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| __all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions'] | |
| import gradio as gr | |
| import pandas as pd | |
| import json | |
| import io | |
| from constants import * | |
| global data_component, data_component_150, filter_component | |
| def upload_file(files): | |
| file_paths = [file.name for file in files] | |
| return file_paths | |
| def compute_scores(input_data): | |
| return [None, [ | |
| input_data["Average_MTScore"], | |
| input_data["Average_CHScore"], | |
| input_data["Average_GPT4o-MTScore"], | |
| input_data["Average_UMT-FVD"], | |
| input_data["Average_UMTScore"] | |
| ]] | |
| def add_new_eval( | |
| input_file, | |
| model_name_textbox: str, | |
| revision_name_textbox: str, | |
| backbone_type_dropdown: str, | |
| model_link: str, | |
| ): | |
| if input_file is None: | |
| return "Error! Empty file!" | |
| else: | |
| input_json = json.load(io.BytesIO(input_file)) | |
| if model_name_textbox not in input_json: | |
| return f"Error! Model '{model_name_textbox}' not found in input file!" | |
| selected_model_data = input_json[model_name_textbox] | |
| scores = compute_scores(selected_model_data) | |
| input_data = scores[1] | |
| input_data = [float(i) for i in input_data] | |
| csv_data = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH) | |
| 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 + ')' | |
| backbone = backbone_type_dropdown | |
| new_data = [ | |
| model_name, | |
| backbone, | |
| input_data[3], | |
| input_data[4], | |
| input_data[0], | |
| input_data[1], | |
| input_data[2], | |
| ] | |
| csv_data.loc[col] = new_data | |
| csv_data.to_csv(CSV_DIR_CHRONOMAGIC_BENCH, index=False) | |
| return "Evaluation successfully submitted!" | |
| def add_new_eval_150( | |
| input_file, | |
| model_name_textbox: str, | |
| revision_name_textbox: str, | |
| backbone_type_dropdown: str, | |
| model_link: str, | |
| ): | |
| if input_file is None: | |
| return "Error! Empty file!" | |
| else: | |
| input_json = json.load(io.BytesIO(input_file)) | |
| if model_name_textbox not in input_json: | |
| return f"Error! Model '{model_name_textbox}' not found in input file!" | |
| selected_model_data = input_json[model_name_textbox] | |
| scores = compute_scores(selected_model_data) | |
| input_data = scores[1] | |
| input_data = [float(i) for i in input_data] | |
| csv_data = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH_150) | |
| 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 + ')' | |
| backbone = backbone_type_dropdown | |
| new_data = [ | |
| model_name, | |
| backbone, | |
| input_data[3], | |
| input_data[4], | |
| input_data[0], | |
| input_data[1], | |
| input_data[2], | |
| ] | |
| csv_data.loc[col] = new_data | |
| csv_data.to_csv(CSV_DIR_CHRONOMAGIC_BENCH_150, index=False) | |
| return "Evaluation (150) successfully submitted!" | |
| def get_baseline_df(): | |
| df = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH) | |
| df = df.sort_values(by="MTScore↑", ascending=False) | |
| present_columns = MODEL_INFO + checkbox_group.value | |
| df = df[present_columns] | |
| return df | |
| def get_baseline_df_150(): | |
| df = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH_150) | |
| df = df.sort_values(by="MTScore↑", ascending=False) | |
| present_columns = MODEL_INFO + checkbox_group_150.value | |
| df = df[present_columns] | |
| return df | |
| def get_all_df(): | |
| df = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH) | |
| df = df.sort_values(by="MTScore↑", ascending=False) | |
| return df | |
| def get_all_df_150(): | |
| df = pd.read_csv(CSV_DIR_CHRONOMAGIC_BENCH_150) | |
| df = df.sort_values(by="MTScore↑", ascending=False) | |
