__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions'] import os import io import gradio as gr import pandas as pd import json import shutil import tempfile import datetime import zipfile import numpy as np 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_type, model_name_textbox, model_ability, revision_name_textbox, access_type, model_link, team_name, contact_email, release_time, model_resolution, model_length, model_fps, model_frame, model_link_optional, ): if input_file is None: return "Error! Empty file!" if model_link == '' or model_name_textbox == '' or contact_email == '': return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True) submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset") submission_repo.git_pull() filename = f"{model_name_textbox}_{model_type}_{model_ability}_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}" now = datetime.datetime.now() update_time = now.strftime("%Y.%m.%d") # Capture update time csv_data = pd.read_csv(CSV_DIR) if revision_name_textbox == '': col = csv_data.shape[0] model_name = model_name_textbox.replace(',',' ') else: model_name = revision_name_textbox.replace(',',' ') model_name_list = csv_data['Model Name'] 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 csv_data[col]['Sampled by'] == "WorldScore" or csv_data[col]['Evaluated by'] == "WorldScore": return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True) if model_link == '': model_name = model_name # no url else: model_name = '[' + model_name + '](' + model_link + ')' if model_link_optional == '': model_link_optional = model_link try: with open(input_file, 'r') as f: upload_data = json.load(f) # add new data print('upload_data:', upload_data) new_data = [model_type, model_name, model_ability] if team_name == '': new_data.append(model_name) new_data.append(model_name) else: new_data.append(team_name) new_data.append(team_name) new_data.append(access_type) new_data.append(update_time) for key in TASK_INFO: value = COLNAME2KEY[key] if value in upload_data: new_data.append(upload_data[value]) else: return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True) csv_data.loc[col] = new_data csv_data = csv_data.to_csv(CSV_DIR, index=False) with open(INFO_DIR,'a') as f: f.write(f"{model_type.replace(',', ' ')}\t{model_name.replace(',', ' ')}\t{model_ability.replace(',', ' ')}\t{model_resolution.replace(',', ' ')}\t{model_length.replace(',', ' ')}\t{model_fps.replace(',', ' ')}\t{model_frame.replace(',', ' ')}\t{model_link_optional.replace(',', ' ')}\t{contact_email.replace(',', ' ')}\n") submission_repo.push_to_hub() print("success update", model_name) return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False) except: return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True) 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=DEFAULT_INFO[0], ascending=False) return df block = gr.Blocks(css=""" .container { max-width: 80%; margin: auto; } /* 添加以下样式来控制表格列宽 */ .gradio-dataframe { overflow-x: auto !important; } .gradio-dataframe table { width: 100% !important; white-space: nowrap !important; } .gradio-dataframe td, .gradio-dataframe th { min-width: fit-content !important; max-width: none !important; white-space: pre-wrap !important; padding: 8px !important; } """) with block: with gr.Column(elem_classes="container"): gr.Markdown( LEADERBORAD_INTRODUCTION ) with gr.Tabs(elem_classes="tab-buttons") as tabs: # Table 0 with gr.TabItem("🏅 WorldScore Benchmark", elem_id="worldscore-tab-table", id=0): with gr.Row(): with gr.Column(scale=0.3): model_type_filter = gr.CheckboxGroup( choices=MODEL_TYPE, value=DEFAULT_MODEL_TYPE, label="Model Type", interactive=True ) ability_filter = gr.CheckboxGroup( choices=ABILITY, value=DEFAULT_ABILITY, label="Ability", interactive=True ) with gr.Column(scale=0.7): sort_by_filter = gr.Radio( choices=TASK_INFO, value=DEFAULT_INFO[0], label="Sort by", interactive=True ) df = get_baseline_df() data_component = gr.components.Dataframe( value=df, headers=COLUMN_NAMES, type="pandas", datatype=DATA_TITILE_TYPE, interactive=False, visible=True, wrap=True ) def on_filter_change(model_types, abilities, sort_by): df = get_baseline_df() # Filter by selected model types df = df[df['Model