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| #!/usr/bin/env python | |
| import os | |
| import datetime | |
| import socket | |
| import gradio as gr | |
| import pandas as pd | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from huggingface_hub import snapshot_download | |
| from src.display.about import ( | |
| CITATION_BUTTON_LABEL, | |
| CITATION_BUTTON_TEXT, | |
| EVALUATION_QUEUE_TEXT, | |
| INTRODUCTION_TEXT, | |
| LLM_BENCHMARKS_TEXT, | |
| LLM_BENCHMARKS_DETAILS, | |
| FAQ_TEXT, | |
| TITLE | |
| ) | |
| from src.display.css_html_js import custom_css | |
| from src.display.utils import ( | |
| BENCHMARK_COLS, | |
| COLS, | |
| EVAL_COLS, | |
| EVAL_TYPES, | |
| NUMERIC_INTERVALS, | |
| TYPES, | |
| AutoEvalColumn, | |
| ModelType, | |
| fields, | |
| WeightType, | |
| Precision | |
| ) | |
| from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO | |
| from src.populate import get_evaluation_queue_df, get_leaderboard_df | |
| from src.submission.submit import add_new_eval | |
| from src.utils import get_dataset_summary_table | |
| def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout): | |
| try: | |
| print(local_dir) | |
| snapshot_download(repo_id=repo_id, local_dir=local_dir, repo_type=repo_type, tqdm_class=tqdm_class, etag_timeout=etag_timeout) | |
| except Exception as e: | |
| restart_space() | |
| def restart_space(): | |
| API.restart_space(repo_id=REPO_ID, token=H4_TOKEN) | |
| def init_space(): | |
| dataset_df = get_dataset_summary_table(file_path='blog/Hallucination-Leaderboard-Summary.csv') | |
| if socket.gethostname() not in {'neuromancer'}: | |
| # sync model_type with open-llm-leaderboard | |
| ui_snapshot_download(repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30) | |
| ui_snapshot_download(repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30) | |
| raw_data, original_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) | |
| return dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df | |
| dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space() | |
| leaderboard_df = original_df.copy() | |
| # Searching and filtering | |
| def update_table(hidden_df: pd.DataFrame, | |
| columns: list, | |
| type_query: list, | |
| precision_query: list, | |
| size_query: list, | |
| query: str): | |
| filtered_df = filter_models(hidden_df, type_query, size_query, precision_query) | |
| filtered_df = filter_queries(query, filtered_df) | |
| df = select_columns(filtered_df, columns) | |
| return df | |
| def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: | |
| return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))] | |
| def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: | |
| # always_here_cols = [AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] | |
| always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] | |
| dummy_col = [AutoEvalColumn.dummy.name] | |
| # We use COLS to maintain sorting | |
| filtered_df = df[ | |
| # always_here_cols + [c for c in COLS if c in df.columns and c in columns] + [AutoEvalColumn.dummy.name] | |
| always_here_cols + [c for c in COLS if c in df.columns and c in columns] + dummy_col | |
| ] | |
| return filtered_df | |
| def filter_queries(query: str, filtered_df: pd.DataFrame): | |
| final_df = [] | |
| if query != "": | |
| queries = [q.strip() for q in query.split(";")] | |
| for _q in queries: | |
| _q = _q.strip() | |
| if _q != "": | |
| temp_filtered_df = search_table(filtered_df, _q) | |
| if len(temp_filtered_df) > 0: | |
| final_df.append(temp_filtered_df) | |
| if len(final_df) > 0: | |
| filtered_df = pd.concat(final_df) | |
| subset = [AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name] | |
| filtered_df = filtered_df.drop_duplicates(subset=subset) | |
| return filtered_df | |
| def filter_models(df: pd.DataFrame, | |
| type_query: list, | |
| size_query: list, | |
| precision_query: list) -> pd.DataFrame: | |
| # Show all models | |
| filtered_df = df | |
| type_emoji = [t[0] for t in type_query] | |
| filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] | |
| filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])] | |
| numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query])) | |
| params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce") | |
| mask = params_column.apply(lambda x: any(numeric_interval.contains(x))) | |
| filtered_df = filtered_df.loc[mask] | |
| return filtered_df | |
| # triggered only once at startup => read query parameter if it exists | |
| def load_query(request: gr.Request): | |
| query = request.query_params.get("query") or "" | |
| return query | |
| 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("Hallucinations Benchmark", | |
| elem_id="llm-benchmark-tab-table", | |
