app.py

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

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

from constants import *
from huggingface_hub import Repository
HF_TOKEN = os.environ.get("HF_TOKEN")

global data_component, filter_component


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

def add_new_eval(
    input_file,
    model_name_textbox: str,
    revision_name_textbox: str,
    model_link: str,
):
    if input_file is None:
        return "Error! Empty file!"

    upload_data=json.loads(input_file)
    submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
    submission_repo.git_pull()
    shutil.copyfile(CSV_DIR, os.path.join(SUBMISSION_NAME, f"{input_file}"))

    csv_data = pd.read_csv(CSV_DIR)

    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 (clickable)']
        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_name
        ]
    for key in TASK_INFO:
        if key in upload_data:
            new_data.append(upload_data[key][0])
        else:
            new_data.append(0)
    csv_data.loc[col] = new_data
    csv_data = csv_data.to_csv(CSV_DIR, index=False)
    submission_repo.push_to_hub()
    return 0

def get_normalized_df(df):
    # final_score = df.drop('name', axis=1).sum(axis=1)
    # df.insert(1, 'Overall Score', final_score)
    normalize_df = df.copy().fillna(0.0)
    for column in normalize_df.columns[1:]:
        min_val = NORMALIZE_DIC[column]['Min']
        max_val = NORMALIZE_DIC[column]['Max']
        print(normalize_df[column], min_val, max_val)
        normalize_df[column] = (normalize_df[column] - min_val) / (max_val - min_val)
    return normalize_df

def calculate_selected_score(df, selected_columns):
    # selected_score = df[selected_columns].sum(axis=1)
    selected_QUALITY = [i for i in selected_columns if i in QUALITY_LIST]
    selected_SEMANTIC = [i for i in selected_columns if i in SEMANTIC_LIST]
    selected_quality_score = df[selected_QUALITY].sum(axis=1)/sum([DIM_WEIGHT[i] for i in selected_QUALITY])
    selected_semantic_score = df[selected_SEMANTIC].sum(axis=1)/sum([DIM_WEIGHT[i] for i in selected_SEMANTIC ])
    if selected_quality_score.isna().any().any() and selected_semantic_score.isna().any().any():
        selected_score =  (selected_quality_score * QUALITY_WEIGHT + selected_semantic_score * SEMANTIC_WEIGHT) / (QUALITY_WEIGHT + SEMANTIC_WEIGHT)
        return selected_score.fillna(0.0)
    if selected_quality_score.isna().any().any():
        return selected_semantic_score
    if selected_semantic_score.isna().any().any():
        return selected_quality_score
    # print(selected_semantic_score,selected_quality_score )
    selected_score =  (selected_quality_score * QUALITY_WEIGHT + selected_semantic_score * SEMANTIC_WEIGHT) / (QUALITY_WEIGHT + SEMANTIC_WEIGHT)
    return selected_score.fillna(0.0)

def get_final_score(df, selected_columns):
    normalize_df = get_normalized_df(df)
    #final_score = normalize_df.drop('name', axis=1).sum(axis=1)
    for name in normalize_df.drop('Model Name (clickable)', axis=1):
        normalize_df[name] = normalize_df[name]*DIM_WEIGHT[name]
    quality_score = normalize_df[QUALITY_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in QUALITY_LIST])
    semantic_score = normalize_df[SEMANTIC_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in SEMANTIC_LIST ])
    final_score =  (quality_score * QUALITY_WEIGHT + semantic_score * SEMANTIC_WEIGHT) / (QUALITY_WEIGHT + SEMANTIC_WEIGHT)
    if 'Total Score' in df:
        df['Total Score'] = final_score
    else:
        df.insert(1, 'Total Score', final_score)
    if 'Semantic Score' in df:
        df['Semantic Score'] = semantic_score
    else:
        df.insert(2, 'Semantic Score', semantic_score)
    if 'Quality Score' in df:
        df['Quality Score'] = quality_score
    else:
        df.insert(3, 'Quality Score', quality_score)
    selected_score = calculate_selected_score(normalize_df, selected_columns)
    if 'Selected Score' in df:
        df['Selected Score'] = selected_score
    else:
        df.insert(1, 'Selected Score', selected_score)
    return df


