import spaces import gradio as gr from hfsearch import (HFSearchResult, search, update_filter, update_df, get_labels, get_valid_labels, get_tags, get_subtag_categories, update_subtag_items, update_tags, update_subtags, search_ref_repos, search_cols, DS_SIZE_CATEGORIES, SPACE_HARDWARES, SPACE_STAGES, TYPES_DESC) from gradio_huggingfacehub_search import HuggingfaceHubSearch CSS = """ .title { align-items: center; text-align: center; } .info { align-items: center; text-align: center; } """ with gr.Blocks(theme="NoCrypt/miku", fill_width=True, css=CSS) as demo: gr.Markdown("# Search Hugging Face🤗", elem_classes="title") with gr.Column(): search_result = gr.State(value=HFSearchResult()) with gr.Tab("Basic Search"): with gr.Group(): with gr.Row(equal_height=True): repo_types = gr.CheckboxGroup(label="Repo type", choices=["model", "dataset", "space", "collection"], value=["model", "dataset", "space"]) filter_str = gr.Textbox(label="Filter", info="String(s) to filter repos", value="") sort = gr.Dropdown(label="Sort", choices=["last_modified", "likes", "downloads", "trending_score"], value="likes") with gr.Accordion("Advanced", open=False): ui_mode = gr.Radio(label="Mode", choices=["PC", "Phone"], value="Phone") with gr.Row(equal_height=True): search_str = gr.Textbox(label="Search", info="A string that will be contained in the returned repo ids", placeholder="bert", value="", lines=1) #author = gr.Textbox(label="Author", info="The author (user or organization)", value="", lines=1) author = HuggingfaceHubSearch(label="Author", placeholder="The author (user or organization)", search_type="user", sumbit_on_select=False) followed = HuggingfaceHubSearch(label="Followed by", placeholder="Extract repos followed by this user", search_type="user", sumbit_on_select=False) with gr.Column(): tags = gr.Textbox(label="Tags", info="Tag(s) to filter repos", value="") with gr.Accordion("Tag input assistance", open=False): with gr.Row(equal_height=True): tag_item = gr.Dropdown(label="Item", choices=get_tags(), value=get_tags()[0], allow_custom_value=True, scale=4) tag_btn = gr.Button("Add", scale=1) with gr.Row(equal_height=True): subtag_cat = gr.Dropdown(label="Category", choices=get_subtag_categories(), value=get_subtag_categories()[0], scale=2) subtag_item = gr.Dropdown(label="Item", choices=[""], value="", allow_custom_value=True, scale=2) subtug_btn = gr.Button("Add", scale=1) with gr.Row(equal_height=True): with gr.Column(): gated_status = gr.Radio(label="Gated status", choices=["gated", "non-gated", "all"], value="all") appr_status = gr.CheckboxGroup(label="Approval method", choices=["auto", "manual"], value=["auto", "manual"]) limit = gr.Number(label="Limit", info="If 0, fetches all models", value=1000, step=1, minimum=0, maximum=10000000) with gr.Tab("for Models"): with gr.Column(): infer_status = gr.Radio(label="Inference status", choices=["warm", "cold", "frozen", "all"], value="all") gr.Markdown("[About the Inference API status (Warm, Cold, Frozen)](https://huggingface.co./docs/api-inference/supported-models)", elem_classes="info") # with gr.Row(equal_height=True): # model_task = gr.Textbox(label="Task", info="String(s) of tasks models were designed for", placeholder="fill-mask", value="") # trained_dataset = gr.Textbox(label="Trained dataset", info="Trained dataset for a model", value="") with gr.Tab("for Datasets"): size_categories = gr.CheckboxGroup(label="Size categories", info="The size of the dataset", choices=DS_SIZE_CATEGORIES, value=[]) # task_categories = gr.Textbox(label="Task categories", info="Identify datasets by the designed task", value="") # task_ids = gr.Textbox(label="Task IDs", info="Identify datasets by the specific task", value="") # language_creators = gr.Textbox(label="Language creators", info="Identify datasets with how the data was curated", value="") # language = gr.Textbox(label="Language", info="String(s) representing two-character language to filter datasets by", value="") # multilinguality = gr.Textbox(label="Multilinguality", info="String(s) representing a filter for datasets that contain multiple languages", value="") with gr.Tab("for Spaces"): with gr.Row(equal_height=True): hardware = gr.CheckboxGroup(label="Specify hardware", choices=SPACE_HARDWARES, value=[]) stage = gr.CheckboxGroup(label="Specify stage", choices=SPACE_STAGES, value=[]) with gr.Accordion("More Advanced", open=False): with gr.Row(equal_height=True): sort_method = gr.Radio(label="Sort method", choices=["ascending order", "descending order"], value="ascending order") fetch_detail = gr.CheckboxGroup(label="Fetch detail", choices=["Space Runtime"], value=["Space Runtime"]) with gr.Row(equal_height=True): show_labels = gr.CheckboxGroup(label="Show items", choices=get_labels(), value=get_valid_labels()) run_button = gr.Button("Search", variant="primary") with gr.Tab("Find Serverless Inference API enabled models"): with gr.Group(): with gr.Row(equal_height=True): infer_repo_types = gr.CheckboxGroup(label="Repo type", choices=["model", "dataset", "space"], value=["model"], visible=False) with