__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()