| id=0): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| search_bar = gr.Textbox(placeholder=" 🔍 Model search (separate multiple queries with `;`)", | |
| show_label=False, | |
| elem_id="search-bar") | |
| with gr.Row(): | |
| shown_columns = gr.CheckboxGroup( | |
| choices=[ | |
| c.name | |
| for c in fields(AutoEvalColumn) | |
| if not c.hidden and not c.never_hidden and not c.dummy | |
| ], | |
| value=[ | |
| c.name | |
| for c in fields(AutoEvalColumn) | |
| if c.displayed_by_default and not c.hidden and not c.never_hidden | |
| ], | |
| label="Select columns to show", | |
| elem_id="column-select", | |
| interactive=True) | |
| with gr.Column(min_width=320): | |
| filter_columns_type = gr.CheckboxGroup( | |
| label="Model types", | |
| choices=[t.to_str() for t in ModelType], | |
| value=[t.to_str() for t in ModelType], | |
| interactive=True, | |
| elem_id="filter-columns-type") | |
| filter_columns_precision = gr.CheckboxGroup( | |
| label="Precision", | |
| choices=[i.value.name for i in Precision], | |
| value=[i.value.name for i in Precision], | |
| interactive=True, | |
| elem_id="filter-columns-precision") | |
| filter_columns_size = gr.CheckboxGroup( | |
| label="Model sizes (in billions of parameters)", | |
| choices=list(NUMERIC_INTERVALS.keys()), | |
| value=list(NUMERIC_INTERVALS.keys()), | |
| interactive=True, | |
| elem_id="filter-columns-size") | |
| # breakpoint() | |
| leaderboard_table = gr.components.Dataframe( | |
| value=leaderboard_df[ | |
| [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value + [AutoEvalColumn.dummy.name] | |
| ] if leaderboard_df.empty is False else leaderboard_df, | |
| headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value, | |
| datatype=TYPES, | |
| elem_id="leaderboard-table", | |
| interactive=False, | |
| visible=True) # column_widths=["2%", "20%"] | |
| # Dummy leaderboard for handling the case when the user uses backspace key | |
| hidden_leaderboard_table_for_search = gr.components.Dataframe( | |
| value=original_df[COLS] if original_df.empty is False else original_df, | |
| headers=COLS, | |
| datatype=TYPES, | |
| visible=False) | |
| search_bar.submit( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_precision, | |
| filter_columns_size, | |
| search_bar, | |
| ], | |
| leaderboard_table) | |
| # Check query parameter once at startup and update search bar | |
| demo.load(load_query, inputs=[], outputs=[search_bar]) | |
| for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size]: | |
| selector.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_precision, | |
| filter_columns_size, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| queue=True) | |
| with gr.TabItem("About", elem_id="llm-benchmark-tab-table", id=2): | |
| gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
| dataset_table = gr.components.Dataframe( | |
| value=dataset_df, | |
| headers=list(dataset_df.columns), | |
| datatype=['str', 'markdown', 'str', 'str', 'str'], | |
| elem_id="dataset-table", | |
| interactive=False, | |
| visible=True, | |
| column_widths=["15%", "20%"]) | |
| gr.Markdown(LLM_BENCHMARKS_DETAILS, elem_classes="markdown-text") | |
| gr.Markdown(FAQ_TEXT, elem_classes="markdown-text") | |
| with gr.TabItem("Submit a model ", elem_id="llm-benchmark-tab-table", id=3): | |
| 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"⏳ Scheduled 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") | |
| revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main") | |
| private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC) | |
| model_type = gr.Dropdown( | |
| choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown], | |
| label="Model type", | |
| multiselect=False, | |
| value=None, | |
| interactive=True) | |
| with gr.Column(): | |
| precision = gr.Dropdown( | |
| choices=[i.value.name for i in Precision if i != Precision.Unknown], | |
| label="Precision", | |
| multiselect=False, | |
| value="float32", | |
| 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)") | |
| submit_button = gr.Button("Submit Eval") | |
| submission_result = gr.Markdown() | |
| submit_button.click( | |
| add_new_eval, | |
| [ | |
| model_name_textbox, | |
| base_model_name_textbox, | |
| revision_name_textbox, | |
| precision, | |
| private, | |
| weight_type, | |
| model_type, | |
| ], | |
| submission_result) | |
| with gr.Row(): | |
| with gr.Accordion("Citing this leaderboard", 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=6 * 60 * 60) | |
| def launch_backend(): | |
| import subprocess | |
| from src.backend.envs import DEVICE | |
| if DEVICE not in {'cpu'}: | |
| _ = subprocess.run(["python", "backend-cli.py"]) | |
| # scheduler.add_job(launch_backend, "interval", seconds=120) | |
| scheduler.start() | |
| demo.queue(default_concurrency_limit=40).launch() | |