app.py

import logging
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
import src.envs as envs
from main_backend import PENDING_STATUS, RUNNING_STATUS, FINISHED_STATUS, FAILED_STATUS
from src.backend import sort_queue
from src.envs import EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, RESULTS_REPO
import src.backend.manage_requests as manage_requests
import socket
import src.display.about as about
from src.display.css_html_js import custom_css
import src.display.utils as utils
import src.populate as populate
from src.populate import get_evaluation_queue_df, get_leaderboard_df
import src.submission.submit as submit
import os
import datetime
import spacy_transformers
import pprint
import src.backend.run_eval_suite as run_eval_suite

pp = pprint.PrettyPrinter(width=80)
TOKEN = os.environ.get("H4_TOKEN", None)
print("TOKEN", TOKEN)

def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout):
    try:
        print("local",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():
    envs.API.restart_space(repo_id=envs.REPO_ID, token=TOKEN)

def init_space():
    #dataset_df = get_dataset_summary_table(file_path='blog/Hallucination-Leaderboard-Summary.csv')


    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)

    original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, utils.COLS, utils.BENCHMARK_COLS)

    finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, utils.EVAL_COLS)
    return original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df

original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
leaderboard_df = original_df.copy()

def process_pending_evals():
    # if len(pending_eval_queue_df) == 0:
    #     print("No pending evaluations found.")
    #     return
    #
    # for _, eval_request in pending_eval_queue_df.iterrows():
    #     import re
    #     model_link = eval_request['model']
    #     match = re.search(r'>([^<]+)<', model_link)
    #     if match:
    #         eval_request['model'] = match.group(1)  # 赋值给 eval_request['model']
    #     else:
    #         eval_request['model'] = model_link  # 如果无法匹配，保留原始字符串
    #
    #     print(f"Evaluating model: {eval_request['model']}")
    #
    #     # 调用评估函数
    #     run_eval_suite.run_evaluation(
    #         eval_request=eval_request,
    #         local_dir=envs.EVAL_RESULTS_PATH_BACKEND,
    #         results_repo=envs.RESULTS_REPO,
    #         batch_size=1,
    #         device=envs.DEVICE,
    #         no_cache=True,
    #         need_check=False,  # 根据需要设定是否需要检查
    #         write_results=False  # 根据需要设定是否写入结果
    #     )
    #     print(f"Finished evaluation for model: {eval_request['model']}")
    #     # Update the status to FINISHED
    #     manage_requests.set_eval_request(
    #         api=envs.API,
    #         eval_request=eval_request,
    #         new_status="FINISHED",
    #         hf_repo=envs.QUEUE_REPO,
    #         local_dir=envs.EVAL_REQUESTS_PATH_BACKEND
    #     )
    current_pending_status = [PENDING_STATUS]
    print('_________________')
    manage_requests.check_completed_evals(
        api=envs.API,
        checked_status=RUNNING_STATUS,
        completed_status=FINISHED_STATUS,
        failed_status=FAILED_STATUS,
        hf_repo=envs.QUEUE_REPO,
        local_dir=envs.EVAL_REQUESTS_PATH_BACKEND,
        hf_repo_results=envs.RESULTS_REPO,
        local_dir_results=envs.EVAL_RESULTS_PATH_BACKEND
    )
    logging.info("Checked completed evals")
    eval_requests = manage_requests.get_eval_requests(
        job_status=current_pending_status,
        hf_repo=envs.QUEUE_REPO,
        local_dir=envs.EVAL_REQUESTS_PATH_BACKEND
    )
    logging.info("Got eval requests")
    eval_requests = sort_queue.sort_models_by_priority(api=envs.API, models=eval_requests)
    logging.info("Sorted eval requests")

    print(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests")
    if len(eval_requests) == 0:
        print("No eval requests found. Exiting.")
        return

    import concurrent.futures

    def process_eval_request(eval_request):
        pp.pprint(eval_request)
        run_eval_suite.run_evaluation(
            eval_request=eval_request,
            local_dir=envs.EVAL_RESULTS_PATH_BACKEND,
            results_repo=envs.RESULTS_REPO,
            batch_size=1,
            device=envs.DEVICE,
            no_cache=True,
            need_check=False,
            write_results=False
        )
        logging.info(f"Eval finished for model {eval_request.model}, now setting status to finished")

