Update app.py

app.py

@@ -1,398 +1,397 @@
-import gradio as gr
-import pandas as pd
-from slider import create_subset_ratios_tab
-from change_output import change_file
-import requests
-import os
-import shutil
-import json
-import pandas as pd
-import subprocess
-import plotly.express as px
-def on_confirm(dataset_radio, num_parts_dropdown, token_counts_radio, line_counts_radio, cyclomatic_complexity_radio, problem_type_radio):
-    # 根据用户选择的参数构建文件路径
-    num_parts = num_parts_dropdown
-    token_counts_split = token_counts_radio
-    line_counts_split = line_counts_radio
-    cyclomatic_complexity_split = cyclomatic_complexity_radio
-
-
-    # 读取数据
-    dataframes = []
-    if token_counts_split=="Equal Frequency Partitioning":
-        token_counts_df = pd.read_csv(f"E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num_parts}/QS/token_counts_QS.csv")
-        dataframes.append(token_counts_df)
-
-    if line_counts_split=="Equal Frequency Partitioning":
-        line_counts_df = pd.read_csv(f"E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num_parts}/QS/line_counts_QS.csv")
-        dataframes.append(line_counts_df)
-
-    if cyclomatic_complexity_split=="Equal Frequency Partitioning":
-        cyclomatic_complexity_df = pd.read_csv(f"E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num_parts}/QS/CC_QS.csv")
-        dataframes.append(cyclomatic_complexity_df)
-    #以下改为直接从一个划分文件中读取即可
-    # if problem_type_radio:
-    #     problem_type_df = pd.read_csv(f"{num_parts}/problem_type_{problem_type_split}.csv")
-    #     dataframes.append(problem_type_df)
-
-    # 如果所有三个radio都有value，将三个文件中的所有行拼接
-    if len(dataframes) > 0:
-        combined_df = dataframes[0]
-        for df in dataframes[1:]:
-            combined_df = pd.merge(combined_df, df, left_index=True, right_index=True, suffixes=('', '_y'))
-            combined_df = combined_df.loc[:, ~combined_df.columns.str.endswith('_y')]  # 去除重复的列
-        return combined_df
-    else:
-        return pd.DataFrame()
-
-
-
-# 定义一个函数来返回数据
-# def show_data(line_counts, token_counts, cyclomatic_complexity, problem_type, show_high, show_medium, show_low):
-#     columns = ["Model"]
-#
-#     if token_counts:
-#         if show_high:
-#             columns.append("Token Counts.I")
-#
-#         if show_medium:
-#             columns.append("Token Counts.II")
-#         if show_low:
-#             columns.append("Token Counts.III")
-#     if line_counts:
-#         if show_high:
-#             columns.append("Line Counts.I")
-#         if show_medium:
-#             columns.append("Line Counts.II")
-#         if show_low:
-#             columns.append("Line Counts.III")
-#     if cyclomatic_complexity:
-#         if show_high:
-#             columns.append("Cyclomatic Complexity.I")
-#         if show_medium:
-#             columns.append("Cyclomatic Complexity.II")
-#         if show_low:
-#             columns.append("Cyclomatic Complexity.III")
-#     if problem_type:
-#         columns.extend(["Problem Type_String", "Problem Type_Math", "Problem Type_Array"])
-#     return data[columns]
-#用于更新数据文件的部分
-def execute_specified_python_files(directory_list, file_list):
-    for directory in directory_list:
-        for py_file in file_list:
-            file_path = os.path.join(directory, py_file)
-            if os.path.isfile(file_path) and py_file.endswith('.py'):
-                print(f"Executing {file_path}...")
-                try:
-                    # 使用subprocess执行Python文件
-                    subprocess.run(['python', file_path], check=True)
-                    print(f"{file_path} executed successfully.")
-                except subprocess.CalledProcessError as e:
-                    print(f"Error executing {file_path}: {e}")
-            else:
-                print(f"File {file_path} does not exist or is not a Python file.")