def get_final_score_quality(df, selected_columns):
    normalize_df = get_normalized_df(df)
    quality_score = normalize_df[QUALITY_TAB].sum(axis=1) / len(QUALITY_TAB)

    if 'Quality Score' in df:
        df['Quality Score'] = quality_score
    else:
        df.insert(1, 'Quality Score', quality_score)
    selected_score = normalize_df[selected_columns].sum(axis=1) / len(selected_columns)
    if 'Selected Score' in df:
        df['Selected Score'] = selected_score
    else:
        df.insert(1, 'Selected Score', selected_score)
    return df

def get_baseline_df():
    submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
    submission_repo.git_pull()
    df = pd.read_csv(CSV_DIR)
    df = get_final_score(df, checkbox_group.value)
    df = df.sort_values(by="Selected Score", ascending=False)
    present_columns = MODEL_INFO + checkbox_group.value
    df = df[present_columns]
    df = convert_scores_to_percentage(df)
    return df

def get_baseline_df_quality():
    submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
    submission_repo.git_pull()
    df = pd.read_csv(QUALITY_DIR)
    df = get_final_score_quality(df, checkbox_group_quality.value)
    df = df.sort_values(by="Selected Score", ascending=False)
    present_columns = MODEL_INFO_TAB_QUALITY + checkbox_group_quality.value
    df = df[present_columns]
    df = convert_scores_to_percentage(df)
    return df

def get_all_df(selected_columns):
    submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
    submission_repo.git_pull()
    df = pd.read_csv(CSV_DIR)
    df = get_final_score(df, selected_columns)
    df = df.sort_values(by="Selected Score", ascending=False)
    return df

def convert_scores_to_percentage(df):
    # 对DataFrame中的每一列（除了'name'列）进行操作
    for column in df.columns[1:]:  # 假设第一列是'name'
        df[column] = round(df[column] * 100,2)  # 将分数转换为百分数
        df[column] = df[column].astype(str) + '%'
    return df

def choose_all_quailty():
    return gr.update(value=QUALITY_LIST)

def choose_all_semantic():
    return gr.update(value=SEMANTIC_LIST)

def disable_all():
    return gr.update(value=[])
    
def enable_all():
    return gr.update(value=TASK_INFO)

def on_filter_model_size_method_change(selected_columns):
    updated_data = get_all_df(selected_columns)
    #print(updated_data)
    # columns:
    selected_columns = [item for item in TASK_INFO if item in selected_columns]
    present_columns = MODEL_INFO + selected_columns
    updated_data = updated_data[present_columns]
    updated_data = updated_data.sort_values(by="Selected Score", ascending=False)
    updated_data = convert_scores_to_percentage(updated_data)
    updated_headers = present_columns
    update_datatype = [DATA_TITILE_TYPE[COLUMN_NAMES.index(x)] for x in updated_headers]
    # print(updated_data,present_columns,update_datatype)
    filter_component = gr.components.Dataframe(
        value=updated_data, 
        headers=updated_headers,
        type="pandas", 
        datatype=update_datatype,
        interactive=False,
        visible=True,
        )
    return filter_component#.value

def on_filter_model_size_method_change_quality(selected_columns):
    updated_data = get_baseline_df_quality()
    #print(updated_data)
    # columns:
    selected_columns = [item for item in QUALITY_TAB if item in selected_columns]
    present_columns = MODEL_INFO_TAB_QUALITY + selected_columns
    updated_data = updated_data[present_columns]
    updated_data = updated_data.sort_values(by="Selected Score", ascending=False)
    updated_data = convert_scores_to_percentage(updated_data)
    updated_headers = present_columns
    update_datatype = [DATA_TITILE_TYPE[COLUMN_NAMES.index(x)] for x in updated_headers]
    # print(updated_data,present_columns,update_datatype)
    filter_component = gr.components.Dataframe(
        value=updated_data, 
        headers=updated_headers,
        type="pandas", 
        datatype=update_datatype,
        interactive=False,
        visible=True,
        )
    return filter_component#.value


