app.py

#!/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'}:
            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%", "16%"])

            # 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()