-# 定义一个函数来生成 CSS 样式
-def generate_css(line_counts, token_counts, cyclomatic_complexity, problem_type, show_high, show_medium, show_low):
-    css = """
-    #dataframe th {
-        background-color: #f2f2f2
-        
-    }
-    """
-    colors = ["#e6f7ff", "#ffeecc", "#e6ffe6", "#ffe6e6"]
-    categories = [line_counts, token_counts, cyclomatic_complexity]
-    category_index = 0
-    column_index = 1
-
-    for category in categories:
-        if category:
-            if show_high:
-                css += f"#dataframe td:nth-child({column_index + 1}) {{ background-color: {colors[category_index]}; }}\n"
-                column_index += 1
-            if show_medium:
-                css += f"#dataframe td:nth-child({column_index + 1}) {{ background-color: {colors[category_index]}; }}\n"
-                column_index += 1
-            if show_low:
-                css += f"#dataframe td:nth-child({column_index + 1}) {{ background-color: {colors[category_index]}; }}\n"
-                column_index += 1
-        category_index += 1
-
-    # 为 Problem Type 相关的三个子列设置固定颜色
-    if problem_type:
-        problem_type_color = "#d4f0fc"  # 你可以选择任何你喜欢的颜色
-        css += f"#dataframe td:nth-child({column_index + 1}) {{ background-color: {problem_type_color}; }}\n"
-        css += f"#dataframe td:nth-child({column_index + 2}) {{ background-color: {problem_type_color}; }}\n"
-        css += f"#dataframe td:nth-child({column_index + 3}) {{ background-color: {problem_type_color}; }}\n"
-
-    # 隐藏 "data" 标识
-    css += """
-    .gradio-container .dataframe-container::before {
-        content: none !important;
-    }
-    """
-
-    return css
-# def update_dataframe(line_counts, token_counts, cyclomatic_complexity, problem_type, show_high, show_medium,
-#                      show_low):
-#     df = show_data(line_counts, token_counts, cyclomatic_complexity, problem_type, show_high, show_medium, show_low)
-#     css = generate_css(line_counts, token_counts, cyclomatic_complexity, problem_type, show_high, show_medium,
-#                        show_low)
-#     return gr.update(value=df), gr.update(value=f"<style>{css}</style>")
-
-
-def generate_file(file_obj, user_string, user_number,dataset_choice):
-    tmpdir = 'tmpdir'
-
-    print('临时文件夹地址：{}'.format(tmpdir))
-    FilePath = file_obj.name
-    print('上传文件的地址：{}'.format(file_obj.name))  # 输出上传后的文件在gradio中保存的绝对地址
-
-    # 将文件复制到临时目录中
-    shutil.copy(file_obj.name, tmpdir)
-
-    # 获取上传Gradio的文件名称
-    FileName = os.path.basename(file_obj.name)
-
-    print(FilePath)
-    # 获取拷贝在临时目录的新的文件地址
-
-    # 打开复制到新路径后的文件
-    with open(FilePath, 'r', encoding="utf-8") as file_obj:
-        # 在本地电脑打开一个新的文件，并且将上传文件内容写入到新文件
-        outputPath = os.path.join('F:/Desktop/test', FileName)
-        data = json.load(file_obj)
-        print("data:", data)
-
-        # 将数据写入新的 JSON 文件
-        with open(outputPath, 'w', encoding="utf-8") as w:
-            json.dump(data, w, ensure_ascii=False, indent=4)
-
-        # 读取文件内容并上传到服务器
-        file_content = json.dumps(data)  # 将数据转换为 JSON 字符串
-        url = "http://localhost:6222/submit"  # 替换为你的后端服务器地址
-        files = {'file': (FileName, file_content, 'application/json')}
-        payload = {
-            'user_string': user_string,
-            'user_number': user_number,
-            'dataset_choice':dataset_choice
-        }
-
-        response = requests.post(url, files=files, data=payload)
-        print(response)
-        #返回服务器处理后的文件
-        if response.status_code == 200:
-            # 获取服务器返回的 JSON 数据
-            output_data = response.json()
-
-            # 保存 JSON 数据到本地
-            output_file_path = os.path.join('E:/python-testn/pythonProject3/hh_1/evaluate_result', 'new-model.json')
-            with open(output_file_path, 'w', encoding="utf-8") as f:
-                json.dump(output_data, f, ensure_ascii=False, indent=4)
-
-            print(f"File saved at: {output_file_path}")
-
-            # 调用更新数据文件的函数
-            directory_list = ['/path/to/directory1', '/path/to/directory2']  # 替换为你的目录路径列表
-            file_list = ['file1.py', 'file2.py', 'file3.py']  # 替换为你想要执行的Python文件列表