block = gr.Blocks()


with block:
    gr.Markdown(
        LEADERBORAD_INTRODUCTION
    )
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        # Table 0
        with gr.TabItem("📊 VBench", elem_id="vbench-tab-table", id=1):
            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",
                        lines=10,
                    )
    
            gr.Markdown(
                TABLE_INTRODUCTION
            )
            with gr.Row():
                with gr.Column(scale=0.2):
                    choosen_q = gr.Button("Select Quality Dimensions")
                    choosen_s = gr.Button("Select Semantic Dimensions")
                    # enable_b = gr.Button("Select All")
                    disable_b = gr.Button("Deselect All")

                with gr.Column(scale=0.8):
                    # selection for column part:
                    checkbox_group = gr.CheckboxGroup(
                        choices=TASK_INFO,
                        value=DEFAULT_INFO,
                        label="Evaluation Dimension",
                        interactive=True,
                    )

            data_component = gr.components.Dataframe(
                value=get_baseline_df, 
                headers=COLUMN_NAMES,
                type="pandas", 
                datatype=DATA_TITILE_TYPE,
                interactive=False,
                visible=True,
                )
    
            choosen_q.click(choose_all_quailty, inputs=None, outputs=[checkbox_group]).then(fn=on_filter_model_size_method_change, inputs=[ checkbox_group], outputs=data_component)
            choosen_s.click(choose_all_semantic, inputs=None, outputs=[checkbox_group]).then(fn=on_filter_model_size_method_change, inputs=[ checkbox_group], outputs=data_component)
            # enable_b.click(enable_all, inputs=None, outputs=[checkbox_group]).then(fn=on_filter_model_size_method_change, inputs=[ checkbox_group], outputs=data_component)
            disable_b.click(disable_all, inputs=None, outputs=[checkbox_group]).then(fn=on_filter_model_size_method_change, inputs=[ checkbox_group], outputs=data_component)
            checkbox_group.change(fn=on_filter_model_size_method_change, inputs=[ checkbox_group], outputs=data_component)

        with gr.TabItem("Video Quaity", elem_id="vbench-tab-table", id=2):
            with gr.Accordion("INSTRUCTION", open=False):
                    citation_button = gr.Textbox(
                        value=QUALITY_CLAIM_TEXT,
                        label="",
                        elem_id="quality-button",
                        lines=10,
                    )
            with gr.Row():
                with gr.Column(scale=1.0):
                    # selection for column part:
                    checkbox_group_quality = gr.CheckboxGroup(
                        choices=QUALITY_TAB,
                        value=QUALITY_TAB,
                        label="Evaluation Quality Dimension",
                        interactive=True,
                    )

            data_component_quality = gr.components.Dataframe(
                value=get_baseline_df_quality, 
                headers=COLUMN_NAMES_QUALITY,
                type="pandas", 
                datatype=DATA_TITILE_TYPE,
                interactive=False,
                visible=True,
                )
    
            checkbox_group_quality.change(fn=on_filter_model_size_method_change_quality, inputs=[checkbox_group_quality], outputs=data_component_quality)
            
        # table 2
        with gr.TabItem("📝 About", elem_id="mvbench-tab-table", id=3):
            gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text")
        
        # table 3 
        with gr.TabItem("🚀 Submit here! ", elem_id="mvbench-tab-table", id=4):
            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="LaVie"
                        )
                    revision_name_textbox = gr.Textbox(
                        label="Revision Model Name", placeholder="LaVie"
                    )

                with gr.Column():
                    model_link = gr.Textbox(
                        label="Model Link", placeholder="https://huggingface.co/decapoda-research/llama-7b-hf"
                    )


            with gr.Column():

                input_file = gr.components.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_link,
                    ],
                )


    def refresh_data():
        value1 = get_baseline_df()
        return value1

    with gr.Row():
        data_run = gr.Button("Refresh")
        data_run.click(on_filter_model_size_method_change, inputs=[checkbox_group], outputs=data_component)


block.launch()