app.py

import os
import json
import gradio as gr
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.display.css_html_js import custom_css
from src.display.utils import (
    BENCHMARK_COLS,
    COLS,
    EVAL_COLS,
    EVAL_TYPES,
    AutoEvalColumn,
    ModelType,
    fields,
    WeightType,
    Precision,
    AddSpecialTokens,
    NumFewShots,
    NUMERIC_INTERVALS,
    TYPES,
)
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)
original_df = LEADERBOARD_DF
leaderboard_df = original_df.copy()
(
    finished_eval_queue_df,
    running_eval_queue_df,
    pending_eval_queue_df,
    failed_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

# Searching and filtering
# def update_table(
#     hidden_df: pd.DataFrame,
#     columns: list,
#     type_query: list,
#     precision_query: str,
#     size_query: list,
#     add_special_tokens_query: list,
#     num_few_shots_query: list,
#     show_deleted: bool,
#     show_merges: bool,
#     show_flagged: bool,
#     query: str,
# ):
#     print(f"Update table called with: type_query={type_query}, precision_query={precision_query}, size_query={size_query}")
#     print(f"hidden_df shape before filtering: {hidden_df.shape}")
    
#     filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, add_special_tokens_query, num_few_shots_query, show_deleted, show_merges, show_flagged)
#     print(f"filtered_df shape after filter_models: {filtered_df.shape}")
    
#     filtered_df = filter_queries(query, filtered_df)
#     print(f"filtered_df shape after filter_queries: {filtered_df.shape}")
    
#     print(f"Filter applied: query={query}, columns={columns}, type_query={type_query}, precision_query={precision_query}")
#     print("Filtered dataframe head:")
#     print(filtered_df.head())

#     df = select_columns(filtered_df, columns)
#     print(f"Final df shape: {df.shape}")
#     print("Final dataframe head:")
#     print(df.head())
#     return df
def update_table(
    hidden_df: pd.DataFrame,
    columns: list,
    type_query: list,
    precision_query: list,
    size_query: list,
    add_special_tokens_query: list,
    num_few_shots_query: list,
    show_deleted: bool,
    show_merges: bool,
    show_flagged: bool,
    query: str,
    architecture_query: list,
    license_query: list
):
    filtered_df = filter_models(
        hidden_df, type_query, size_query, precision_query,
        add_special_tokens_query, num_few_shots_query,
        show_deleted, show_merges, show_flagged,
        architecture_query, license_query
    )
    filtered_df = filter_queries(query, filtered_df)
    df = select_columns(filtered_df, columns)
    return df


def load_query(request: gr.Request):  # triggered only once at startup => read query parameter if it exists
    query = request.query_params.get("query") or ""
    return query, query # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed


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,
    ]
    # 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]
    ]
    return filtered_df


def filter_queries(query: str, filtered_df: pd.DataFrame):
    """Added by Abishek"""
    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)
            filtered_df = filtered_df.drop_duplicates(
                subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
            )

    return filtered_df


# def filter_models(
#     df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, add_special_tokens_query: list, num_few_shots_query: list, show_deleted: bool, show_merges: bool, show_flagged: bool
# ) -> pd.DataFrame:
#     print(f"Initial df shape: {df.shape}")
#     print(f"Initial df content:\n{df}")

#     filtered_df = df

#     # Model Type フィルタリング
#     type_emoji = [t.split()[0] for t in type_query]
#     filtered_df = filtered_df[filtered_df['T'].isin(type_emoji)]
#     print(f"After type filter: {filtered_df.shape}")

#     # Precision フィルタリング
#     filtered_df = filtered_df[filtered_df['Precision'].isin(precision_query + ['Unknown', '?'])]
#     print(f"After precision filter: {filtered_df.shape}")

#     # Model Size フィルタリング
#     if 'Unknown' in size_query:
#         size_mask = filtered_df['#Params (B)'].isna() | (filtered_df['#Params (B)'] == 0)
#     else:
#         size_mask = filtered_df['#Params (B)'].apply(lambda x: any(x in NUMERIC_INTERVALS[s] for s in size_query if s != 'Unknown'))
#     filtered_df = filtered_df[size_mask]
#     print(f"After size filter: {filtered_df.shape}")