-
-            execute_specified_python_files(directory_list, file_list)
-
-            return {"status": "success", "message": "File received and saved"}
-        else:
-            return {"status": "error", "message": response.text}
-
-        # 返回服务器响应
-    return {"status": "success", "message": response.text}
-
-def update_radio_options(token_counts, line_counts, cyclomatic_complexity, problem_type):
-    options = []
-    if token_counts:
-        options.append("Token Counts in Prompt")
-    if line_counts:
-        options.append("Line Counts in Prompt")
-    if cyclomatic_complexity:
-        options.append("Cyclomatic Complexity")
-    if problem_type:
-        options.append("Problem Type")
-
-    return gr.update(choices=options)
-
-def plot_csv(radio,num):
-    # 读取本地的CSV文件
-    #token_counts_df = pd.read_csv(f"{num_parts}/QS/token_counts_QS.csv")
-    if radio=="Line Counts in Prompt":
-        radio_choice="line_counts"
-        file_path = f'E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
-    elif radio=="Token Counts in Prompt":
-        radio_choice="token_counts"
-        file_path = f'E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
-    elif radio=="Cyclomatic Complexity":
-        radio_choice="CC"
-        file_path = f'E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
-    elif radio=="Problem Type":
-        radio_choice="problem_type"
-        file_path = f'E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/cata_result.csv'
-    print("test!")
-
-    # file_path="E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/3/QS/CC_QS.csv"
-    df = pd.read_csv(file_path)
-    # 将第一列作为索引
-    df.set_index('Model', inplace=True)
-
-    # 转置数据框，使得模型作为列，横轴作为行
-    df_transposed = df.T
-
-    # 使用plotly绘制折线图
-    fig = px.line(df_transposed, x=df_transposed.index, y=df_transposed.columns,
-                  title='Model Evaluation Results',
-                  labels={'value': 'Evaluation Score', 'index': 'Evaluation Metric'},
-                  color_discrete_sequence=px.colors.qualitative.Plotly)
-
-    # 设置悬停效果
-    fig.update_traces(hovertemplate='%{y}')
-
-    return fig
-
-
-
-    # 创建 Gradio 界面
-with gr.Blocks() as iface:
-    gr.HTML("""
-               <style>
-                   .title {
-                       text-align: center;
-                       font-size: 3em;
-                       font-weight: bold;
-                       margin-bottom: 0.5em;
-                   }
-                   .subtitle {
-                       text-align: center;
-                       font-size: 2em;
-                       margin-bottom: 1em;
-                   }
-               </style>
-               <div class="title">📊 Demo-Leaderboard 📊</div>
-           """)
-
-    with gr.Tabs() as tabs:
-        with gr.TabItem("evaluation_result"):
-            with gr.Row():
-                with gr.Column(scale=2):
-                    with gr.Row():
-                        with gr.Column():
-                            dataset_radio = gr.Radio(["HumanEval", "MBPP"], label="Select Dataset ")
-
-                    with gr.Row():
-                        custom_css = """  
-                            <style>  
-                                .markdown-class {  
-                                    font-family: 'Helvetica', sans-serif;
-                                    font-size: 17px; 
-                                    font-weight: bold;  
-                                    color: #333; 
-                                }  
-                            </style>  
-                            """
-
-                        with gr.Column():
-                            gr.Markdown(
-                                f"{custom_css}<div class='markdown-class'> Choose Classification Perspective </div>")
-
-                            token_counts_checkbox = gr.Checkbox(label="Token Counts in Prompt ")
-                            line_counts_checkbox = gr.Checkbox(label="Line Counts in Prompt ")
-                            cyclomatic_complexity_checkbox = gr.Checkbox(label="Cyclomatic Complexity ")