Type'].isin(model_types)] # Filter by selected abilities df = df[df['Ability'].isin(abilities)] # Sort by selected sort by df = df.sort_values(by=sort_by, ascending=False) return gr.Dataframe( value=df, headers=COLUMN_NAMES, type="pandas", datatype=DATA_TITILE_TYPE, interactive=False, visible=True, wrap=True ) model_type_filter.change( fn=on_filter_change, inputs=[model_type_filter, ability_filter, sort_by_filter], outputs=data_component ) ability_filter.change( fn=on_filter_change, inputs=[model_type_filter, ability_filter, sort_by_filter], outputs=data_component ) sort_by_filter.change( fn=on_filter_change, inputs=[model_type_filter, ability_filter, sort_by_filter], outputs=data_component ) with gr.TabItem("🚀 Submit here! ", elem_id="submit-tab-table", id=1): with gr.Row(): gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text") with gr.Row(): gr.Markdown("# Submit your evaluation json file here!", elem_classes="markdown-text") with gr.Row(): gr.Markdown("Here is a required field", elem_classes="markdown-text") with gr.Row(): with gr.Column(): model_type = gr.Dropdown(["Video", "3D", "4D"], label="Model Type") model_name_textbox = gr.Textbox( label="Model name", placeholder="Your model name" ) model_ability = gr.Dropdown(["I2V", "T2V"], label="Model Ability") revision_name_textbox = gr.Textbox( label="Revision Model Name (Optional)", placeholder="If you need to update the previous submissions, please fill in this line" ) with gr.Column(): access_type = gr.Dropdown(["Open Source", "Ready to Open Source", "API", "Close"], label="Access") model_link = gr.Textbox( label="Link (Website/Paper Link/Github/HuggingFace)", placeholder="If filling in the wrong information, your results may be removed." ) team_name = gr.Textbox( label="Your Team Name", placeholder="If left blank, it will be your model name" ) contact_email = gr.Textbox( label="E-Mail (Will not be displayed)", placeholder="Contact email" ) with gr.Row(): gr.Markdown("The following is optional and will be synced to [GitHub](https://github.com/haoyi-duan/WorldScore/blob/main/README.md#world-generation-models-info)", elem_classes="markdown-text") with gr.Row(): release_time = gr.Textbox(label="Version", placeholder="2025.03.29") model_resolution = gr.Textbox(label="Resolution", placeholder="WidthxHeight") model_length = gr.Textbox(label="Video Length (s)", placeholder="float") model_fps = gr.Textbox(label="FPS", placeholder="int") model_frame = gr.Textbox(label="Frame Number", placeholder="int") model_link_optional = gr.Textbox(label="Link", placeholder='optional') with gr.Column(): input_file = gr.components.File(label = "Click to Upload a JSON File", file_count="single", file_types=[".json"]) submit_button = gr.Button("Submit Eval") submit_succ_button = gr.Markdown("Submit Success! Please press refresh and return to WorldScore Benchmark!", visible=False) fail_textbox = gr.Markdown('Please ensure that the `Model Name`, `Project Page`, and `E-mail` are filled in correctly and the uploaded json file is valid.', elem_classes="markdown-text",visible=False) submission_result = gr.Markdown() submit_button.click( add_new_eval, inputs = [ input_file, model_type, model_name_textbox, model_ability, revision_name_textbox, access_type, model_link, team_name, contact_email, release_time, model_resolution, model_length, model_fps, model_frame, model_link_optional, ], outputs=[submit_button, submit_succ_button, fail_textbox] ) def refresh_data(): model_type_filter.value = DEFAULT_MODEL_TYPE ability_filter.value = DEFAULT_ABILITY sort_by_filter.value = DEFAULT_INFO[0] df = get_baseline_df() df = df[df['Model Type'].isin(model_type_filter.value)] df = df[df['Ability'].isin(ability_filter.value)] df = df.sort_values(by=sort_by_filter.value, ascending=False) data_component = gr.Dataframe( value=df, headers=COLUMN_NAMES, type="pandas", datatype=DATA_TITILE_TYPE, interactive=False, visible=True, wrap=True ) return data_component, model_type_filter, ability_filter, sort_by_filter with gr.Row(elem_classes="container"): with gr.Column(): data_run = gr.Button("Refresh") data_run.click( refresh_data, inputs=[], outputs=[data_component, model_type_filter, ability_filter, sort_by_filter] ) block.launch()