gr.Column(): infer_infer_status = gr.Radio(label="Inference status", choices=["warm", "cold", "frozen", "all"], value="warm") gr.Markdown("[About the Inference API status (Warm, Cold, Frozen)](https://huggingface.co./docs/api-inference/supported-models)", elem_classes="info") with gr.Column(): infer_gated_status = gr.Radio(label="Gated status", choices=["gated", "non-gated", "all"], value="all") infer_appr_status = gr.CheckboxGroup(label="Approval method", choices=["auto", "manual"], value=["auto", "manual"]) infer_run_button = gr.Button("Search", variant="primary") with gr.Tab("Find recommended repos"): with gr.Group(): with gr.Row(equal_height=True): #rec_repo_id = gr.Textbox(label="Repo ID", info="Input your favorite repo", value="") rec_repo_id = HuggingfaceHubSearch(label="Repo ID", placeholder="Input your favorite Repo ID", search_type=["model", "dataset", "space"], sumbit_on_select=False) rec_repo_types = gr.CheckboxGroup(label="Repo type", choices=["model", "dataset", "space", "collection"], value=["model", "dataset", "space", "collection"]) with gr.Row(equal_height=True): rec_sort = gr.Dropdown(label="Sort", choices=["last_modified", "likes", "downloads", "downloads_all_time", "trending_score"], value="likes") rec_limit = gr.Number(label="Limit", value=20, step=1, minimum=1, maximum=1000) with gr.Accordion("Advanced", open=False): with gr.Row(equal_height=True): rec_show_labels = gr.CheckboxGroup(label="Show items", choices=get_labels(), value=get_valid_labels()) rec_run_button = gr.Button("Search", variant="primary") with gr.Tab("Find collections"): with gr.Group(): with gr.Row(equal_height=True): #rec_repo_id = gr.Textbox(label="Repo ID", info="Input your favorite repo", value="") col_repo_id = HuggingfaceHubSearch(label="Repo ID or User ID", placeholder="Input your favorite Repo ID or User ID", search_type=["model", "dataset", "space", "user"], sumbit_on_select=False) col_sort = gr.Dropdown(label="Sort", choices=["last_modified", "trending_score", "likes"], value="likes") with gr.Row(equal_height=True): col_repo_limit = gr.Number(label="Limit for repos", value=50, step=1, minimum=1, maximum=100) col_user_limit = gr.Number(label="Limit for users", value=10, step=1, minimum=0, maximum=50) with gr.Accordion("Advanced", open=False): col_show_labels = gr.CheckboxGroup(label="Show items", choices=get_labels(), value=get_valid_labels()) col_run_button = gr.Button("Search", variant="primary") with gr.Group(): with gr.Accordion("Filter", open=False): hide_labels = gr.CheckboxGroup(label="Hide items", choices=[], value=[], visible=False) with gr.Row(equal_height=True): filter_item1 = gr.Dropdown(label="Filter item", choices=[""], value="", visible=False) filter1 = gr.Dropdown(label="Filter", choices=[""], value="", allow_custom_value=True, visible=False) filter_btn = gr.Button("Apply filter", variant="secondary", visible=False) types_desc = gr.Markdown(TYPES_DESC, elem_classes="info") result_df = gr.DataFrame(label="Results", type="pandas", value=None, interactive=False) run_button.click(search, [repo_types, sort, sort_method, filter_str, search_str, author, tags, infer_status, gated_status, appr_status, size_categories, limit, hardware, stage, followed, fetch_detail, show_labels, ui_mode, search_result], [result_df, hide_labels, search_result])\ .success(update_filter, [filter_item1, search_result], [filter_item1, filter1, filter_btn, search_result], queue=False) infer_run_button.click(search, [infer_repo_types, sort, sort_method, filter_str, search_str, author, tags, infer_infer_status, infer_gated_status, infer_appr_status, size_categories, limit, hardware, stage, followed, fetch_detail, show_labels, ui_mode, search_result], [result_df, hide_labels, search_result])\ .success(update_filter, [filter_item1, search_result], [filter_item1, filter1, filter_btn, search_result], queue=False) gr.on(triggers=[hide_labels.change, filter_btn.click], fn=update_df, inputs=[hide_labels, filter_item1, filter1, search_result], outputs=[result_df, search_result], trigger_mode="once", queue=False, show_api=False) filter_item1.change(update_filter, [filter_item1, search_result], [filter_item1, filter1, filter_btn, search_result], queue=False, show_api=False) subtag_cat.change(update_subtag_items, [subtag_cat], [subtag_item], queue=False, show_api=False) subtug_btn.click(update_subtags, [tags, subtag_cat, subtag_item], [tags], queue=False, show_api=False) tag_btn.click(update_tags, [tags, tag_item], [tags], queue=False, show_api=False) gr.on(triggers=[rec_run_button.click, rec_repo_id.submit], fn=search_ref_repos, inputs=[rec_repo_id, rec_repo_types, rec_sort, rec_show_labels, rec_limit, ui_mode, search_result], outputs=[result_df, hide_labels, search_result]) gr.on(triggers=[col_run_button.click, col_repo_id.submit], fn=search_cols, inputs=[col_repo_id, col_sort, col_show_labels, col_repo_limit, col_user_limit, ui_mode, search_result], outputs=[result_df, hide_labels, search_result]) demo.queue().launch(ssr_mode=False)