EmoBench

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# import gradio as gr
# from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
# import pandas as pd
# from apscheduler.schedulers.background import BackgroundScheduler
# from huggingface_hub import snapshot_download

# from src.about import (
#     CITATION_BUTTON_LABEL,
#     CITATION_BUTTON_TEXT,
#     EVALUATION_QUEUE_TEXT,
#     INTRODUCTION_TEXT,
#     LLM_BENCHMARKS_TEXT,
#     TITLE,
# )
# from src.display.css_html_js import custom_css
# from src.display.utils import (
#     BENCHMARK_COLS,
#     COLS,
#     EVAL_COLS,
#     EVAL_TYPES,
#     AutoEvalColumn,
#     ModelType,
#     fields,
#     WeightType,
#     Precision
# )
# from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
# from src.populate import get_evaluation_queue_df, get_leaderboard_df
# from src.submission.submit import add_new_eval


# def restart_space():
#     API.restart_space(repo_id=REPO_ID)

# ### Space initialisation
# try:
#     print(EVAL_REQUESTS_PATH)
#     snapshot_download(
#         repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
#     )
# except Exception:
#     restart_space()
# try:
#     print(EVAL_RESULTS_PATH)
#     snapshot_download(
#         repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
#     )
# except Exception:
#     restart_space()


# LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)

# (
#     finished_eval_queue_df,
#     running_eval_queue_df,
#     pending_eval_queue_df,
# ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

# def init_leaderboard(dataframe):
#     if dataframe is None or dataframe.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")
#     return Leaderboard(
#         value=dataframe,
#         datatype=[c.type for c in fields(AutoEvalColumn)],
#         select_columns=SelectColumns(
#             default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
#             cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
#             label="Select Columns to Display:",
#         ),
#         search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
#         hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
#         filter_columns=[
#             ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
#             ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
#             ColumnFilter(
#                 AutoEvalColumn.params.name,
#                 type="slider",
#                 min=0.01,
#                 max=150,
#                 label="Select the number of parameters (B)",
#             ),
#             ColumnFilter(
#                 AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
#             ),
#         ],
#         bool_checkboxgroup_label="Hide models",
#         interactive=False,
#     )


# demo = gr.Blocks(css=custom_css)
# with demo:
#     gr.HTML(TITLE)
#     gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

#     with gr.Tabs(elem_classes="tab-buttons") as tabs:
#         with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
#             leaderboard = init_leaderboard(LEADERBOARD_DF)

#         with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
#             gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

#         with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
#             with gr.Column():
#                 with gr.Row():
#                     gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")

#                 with gr.Column():
#                     with gr.Accordion(
#                         f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
#                         open=False,
#                     ):
#                         with gr.Row():
#                             finished_eval_table = gr.components.Dataframe(
#                                 value=finished_eval_queue_df,
#                                 headers=EVAL_COLS,
#                                 datatype=EVAL_TYPES,
#                                 row_count=5,
#                             )
#                     with gr.Accordion(
#                         f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
#                         open=False,
#                     ):
#                         with gr.Row():
#                             running_eval_table = gr.components.Dataframe(
#                                 value=running_eval_queue_df,
#                                 headers=EVAL_COLS,
#                                 datatype=EVAL_TYPES,
#                                 row_count=5,
#                             )

#                     with gr.Accordion(
#                         f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
#                         open=False,
#                     ):
#                         with gr.Row():
#                             pending_eval_table = gr.components.Dataframe(
#                                 value=pending_eval_queue_df,
#                                 headers=EVAL_COLS,
#                                 datatype=EVAL_TYPES,
#                                 row_count=5,
#                             )
#             with gr.Row():
#                 gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")

#             with gr.Row():
#                 with gr.Column():
#                     model_name_textbox = gr.Textbox(label="Model name")
#                     revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
#                     model_type = gr.Dropdown(
#                         choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
#                         label="Model type",
#                         multiselect=False,
#                         value=None,
#                         interactive=True,
#                     )