-                            problem_type_checkbox = gr.Checkbox(label="Problem Type ")
-
-                        with gr.Column():
-                            gr.Markdown("<div class='markdown-class'>Choose Subsets </div>")
-                            num_parts_dropdown = gr.Dropdown(choices=[3, 4, 5, 6, 7, 8], label="Number of Subsets")
-
-            with gr.Row():
-                with gr.Column():
-                    token_counts_radio = gr.Radio(
-                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"], label="Select Dataset",
-                        visible=False)
-                with gr.Column():
-                    line_counts_radio = gr.Radio(
-                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"], label="Select Dataset",
-                        visible=False)
-                with gr.Column():
-                    cyclomatic_complexity_radio = gr.Radio(
-                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"], label="Select Dataset",
-                        visible=False)
-
-            token_counts_checkbox.change(fn=lambda x: toggle_radio(x, token_counts_radio),
-                                         inputs=token_counts_checkbox, outputs=token_counts_radio)
-            line_counts_checkbox.change(fn=lambda x: toggle_radio(x, line_counts_radio),
-                                        inputs=line_counts_checkbox, outputs=line_counts_radio)
-            cyclomatic_complexity_checkbox.change(fn=lambda x: toggle_radio(x, cyclomatic_complexity_radio),
-                                                  inputs=cyclomatic_complexity_checkbox,
-                                                  outputs=cyclomatic_complexity_radio)
-
-            with gr.Tabs() as inner_tabs:
-                with gr.TabItem("Leaderboard"):
-                    dataframe_output = gr.Dataframe(elem_id="dataframe")
-                    css_output = gr.HTML()
-                    confirm_button = gr.Button("Confirm ")
-                    confirm_button.click(fn=on_confirm, inputs=[dataset_radio, num_parts_dropdown, token_counts_radio,
-                                                                line_counts_radio, cyclomatic_complexity_radio],
-                                         outputs=dataframe_output)
-
-                with gr.TabItem("Line chart"):
-                    select_radio = gr.Radio(choices=[])
-                    checkboxes = [token_counts_checkbox, line_counts_checkbox, cyclomatic_complexity_checkbox,
-                                  problem_type_checkbox]
-                    for checkbox in checkboxes:
-                        checkbox.change(fn=update_radio_options, inputs=checkboxes, outputs=select_radio)
-                    select_radio.change(fn=plot_csv, inputs=[select_radio, num_parts_dropdown],
-                                        outputs=gr.Plot(label="Line Plot "))
-
-        with gr.TabItem("upload"):
-            gr.Markdown("Upload a JSON file")
-            with gr.Row():
-                with gr.Column():
-                    string_input = gr.Textbox(label="Enter the Model Name")
-                    number_input = gr.Number(label="Select the Number of Samples")
-                    dataset_choice = gr.Dropdown(label="Select Dataset", choices=["humaneval", "mbpp"])
-                with gr.Column():
-                    file_input = gr.File(label="Upload Generation Result in JSON file")
-                    upload_button = gr.Button("Confirm and Upload")
-
-            json_output = gr.JSON(label="")
-
-            upload_button.click(fn=generate_file, inputs=[file_input, string_input, number_input, dataset_choice],
-                                outputs=json_output)
-
-
-    # 定义事件处理函数
-    def toggle_radio(checkbox, radio):
-        return gr.update(visible=checkbox)
-
-
-
-    css = """  
-        #scale1 {  
-    border: 1px solid rgba(0, 0, 0, 0.2); /* 使用浅色边框，并带有透明度 */  
-    padding: 10px; /* 添加内边距 */  
-    border-radius: 8px; /* 更圆滑的圆角 */  
-    background-color: #f9f9f9; /* 背景颜色 */  
-    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); /* 添加阴影效果 */  
-}  