        # Update the status to FINISHED
        manage_requests.set_eval_request(
            api=envs.API,
            eval_request=eval_request,
            new_status=FINISHED_STATUS,
            hf_repo=envs.QUEUE_REPO,
            local_dir=envs.EVAL_REQUESTS_PATH_BACKEND
        )

    # 定义线程池的数量
    max_workers = 5  # 你可以根据你的需求设置合适的数量

    # 使用 ThreadPoolExecutor 来并行执行多个 eval_request
    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = [executor.submit(process_eval_request, eval_request) for eval_request in eval_requests]

        # 等待所有任务完成
        concurrent.futures.wait(futures)
    # for eval_request in eval_requests:
    #     pp.pprint(eval_request)
    #     run_eval_suite.run_evaluation(
    #         eval_request=eval_request,
    #         local_dir=envs.EVAL_RESULTS_PATH_BACKEND,
    #         results_repo=envs.RESULTS_REPO,
    #         batch_size=1,
    #         device=envs.DEVICE,
    #         no_cache=True,
    #         need_check= False,
    #         write_results= False
    #     )
    #     logging.info(f"Eval finished for model {eval_request.model}, now setting status to finished")
    #
    #     # Update the status to FINISHED
    #     manage_requests.set_eval_request(
    #         api=envs.API,
    #         eval_request=eval_request,
    #         new_status=FINISHED_STATUS,
    #         hf_repo=envs.QUEUE_REPO,
    #         local_dir=envs.EVAL_REQUESTS_PATH_BACKEND
    #     )


# Searching and filtering
def update_table(
    hidden_df: pd.DataFrame,
    columns: list,
    #type_query: list,
    precision_query: str,
    size_query: list,
    show_deleted: bool,
    query: str,
):
    # filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
    filtered_df = filter_models(hidden_df, size_query, precision_query, show_deleted)
    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[utils.AutoEvalColumn.dummy.name].str.contains(query, case=False))]


def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
    always_here_cols = [
        #utils.AutoEvalColumn.model_type_symbol.name,
        utils.AutoEvalColumn.model.name,
    ]
    # We use COLS to maintain sorting
    filtered_df = df[
        always_here_cols + [c for c in utils.COLS if c in df.columns and c in columns] + [utils.AutoEvalColumn.dummy.name]
    ]
    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=[utils.AutoEvalColumn.model.name, utils.AutoEvalColumn.precision.name, utils.AutoEvalColumn.revision.name]
            )

    return filtered_df


def filter_models(
    # df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
    df: pd.DataFrame, 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[utils.AutoEvalColumn.still_on_hub.name]]

    filtered_df = df
    
    # type_emoji = [t[0] for t in type_query]
    #filtered_df = filtered_df.loc[df[utils.AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
    filtered_df = filtered_df.loc[df[utils.AutoEvalColumn.precision.name].isin(precision_query + ["None"])]

    numeric_interval = pd.IntervalIndex(sorted([utils.NUMERIC_INTERVALS[s] for s in size_query]))
    params_column = pd.to_numeric(df[utils.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(about.TITLE)
    gr.Markdown(about.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 utils.fields(utils.AutoEvalColumn)
                                if not c.hidden and not c.never_hidden and not c.dummy
                            ],
                            value=[
                                c.name
                                for c in utils.fields(utils.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 gated/private/deleted 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 utils.ModelType],
                    #     value=[t.to_str() for t in utils.ModelType],
                    #     interactive=True,
                    #     elem_id="filter-columns-type",
                    # )
                    filter_columns_precision = gr.CheckboxGroup(
                        label="Precision",
                        choices=[i.value.name for i in utils.Precision],
                        value=[i.value.name for i in utils.Precision],
                        interactive=True,
                        elem_id="filter-columns-precision",
                    )
                    filter_columns_size = gr.CheckboxGroup(
                        label="Model sizes (in billions of parameters)",
                        choices=list(utils.NUMERIC_INTERVALS.keys()),
                        value=list(utils.NUMERIC_INTERVALS.keys()),
                        interactive=True,
                        elem_id="filter-columns-size",
                    )

