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_1,
    EVALUATION_EXAMPLE_IMG,
    LLM_BENCHMARKS_TEXT_2,
    # ENTITY_DISTRIBUTION_IMG,
    LLM_BENCHMARKS_TEXT_3,
    TITLE,
    LOGO
)
from src.display.css_html_js import custom_css
from src.display.utils import (
    DATASET_BENCHMARK_COLS,
    TYPES_BENCHMARK_COLS,
    DATASET_COLS,
    Clinical_TYPES_COLS,
    EVAL_COLS,
    EVAL_TYPES,
    NUMERIC_INTERVALS,
    TYPES,
    AutoEvalColumn,
    ModelType,
    ModelArch,
    PromptTemplateName,
    Precision,
    WeightType,
    fields,
)
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, PLACEHOLDER_DATASET_WISE_NORMALIZATION_CONFIG


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()

# Span based results
_, span_based_datasets_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, DATASET_COLS, DATASET_BENCHMARK_COLS, "SpanBasedWithPartialOverlap", "datasets")
span_based_datasets_leaderboard_df = span_based_datasets_original_df.copy()

_, span_based_types_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, Clinical_TYPES_COLS, TYPES_BENCHMARK_COLS, "SpanBasedWithPartialOverlap", "clinical_types")
span_based_types_leaderboard_df = span_based_types_original_df.copy()

# Token based results
_, token_based_datasets_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, DATASET_COLS, DATASET_BENCHMARK_COLS, "TokenBasedWithMacroAverage", "datasets")
token_based_datasets_leaderboard_df = token_based_datasets_original_df.copy()

_, token_based_types_original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, Clinical_TYPES_COLS, TYPES_BENCHMARK_COLS, "TokenBasedWithMacroAverage", "clinical_types")
token_based_types_leaderboard_df = token_based_types_original_df.copy()


(
    finished_eval_queue_df,
    running_eval_queue_df,
    pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)


def update_df(evaluation_metric, shown_columns, subset="datasets"):
    print(evaluation_metric)

    if subset == "datasets":
        match evaluation_metric:
            case "Span Based":
                leaderboard_table_df = span_based_datasets_leaderboard_df.copy()
                hidden_leader_board_df = span_based_datasets_original_df
            case "Token Based":
                leaderboard_table_df = token_based_datasets_leaderboard_df.copy()
                hidden_leader_board_df = token_based_datasets_original_df
            case _:
                pass
    else:
        match evaluation_metric:
            case "Span Based":
                leaderboard_table_df = span_based_types_leaderboard_df.copy()
                hidden_leader_board_df = span_based_types_original_df
            case "Token Based":
                leaderboard_table_df = token_based_types_leaderboard_df.copy()
                hidden_leader_board_df = token_based_types_original_df
            case _:
                pass        
    

    value_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns

    return leaderboard_table_df[value_cols], hidden_leader_board_df


# Searching and filtering
def update_table(
    hidden_df: pd.DataFrame,
    columns: list,
    query: str,
    type_query: list = None,
    architecture_query: list = None,
    size_query: list = None,
    precision_query: str = None,
    show_deleted: bool = False,
):
    filtered_df = filter_models(hidden_df, type_query, architecture_query, size_query, precision_query, show_deleted)
    filtered_df = filter_queries(query, filtered_df)
    df = select_columns(filtered_df, columns, list(hidden_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, cols: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]]
    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,
                    # AutoEvalColumn.precision.name,
                    # AutoEvalColumn.revision.name,
                ]
            )

    return filtered_df


def filter_models(
    df: pd.DataFrame, type_query: list, architecture_query: list, size_query: list, precision_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]

    filtered_df = df

    if type_query is not None:
        type_emoji = [t[0] for t in type_query]
        filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]

    if architecture_query is not None:
        arch_types = [t for t in architecture_query]
        filtered_df = filtered_df.loc[df[AutoEvalColumn.architecture.name].isin(arch_types)]
            # filtered_df = filtered_df.loc[df[AutoEvalColumn.architecture.name].isin(architecture_query + ["None"])]
    
    if precision_query is not None:
        if AutoEvalColumn.precision.name in df.columns:
            filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]

