Update app.py

app.py

@@ -18,19 +18,19 @@ def on_confirm(dataset_radio, num_parts_dropdown, token_counts_radio, line_count
         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"E:/python-testn/pythonProject3/hh_2/dividing_into_different_subsets/{num_parts}/EI/token_counts_EI.csv")
+        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"E:/python-testn/pythonProject3/hh_2/dividing_into_different_subsets/{num_parts}/QS/line_counts_QS.csv")
+        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"E:/python-testn/pythonProject3/hh_2/dividing_into_different_subsets/{num_parts}/EI/line_counts_EI.csv")
+        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"E:/python-testn/pythonProject3/hh_2/dividing_into_different_subsets/{num_parts}/QS/CC_QS.csv")
+        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"E:/python-testn/pythonProject3/hh_2/dividing_into_different_subsets/{num_parts}/EI/CC_EI.csv")
+        CC_df = pd.read_csv(f"/home/user/app/dividing_into_different_subsets/{num_parts}/EI/CC_EI.csv")
         dataframes.append(CC_df)
 
 
@@ -119,31 +119,30 @@ def generate_file(file_obj, user_string, user_number,dataset_choice):
 
     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
+    print('上传文件的地址：{}'.format(file_obj.name)) 
+
 
-    # 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
+       
+        file_content = json.dumps(data) 
+        url = "http://localhost:6222/submit"  
         files = {'file': (FileName, file_content, 'application/json')}
         payload = {
             'user_string': user_string,
@@ -153,21 +152,21 @@ def generate_file(file_obj, user_string, user_number,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_2/evaluate_result', 'new-model.json')
+          
+            output_file_path = os.path.join('/home/user/app/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 = ['E:\python-testn\pythonProject3\hh_2\dividing_into_different_subsets\5\QS']  # 替换为你的目录路径列表
-            file_list = ["calculate_humaneval_result.py"]  # 替换为你想要执行的Python文件列表
+       
+            directory_list = ['/home/user/app/dividing_into_different_subsets\5\QS']  
+            file_list = ["calculate_humaneval_result.py"] 
 
             execute_specified_python_files(directory_list, file_list)
 
@@ -175,7 +174,7 @@ def generate_file(file_obj, user_string, user_number,dataset_choice):
         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):
@@ -192,44 +191,32 @@ def update_radio_options(token_counts, line_counts, cyclomatic_complexity, probl
     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=="The Number of Tokens in Problem Descriptions":
         radio_choice="token_counts"
-        file_path = f'E:/python-testn/pythonProject3/hh_2/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
+        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'E:/python-testn/pythonProject3/hh_2/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
+        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'E:/python-testn/pythonProject3/hh_2/dividing_into_different_subsets/{num}/QS/{radio_choice}_QS.csv'
+        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'E:/python-testn/pythonProject3/hh_2/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"
+        file_path = f'/home/user/app/dividing_into_different_subsets/cata_result.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
 import gradio as gr
 
 with gr.Blocks() as iface:
@@ -349,7 +336,7 @@ with gr.Blocks() as iface:
                                 outputs=json_output)
 
 
-    # 定义事件处理函数
+  
     def toggle_radio(checkbox, radio):
         return gr.update(visible=checkbox)
 
@@ -367,12 +354,4 @@ 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()