app.py

import json
import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
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,
    TASK_TEXT,
    SUBMIT_TEMPLATE,
    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
)
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

import pdb

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()

task = ['Overall', 'Acrostic', 'Crossword', 'Cryptogram', 'Logic_Puzzle', 'Sudoku', 'Drop_Quote']
leaderboard_dict = {}
for t in task:
    leaderboard_dict[t] = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS, task=t)

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

def init_leaderboard(dataframe):
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")

    # pdb.set_trace()
    def highlight_max_bold(s):
        return ['font-weight: bold' if v == s.max() and v != s.min() else '' for v in s]
    num_cols = dataframe.select_dtypes(include=['float']).columns
    styler = dataframe.style.format({col: "{:.1f}" for col in num_cols})
    styler = styler.apply(highlight_max_bold, subset=num_cols)
    return gr.components.Dataframe(
        value=styler,
        headers=[c.name for c in fields(AutoEvalColumn)],
        datatype=[c.type for c in fields(AutoEvalColumn)],
        row_count=10,
        interactive=False,
        column_widths=[180, 60, 80, 80, 80, 80, 60],
    )

    # return Leaderboard(
    #     value=dataframe,
    #     datatype=[c.type for c in fields(AutoEvalColumn)],
    #     select_columns=SelectColumns(
    #         default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
    #         cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
    #         label="Select Columns to Display:",
    #     ),
    #     # search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
    #     # hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
    #     # filter_columns=[
    #     #     ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
    #     #     ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
    #     #     ColumnFilter(
    #     #         AutoEvalColumn.params.name,
    #     #         type="slider",
    #     #         min=0.01,
    #     #         max=150,
    #     #         label="Select the number of parameters (B)",
    #     #     ),
    #     #     ColumnFilter(
    #     #         AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
    #     #     ),
    #     # ],
    #     # bool_checkboxgroup_label="Hide models",
    #     interactive=False,
    # )

def process_json(file):
    """ 读取用户上传的 JSON 文件并返回解析后的数据 """
    try:
        with open(file.name, 'r', encoding='utf-8') as f:
            data = json.load(f)
        return json.dumps(data, indent=4, ensure_ascii=False)  # 格式化 JSON 以便显示
    except Exception as e:
        return str(e)

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

    with gr.Tabs(elem_id="main-tabs", elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
            # leaderboard = init_leaderboard(LEADERBOARD_DF)
            with gr.Tabs():
                for i, t in enumerate(task):
                    with gr.TabItem(t.replace("_", " "), elem_id=f"llm-benchmark-tab-table-{t}", id=i):
                        if TASK_TEXT.get(t, None):
                            gr.Markdown(TASK_TEXT[t], elem_classes="markdown-text")
                        leaderboard = init_leaderboard(leaderboard_dict[t])


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

            gr.Markdown("## Submission Template", elem_classes="markdown-text")
            gr.Markdown(SUBMIT_TEMPLATE, elem_classes="markdown-text", height=250)
            
            file_input = gr.File(label="Upload JSON File", file_types=[".json"], height=150)
            json_output = gr.JSON(label="Parsed JSON Data")  # 输出 JSON 数据
            submit_button = gr.Button("Submit")
            submit_button.click(fn=process_json, inputs=file_input, outputs=json_output)


    with gr.Row():
        # gr.Markdown()
        citation_button = gr.Textbox(
            value=CITATION_BUTTON_TEXT,
            label=CITATION_BUTTON_LABEL,
            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()