| return df | |
| block = gr.Blocks() | |
| with block: | |
| gr.Markdown( | |
| LEADERBORAD_INTRODUCTION | |
| ) | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| # table 1 | |
| with gr.TabItem("🏅 ChronoMagic-Bench", elem_id="ChronoMagic-Bench-tab-table", id=0): | |
| 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", | |
| show_copy_button=True | |
| ) | |
| gr.Markdown( | |
| TABLE_INTRODUCTION | |
| ) | |
| checkbox_group = gr.CheckboxGroup( | |
| choices=ALL_RESULTS, | |
| value=SELECTED_RESULTS, | |
| label="Select options", | |
| interactive=True, | |
| ) | |
| 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): | |
| selected_columns = [item for item in ALL_RESULTS 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 | |
| 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, | |
| ) | |
| return filter_component | |
| checkbox_group.change(fn=on_checkbox_group_change, inputs=checkbox_group, outputs=data_component) | |
| # table 2 | |
| with gr.TabItem("🏅 ChronoMagic-Bench-150", elem_id="ChronoMagic-Bench-150-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", | |
| show_copy_button=True | |
| ) | |
| gr.Markdown( | |
| TABLE_INTRODUCTION | |
| ) | |
| checkbox_group_150 = gr.CheckboxGroup( | |
| choices=ALL_RESULTS, | |
| value=SELECTED_RESULTS_150, | |
| label="Select options", | |
| interactive=True, | |
| ) | |
| data_component_150 = gr.components.Dataframe( | |
| value=get_baseline_df_150, | |
| headers=COLUMN_NAMES, | |
| type="pandas", | |
| datatype=DATA_TITILE_TYPE, | |
| interactive=False, | |
| visible=True, | |
| ) | |
| def on_checkbox_group_150_change(selected_columns): | |
| selected_columns = [item for item in ALL_RESULTS if item in selected_columns] | |
| present_columns = MODEL_INFO + selected_columns | |
| updated_data = get_all_df_150()[present_columns] | |
| updated_data = updated_data.sort_values(by=present_columns[1], ascending=False) | |
| updated_headers = present_columns | |
| 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, | |
| ) | |
| return filter_component | |
| checkbox_group_150.change(fn=on_checkbox_group_150_change, inputs=checkbox_group_150, outputs=data_component_150) | |
| # table 3 | |
| with gr.TabItem("🚀 Submit here! ", elem_id="seed-benchmark-tab-table", id=2): | |
| 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="MagicTime" | |
| ) | |
| revision_name_textbox = gr.Textbox( | |
| label="Revision Model Name", placeholder="MagicTime" | |
| ) | |
| backbone_type_dropdown = gr.Dropdown( | |
| label="Backbone Type", | |
| choices=["DiT", "U-Net"], | |
| value="DiT" | |
| ) | |
| model_link = gr.Textbox( | |
| label="Model Link", placeholder="https://github.com/PKU-YuanGroup/MagicTime" | |
| ) | |
| with gr.Column(): | |
| input_file = gr.File(label="Click to Upload a json File", type='binary') | |
| submit_button = gr.Button("Submit Eval (ChronoMagic-Bench)") | |
| submit_button_150 = gr.Button("Submit Eval (ChronoMagic-Bench-150)") | |
| submission_result = gr.Markdown() | |
| submit_button.click( | |
| add_new_eval, | |
| inputs=[ | |
| input_file, | |
| model_name_textbox, | |
| revision_name_textbox, | |
| backbone_type_dropdown, | |
| model_link, | |
| ], | |
| outputs=submission_result, | |
| ) | |
| submit_button_150.click( | |
| add_new_eval_150, | |
| inputs=[ | |
| input_file, | |
| model_name_textbox, | |
| revision_name_textbox, | |
| backbone_type_dropdown, | |
| model_link, | |
| ], | |
| outputs = submission_result, | |
| ) | |
| with gr.Row(): | |
| data_run = gr.Button("Refresh") | |
| data_run.click( | |
| get_baseline_df, outputs=data_component | |
| ) | |
| data_run.click( | |
| get_baseline_df_150, outputs=data_component_150 | |
| ) | |
| block.launch() |