#                 with gr.Column():
#                     precision = gr.Dropdown(
#                         choices=[i.value.name for i in Precision if i != Precision.Unknown],
#                         label="Precision",
#                         multiselect=False,
#                         value="float16",
#                         interactive=True,
#                     )
#                     weight_type = gr.Dropdown(
#                         choices=[i.value.name for i in WeightType],
#                         label="Weights type",
#                         multiselect=False,
#                         value="Original",
#                         interactive=True,
#                     )
#                     base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")

#             submit_button = gr.Button("Submit Eval")
#             submission_result = gr.Markdown()
#             submit_button.click(
#                 add_new_eval,
#                 [
#                     model_name_textbox,
#                     base_model_name_textbox,
#                     revision_name_textbox,
#                     precision,
#                     weight_type,
#                     model_type,
#                 ],
#                 submission_result,
#             )

#     with gr.Row():
#         with gr.Accordion("📙 Citation", open=False):
#             citation_button = gr.Textbox(
#                 value=CITATION_BUTTON_TEXT,
#                 label=CITATION_BUTTON_LABEL,
#                 lines=20,
#                 elem_id="citation-button",
#                 show_copy_button=True,
#             )

# scheduler = BackgroundScheduler()
# scheduler.add_job(restart_space, "interval", seconds=1800)
# scheduler.start()
# demo.queue(default_concurrency_limit=40).launch()
__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 download_csv():
    # pull the results and return this file!
    submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
    submission_repo.git_pull()
    return CSV_DIR, gr.update(visible=True)

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_type: str,
    model_link: str,
    model_size: str,
    LLM_type: str,
    LLM_name_textbox: 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()
    csv_data = pd.read_csv(CSV_DIR)

    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
        ]
    for key in TASK_INFO:
        if key in upload_data:
            new_data.append(upload_data[key])
        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_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 = df.sort_values(by="Avg", ascending=False)
    present_columns = MODEL_INFO + checkbox_group.value
    df = df[present_columns]
    return df

def get_all_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 = df.sort_values(by="Avg", ascending=False)
    return df

def on_filter_model_size_method_change(selected_columns):
    updated_data = get_all_df()

    # columns:
    selected_columns = [item for item in TASK_INFO if item in selected_columns]
    present_columns = MODEL_INFO + selected_columns
    # print("selected_columns",'|'.join(selected_columns))
    updated_data = updated_data[present_columns]
    updated_data = updated_data.sort_values(by=selected_columns[0], ascending=False)
    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:
        with gr.TabItem("📊 MVBench", elem_id="mvbench-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
            )

            # selection for column part:
            checkbox_group = gr.CheckboxGroup(
                choices=TASK_INFO,
                value=AVG_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,
                )


            checkbox_group.change(fn=on_filter_model_size_method_change, inputs=[ checkbox_group], outputs=data_component)

        # table 2
        with gr.TabItem("📝 About", elem_id="mvbench-tab-table", id=2):
            gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text")
        
        # table 3 
        with gr.TabItem("🚀 Submit here! ", elem_id="mvbench-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-7B"
                    )
                    model_type = gr.Dropdown(
                        choices=[                         
                            "LLM",
                            "ImageLLM",
                            "VideoLLM",
                            "Other", 
                        ], 
                        label="Model type", 
                        multiselect=False,
                        value="ImageLLM",
                        interactive=True,
                    )
                    


                with gr.Column():
                    LLM_type = gr.Dropdown(
                        choices=["Vicuna-7B", "Flan-T5-XL", "LLaMA-7B", "InternLM-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"
                    )
                    model_link = gr.Textbox(
                        label="Model Link", placeholder="https://huggingface.co/decapoda-research/llama-7b-hf"
                    )
                    model_size = gr.Textbox(
                        label="Model size", placeholder="7B(Input content format must be 'number+B' or '-')"
                    )

            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_type,
                        model_link,
                        model_size,
                        LLM_type,
                        LLM_name_textbox,
                    ],
                )


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

    with gr.Row():
        data_run = gr.Button("Refresh")
    with gr.Row():
        result_download = gr.Button("Download Leaderboard")
        file_download = gr.File(label="download the csv of leaderborad.", visible=False)
        data_run.click(on_filter_model_size_method_change, inputs=[checkbox_group], outputs=data_component)
        result_download.click(download_csv, inputs=None, outputs= [file_download,file_download])


block.launch()

