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| __all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions'] | |
| import gradio as gr | |
| import pandas as pd | |
| import re | |
| import pdb | |
| import tempfile | |
| import os | |
| from constants import * | |
| from src.compute import compute_scores | |
| global data_component, filter_component | |
| from huggingface_hub import HfApi | |
| hf_token = os.getenv('HF_TOKEN') | |
| api = HfApi(token=hf_token) | |
| def validate_model_size(s): | |
| pattern = r'^\d+B$|^-$' | |
| if re.match(pattern, s): | |
| return s | |
| else: | |
| return '-' | |
| 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, | |
| model_type: str, | |
| model_size: str, | |
| notes: str, | |
| ): | |
| if input_file is None: | |
| return "Error! Empty file!" | |
| else: | |
| model_size = validate_model_size(model_size) | |
| input_file = compute_scores(input_file) | |
| input_data = input_file[1] | |
| input_data = [float(i) for i in input_data] | |
| csv_data = pd.read_csv(CSV_DIR) | |
| if revision_name_textbox == '': | |
| col = csv_data.shape[0] | |
| model_name = model_name_textbox | |
| name_list = [name.split(']')[0][1:] if name.endswith(')') else name for name in csv_data['Model']] | |
| assert model_name not in name_list | |
| else: | |
| model_name = revision_name_textbox | |
| model_name_list = csv_data['Model'] | |
| name_list = [name.split(']')[0][1:] if name.endswith(')') else name 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, | |
| model_type, | |
| model_size, | |
| input_data[0], | |
| input_data[1], | |
| input_data[2], | |
| input_data[3], | |
| input_data[4], | |
| input_data[5], | |
| input_data[6], | |
| input_data[7], | |
| input_data[8], | |
| input_data[9], | |
| input_data[10], | |
| input_data[11], | |
| input_data[12], | |
| input_data[13], | |
| input_data[14], | |
| input_data[15], | |
| input_data[16], | |
| input_data[17], | |
| input_data[18], | |
| input_data[19], | |
| input_data[20], | |
| input_data[21], | |
| input_data[22], | |
| input_data[23], | |
| input_data[24], | |
| notes, | |
| ] | |
| # print(len(new_data), col) | |
| # print(csv_data.loc[col]) | |
| # print(model_name, model_type, model_size) | |
| csv_data.loc[col] = new_data | |
| # with open(f'./file/{model_name}.json','w' ,encoding='utf-8') as f: | |
| # json.dump(new_data, f) | |
| csv_data.to_csv(CSV_DIR, index=False) | |
| # push newly added result | |
| api.upload_file( | |
| path_or_fileobj=CSV_DIR, | |
| path_in_repo=CSV_DIR, | |
| repo_id="lyx97/TempCompass", | |
| repo_type="space", | |
| ) | |
| return 0 | |
| def get_baseline_df(): | |
| # pdb.set_trace() | |
| df = pd.read_csv(CSV_DIR) | |
| df = df.sort_values(by="Avg. All", ascending=False) | |
| present_columns = MODEL_INFO + checkbox_group.value | |
| df = df[present_columns] | |
| return df | |
| def get_all_df(): | |
| df = pd.read_csv(CSV_DIR) | |
| df = df.sort_values(by="Avg. All", ascending=False) | |
| return df | |
| block = gr.Blocks() | |
| with block: | |
| gr.Markdown( | |
| LEADERBORAD_INTRODUCTION | |
| ) | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| with gr.TabItem("🏅 TempCompass Benchmark", elem_id="video-benchmark-tab-table", id=0): | |
| gr.Markdown( | |
| TABLE_INTRODUCTION | |
| ) | |
| # selection for column part: | |
| checkbox_group = gr.CheckboxGroup( | |
| choices=TASK_INFO, | |
| value=AVG_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_all_df()[present_columns] | |
| updated_data = updated_data.sort_values(by=present_columns[1], ascending=False) | |
| updated_headers = present_columns | |
| print(updated_headers) | |
| print([COLUMN_NAMES.index(x) for x in updated_headers]) | |
| 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.constructor_args['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="Video-LLaVA-7B" | |
| ) | |
| revision_name_textbox = gr.Textbox( | |
| label="Revision Model Name", placeholder="Video-LLaVA-7B" | |
| ) | |
| model_link = gr.Textbox( | |
| label="Model Link", placeholder="https://huggingface.co/LanguageBind/Video-LLaVA-7B" | |
| ) | |
| model_type = gr.Dropdown( | |
| choices=[ | |
| "LLM", | |
| "ImageLLM", | |
| "VideoLLM", | |
| "Other", | |
| ], | |
| label="Model type", | |
| multiselect=False, | |
| value=None, | |
| interactive=True, | |
| ) | |
| model_size = gr.Textbox( | |
| label="Model size", placeholder="7B(Input content format must be 'number+B' or '-', default is '-')" | |
| ) | |
| notes = gr.Textbox( | |
| label="Notes", placeholder="Other details of the model or evaluation, e.g., which answer prompt is used." | |
| ) | |
| with gr.Column(): | |
| input_file = gr.File(label="Click to Upload a json File", 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, | |
| model_type, | |
| model_size, | |
| notes, | |
| ], | |
| # outputs = submission_result, | |
| ) | |
| with gr.Row(): | |
| data_run = gr.Button("Refresh") | |
| data_run.click( | |
| get_baseline_df, outputs=data_component | |
| ) | |
| 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, | |
| ) | |
| # block.load(get_baseline_df, outputs=data_title) | |
| block.launch() |