Update app.py

app.py

@@ -1,5 +1,7 @@
 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
@@ -7,48 +9,82 @@ import json
 import pandas as pd
 import subprocess
 import plotly.express as px
-def on_confirm( num_parts_dropdown, token_counts_radio, line_counts_radio, cyclomatic_complexity_radio, problem_type_checkbox):
+def on_confirm(dataset_radio, num_parts_dropdown, token_counts_radio, line_counts_radio, cyclomatic_complexity_radio, problem_type_checkbox):
+    # 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_radio=="Equal Frequency Partitioning":#等频划分，每个子集数据点的数量基本一致
-        token_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/QS/token_counts_QS.csv")
-        dataframes.append(token_counts_df)
-    if token_counts_radio=="Equal Interval Partitioning":
-        token_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/EI/token_counts_EI.csv")
-        dataframes.append(token_counts_df)
-    if line_counts_radio=="Equal Frequency Partitioning":#等频划分，每个子集数据点的数量基本一致
-        line_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/QS/line_counts_QS.csv")
-        dataframes.append(line_counts_df)
-    if token_counts_radio=="Equal Interval Partitioning":
-        line_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/EI/line_counts_EI.csv")
-        dataframes.append(line_counts_df)
-    if cyclomatic_complexity_radio=="Equal Frequency Partitioning":#等频划分，每个子集数据点的数量基本一致
-        CC_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/QS/CC_QS.csv")
-        dataframes.append(CC_df)
-    if token_counts_radio=="Equal Interval Partitioning":
-        CC_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/EI/CC_EI.csv")
-        dataframes.append(CC_df)
-
-
-
-
-    if problem_type_checkbox:
-        problem_type_df = pd.read_csv("/home/user/app/dividing_into_different_subsets/cata_result.csv")
-        dataframes.append(problem_type_df)
-
+    if dataset_radio == "HumanEval":
+        if token_counts_radio=="Equal Frequency Partitioning":#等频划分，每个子集数据点的数量基本一致
+            token_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/QS/token_counts_QS.csv")
+            dataframes.append(token_counts_df)
+        if token_counts_radio=="Equal Interval Partitioning":
+            token_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/EI/token_counts_EI.csv")
+            dataframes.append(token_counts_df)
+        if line_counts_radio=="Equal Frequency Partitioning":#等频划分，每个子集数据点的数量基本一致
+            line_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/QS/line_counts_QS.csv")
+            dataframes.append(line_counts_df)
+        if token_counts_radio=="Equal Interval Partitioning":
+            line_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/EI/line_counts_EI.csv")
+            dataframes.append(line_counts_df)
+        if cyclomatic_complexity_radio=="Equal Frequency Partitioning":#等频划分，每个子集数据点的数量基本一致
+            CC_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/QS/CC_QS.csv")
+            dataframes.append(CC_df)
+        if token_counts_radio=="Equal Interval Partitioning":
+            CC_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/EI/CC_EI.csv")
+            dataframes.append(CC_df)
+
+
+
+    #以下改为直接从一个划分文件中读取即可
+        if problem_type_checkbox:
+            problem_type_df = pd.read_csv("/home/user/app/dividing_into_different_subsets/cata_result.csv")
+            dataframes.append(problem_type_df)
+    if dataset_radio == "MBPP":
+        if token_counts_radio=="Equal Frequency Partitioning":#等频划分，每个子集数据点的数量基本一致
+            token_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets_mbpp/{num_parts}/QS/token_counts_QS.csv")
+            dataframes.append(token_counts_df)
+        if token_counts_radio=="Equal Interval Partitioning":
+            token_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets_mbpp/{num_parts}/EI/token_counts_EI.csv")
+            dataframes.append(token_counts_df)
+        if line_counts_radio=="Equal Frequency Partitioning":#等频划分，每个子集数据点的数量基本一致
+            line_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets_mbpp/{num_parts}/QS/line_counts_QS.csv")
+            dataframes.append(line_counts_df)
+        if token_counts_radio=="Equal Interval Partitioning":
+            line_counts_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets_mbpp/{num_parts}/EI/line_counts_EI.csv")
+            dataframes.append(line_counts_df)
+        if cyclomatic_complexity_radio=="Equal Frequency Partitioning":#等频划分，每个子集数据点的数量基本一致
+            CC_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets_mbpp/{num_parts}/QS/CC_QS.csv")
+            dataframes.append(CC_df)
+        if token_counts_radio=="Equal Interval Partitioning":
+            CC_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets_mbpp/{num_parts}/EI/CC_EI.csv")
+            dataframes.append(CC_df)
+
+
+
+    #以下改为直接从一个划分文件中读取即可
+        if problem_type_checkbox:
+            problem_type_df = pd.read_csv("/home/user/app/dividing_into_different_subsets_mbpp/cata_result.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')]  
+            combined_df = combined_df.loc[:, ~combined_df.columns.str.endswith('_y')]  # 去除重复的列
         return combined_df
     else:
         return pd.DataFrame()
 
