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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,
DS_SIZE_CATEGORIES, SPACE_HARDWARES, SPACE_STAGES)
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("Normal Search"):
with gr.Group():
with gr.Row(equal_height=True):
repo_types = gr.CheckboxGroup(label="Repo type", choices=["model", "dataset", "space"], value=["model", "dataset", "space"])
with gr.Accordion("Advanced", open=False):
with gr.Row(equal_height=True):
filter_str = gr.Textbox(label="Filter", info="String(s) to filter repos", value="")
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)
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.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"])
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.Row(equal_height=True):
sort = gr.Radio(label="Sort", choices=["last_modified", "likes", "downloads", "trending_score"], value="likes")
sort_method = gr.Radio(label="Sort method", choices=["ascending order", "descending order"], value="ascending order")
limit = gr.Number(label="Limit", info="If 0, fetches all models", value=1000, step=1, minimum=0, maximum=10000000)
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.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)
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, fetch_detail, show_labels, 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, fetch_detail, show_labels, 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)
demo.queue().launch()
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