	# import gradio as gr
	# from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
	# import pandas as pd
	# from apscheduler.schedulers.background import BackgroundScheduler
	# from huggingface_hub import snapshot_download

	# from src.about import (
	# CITATION_BUTTON_LABEL,
	# CITATION_BUTTON_TEXT,
	# EVALUATION_QUEUE_TEXT,
	# INTRODUCTION_TEXT,
	# LLM_BENCHMARKS_TEXT,
	# TITLE,
	# )
	# from src.display.css_html_js import custom_css
	# from src.display.utils import (
	# BENCHMARK_COLS,
	# COLS,
	# EVAL_COLS,
	# EVAL_TYPES,
	# AutoEvalColumn,
	# ModelType,
	# fields,
	# WeightType,
	# Precision
	# )
	# from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
	# from src.populate import get_evaluation_queue_df, get_leaderboard_df
	# from src.submission.submit import add_new_eval


	# def restart_space():
	# API.restart_space(repo_id=REPO_ID)

	# ### Space initialisation
	# try:
	# print(EVAL_REQUESTS_PATH)
	# snapshot_download(
	# repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
	# )
	# except Exception:
	# restart_space()
	# try:
	# print(EVAL_RESULTS_PATH)
	# snapshot_download(
	# repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
	# )
	# except Exception:
	# restart_space()


	# LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)

	# (
	# finished_eval_queue_df,
	# running_eval_queue_df,
	# pending_eval_queue_df,
	# ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

	# def init_leaderboard(dataframe):
	# if dataframe is None or dataframe.empty:
	# raise ValueError("Leaderboard DataFrame is empty or None.")
	# return Leaderboard(
	# value=dataframe,
	# datatype=[c.type for c in fields(AutoEvalColumn)],
	# select_columns=SelectColumns(
	# default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
	# cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
	# label="Select Columns to Display:",
	# ),
	# search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
	# hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
	# filter_columns=[
	# ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
	# ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
	# ColumnFilter(
	# AutoEvalColumn.params.name,
	# type="slider",
	# min=0.01,
	# max=150,
	# label="Select the number of parameters (B)",
	# ),
	# ColumnFilter(
	# AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
	# ),
	# ],
	# bool_checkboxgroup_label="Hide models",
	# interactive=False,
	# )


	# demo = gr.Blocks(css=custom_css)
	# with demo:
	# gr.HTML(TITLE)
	# gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

	# with gr.Tabs(elem_classes="tab-buttons") as tabs:
	# with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
	# leaderboard = init_leaderboard(LEADERBOARD_DF)

	# with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
	# gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

	# with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
	# with gr.Column():
	# with gr.Row():
	# gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")

	# with gr.Column():
	# with gr.Accordion(
	# f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
	# open=False,
	# ):
	# with gr.Row():
	# finished_eval_table = gr.components.Dataframe(
	# value=finished_eval_queue_df,
	# headers=EVAL_COLS,
	# datatype=EVAL_TYPES,
	# row_count=5,
	# )
	# with gr.Accordion(
	# f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
	# open=False,
	# ):
	# with gr.Row():
	# running_eval_table = gr.components.Dataframe(
	# value=running_eval_queue_df,
	# headers=EVAL_COLS,
	# datatype=EVAL_TYPES,
	# row_count=5,
	# )

	# with gr.Accordion(
	# f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
	# open=False,
	# ):
	# with gr.Row():
	# pending_eval_table = gr.components.Dataframe(
	# value=pending_eval_queue_df,
	# headers=EVAL_COLS,
	# datatype=EVAL_TYPES,
	# row_count=5,
	# )
	# with gr.Row():
	# gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")

	# with gr.Row():
	# with gr.Column():
	# model_name_textbox = gr.Textbox(label="Model name")
	# revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
	# model_type = gr.Dropdown(
	# choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
	# label="Model type",
	# multiselect=False,
	# value=None,
	# interactive=True,
	# )