-        }  
-        """
-    gr.HTML(f"<style>{css}</style>")
-
-
-
-
-    # 初始化数据表格
-    # initial_df = show_data(False, False, False, False, False, False, False)
-    # initial_css = generate_css(False, False, False, False, True, False, False)
-    # dataframe_output.value = initial_df
-    # css_output.value = f"<style>{initial_css}</style>"
-
-
-
-
-# 启动界面
+import gradio as gr
+import pandas as pd
+from change_output import change_file
+import requests
+import os
+import shutil
+import json
+import pandas as pd
+import subprocess
+import plotly.express as px
+def on_confirm(dataset_radio, num_parts_dropdown, token_counts_radio, line_counts_radio, cyclomatic_complexity_radio, problem_type_radio):
+    # 根据用户选择的参数构建文件路径
+    num_parts = num_parts_dropdown
+    token_counts_split = token_counts_radio
+    line_counts_split = line_counts_radio
+    cyclomatic_complexity_split = cyclomatic_complexity_radio
+
+
+    # 读取数据
+    dataframes = []
+    if token_counts_split=="Equal Frequency Partitioning":
+        token_counts_df = pd.read_csv(f"E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num_parts}/QS/token_counts_QS.csv")
+        dataframes.append(token_counts_df)
+
+    if line_counts_split=="Equal Frequency Partitioning":
+        line_counts_df = pd.read_csv(f"E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num_parts}/QS/line_counts_QS.csv")
+        dataframes.append(line_counts_df)
+
+    if cyclomatic_complexity_split=="Equal Frequency Partitioning":
+        cyclomatic_complexity_df = pd.read_csv(f"E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num_parts}/QS/CC_QS.csv")
+        dataframes.append(cyclomatic_complexity_df)
+    #以下改为直接从一个划分文件中读取即可
+    # if problem_type_radio:
+    #     problem_type_df = pd.read_csv(f"{num_parts}/problem_type_{problem_type_split}.csv")
+    #     dataframes.append(problem_type_df)
+
+    # 如果所有三个radio都有value，将三个文件中的所有行拼接
+    if len(dataframes) > 0:
+        combined_df = dataframes[0]
+        for df in dataframes[1:]:
+            combined_df = pd.merge(combined_df, df, left_index=True, right_index=True, suffixes=('', '_y'))
+            combined_df = combined_df.loc[:, ~combined_df.columns.str.endswith('_y')]  # 去除重复的列
+        return combined_df
+    else:
+        return pd.DataFrame()
+
+
+
+# 定义一个函数来返回数据
+# def show_data(line_counts, token_counts, cyclomatic_complexity, problem_type, show_high, show_medium, show_low):
+#     columns = ["Model"]
+#
+#     if token_counts:
+#         if show_high:
+#             columns.append("Token Counts.I")
+#
+#         if show_medium:
+#             columns.append("Token Counts.II")
+#         if show_low:
+#             columns.append("Token Counts.III")
+#     if line_counts:
+#         if show_high:
+#             columns.append("Line Counts.I")
+#         if show_medium:
+#             columns.append("Line Counts.II")
+#         if show_low:
+#             columns.append("Line Counts.III")
+#     if cyclomatic_complexity:
+#         if show_high:
+#             columns.append("Cyclomatic Complexity.I")
+#         if show_medium:
+#             columns.append("Cyclomatic Complexity.II")
+#         if show_low:
+#             columns.append("Cyclomatic Complexity.III")
+#     if problem_type:
+#         columns.extend(["Problem Type_String", "Problem Type_Math", "Problem Type_Array"])
+#     return data[columns]
+#用于更新数据文件的部分
+def execute_specified_python_files(directory_list, file_list):
+    for directory in directory_list:
+        for py_file in file_list:
+            file_path = os.path.join(directory, py_file)
+            if os.path.isfile(file_path) and py_file.endswith('.py'):
+                print(f"Executing {file_path}...")
+                try:
+                    # 使用subprocess执行Python文件
+                    subprocess.run(['python', file_path], check=True)
+                    print(f"{file_path} executed successfully.")
+                except subprocess.CalledProcessError as e:
+                    print(f"Error executing {file_path}: {e}")
+            else:
+                print(f"File {file_path} does not exist or is not a Python file.")
