app.py

import subprocess
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,
    nc_tasks,
    nr_tasks,
    lp_tasks,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
    BENCHMARK_COLS,
    COLS,
    COLS_NC,
    COLS_NR,
    COLS_LP,
    EVAL_COLS,
    EVAL_TYPES,
    NUMERIC_INTERVALS,
    TYPES,
    AutoEvalColumn_NodeClassification,
    AutoEvalColumn_NodeRegression,
    AutoEvalColumn_LinkPrediction,
    #AutoEvalColumn,
    ModelType,
    TASK_LIST,
    OFFICIAL,
    HONOR,
    fields,
    WeightType,
    Precision
)
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)

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()


# Searching and filtering
def update_table(
    hidden_df: pd.DataFrame,
    columns: list,
    query: str,
):
    #filtered_df = filter_models(hidden_df, size_query, show_deleted)
    filtered_df = filter_queries(query, hidden_df)
    df = select_columns(filtered_df, columns)
    return df


def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
    return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))]


def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
    always_here_cols = [
        "Model"
    ]
    # 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] 
    ]
    return filtered_df


def filter_queries(query: str, filtered_df: pd.DataFrame) -> 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]
            )

    return filtered_df


def filter_models(
    df: pd.DataFrame, size_query: list, show_deleted: bool
) -> pd.DataFrame:
    # 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] == 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

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("🏅 Entity Classification Leaderboard", elem_id="llm-benchmark-tab-table", id=0):
            COLS = COLS_NC
            AutoEvalColumn = AutoEvalColumn_NodeClassification
            original_df = get_leaderboard_df(EVAL_REQUESTS_PATH, "Node Classification")
            leaderboard_df = original_df.copy()

            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
                            ],
                            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,
                        )
                    
            print(leaderboard_df)
            print(fields(AutoEvalColumn))
            leaderboard_table = gr.components.Dataframe(
                value=leaderboard_df[
                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
                    + shown_columns.value
                ],
                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,
            )

            # 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,
                    search_bar,
                ],
                leaderboard_table,
            )
            for selector in [shown_columns]:
                selector.change(
                    update_table,
                    [
                        hidden_leaderboard_table_for_search,
                        shown_columns,
                        search_bar,
                    ],
                    leaderboard_table,
                    queue=True,
                )
            gr.Markdown("Evaluation metric: AUROC ⬆️")

        
        with gr.TabItem("🏅 Entity Regression Leaderboard", elem_id="llm-benchmark-tab-table", id=1):
            COLS = COLS_NR
            AutoEvalColumn = AutoEvalColumn_NodeRegression
            original_df = get_leaderboard_df(EVAL_REQUESTS_PATH, "Node Regression")
            leaderboard_df = original_df.copy()
            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
                            ],
                            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,
                        )
                    
            print(leaderboard_df)
            print(fields(AutoEvalColumn))
            leaderboard_table = gr.components.Dataframe(
                value=leaderboard_df[
                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
                    + shown_columns.value
                ],
                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,
            )

            # 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,
                    search_bar,
                ],
                leaderboard_table,
            )
            for selector in [shown_columns]:
                selector.change(
                    update_table,
                    [
                        hidden_leaderboard_table_for_search,
                        shown_columns,
                        search_bar,
                    ],
                    leaderboard_table,
                    queue=True,
                )
            gr.Markdown("Evaluation metric: MAE ⬇️")

        with gr.TabItem("🏅 Recommendation Leaderboard", elem_id="llm-benchmark-tab-table", id=2):
            COLS = COLS_LP
            AutoEvalColumn = AutoEvalColumn_LinkPrediction
            original_df = get_leaderboard_df(EVAL_REQUESTS_PATH, "Link Prediction")
            leaderboard_df = original_df.copy()
            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
                            ],
                            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,
                        )
                    
            print(leaderboard_df)
            print(fields(AutoEvalColumn))
            leaderboard_table = gr.components.Dataframe(
                value=leaderboard_df[
                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
                    + shown_columns.value
                ],
                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,
            )

            # 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,
                    search_bar,
                ],
                leaderboard_table,
            )
            for selector in [shown_columns]:
                selector.change(
                    update_table,
                    [
                        hidden_leaderboard_table_for_search,
                        shown_columns,
                        search_bar,
                    ],
                    leaderboard_table,
                    queue=True,
                )
            gr.Markdown("Evaluation metric: MAP ⬆️")


        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.Row():
                gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")

            with gr.Row():
                with gr.Column():
                    author_name_textbox = gr.Textbox(label="Your name")
                    email_textbox = gr.Textbox(label="Your email")
                    relbench_version_textbox = gr.Textbox(label="RelBench version")

                    model_name_textbox = gr.Textbox(label="Model name")
                    
                    '''
                    dataset_name_textbox = gr.Dropdown(
                        choices=[t.value.name for t in TASK_LIST],
                        label="Task name (e.g. rel-amazon-user-churn)",
                        multiselect=False,
                        value=None,
                        interactive=True,
                    )
                    '''
                    
                    official_or_not = gr.Dropdown(
                        choices=[i.value.name for i in OFFICIAL],
                        label="Is it an official submission?",
                        multiselect=False,
                        value=None,
                        interactive=True,
                    )
                    paper_url_textbox = gr.Textbox(label="Paper URL Link")
                    github_url_textbox = gr.Textbox(label="GitHub URL Link")
                    #parameters_textbox = gr.Textbox(label="Number of parameters")
                    task_track = gr.Dropdown(
                        choices=['Entity Classification', 'Entity Regression', 'Recommendation'],
                        label="Choose the task track",
                        multiselect=False,
                        value=None,
                        interactive=True,
                    )
                    honor_code = gr.Dropdown(
                        choices=[i.value.name for i in HONOR],
                        label="Do you agree to the honor code?",
                        multiselect=False,
                        value=None,
                        interactive=True,
                    )


                with gr.Column():
                    test_performance = gr.Textbox(lines = 16, label="Test set performance, use {task: [mean,std]} format e.g. {'rel-amazon/user-churn': [0.352,0.023], 'rel-amazon/user-ltv': [0.304,0.022], ...}")
                    valid_performance = gr.Textbox(lines = 16, label="Validation set performance, use {task: [mean,std]} format e.g. {'rel-amazon/user-churn': [0.352,0.023], 'rel-amazon/user-ltv': [0.304,0.022], ...}")
                    

            submit_button = gr.Button("Submit Eval")
            submission_result = gr.Markdown()
            submit_button.click(
                add_new_eval,
                [
                    author_name_textbox,
                    email_textbox,
                    relbench_version_textbox,
                    model_name_textbox,
                    official_or_not,
                    test_performance,
                    valid_performance,
                    paper_url_textbox,
                    github_url_textbox,
                    #parameters_textbox,
                    honor_code,
                    task_track
                ],
                submission_result,
            )


scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()