            leaderboard_table = gr.components.Dataframe(
                value=leaderboard_df[
                    [c.name for c in utils.fields(utils.AutoEvalColumn) if c.never_hidden]
                    + shown_columns.value
                    + [utils.AutoEvalColumn.dummy.name]
                ].sort_values(by="Overall Humanlike %", ascending=False),
                headers=[c.name for c in utils.fields(utils.AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                datatype=utils.TYPES,
                elem_id="leaderboard-table",
                interactive=False,
                visible=True,
                column_widths=["33%", "33%"]
            )

            # Dummy leaderboard for handling the case when the user uses backspace key
            hidden_leaderboard_table_for_search = gr.components.Dataframe(
                value=original_df[utils.COLS],
                headers=utils.COLS,
                datatype=utils.TYPES,
                visible=False,
            )
            search_bar.submit(
                update_table,
                [
                    hidden_leaderboard_table_for_search,
                    shown_columns,
                    #filter_columns_type,
                    filter_columns_precision,
                    filter_columns_size,
                    deleted_models_visibility,
                    search_bar,
                ],
                leaderboard_table,
            )
            # for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
            for selector in [shown_columns, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
                selector.change(
                    update_table,
                    [
                        hidden_leaderboard_table_for_search,
                        shown_columns,
                        #filter_columns_type,
                        filter_columns_precision,
                        filter_columns_size,
                        deleted_models_visibility,
                        search_bar,
                    ],
                    leaderboard_table,
                    queue=True,
                )

        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
            gr.Markdown(about.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(about.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=utils.EVAL_COLS,
                                datatype=utils.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=utils.EVAL_COLS,
                                datatype=utils.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=utils.EVAL_COLS,
                                datatype=utils.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 utils.ModelType if t != utils.ModelType.Unknown],
                        label="Model type",
                        multiselect=False,
                        value=None,
                        interactive=True,
                    )

                with gr.Column():
                    precision = gr.Dropdown(
                        choices=[i.value.name for i in utils.Precision if i != utils.Precision.Unknown],
                        label="Precision",
                        multiselect=False,
                        value="float16",
                        interactive=True,
                    )
                    weight_type = gr.Dropdown(
                        choices=[i.value.name for i in utils.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(
                submit.add_new_eval,
                [
                    model_name_textbox,
                    base_model_name_textbox,
                    revision_name_textbox,
                    precision,
                    weight_type,
                    model_type,
                ],
                submission_result,
            )

    with gr.Row():
        with gr.Accordion("📙 Citation", open=False):
            citation_button = gr.Textbox(
                value=about.CITATION_BUTTON_TEXT,
                label=about.CITATION_BUTTON_LABEL,
                lines=20,
                elem_id="citation-button",
                show_copy_button=True,
            )


# 在初始化完成后调用
# original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
# process_pending_evals()

# try:
#     print(envs.EVAL_REQUESTS_PATH)
#     snapshot_download(
#         repo_id=envs.QUEUE_REPO, local_dir=envs.EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
#     )
# except Exception:
#     restart_space()
# try:
#     print(envs.EVAL_RESULTS_PATH)
#     snapshot_download(
#         repo_id=envs.RESULTS_REPO, local_dir=envs.EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
#     )
# except Exception:
#     restart_space()

# raw_data, original_df = populate.get_leaderboard_df(envs.RESULTS_REPO, envs.QUEUE_REPO, utils.COLS, utils.BENCHMARK_COLS)




(
    finished_eval_queue_df,
    running_eval_queue_df,
    pending_eval_queue_df,
) = populate.get_evaluation_queue_df(envs.EVAL_REQUESTS_PATH, utils.EVAL_COLS)



def background_init_and_process():
    global original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df
    original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
    process_pending_evals()

scheduler = BackgroundScheduler()
scheduler.add_job(background_init_and_process, 'date', run_date=datetime.datetime.now())  # 立即执行
scheduler.add_job(restart_space, "interval", seconds=1720000)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()