+# 定义一个函数来生成 CSS 样式
+def generate_css(line_counts, token_counts, cyclomatic_complexity, problem_type, show_high, show_medium, show_low):
+    css = """
+    #dataframe th {
+        background-color: #f2f2f2
+        
+    }
+    """
+    colors = ["#e6f7ff", "#ffeecc", "#e6ffe6", "#ffe6e6"]
+    categories = [line_counts, token_counts, cyclomatic_complexity]
+    category_index = 0
+    column_index = 1
+
+    for category in categories:
+        if category:
+            if show_high:
+                css += f"#dataframe td:nth-child({column_index + 1}) {{ background-color: {colors[category_index]}; }}\n"
+                column_index += 1
+            if show_medium:
+                css += f"#dataframe td:nth-child({column_index + 1}) {{ background-color: {colors[category_index]}; }}\n"
+                column_index += 1
+            if show_low:
+                css += f"#dataframe td:nth-child({column_index + 1}) {{ background-color: {colors[category_index]}; }}\n"
+                column_index += 1
+        category_index += 1
+
+    # 为 Problem Type 相关的三个子列设置固定颜色
+    if problem_type:
+        problem_type_color = "#d4f0fc"  # 你可以选择任何你喜欢的颜色
+        css += f"#dataframe td:nth-child({column_index + 1}) {{ background-color: {problem_type_color}; }}\n"
+        css += f"#dataframe td:nth-child({column_index + 2}) {{ background-color: {problem_type_color}; }}\n"
+        css += f"#dataframe td:nth-child({column_index + 3}) {{ background-color: {problem_type_color}; }}\n"
+
+    # 隐藏 "data" 标识
+    css += """
+    .gradio-container .dataframe-container::before {
+        content: none !important;
+    }
+    """
+
+    return css
+# def update_dataframe(line_counts, token_counts, cyclomatic_complexity, problem_type, show_high, show_medium,
+#                      show_low):
+#     df = show_data(line_counts, token_counts, cyclomatic_complexity, problem_type, show_high, show_medium, show_low)
+#     css = generate_css(line_counts, token_counts, cyclomatic_complexity, problem_type, show_high, show_medium,
+#                        show_low)
+#     return gr.update(value=df), gr.update(value=f"<style>{css}</style>")
+
+
+def generate_file(file_obj, user_string, user_number,dataset_choice):
+    tmpdir = 'tmpdir'
+
+    print('临时文件夹地址：{}'.format(tmpdir))
+    FilePath = file_obj.name
+    print('上传文件的地址：{}'.format(file_obj.name))  # 输出上传后的文件在gradio中保存的绝对地址
+
+    # 将文件复制到临时目录中
+    shutil.copy(file_obj.name, tmpdir)
+
+    # 获取上传Gradio的文件名称
+    FileName = os.path.basename(file_obj.name)
+
+    print(FilePath)
+    # 获取拷贝在临时目录的新的文件地址
+
+    # 打开复制到新路径后的文件
+    with open(FilePath, 'r', encoding="utf-8") as file_obj:
+        # 在本地电脑打开一个新的文件，并且将上传文件内容写入到新文件
+        outputPath = os.path.join('F:/Desktop/test', FileName)
+        data = json.load(file_obj)
+        print("data:", data)
+
+        # 将数据写入新的 JSON 文件
+        with open(outputPath, 'w', encoding="utf-8") as w:
+            json.dump(data, w, ensure_ascii=False, indent=4)
+
+        # 读取文件内容并上传到服务器
+        file_content = json.dumps(data)  # 将数据转换为 JSON 字符串
+        url = "http://localhost:6222/submit"  # 替换为你的后端服务器地址
+        files = {'file': (FileName, file_content, 'application/json')}
+        payload = {
+            'user_string': user_string,
+            'user_number': user_number,
+            'dataset_choice':dataset_choice
+        }
+
+        response = requests.post(url, files=files, data=payload)
+        print(response)
+        #返回服务器处理后的文件
+        if response.status_code == 200:
+            # 获取服务器返回的 JSON 数据
+            output_data = response.json()
+
+            # 保存 JSON 数据到本地
+            output_file_path = os.path.join('E:/python-testn/pythonProject3/hh_1/evaluate_result', 'new-model.json')
+            with open(output_file_path, 'w', encoding="utf-8") as f:
+                json.dump(output_data, f, ensure_ascii=False, indent=4)
+
+            print(f"File saved at: {output_file_path}")
+
+            # 调用更新数据文件的函数
+            directory_list = ['/path/to/directory1', '/path/to/directory2']  # 替换为你的目录路径列表
+            file_list = ['file1.py', 'file2.py', 'file3.py']  # 替换为你想要执行的Python文件列表
+
+            execute_specified_python_files(directory_list, file_list)
+
+            return {"status": "success", "message": "File received and saved"}
