__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, num_frame: 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, num_frame, 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], input_data[25], input_data[26], input_data[27], input_data[28], input_data[29], 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 '-')" ) num_frame = gr.Textbox( label="Frames", placeholder="The number of frames sampled from video, 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, num_frame, 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()