Upload app.py

app.py

@@ -0,0 +1,156 @@
+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()