+        else:
+            return {"status": "error", "message": response.text}
+
+        # 返回服务器响应
+    return {"status": "success", "message": response.text}
+
+def update_radio_options(token_counts, line_counts, cyclomatic_complexity, problem_type):
+    options = []
+    if token_counts:
+        options.append("Token Counts in Prompt")
+    if line_counts:
+        options.append("Line Counts in Prompt")
+    if cyclomatic_complexity:
+        options.append("Cyclomatic Complexity")
+    if problem_type:
+        options.append("Problem Type")
+
+    return gr.update(choices=options)
+
+def plot_csv(radio,num):
+    # 读取本地的CSV文件
+    #token_counts_df = pd.read_csv(f"{num_parts}/QS/token_counts_QS.csv")
+    if radio=="Line Counts in Prompt":
+        radio_choice="line_counts"
+        file_path = f'E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
+    elif radio=="Token Counts in Prompt":
+        radio_choice="token_counts"
+        file_path = f'E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
+    elif radio=="Cyclomatic Complexity":
+        radio_choice="CC"
+        file_path = f'E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
+    elif radio=="Problem Type":
+        radio_choice="problem_type"
+        file_path = f'E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/cata_result.csv'
+    print("test!")
+
+    # file_path="E:/python-testn/pythonProject3/hh_1/dividing_into_different_subsets/3/QS/CC_QS.csv"
+    df = pd.read_csv(file_path)
+    # 将第一列作为索引
+    df.set_index('Model', inplace=True)
+
+    # 转置数据框，使得模型作为列，横轴作为行
+    df_transposed = df.T
+
+    # 使用plotly绘制折线图
+    fig = px.line(df_transposed, x=df_transposed.index, y=df_transposed.columns,
+                  title='Model Evaluation Results',
+                  labels={'value': 'Evaluation Score', 'index': 'Evaluation Metric'},
+                  color_discrete_sequence=px.colors.qualitative.Plotly)
+
+    # 设置悬停效果
+    fig.update_traces(hovertemplate='%{y}')
+
+    return fig
+
+
+
+    # 创建 Gradio 界面
+with gr.Blocks() as iface:
+    gr.HTML("""
+               <style>
+                   .title {
+                       text-align: center;
+                       font-size: 3em;
+                       font-weight: bold;
+                       margin-bottom: 0.5em;
+                   }
+                   .subtitle {
+                       text-align: center;
+                       font-size: 2em;
+                       margin-bottom: 1em;
+                   }
+               </style>
+               <div class="title">📊 Demo-Leaderboard 📊</div>
+           """)
+
+    with gr.Tabs() as tabs:
+        with gr.TabItem("evaluation_result"):
+            with gr.Row():
+                with gr.Column(scale=2):
+                    with gr.Row():
+                        with gr.Column():
+                            dataset_radio = gr.Radio(["HumanEval", "MBPP"], label="Select Dataset ")
+
+                    with gr.Row():
+                        custom_css = """  
+                            <style>  
+                                .markdown-class {  
+                                    font-family: 'Helvetica', sans-serif;
+                                    font-size: 17px; 
+                                    font-weight: bold;  
+                                    color: #333; 
+                                }  
+                            </style>  
+                            """
+
+                        with gr.Column():
+                            gr.Markdown(
+                                f"{custom_css}<div class='markdown-class'> Choose Classification Perspective </div>")
+
+                            token_counts_checkbox = gr.Checkbox(label="Token Counts in Prompt ")
+                            line_counts_checkbox = gr.Checkbox(label="Line Counts in Prompt ")
+                            cyclomatic_complexity_checkbox = gr.Checkbox(label="Cyclomatic Complexity ")
+                            problem_type_checkbox = gr.Checkbox(label="Problem Type ")
+
+                        with gr.Column():
+                            gr.Markdown("<div class='markdown-class'>Choose Subsets </div>")