    if size_query is not 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

def change_submit_request_form(model_architecture):
    match model_architecture:
        case "Encoder":
            return (
                gr.Textbox(label="Threshold for gliner models", visible=False), 
                gr.Radio(
                        choices=["True", "False"],
                        label="Load GLiNER Tokenizer",
                        visible=False
                    ),
                gr.Dropdown(
                        choices=[prompt_template.value for prompt_template in PromptTemplateName],
                        label="Prompt for generation",
                        multiselect=False,
                        # value="HTML Highlighted Spans",
                        interactive=True,
                        visible=False
                    )
            )
        case "Decoder":
            return (
                gr.Textbox(label="Threshold for gliner models", visible=False), 
                gr.Radio(
                        choices=["True", "False"],
                        label="Load GLiNER Tokenizer",
                        visible=False
                    ),
                gr.Dropdown(
                        choices=[prompt_template.value for prompt_template in PromptTemplateName],
                        label="Prompt for generation",
                        multiselect=False,
                        # value="HTML Highlighted Spans",
                        interactive=True,
                        visible=True
                    )
            )
        case "GLiNER Encoder":
            return (
                gr.Textbox(label="Threshold for gliner models", visible=True), 
                gr.Radio(
                        choices=["True", "False"],
                        label="Load GLiNER Tokenizer",
                        visible=True
                    ),
                gr.Dropdown(
                        choices=[prompt_template.value for prompt_template in PromptTemplateName],
                        label="Prompt for generation",
                        multiselect=False,
                        # value="HTML Highlighted Spans",
                        interactive=True,
                        visible=False
                    )
            )

            
demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)
    gr.HTML(LOGO, elem_classes="logo")
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 NER Datasets", 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.clinical_type_col],
                            value=[
                                c.name
                                for c in fields(AutoEvalColumn)
                                if c.displayed_by_default and not c.hidden and not c.never_hidden and not c.clinical_type_col
                            ],
                            label="Select columns to show",
                            elem_id="column-select",
                            interactive=True,
                        )
                    # with gr.Row():
                    #     deleted_models_visibility = gr.Checkbox(
                    #         value=False, label="Show gated/private/deleted models", interactive=True
                    #     )
                with gr.Column(min_width=320):
                    # with gr.Box(elem_id="box-filter"):

                    eval_metric = gr.Radio(
                            choices=["Span Based", "Token Based"],
                            value = "Span Based",
                            label="Evaluation Metric",
                        )
                    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_architecture = gr.CheckboxGroup(
                        label="Architecture Types",
                        choices=[i.value.name for i in ModelArch],
                        value=[i.value.name for i in ModelArch],
                        interactive=True,
                        elem_id="filter-columns-architecture",
                    )
                    # 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",
                    # )

            datasets_leaderboard_df, datasets_original_df = update_df(eval_metric.value, shown_columns.value, subset="datasets")

            leaderboard_table = gr.components.Dataframe(
                value=datasets_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=datasets_original_df[DATASET_COLS],
                headers=DATASET_COLS,
                datatype=TYPES,
                visible=False,
            )

            eval_metric.change(
                lambda a, b: update_df(a,b, "datasets") , 
                inputs=[
                    eval_metric,
                    shown_columns,
                ], 
                outputs=[
                        leaderboard_table, 
                        hidden_leaderboard_table_for_search,
                        ]
                )
                        
            search_bar.submit(
                update_table,
                [
                    hidden_leaderboard_table_for_search,
                    shown_columns,
                    search_bar,
                    filter_columns_type,
                    filter_columns_architecture
                ],
                leaderboard_table,
            )
            for selector in [
                shown_columns,
                filter_columns_type,
                filter_columns_architecture,
                # filter_columns_size,
                # deleted_models_visibility,
            ]:
                selector.change(
                    update_table,
                    [
                        hidden_leaderboard_table_for_search,
                        shown_columns,
                        search_bar,
                        filter_columns_type,
                        filter_columns_architecture,
                    ],
                    leaderboard_table,
                    queue=True,
                )