 
 
+
 def execute_specified_python_files(directory_list, file_list):
     for directory in directory_list:
         for py_file in file_list:
@@ -56,12 +92,14 @@ def execute_specified_python_files(directory_list, file_list):
             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 {
@@ -86,8 +124,10 @@ def generate_css(line_counts, token_counts, cyclomatic_complexity, problem_type,
                 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" 
+        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"
@@ -102,35 +142,34 @@ def generate_css(line_counts, token_counts, cyclomatic_complexity, problem_type,
     return css
 
 
+
 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)) 
-
+    print('上传文件的地址：{}'.format(file_obj.name))  
 
     shutil.copy(file_obj.name, tmpdir)
 
- 
+   
     FileName = os.path.basename(file_obj.name)
 
     print(FilePath)
- 
-
-
+  
     with open(FilePath, 'r', encoding="utf-8") as file_obj:
+      
         outputPath = os.path.join('F:/Desktop/test', FileName)
         data = json.load(file_obj)
         print("data:", data)
 
-       
+    
         with open(outputPath, 'w', encoding="utf-8") as w:
             json.dump(data, w, ensure_ascii=False, indent=4)
 
-       
+    
         file_content = json.dumps(data) 
-        url = "http://localhost:6222/submit"  
+        url = "http://localhost:6222/submit"
         files = {'file': (FileName, file_content, 'application/json')}
         payload = {
             'user_string': user_string,
@@ -140,21 +179,21 @@ def generate_file(file_obj, user_string, user_number,dataset_choice):
 
         response = requests.post(url, files=files, data=payload)
         print(response)
-      
+        
         if response.status_code == 200:
            
             output_data = response.json()
 
           
-            output_file_path = os.path.join('/home/user/app/evaluate_result', 'new-model.json')
+            output_file_path = os.path.join('E:/python-testn/pythonProject3/hh_2/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}")
 
-       
-            directory_list = ['/home/user/app/dividing_into_different_subsets\5\QS']  
-            file_list = ["calculate_humaneval_result.py"] 
+           
+            directory_list = ['E:\python-testn\pythonProject3\hh_2\dividing_into_different_subsets\5\QS'] 
+            file_list = ["calculate_cata_result.py"] 
 
             execute_specified_python_files(directory_list, file_list)
 
@@ -162,7 +201,7 @@ def generate_file(file_obj, user_string, user_number,dataset_choice):
         else:
             return {"status": "error", "message": response.text}
 
-        
+       
     return {"status": "success", "message": response.text}
 
 def update_radio_options(token_counts, line_counts, cyclomatic_complexity, problem_type):
@@ -178,38 +217,76 @@ def update_radio_options(token_counts, line_counts, cyclomatic_complexity, probl
 
     return gr.update(choices=options)
 
-def plot_csv(radio,num):
-   
-    if radio=="The Number of Tokens in Problem Descriptions":
-        radio_choice="token_counts"
-        file_path = f'/home/user/app/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
-    elif radio=="The Number of Lines in Problem Descriptions":
-        radio_choice="line_counts"
-        file_path = f'/home/user/app/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
-    elif radio=="The Complexity of Reference Code":
-        radio_choice="CC"
-        file_path = f'/home/user/app/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
-    elif radio=="Problem Type":
-        radio_choice="problem_type"
-        file_path = f'/home/user/app/dividing_into_different_subsets/cata_result.csv'
+def plot_csv(dataset_radio,radio,num):
+    print(dataset_radio,radio)
+    if dataset_radio=="HumanEval":
+    
+        if radio=="The Number of Tokens in Problem Descriptions":
+            radio_choice="token_counts"
+            file_path = f'/home/user/app/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
+        elif radio=="The Number of Lines in Problem Descriptions":
+            radio_choice="line_counts"
+            file_path = f'/home/user/app/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
+        elif radio=="The Complexity of Reference Code":
+            radio_choice="CC"
+            file_path = f'/home/user/app/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
+        elif radio=="Problem Type":
+            radio_choice="problem_type"
+            file_path = f'/home/user/app/dividing_into_different_subsets/cata_result.csv'
+        print("test!")
+    elif dataset_radio=="MBPP":
+        if radio=="The Number of Tokens in Problem Descriptions":
+            radio_choice="token_counts"
+            file_path = f'/home/user/app/dividing_into_different_subsets_mbpp/{num}/QS/{radio_choice}_QS.csv'
+        elif radio=="The Number of Lines in Problem Descriptions":
+            radio_choice="line_counts"
+            file_path = f'/home/user/app/dividing_into_different_subsets_mbpp/{num}/QS/{radio_choice}_QS.csv'
+        elif radio=="The Complexity of Reference Code":
+            radio_choice="CC"
+            file_path = f'/home/user/app/dividing_into_different_subsets_mbpp/{num}/QS/{radio_choice}_QS.csv'
+        elif radio=="Problem Type":
+            radio_choice="problem_type"
+            file_path = f'/home/user/app/dividing_into_different_subsets_mbpp/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
 