#     # Add Special Tokens フィルタリング
#     filtered_df = filtered_df[filtered_df['Add Special Tokens'].isin(add_special_tokens_query + ['Unknown', '?'])]
#     print(f"After add_special_tokens filter: {filtered_df.shape}")

#     # Num Few Shots フィルタリング
#     filtered_df = filtered_df[filtered_df['Few-shot'].astype(str).isin([str(x) for x in num_few_shots_query] + ['Unknown', '?'])]
#     print(f"After num_few_shots filter: {filtered_df.shape}")

#     # Show deleted models フィルタリング
#     if not show_deleted:
#         filtered_df = filtered_df[filtered_df['Available on the hub'] == True]
#     print(f"After show_deleted filter: {filtered_df.shape}")

#     print("Filtered dataframe head:")
#     print(filtered_df.head())
#     return filtered_df
def filter_models(
    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list,
    add_special_tokens_query: list, num_few_shots_query: list,
    show_deleted: bool, show_merges: bool, show_flagged: bool,
    architecture_query: list, license_query: list
) -> pd.DataFrame:
    print(f"Initial df shape: {df.shape}")

    # Model Type フィルタリング
    type_emoji = [t.split()[0] for t in type_query]
    filtered_df = df[df['T'].isin(type_emoji)]
    print(f"After type filter: {filtered_df.shape}")

    # Precision フィルタリング
    filtered_df = filtered_df[filtered_df['Precision'].isin(precision_query + ['Unknown', '?'])]
    print(f"After precision filter: {filtered_df.shape}")

    # Model Size フィルタリング
    if 'Unknown' in size_query:
        size_mask = filtered_df['#Params (B)'].isna() | (filtered_df['#Params (B)'] == 0)
    else:
        size_mask = filtered_df['#Params (B)'].apply(lambda x: any(pd.Interval(NUMERIC_INTERVALS[s].left, NUMERIC_INTERVALS[s].right).contains(x) for s in size_query if s != 'Unknown'))
    filtered_df = filtered_df[size_mask]
    print(f"After size filter: {filtered_df.shape}")

    # Add Special Tokens フィルタリング
    filtered_df = filtered_df[filtered_df['Add Special Tokens'].isin(add_special_tokens_query + ['Unknown', '?'])]
    print(f"After add_special_tokens filter: {filtered_df.shape}")

    # Num Few Shots フィルタリング
    filtered_df = filtered_df[filtered_df['Few-shot'].astype(str).isin([str(x) for x in num_few_shots_query] + ['Unknown', '?'])]
    print(f"After num_few_shots filter: {filtered_df.shape}")

    # Architecture フィルタリング
    if architecture_query:
        filtered_df = filtered_df[filtered_df['Architecture'].isin(architecture_query)]
    print(f"After architecture filter: {filtered_df.shape}")

    # License フィルタリング
    if license_query:
        filtered_df = filtered_df[filtered_df['Hub License'].isin(license_query)]
    print(f"After license filter: {filtered_df.shape}")

    # Show deleted models フィルタリング
    if not show_deleted:
        filtered_df = filtered_df[filtered_df['Available on the hub'] == True]
    print(f"After show_deleted filter: {filtered_df.shape}")

    print("Filtered dataframe head:")
    print(filtered_df.head())
    return filtered_df

leaderboard_df = filter_models(leaderboard_df, [t.to_str(" : ") for t in ModelType], list(NUMERIC_INTERVALS.keys()), [i.value.name for i in Precision], [i.value.name for i in AddSpecialTokens], [i.value.name for i in NumFewShots], False, False, False)