+                            num_parts_dropdown = gr.Dropdown(choices=[3, 4, 5, 6, 7, 8], label="Number of Subsets")
+
+            with gr.Row():
+                with gr.Column():
+                    token_counts_radio = gr.Radio(
+                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"], label="Select Dataset",
+                        visible=False)
+                with gr.Column():
+                    line_counts_radio = gr.Radio(
+                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"], label="Select Dataset",
+                        visible=False)
+                with gr.Column():
+                    cyclomatic_complexity_radio = gr.Radio(
+                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"], label="Select Dataset",
+                        visible=False)
+
+            token_counts_checkbox.change(fn=lambda x: toggle_radio(x, token_counts_radio),
+                                         inputs=token_counts_checkbox, outputs=token_counts_radio)
+            line_counts_checkbox.change(fn=lambda x: toggle_radio(x, line_counts_radio),
+                                        inputs=line_counts_checkbox, outputs=line_counts_radio)
+            cyclomatic_complexity_checkbox.change(fn=lambda x: toggle_radio(x, cyclomatic_complexity_radio),
+                                                  inputs=cyclomatic_complexity_checkbox,
+                                                  outputs=cyclomatic_complexity_radio)
+
+            with gr.Tabs() as inner_tabs:
+                with gr.TabItem("Leaderboard"):
+                    dataframe_output = gr.Dataframe(elem_id="dataframe")
+                    css_output = gr.HTML()
+                    confirm_button = gr.Button("Confirm ")
+                    confirm_button.click(fn=on_confirm, inputs=[dataset_radio, num_parts_dropdown, token_counts_radio,
+                                                                line_counts_radio, cyclomatic_complexity_radio],
+                                         outputs=dataframe_output)
+
+                with gr.TabItem("Line chart"):
+                    select_radio = gr.Radio(choices=[])
+                    checkboxes = [token_counts_checkbox, line_counts_checkbox, cyclomatic_complexity_checkbox,
+                                  problem_type_checkbox]
+                    for checkbox in checkboxes:
+                        checkbox.change(fn=update_radio_options, inputs=checkboxes, outputs=select_radio)
+                    select_radio.change(fn=plot_csv, inputs=[select_radio, num_parts_dropdown],
+                                        outputs=gr.Plot(label="Line Plot "))
+
+        with gr.TabItem("upload"):
+            gr.Markdown("Upload a JSON file")
+            with gr.Row():
+                with gr.Column():
+                    string_input = gr.Textbox(label="Enter the Model Name")
+                    number_input = gr.Number(label="Select the Number of Samples")
+                    dataset_choice = gr.Dropdown(label="Select Dataset", choices=["humaneval", "mbpp"])
+                with gr.Column():
+                    file_input = gr.File(label="Upload Generation Result in JSON file")
+                    upload_button = gr.Button("Confirm and Upload")
+
+            json_output = gr.JSON(label="")
+
+            upload_button.click(fn=generate_file, inputs=[file_input, string_input, number_input, dataset_choice],
+                                outputs=json_output)
+
+
+    # 定义事件处理函数
+    def toggle_radio(checkbox, radio):
+        return gr.update(visible=checkbox)
+
+
+
+    css = """  
+        #scale1 {  
+    border: 1px solid rgba(0, 0, 0, 0.2); /* 使用浅色边框，并带有透明度 */  
+    padding: 10px; /* 添加内边距 */  
+    border-radius: 8px; /* 更圆滑的圆角 */  
+    background-color: #f9f9f9; /* 背景颜色 */  
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); /* 添加阴影效果 */  
+}  
+        }  
+        """
+    gr.HTML(f"<style>{css}</style>")
+
+
+
+
+    # 初始化数据表格
+    # initial_df = show_data(False, False, False, False, False, False, False)
+    # initial_css = generate_css(False, False, False, False, True, False, False)
+    # dataframe_output.value = initial_df
+    # css_output.value = f"<style>{initial_css}</style>"
+
+
+
+
+# 启动界面
 iface.launch()