app.py

import os.path
import gradio as gr
import pandas as pd
from constants import *

# ------------ 下载链接 ------------
def get_download_link_model(task, dataset, example):
    _task_path = TASK_PATH_MAPPING[task]
    _dataset_path = DATASET_PATH_MAPPING[dataset]
    _example_path = EXAMPLE_PATH_MAPPING[example]
    return os.path.join("data", _task_path, _dataset_path, "weight", f"{_example_path}.zip")

def get_download_link_json(task, dataset, example):
    _task_path = TASK_PATH_MAPPING[task]
    _dataset_path = DATASET_PATH_MAPPING[dataset]
    _example_path = EXAMPLE_PATH_MAPPING[example]
    if _task_path == "common":
        return os.path.join("data", _task_path, _dataset_path, "json", f"{_example_path}.jsonl")
    else:
        return os.path.join("data", _task_path, _dataset_path, "json", f"{_example_path}.json")

# ------------ 数据读取 + 平均准确率 ------------
def get_data(task, dataset, example):
    _task_path = TASK_PATH_MAPPING[task]
    _dataset_path = DATASET_PATH_MAPPING[dataset]
    _example_path = EXAMPLE_PATH_MAPPING[example]
    csv_file = os.path.join("data", _task_path, _dataset_path, "csv", f"{_example_path}.csv")
    if not os.path.exists(csv_file):
        return None, None

    read_data = pd.read_csv(csv_file)
    data = pd.DataFrame(columns=COLUMN_NAMES)
    average_acc = None

    if _task_path == "coding":
        for _, row in read_data.iterrows():
            data = pd.concat([data, pd.DataFrame([{
                "Prompt": row["prompt"],
                "Pass@1": round(float(row["pass@1"]) * 100, 3),
                "Pass@5": round(float(row["pass@5"]) * 100, 3),
                "Pass@10": round(float(row["pass@10"]) * 100, 3),
                "Correctness": "N/A"
            }])], ignore_index=True)
        # 仅对 HumanEval 数据集计算三列平均
        if "HumanEval" in dataset:
            p1_mean = round(read_data["pass@1"].mean() * 100, 3)
            p5_mean = round(read_data["pass@5"].mean() * 100, 3)
            p10_mean = round(read_data["pass@10"].mean() * 100, 3)
            average_acc = f"{p1_mean} / {p5_mean} / {p10_mean}"
    elif _task_path in ["common", "math"]:
        for _, row in read_data.iterrows():
            data = pd.concat([data, pd.DataFrame([{
                "Prompt": row["prompt"],
                "Pass@1": None,
                "Pass@5": None,
                "Pass@10": None,
                "Correctness": "✅" if row["correctness"] else "❌"
            }])], ignore_index=True)
        average_acc = round(read_data["correctness"].mean() * 100, 3)

    return data, average_acc

# ------------ Gradio UI ------------
with gr.Blocks() as demo_board:
    gr.HTML(DND_HEADER)
    gr.Markdown(DND_INTRODUCTION)

    task = gr.Radio(
        label="Task",
        choices=TASK_LIST,
        value=TASK_LIST[0],
        interactive=True,
    )
    dataset = gr.Radio(
        label="Dataset",
        choices=TASK_DATASET_LIST[task.value],
        value=TASK_DATASET_LIST[task.value][0],
        interactive=True
    )
    example = gr.Radio(
        label="Example",
        choices=EXAMPLE_LIST,
        value=EXAMPLE_LIST[0],
        interactive=True,
    )

    # 平均准确率（放在 Prompt 表格上方）
    average_acc_display = gr.Textbox(
        label="Average Accuracy (%)",
        value=lambda: str(get_data(task.value, dataset.value, example.value)[1]),
        interactive=False,
        visible=True,
        scale=0,
        max_lines=1,
        min_width=160
    )

    # Prompt 表格
    board = gr.components.Dataframe(
        value=lambda: get_data(task.value, dataset.value, example.value)[0],
        column_widths=["60%", "10%", "10%", "10%", "10%"],
        headers=COLUMN_NAMES,
        type="pandas",
        datatype=DATA_TITLE_TYPE,
        interactive=False,
        visible=True,
        max_height=500,
    )

    # 联动更新：task -> dataset
    task.change(
        lambda t: gr.Radio(
            label="Dataset",
            choices=TASK_DATASET_LIST[t],
            value=TASK_DATASET_LIST[t][0],
            interactive=True,
        ),
        inputs=[task],
        outputs=dataset
    )

    # 联动更新：task / dataset / example -> 表格 + 平均准确率
    for component in [task, dataset, example]:
        component.change(
            lambda t, d, e: (get_data(t, d, e)[0], str(get_data(t, d, e)[1])),
            inputs=[task, dataset, example],
            outputs=[board, average_acc_display]
        )

    # 下载按钮
    with gr.Row():
        json_downloader = gr.DownloadButton("Download JSON", visible=True)
        model_downloader = gr.DownloadButton("Download Model", visible=True)

        json_downloader.click(
            fn=get_download_link_json,
            inputs=[task, dataset, example],
            outputs=json_downloader,
        )
        model_downloader.click(
            fn=get_download_link_model,
            inputs=[task, dataset, example],
            outputs=model_downloader,
        )

    # 引用文本
    citation_button = gr.Textbox(
        value=CITATION_BUTTON_TEXT,
        label=CITATION_BUTTON_LABEL,
        elem_id="citation-button",
        lines=6,
        show_copy_button=True,
    )

# 启动
demo_board.launch()