	# with gr.Column():
	# precision = gr.Dropdown(
	# choices=[i.value.name for i in Precision if i != Precision.Unknown],
	# label="Precision",
	# multiselect=False,
	# value="float16",
	# interactive=True,
	# )
	# weight_type = gr.Dropdown(
	# choices=[i.value.name for i in WeightType],
	# label="Weights type",
	# multiselect=False,
	# value="Original",
	# interactive=True,
	# )
	# base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")

	# submit_button = gr.Button("Submit Eval")
	# submission_result = gr.Markdown()
	# submit_button.click(
	# add_new_eval,
	# [
	# model_name_textbox,
	# base_model_name_textbox,
	# revision_name_textbox,
	# precision,
	# weight_type,
	# model_type,
	# ],
	# submission_result,
	# )

	# with gr.Row():
	# with gr.Accordion("📙 Citation", open=False):
	# citation_button = gr.Textbox(
	# value=CITATION_BUTTON_TEXT,
	# label=CITATION_BUTTON_LABEL,
	# lines=20,
	# elem_id="citation-button",
	# show_copy_button=True,
	# )

	# scheduler = BackgroundScheduler()
	# scheduler.add_job(restart_space, "interval", seconds=1800)
	# scheduler.start()
	# demo.queue(default_concurrency_limit=40).launch()
	__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 download_csv():
	# pull the results and return this file!
	submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
	submission_repo.git_pull()
	return CSV_DIR, gr.update(visible=True)

	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_type: str,
	model_link: str,
	model_size: str,
	LLM_type: str,
	LLM_name_textbox: 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()
	csv_data = pd.read_csv(CSV_DIR)

	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
	]
	for key in TASK_INFO:
	if key in upload_data:
	new_data.append(upload_data[key])
	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_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 = df.sort_values(by="Avg", ascending=False)
	present_columns = MODEL_INFO + checkbox_group.value
	df = df[present_columns]
	return df

	def get_all_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 = df.sort_values(by="Avg", ascending=False)
	return df

	def on_filter_model_size_method_change(selected_columns):
	updated_data = get_all_df()

	# columns:
	selected_columns = [item for item in TASK_INFO if item in selected_columns]
	present_columns = MODEL_INFO + selected_columns
	# print("selected_columns",'\|'.join(selected_columns))
	updated_data = updated_data[present_columns]
	updated_data = updated_data.sort_values(by=selected_columns[0], ascending=False)
	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:
	with gr.TabItem("📊 MVBench", elem_id="mvbench-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
	)

	# selection for column part:
	checkbox_group = gr.CheckboxGroup(
	choices=TASK_INFO,
	value=AVG_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,
	)


	checkbox_group.change(fn=on_filter_model_size_method_change, inputs=[ checkbox_group], outputs=data_component)

	# table 2
	with gr.TabItem("📝 About", elem_id="mvbench-tab-table", id=2):
	gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text")

	# table 3
	with gr.TabItem("🚀 Submit here! ", elem_id="mvbench-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-7B"
	)
	model_type = gr.Dropdown(
	choices=[
	"LLM",
	"ImageLLM",
	"VideoLLM",
	"Other",
	],
	label="Model type",
	multiselect=False,
	value="ImageLLM",
	interactive=True,
	)



	with gr.Column():
	LLM_type = gr.Dropdown(
	choices=["Vicuna-7B", "Flan-T5-XL", "LLaMA-7B", "InternLM-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"
	)
	model_link = gr.Textbox(
	label="Model Link", placeholder="https://huggingface.co/decapoda-research/llama-7b-hf"
	)
	model_size = gr.Textbox(
	label="Model size", placeholder="7B(Input content format must be 'number+B' or '-')"
	)

	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_type,
	model_link,
	model_size,
	LLM_type,
	LLM_name_textbox,
	],
	)


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

	with gr.Row():
	data_run = gr.Button("Refresh")
	with gr.Row():
	result_download = gr.Button("Download Leaderboard")
	file_download = gr.File(label="download the csv of leaderborad.", visible=False)
	data_run.click(on_filter_model_size_method_change, inputs=[checkbox_group], outputs=data_component)
	result_download.click(download_csv, inputs=None, outputs= [file_download,file_download])


	block.launch()