app.py

import gradio as gr
import pandas as pd
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_checkbox):
    num_parts = num_parts_dropdown
    current_dir = os.getcwd()
    print("当前工作目录的路径：", current_dir)


    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("E:/python-testn/pythonProject3/hh_2/dividing_into_different_subsets/cata_result.csv")
        dataframes.append(problem_type_df)

    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()



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)) 


    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"  
        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)
      
        if response.status_code == 200:
           
            output_data = response.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}")

       
            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)

            return {"status": "success", "message": "File received and saved"}
        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):
    options = []
    if token_counts:
        options.append("The Number of Tokens in Problem Descriptions")
    if line_counts:
        options.append("The Number of Lines in Problem Descriptions")
    if cyclomatic_complexity:
        options.append("The Complexity of Reference Code")
    if problem_type:
        options.append("Problem Type")

    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'
    df = pd.read_csv(file_path)
    df.set_index('Model', inplace=True)

    df_transposed = df.T
    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)
    fig.update_traces(hovertemplate='%{y}')

    return fig


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;
                       font-weight: bold;
                       margin-bottom: 0.5em;
                   }
                   .subtitle {
                       text-align: center;
                       font-size: 2em;
                       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 = """  
                    <style>  
                        .markdown-class {  
                            font-family: 'Helvetica', sans-serif;
                            font-size: 20px; 
                            font-weight: bold;  
                            color: #333; 
                        }  
                    </style>  
                    """

                with gr.Column():
                    gr.Markdown(
                        f"{custom_css}<div class='markdown-class'> Choose Division Perspective </div>")

                    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")
                    cyclomatic_complexity_checkbox = gr.Checkbox(label="III-The Complexity of Reference Code")
                    problem_type_checkbox = gr.Checkbox(label="IV-Problem Types ")
                css_code = """  
                    .dropdown-container {  
                        display: none;  
                    }  
                """

                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="Choose the Number of Subsets")

            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",
                        visible=False)
                with gr.Column():
                    line_counts_radio = gr.Radio(
                        ["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",
                        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("Ranking Table"):
                    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,problem_type_checkbox],
                                         outputs=dataframe_output)

                with gr.TabItem("Line chart"):
                    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],
                                        outputs=gr.Plot(label="Line Plot "))

        with gr.TabItem("Upload Inference File"):
            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)


  
    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; 
    background-color: #f9f9f9; 
    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); 
}  
        }  
        """
    gr.HTML(f"<style>{css}</style>")


iface.launch()