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| __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) | |
| block.launch() |