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("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        search_bar = gr.Textbox(
                            placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
                            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.Row():
                        deleted_models_visibility = gr.Checkbox(
                            value=False, label="Show private/deleted models", interactive=True
                        )
                        merged_models_visibility = gr.Checkbox(
                            value=False, label="Show merges", interactive=True
                        )
                        flagged_models_visibility = gr.Checkbox(
                            value=False, label="Show flagged models", interactive=True
                        )
                with gr.Column(min_width=320):
                    #with gr.Box(elem_id="box-filter"):
                    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",
                    )
                    filter_columns_add_special_tokens = gr.CheckboxGroup(
                        label="Add Special Tokens",
                        choices=[i.value.name for i in AddSpecialTokens],
                        value=[i.value.name for i in AddSpecialTokens],
                        interactive=True,
                        elem_id="filter-columns-add-special-tokens",
                    )
                    filter_columns_num_few_shots = gr.CheckboxGroup(
                        label="Num Few Shots",
                        choices=[i.value.name for i in NumFewShots],
                        value=[i.value.name for i in NumFewShots],
                        interactive=True,
                        elem_id="filter-columns-num-few-shots",
                    )

            leaderboard_df_filtered = filter_models(leaderboard_df, [t.to_str(" : ") for t in ModelType], list(NUMERIC_INTERVALS.keys()), [i.value.name for i in Precision], [i.value.name for i in AddSpecialTokens], [i.value.name for i in NumFewShots], False, False, False)

            leaderboard_table = gr.components.Dataframe(
                value=leaderboard_df_filtered,
                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,
            )
            print("Leaderboard table initial value:")
            print(leaderboard_table.value)
            print(f"Leaderboard table shape: {leaderboard_table.value.shape if isinstance(leaderboard_table.value, pd.DataFrame) else 'Not a DataFrame'}")
            
            # Dummy leaderboard for handling the case when the user uses backspace key
            hidden_leaderboard_table_for_search = gr.components.Dataframe(
                value=original_df[COLS],
                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,
                    filter_columns_add_special_tokens,
                    filter_columns_num_few_shots,
                    deleted_models_visibility,
                    merged_models_visibility,
                    flagged_models_visibility,
                    search_bar,
                ],
                leaderboard_table,
            )

            # Define a hidden component that will trigger a reload only if a query parameter has be set
            hidden_search_bar = gr.Textbox(value="", visible=False)
            hidden_search_bar.change(
                update_table,
                [
                    hidden_leaderboard_table_for_search,
                    shown_columns,
                    filter_columns_type,
                    filter_columns_precision,
                    filter_columns_size,
                    filter_columns_add_special_tokens,
                    filter_columns_num_few_shots,
                    deleted_models_visibility,
                    merged_models_visibility,
                    flagged_models_visibility,
                    search_bar,
                ],
                leaderboard_table,
            )
            # Check query parameter once at startup and update search bar + hidden component
            demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar])
            
            for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, filter_columns_add_special_tokens, filter_columns_num_few_shots, deleted_models_visibility, merged_models_visibility, flagged_models_visibility]:
                selector.change(
                    update_table,
                    [
                        hidden_leaderboard_table_for_search,
                        shown_columns,
                        filter_columns_type,
                        filter_columns_precision,
                        filter_columns_size,
                        filter_columns_add_special_tokens,
                        filter_columns_num_few_shots,
                        deleted_models_visibility,
                        merged_models_visibility,
                        flagged_models_visibility,
                        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")

        with gr.TabItem("🚀 Submit here! ", 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"⏳ 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.Accordion(
                        f"❎ Failed Evaluation Queue ({len(failed_eval_queue_df)})",
                        open=False,
                    ):
                        with gr.Row():
                            failed_eval_table = gr.components.Dataframe(
                                value=failed_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")
                    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="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)")
                    add_special_tokens = gr.Dropdown(
                        choices=[i.value.name for i in AddSpecialTokens if i != AddSpecialTokens.Unknown],
                        label="AddSpecialTokens",
                        multiselect=False,
                        value="False",
                        interactive=True,
                    )

            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,
                    weight_type,
                    model_type,
                    add_special_tokens,
                ],
                submission_result,
            )

    with gr.Row():
        with gr.Accordion("📙 Citation", 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=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()