app.py

import gradio as gr
import pandas as pd

from apscheduler.schedulers.background import BackgroundScheduler

from src.display.css_html_js import custom_css

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.utils import (
    BENCHMARK_COLS,
    COLS,
    EVAL_COLS,
    EVAL_TYPES,
    NUMERIC_INTERVALS,
    TYPES,
    AutoEvalColumn,
    ModelType,
    fields,
    WeightType,
    Precision
)

from src.populate import get_evaluation_queue_df, get_leaderboard_df

from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO
from src.submission.submit import add_new_eval

from src.display.utils import Tasks

from huggingface_hub import snapshot_download

## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## -------

def restart_space():
    API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)

def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout):
    try:
        print(f"local_dir for snapshot download = {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:
        print(f"ui_snapshot_download failed. restarting space...")
        restart_space()

# Searching and filtering
def update_table(hidden_df: pd.DataFrame, columns: list, type_query: list, precision_query: list, size_query: list, query: str):
    print(f"hidden_df = {hidden_df}")
    show_deleted = True
    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)

    print(f"filtered_df = {filtered_df}")
    filtered_df = filter_queries(query, filtered_df)
    df = select_columns(filtered_df, columns)
    print(f"df = {df}")
    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,
    ]
    # 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):
    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, show_deleted: bool) -> pd.DataFrame:

    print(f"filter_models()'s df: {df}\n")
    # Show all models
    if show_deleted:
        filtered_df = df
    else:  # Show only still on the hub models
        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] is True]

    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


## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- ## ------- 

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)

print(f"COLS = {COLS}")


raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) # k the problem is that the results are only saved in _bk dirs. 
leaderboard_df = original_df.copy()
print(f"leaderboard_df = {leaderboard_df}")


################################################################################################################################
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:

        # toggle break 1: this tab just RENDERS existing result files on remote repo. 
        with gr.TabItem("Benchmarks", 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",
                    )

            # 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%"]
            # )
            leaderboard_table = gr.components.Dataframe(
                # value=leaderboard_df,
                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
            )
            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,
                )

        # toggle break 2: Submission -> runs add_new_eval() (actual evaluation is done on backend when backend-cli.py is run.)
        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.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")
                    # You can use the revision parameter to point to the specific commit hash when downloading.
                    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,
                    )


                    requested_tasks = gr.CheckboxGroup(
                        choices=[ (i.value.col_name, i.value) for i in Tasks], 

                        label="Select tasks",
                        elem_id="task-select",
                        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()

            # we need to add task specification argument here as well. 
            submit_button.click(
                add_new_eval,
                [
                    model_name_textbox,

                    requested_tasks, # is this a list of str or class Task? i think it's Task. 

                    base_model_name_textbox,
                    revision_name_textbox,
                    precision,
                    private,
                    weight_type,
                    model_type,
                ],
                submission_result)



# demo.launch()

####

scheduler = BackgroundScheduler()

scheduler.add_job(restart_space, "interval", seconds=6 * 60 * 60)

scheduler.start()
# demo.queue(default_concurrency_limit=40).launch()

# demo.launch(show_api=False, enable_queue=False)
demo.launch() # TypeError: Blocks.launch() got an unexpected keyword argument 'enable_queue'