Spaces:
Running
Running
from typing import List, Dict | |
import httpx | |
import gradio as gr | |
import pandas as pd | |
from huggingface_hub import HfApi, ModelCard | |
def search_hub(query: str, search_type: str) -> pd.DataFrame: | |
api = HfApi() | |
if search_type == "Models": | |
results = api.list_models(search=query) | |
data = [{"id": model.modelId, "author": model.author, "downloads": model.downloads} for model in results] | |
elif search_type == "Datasets": | |
results = api.list_datasets(search=query) | |
data = [{"id": dataset.id, "author": dataset.author, "downloads": dataset.downloads} for dataset in results] | |
elif search_type == "Spaces": | |
results = api.list_spaces(search=query) | |
data = [{"id": space.id, "author": space.author} for space in results] | |
else: | |
data = [] | |
return pd.DataFrame(data) | |
def open_url(row): | |
if row is not None and not row.empty: | |
url = f"https://huggingface.co/{row.iloc[0]['id']}" | |
return f'<a href="{url}" target="_blank">{url}</a>' | |
else: | |
return "" | |
def load_metadata(row, search_type): | |
if row is not None and not row.empty: | |
item_id = row.iloc[0]['id'] | |
if search_type == "Models": | |
try: | |
card = ModelCard.load(item_id) | |
return card | |
except Exception as e: | |
return f"Error loading model card: {str(e)}" | |
elif search_type == "Datasets": | |
api = HfApi() | |
metadata = api.dataset_info(item_id) | |
return str(metadata) | |
elif search_type == "Spaces": | |
api = HfApi() | |
metadata = api.space_info(item_id) | |
return str(metadata) | |
else: | |
return "" | |
else: | |
return "" | |
with gr.Blocks() as demo: | |
gr.Markdown("## Search the Hugging Face Hub") | |
with gr.Row(): | |
search_query = gr.Textbox(label="Search Query") | |
search_type = gr.Radio(["Models", "Datasets", "Spaces"], label="Search Type", value="Models") | |
search_button = gr.Button("Search") | |
results_df = gr.DataFrame(label="Search Results", wrap=True, interactive=True) | |
url_output = gr.HTML(label="URL") | |
metadata_output = gr.Textbox(label="Metadata", lines=10) | |
search_button.click(search_hub, inputs=[search_query, search_type], outputs=[results_df]) | |
results_df.select(open_url, outputs=[url_output]) | |
results_df.select(load_metadata, inputs=[results_df, search_type], outputs=[metadata_output]) | |
demo.launch(debug=True) |