app.py

__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']

import gradio as gr
import pandas as pd
import json
import pdb
import tempfile

from constants import *
from src.auto_leaderboard.model_metadata_type import ModelType

global data_component, filter_component


def upload_file(files):
    file_paths = [file.name for file in files]
    return file_paths

def prediction_analyse(prediction_content):
    # pdb.set_trace()
    predictions = prediction_content.split("\n")

    # 读取 ground_truth JSON 文件
    with open("./file/SEED-Bench.json", "r") as file:
        ground_truth_data = json.load(file)["questions"]

    # 将 ground_truth 数据转换为以 question_id 为键的字典
    ground_truth = {item["question_id"]: item for item in ground_truth_data}

    # 初始化结果统计字典
    results = {i: {"correct": 0, "total": 0} for i in range(1, 13)}

    # 遍历 predictions，计算每个 question_type_id 的正确预测数和总预测数
    for prediction in predictions:
        # pdb.set_trace()
        prediction = prediction.strip()
        if not prediction:
            continue
        try:
            prediction = json.loads(prediction)
        except json.JSONDecodeError:
            print(f"Warning: Skipping invalid JSON data in line: {prediction}")
            continue
        question_id = prediction["question_id"]
        gt_item = ground_truth[question_id]
        question_type_id = gt_item["question_type_id"]

        if prediction["prediction"] == gt_item["answer"]:
            results[question_type_id]["correct"] += 1

        results[question_type_id]["total"] += 1
    
    return results

def add_new_eval(
    input_file,
    model_name_textbox: str,
    revision_name_textbox: str,
    model_type: str,
    model_link: str,
    LLM_type: str,
    LLM_name_textbox: str,
    Evaluation_dimension: str,
):
    if input_file is None:
        return "Error! Empty file!"
    else:
        content = input_file.decode("utf-8")
        prediction = prediction_analyse(content)
        csv_data = pd.read_csv(CSV_DIR)

        Start_dimension, End_dimension = 1, 13
        if Evaluation_dimension == 'Image':
            End_dimension = 10
        elif Evaluation_dimension == 'Video':
            Start_dimension = 10
        each_task_accuracy = {i: round(prediction[i]["correct"] / prediction[i]["total"] * 100, 1) if i >= Start_dimension and i < End_dimension else 0 for i in range(1, 13)}

        # count for average image\video\all
        total_correct_image = sum(prediction[i]["correct"] for i in range(1, 10))
        total_correct_video = sum(prediction[i]["correct"] for i in range(10, 13))

        total_image = sum(prediction[i]["total"] for i in range(1, 10))
        total_video = sum(prediction[i]["total"] for i in range(10, 13))

        if Evaluation_dimension != 'Video':
            average_accuracy_image = round(total_correct_image / total_image * 100, 1)
        else:
            average_accuracy_image = 0
        
        if Evaluation_dimension != 'Image':
            average_accuracy_video = round(total_correct_video / total_video * 100, 1)
        else:
            average_accuracy_video = 0
        
        if Evaluation_dimension == 'All':
            overall_accuracy = round((total_correct_image + total_correct_video) / (total_image + total_video) * 100, 1)
        else:
            overall_accuracy = 0

        if LLM_type == 'Other':
            LLM_name = LLM_name_textbox
        else:
            LLM_name = LLM_type
        
        if revision_name_textbox == '':
            col = csv_data.shape[0]
            model_name = model_name_textbox
        else:
            model_name = revision_name_textbox
            model_name_list = csv_data['Model']
            name_list = [name.split(']')[0][1:] for name in model_name_list]
            if revision_name_textbox not in name_list:
                col = csv_data.shape[0]
            else:
                col = name_list.index(revision_name_textbox)    
        
        if model_link == '':
            model_name = model_name  # no url
        else:
            model_name = '[' + model_name + '](' + model_link + ')'

        # add new data
        new_data = [
            model_type, 
            model_name, 
            LLM_name, 
            each_task_accuracy[1],
            each_task_accuracy[2],
            each_task_accuracy[3],
            each_task_accuracy[4],
            each_task_accuracy[5],
            each_task_accuracy[6],
            each_task_accuracy[7],
            each_task_accuracy[8],
            each_task_accuracy[9],
            average_accuracy_image,
            each_task_accuracy[10],
            each_task_accuracy[11],
            each_task_accuracy[12], 
            average_accuracy_video, 
            overall_accuracy]
        # pdb.set_trace()
        csv_data.loc[col] = new_data
        csv_data = csv_data.to_csv(CSV_DIR, index=False)
    return 0

def get_baseline_df():
    df = pd.read_csv(CSV_DIR)
    return df

block = gr.Blocks()


with block:
    gr.Markdown(
        LEADERBORAD_INTRODUCTION
    )
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 SEED Benchmark", elem_id="seed-benchmark-tab-table", id=0):
            with gr.Row():
                with gr.Accordion("Citation", open=False):
                    citation_button = gr.Textbox(
                        value=CITATION_BUTTON_TEXT,
                        label=CITATION_BUTTON_LABEL,
                        elem_id="citation-button",
                    ).style(show_copy_button=True)
    
            gr.Markdown(
                TABLE_INTRODUCTION
            )

