO, SUBMISSIONS_REPO, RESULTS_REPO | |
| from src.logger import get_logger | |
| from src.populate import get_evaluation_queue_df, get_leaderboard_df | |
| from src.submission.submit import add_new_solutions | |
| logger = get_logger(__name__) | |
| SPLIT = "warmup" # TODO temp | |
| SKIP_VALIDATION = True # TODO temp | |
| def restart_space(): | |
| API.restart_space(repo_id=REPO_ID) | |
| lbdb = F1Data(cp_ds_name=CODE_PROBLEMS_REPO, sub_ds_name=SUBMISSIONS_REPO, res_ds_name=RESULTS_REPO, split=SPLIT) | |
| leaderboard_df = get_leaderboard_df(RESULTS_REPO) | |
| logger.info("Initialized LBDB") | |
| # ( | |
| # finished_eval_queue_df, | |
| # running_eval_queue_df, | |
| # pending_eval_queue_df, | |
| # ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) | |
| def init_leaderboard(dataframe): | |
| if dataframe is None or dataframe.empty: | |
| raise ValueError("Leaderboard DataFrame is empty or None.") | |
| return Leaderboard( | |
| value=dataframe, | |
| datatype=[c.type for c in fields(AutoEvalColumn)], | |
| select_columns=SelectColumns( | |
| default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default], | |
| cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden], | |
| label="Select Columns to Display:", | |
| ), | |
| search_columns=[AutoEvalColumn.system.name, AutoEvalColumn.system_type.name], | |
| hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], | |
| filter_columns=[ | |
| ColumnFilter(AutoEvalColumn.system_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=150, | |
| # label="Select the number of parameters (B)", | |
| # ), | |
| # ColumnFilter(AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True), | |
| ], | |
| bool_checkboxgroup_label="Hide models", | |
| interactive=False, | |
| ) | |
| # Display image using Markdown | |
| # banner = "" | |
| demo = gr.Blocks(css=custom_css) | |
| with demo: | |
| gr.Image( | |
| "assets/banner.png", | |
| interactive=False, | |
| show_label=False, | |
| show_download_button=False, | |
| container=False, | |
| ) | |
| # gr.Markdown(banner) | |
| gr.HTML( | |
| """ | |
| <style> | |
| body { | |
| background-color: #121212; | |
| color: white; | |
| margin: 0; /* Reset browser default */ | |
| } | |
| /* Outer container margin & spacing */ | |
| .gradio-container { | |
| max-width: 1100px; | |
| margin: 2rem auto; /* top/bottom spacing + horizontal centering */ | |
| padding: 2rem; /* inner spacing */ | |
| background-color: rgba(0, 0, 0, 0.6); /* optional: semi-transparent panel */ | |
| border-radius: 12px; /* rounded corners */ | |
| } | |
| </style> | |
| """ | |
| ) | |
| gr.HTML(TITLE) | |
| gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| with gr.TabItem("🏅 FormulaOne Leaderboard", elem_id="formulaone-leaderboar-tab-table", id=0): | |
| leaderboard = init_leaderboard(leaderboard_df) | |
| # with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=1): | |
| # logger.info("Tab about") | |
| # gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
| with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=2): | |
| logger.info("Tab submission") | |
| 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 solutions here!", elem_classes="markdown-text") | |
| with gr.Row(): | |
| with gr.Column(): | |
| system_name_textbox = gr.Textbox(label=AutoEvalColumn.system.name) | |
| org_textbox = gr.Textbox(label=AutoEvalColumn.organization.name) | |
| # revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main") | |
| sys_type_dropdown = gr.Dropdown( | |
| choices=[t.to_str(" ") for t in ModelType], | |
| label=AutoEvalColumn.system_type.name, | |
| multiselect=False, | |
| value=ModelType.LLM.to_str(" "), | |
| interactive=True, | |
| ) | |
| # with gr.Column(): | |
| submission_file = gr.File(label="JSONL solutions file", file_types=[".jsonl"]) | |
| # precision = gr.Dropdown( | |
| # choices=[i.value.name for i in Precision if i != Precision.Unknown], | |
| # label="Precision", | |
| # multiselect=False, | |
| # value="float16", | |
| # interactive=True, | |
| # ) | |
| # weight_type = gr.Dropdown( | |
| # choices=[i.value.name for i in WeightType], | |
| # label="Weights type", | |
| # multiselect=False, | |
| # value="Original", | |
| # interactive=True, | |
| # ) | |
| # base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)") | |
| logger.info("Submit button") | |
| submit_button = gr.Button("Submit") | |
| submission_result = gr.Markdown() | |
| def add_solution_cbk(system_name, org, sys_type, submission_path): | |
| return add_new_solutions( | |
| lbdb, system_name, org, sys_type, submission_path, skip_validation=SKIP_VALIDATION | |
| ) | |
| submit_button.click( | |
| add_solution_cbk, | |
| [ | |
| system_name_textbox, | |
| org_textbox, | |
| sys_type_dropdown, | |
| submission_file, | |
| ], | |
| submission_result, | |
| ) | |
| with gr.Row(): | |
| logger.info("Citation") | |
| with gr.Accordion(CITATION_BUTTON_LABEL, open=False): | |
| gr.Code( | |
| value=CITATION_BUTTON_TEXT.strip(), | |
| elem_id="citation-block", | |
| ) | |
| # citation_button = gr.Textbox( | |
| # value=CITATION_BUTTON_TEXT, | |
| # # label=CITATION_BUTTON_LABEL, | |
| # lines=20, | |
| # elem_id="citation-button", | |
| # show_copy_button=True, | |
| # ) | |
| logger.info("Scheduler") | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job(restart_space, "interval", seconds=1800) | |
| scheduler.start() | |
| logger.info("Launch") | |
| demo.queue(default_concurrency_limit=40).launch() | |
| logger.info("Done") | |