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import gradio as gr |
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from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns |
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import pandas as pd |
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from apscheduler.schedulers.background import BackgroundScheduler |
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from huggingface_hub import snapshot_download |
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from src.about import ( |
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CITATION_BUTTON_LABEL, |
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CITATION_BUTTON_TEXT, |
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EVALUATION_QUEUE_TEXT, |
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INTRODUCTION_TEXT, |
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LLM_BENCHMARKS_TEXT, |
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TITLE, |
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) |
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from src.display.css_html_js import custom_css |
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from src.display.utils import ( |
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BENCHMARK_COLS, |
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COLS, |
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EVAL_COLS, |
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EVAL_TYPES, |
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AutoEvalColumn, |
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ModelTraining, |
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fields, |
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MalteseTraining |
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) |
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from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN |
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from src.populate import get_evaluation_queue_df, get_leaderboard_df |
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from src.submission.submit import add_new_eval, read_configuration |
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def restart_space(): |
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API.restart_space(repo_id=REPO_ID) |
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try: |
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print(EVAL_REQUESTS_PATH) |
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snapshot_download( |
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repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN |
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) |
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except Exception: |
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restart_space() |
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try: |
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print(EVAL_RESULTS_PATH) |
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snapshot_download( |
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repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN |
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) |
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except Exception: |
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restart_space() |
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LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) |
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( |
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finished_eval_queue_df, |
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running_eval_queue_df, |
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pending_eval_queue_df, |
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) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) |
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def init_leaderboard(dataframe): |
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if dataframe is None or dataframe.empty: |
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raise ValueError("Leaderboard DataFrame is empty or None.") |
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return Leaderboard( |
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value=dataframe, |
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datatype=[c.type for c in fields(AutoEvalColumn)], |
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select_columns=SelectColumns( |
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default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default], |
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cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden], |
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label="Select Columns to Display:", |
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), |
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search_columns=[AutoEvalColumn.model.name], |
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hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], |
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filter_columns=[ |
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ColumnFilter(AutoEvalColumn.model_training.name, type="checkboxgroup", label="Model types"), |
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ColumnFilter(AutoEvalColumn.maltese_training.name, type="checkboxgroup", label="Maltese training"), |
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ColumnFilter( |
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AutoEvalColumn.language_count.name, |
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type="slider", |
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min=1, |
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max=1000, |
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label="Number of languages during training", |
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), |
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ColumnFilter( |
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AutoEvalColumn.params.name, |
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type="slider", |
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min=0.01, |
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max=150, |
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label="Select the number of parameters (B)", |
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), |
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ColumnFilter(AutoEvalColumn.prompt_version.name, type="checkboxgroup", label="Prompt Version"), |
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ColumnFilter(AutoEvalColumn.n_shot.name, type="slider", min=0, max=100, label="Number of Shots"), |
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ColumnFilter( |
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AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True |
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), |
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], |
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bool_checkboxgroup_label="Hide models", |
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interactive=False, |
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) |
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demo = gr.Blocks(css=custom_css) |
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with demo: |
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gr.HTML(TITLE) |
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gr.HTML(INTRODUCTION_TEXT, elem_classes="markdown-text") |
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with gr.Tabs(elem_classes="tab-buttons") as tabs: |
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with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0): |
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leaderboard = init_leaderboard(LEADERBOARD_DF) |
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with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): |
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gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") |
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with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3): |
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with gr.Column(): |
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with gr.Row(): |
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gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") |
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with gr.Column(): |
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with gr.Accordion( |
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f"✅ Finished Evaluations ({len(finished_eval_queue_df)})", |
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open=False, |
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): |
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with gr.Row(): |
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finished_eval_table = gr.components.Dataframe( |
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value=finished_eval_queue_df, |
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headers=EVAL_COLS, |
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datatype=EVAL_TYPES, |
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row_count=5, |
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) |
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with gr.Accordion( |
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f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})", |
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open=False, |
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): |
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with gr.Row(): |
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running_eval_table = gr.components.Dataframe( |
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value=running_eval_queue_df, |
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headers=EVAL_COLS, |
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datatype=EVAL_TYPES, |
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row_count=5, |
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) |
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with gr.Accordion( |
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f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})", |
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open=False, |
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): |
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with gr.Row(): |
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pending_eval_table = gr.components.Dataframe( |
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value=pending_eval_queue_df, |
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headers=EVAL_COLS, |
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datatype=EVAL_TYPES, |
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row_count=5, |
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) |
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with gr.Row(): |
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gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text") |
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with gr.Row(): |
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files = gr.File( |
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label="Files (Configuration File & Prediction Outputs)", |
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file_count="directory", |
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type="filepath", |
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) |
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with gr.Row(equal_height=True): |
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with gr.Column(): |
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model_name = gr.Textbox( |
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label="Model name", |
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info="Read automatically from the results file.", |
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interactive=False, |
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) |
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version = gr.Textbox( |
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label="Prompt Version", |
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info="Read automatically from the results file.", |
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interactive=False, |
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) |
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n_shots = gr.Number( |
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label="Number of Shots", |
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info="Read automatically from the results file.", |
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interactive=False, |
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) |
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with gr.Column(): |
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model_training = gr.Dropdown( |
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choices=[t.to_str(": ") for t in ModelTraining if t != ModelTraining.NK], |
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label="Model Training", |
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info="How to model is trained.", |
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multiselect=False, |
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value=None, |
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interactive=True, |
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) |
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maltese_training = gr.Dropdown( |
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choices=[t.to_str(": ") for t in MalteseTraining if t != ModelTraining.NK], |
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label="Maltese Training", |
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info="The last stage of training in which Maltese was included.", |
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multiselect=False, |
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value=None, |
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interactive=True, |
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) |
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language_count = gr.Number( |
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label="Number of languages", |
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info="Include languages for all training stages. Set to 0 if unknown.", |
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minimum=0, |
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interactive=True, |
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) |
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submit_button = gr.Button("Submit Eval") |
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submission_result = gr.Markdown() |
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configuration = gr.State() |
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file_paths = gr.State() |
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files.change(read_configuration, |
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files, |
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[configuration, file_paths, model_name, version, n_shots, submission_result]) |
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submit_button.click( |
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add_new_eval, |
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[ |
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model_training, |
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maltese_training, |
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language_count, |
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configuration, |
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file_paths |
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], |
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submission_result, |
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) |
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with gr.Row(): |
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with gr.Accordion("📙 Citation", open=False): |
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citation_button = gr.Textbox( |
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value=CITATION_BUTTON_TEXT, |
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label=CITATION_BUTTON_LABEL, |
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lines=20, |
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elem_id="citation-button", |
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show_copy_button=True, |
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) |
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scheduler = BackgroundScheduler() |
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scheduler.add_job(restart_space, "interval", seconds=1800) |
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scheduler.start() |
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demo.queue(default_concurrency_limit=40).launch() |
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