+def toggle_radio(checkbox, radio):
+    return gr.update(visible=checkbox)
+
+def toggle_line_counts_visibility(dataset):
+    if dataset == "MBPP":
+        return gr.update(visible=False)
+    else:
+        return gr.update(visible=True)
 
+    # 创建 Gradio 界面
 import gradio as gr
 
 with gr.Blocks() as iface:
     gr.HTML("""
                <style>
+                   # body {
+                   #     max-width: 50%; /* 设置最大宽度为50% */
+                   #     margin: 0 auto; /* 将内容居中 */
+                   # }
                    .title {
                        text-align: center;
                        font-size: 3em;
@@ -222,12 +299,17 @@ with gr.Blocks() as iface:
                        margin-bottom: 1em;
                    }
                </style>
-               
+
            """)
 
     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 = """  
@@ -247,6 +329,8 @@ with gr.Blocks() as iface:
 
                     token_counts_checkbox = gr.Checkbox(label="I-The Number of Tokens in Problem Descriptions")
                     line_counts_checkbox = gr.Checkbox(label="II-The Number of Lines in Problem Descriptions")
+                    dataset_radio.change(fn=toggle_line_counts_visibility, inputs=dataset_radio,
+                                         outputs=line_counts_checkbox)
                     cyclomatic_complexity_checkbox = gr.Checkbox(label="III-The Complexity of Reference Code")
                     problem_type_checkbox = gr.Checkbox(label="IV-Problem Types ")
                 css_code = """  
@@ -262,15 +346,18 @@ with gr.Blocks() as iface:
             with gr.Row():
                 with gr.Column():
                     token_counts_radio = gr.Radio(
-                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"], label="Choose the Division Method for Perspective-I",
+                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"],
+                        label="Choose the Division Method for Perspective-I",
                         visible=False)
                 with gr.Column():
                     line_counts_radio = gr.Radio(
-                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"], label="Choose the Division Method for Perspective-II",
+                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"],
+                        label="Choose the Division Method for Perspective-II",
                         visible=False)
                 with gr.Column():
                     cyclomatic_complexity_radio = gr.Radio(
-                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"], label="Choose the Division Method for Perspective-III",
+                        ["Equal Frequency Partitioning", "Equal Interval Partitioning"],
+                        label="Choose the Division Method for Perspective-III",
                         visible=False)
 
             token_counts_checkbox.change(fn=lambda x: toggle_radio(x, token_counts_radio),
@@ -286,17 +373,18 @@ with gr.Blocks() as iface:
                     dataframe_output = gr.Dataframe(elem_id="dataframe")
                     css_output = gr.HTML()
                     confirm_button = gr.Button("Confirm ")
-                    confirm_button.click(fn=on_confirm, inputs=[num_parts_dropdown, token_counts_radio,
-                                                                line_counts_radio, cyclomatic_complexity_radio,problem_type_checkbox],
+                    confirm_button.click(fn=on_confirm, inputs=[dataset_radio, num_parts_dropdown, token_counts_radio,
+                                                                line_counts_radio, cyclomatic_complexity_radio,
+                                                                problem_type_checkbox],
                                          outputs=dataframe_output)
 
                 with gr.TabItem("Line chart"):
-                    select_radio = gr.Radio(choices=[],label="Select One Perpective")
+                    select_radio = gr.Radio(choices=[], label="Select One Perpective")
                     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],
+                    select_radio.change(fn=plot_csv, inputs=[dataset_radio, select_radio, num_parts_dropdown],
                                         outputs=gr.Plot(label="Line Plot "))
 
         with gr.TabItem("Upload Inference File"):
@@ -315,17 +403,11 @@ with gr.Blocks() as iface:
             upload_button.click(fn=generate_file, inputs=[file_input, string_input, number_input, dataset_choice],
                                 outputs=json_output)
 
-
-  
-    def toggle_radio(checkbox, radio):
-        return gr.update(visible=checkbox)
-
-
     css = """  
         #scale1 {  
     border: 1px solid rgba(0, 0, 0, 0.2); 
     padding: 10px;  
-    border-radius: 8px; 
+    border-radius: 8px;
     background-color: #f9f9f9; 
     box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); 
 }  
@@ -333,5 +415,11 @@ with gr.Blocks() as iface:
         """
     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()