            
        with gr.TabItem("🏅 Clinical Types", elem_id="llm-benchmark-tab-table", id=4):
            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.dataset_task_col],
                            value=[
                                c.name
                                for c in fields(AutoEvalColumn)
                                if c.displayed_by_default and not c.hidden and not c.never_hidden and not c.dataset_task_col
                            ],
                            label="Select columns to show",
                            elem_id="column-select",
                            interactive=True,
                        )
                    # with gr.Row():
                    #     deleted_models_visibility = gr.Checkbox(
                    #         value=False, label="Show gated/private/deleted models", interactive=True
                    #     )
                with gr.Column(min_width=320):

                    eval_metric = gr.Radio(
                            choices=["Span Based", "Token Based"],
                            value = "Span Based",
                            label="Evaluation Metric",
                        )                    
                    # 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_architecture = gr.CheckboxGroup(
                        label="Architecture Types",
                        choices=[i.value.name for i in ModelArch],
                        value=[i.value.name for i in ModelArch],
                        interactive=True,
                        elem_id="filter-columns-architecture",
                    )
                    # 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",
                    # )
            types_leaderboard_df, types_original_df = update_df(eval_metric.value, shown_columns.value, subset="clinical_types")

            leaderboard_table = gr.components.Dataframe(
                value=types_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=types_original_df[Clinical_TYPES_COLS],
                headers=Clinical_TYPES_COLS,
                datatype=TYPES,
                visible=False,
            )

            eval_metric.change(
                fn=lambda a, b: update_df(a,b, "clinical_types"), 
                inputs=[
                    eval_metric,
                    shown_columns,
                ], 
                outputs=[
                        leaderboard_table, 
                        hidden_leaderboard_table_for_search
                        ]
                    )
                        
            search_bar.submit(
                update_table,
                [
                    hidden_leaderboard_table_for_search,
                    shown_columns,
                    search_bar,
                    filter_columns_type,
                    filter_columns_architecture,
                ],
                leaderboard_table,
            )
            for selector in [
                shown_columns,
                filter_columns_type,
                filter_columns_architecture,
                # filter_columns_precision,
                # filter_columns_size,
                # deleted_models_visibility,
            ]:
                selector.change(
                    update_table,
                    [
                        hidden_leaderboard_table_for_search,
                        shown_columns,
                        search_bar,
                        filter_columns_type,
                        filter_columns_architecture,
                    ],
                    leaderboard_table,
                    queue=True,
                )

        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
            gr.Markdown(LLM_BENCHMARKS_TEXT_1, elem_classes="markdown-text")
            gr.HTML(EVALUATION_EXAMPLE_IMG, elem_classes="logo")
            gr.Markdown(LLM_BENCHMARKS_TEXT_2, elem_classes="markdown-text")
            # gr.HTML(ENTITY_DISTRIBUTION_IMG, elem_classes="logo")
            gr.Markdown(LLM_BENCHMARKS_TEXT_3, 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.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_arch = gr.Radio(
                        choices=[t.to_str(" : ") for t in ModelArch if t != ModelArch.Unknown],
                        label="Model Architecture",
                    )
                    
                    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():
                    label_normalization_map = gr.Textbox(lines=6, label="Label Normalization Map", placeholder=PLACEHOLDER_DATASET_WISE_NORMALIZATION_CONFIG)
                    gliner_threshold = gr.Textbox(label="Threshold for GLiNER models", visible=False)
                    gliner_tokenizer_bool = gr.Radio(
                        choices=["True", "False"],
                        label="Load GLiNER Tokenizer",
                        visible=False
                    )
                    prompt_name = gr.Dropdown(
                        choices=[prompt_template.value for prompt_template in PromptTemplateName],
                        label="Prompt for generation",
                        multiselect=False,
                        value="HTML Highlighted Spans",
                        interactive=True,
                        visible=False
                    )# should be a dropdown

                    # parsing_function - this is tied to the prompt & therefore does not need to be specified
                    # generation_parameters = gr.Textbox(label="Generation params in json format") just default for now

                    model_arch.change(fn=change_submit_request_form, inputs=model_arch, outputs=[
                        gliner_threshold, 
                        gliner_tokenizer_bool, 
                        prompt_name])

            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,
                    model_arch,
                    label_normalization_map,
                    gliner_threshold,
                    gliner_tokenizer_bool,
                    prompt_name,
                    # weight_type,
                    model_type,
                ],
                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(allowed_paths=['./assets/'])