            # selection for column part:
            checkbox_group = gr.CheckboxGroup(
                choices=TASK_INFO,
                value=TASK_INFO,
                label="Select options",
                interactive=True,
            )

            # 创建数据帧组件
            data_component = gr.components.Dataframe(
                value=get_baseline_df, 
                headers=COLUMN_NAMES,
                type="pandas", 
                datatype=DATA_TITILE_TYPE,
                interactive=False,
                visible=True,
                )
    
            def on_checkbox_group_change(selected_columns):
                # pdb.set_trace()
                selected_columns = [item for item in TASK_INFO if item in selected_columns]
                present_columns = MODEL_INFO + selected_columns
                updated_data = get_baseline_df()[present_columns]
                updated_headers = present_columns
                update_datatype = [DATA_TITILE_TYPE[COLUMN_NAMES.index(x)] for x in updated_headers]

                filter_component = gr.components.Dataframe(
                    value=updated_data, 
                    headers=updated_headers,
                    type="pandas", 
                    datatype=update_datatype,
                    interactive=False,
                    visible=True,
                    )
                # pdb.set_trace()
        
                return filter_component.value

            # 将复选框组关联到处理函数
            checkbox_group.change(fn=on_checkbox_group_change, inputs=checkbox_group, outputs=data_component)

        # table 2
        with gr.TabItem("📝 About", elem_id="seed-benchmark-tab-table", id=2):
            gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text")
        
        # table 3 
        with gr.TabItem("🚀 Submit here! ", elem_id="seed-benchmark-tab-table", id=3):
            gr.Markdown(LEADERBORAD_INTRODUCTION, elem_classes="markdown-text")

            with gr.Row():
                gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")

            with gr.Row():
                gr.Markdown("# ✉️✨ Submit your model evaluation json file here!", elem_classes="markdown-text")

            with gr.Row():
                with gr.Column():
                    model_name_textbox = gr.Textbox(
                        label="Model Name", placeholder="LLaMA-7B"
                        )
                    revision_name_textbox = gr.Textbox(
                        label="Revision Model Name", placeholder="LLaMA"
                    )
                    model_type = gr.Dropdown(
                        choices=[                         
                            "LLM",
                            "ImageLLM",
                            "VideoLLM",
                            "Other", 
                        ], 
                        label="Model Type", 
                        multiselect=False,
                        value="ImageLLM",
                        interactive=True,
                    )
                    model_link = gr.Textbox(
                        label="Model Link", placeholder="https://huggingface.co/decapoda-research/llama-7b-hf"
                    )

                with gr.Column():
                    
                    LLM_type = gr.Dropdown(
                        choices=["Vicuna-7B", "Flan-T5-XL", "LLaMA-7B", "Other"],
                        label="LLM Type", 
                        multiselect=False,
                        value="LLaMA-7B",
                        interactive=True,
                    )
                    LLM_name_textbox = gr.Textbox(
                        label="LLM Model (for Other)",
                        placeholder="LLaMA-13B"
                    )
                    Evaluation_dimension = gr.Dropdown(
                        choices=["All", "Image", "Video"],
                        label="Evaluation Dimension", 
                        multiselect=False,
                        value="All",
                        interactive=True,
                    )

            with gr.Column():

                input_file = gr.inputs.File(label = "Click to Upload a json File", file_count="single", type='binary')
                submit_button = gr.Button("Submit Eval")
    
                submission_result = gr.Markdown()
                submit_button.click(
                    add_new_eval,
                    inputs = [
                        input_file,
                        model_name_textbox,
                        revision_name_textbox,
                        model_type,
                        model_link,
                        LLM_type,
                        LLM_name_textbox,
                        Evaluation_dimension,
                    ],
                    # outputs = submission_result,
                )


    with gr.Row():
        data_run = gr.Button("Refresh")
        data_run.click(
            get_baseline_df, outputs=data_component
        )

    # block.load(get_baseline_df, outputs=data_title)

block.launch()