app.py

from typing import List, Dict
import httpx
import gradio as gr
import pandas as pd
from huggingface_hub import HfApi

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(url):
    if url:
        return f'<a href="{url}" target="_blank">{url}</a>'
    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=False)
    url_output = gr.HTML(label="URL")

    search_button.click(search_hub, inputs=[search_query, search_type], outputs=[results_df])
    results_df.select(lambda row: open_url(f"https://huggingface.co/{row.iloc[0]}"), outputs=[url_output])

demo.launch(debug=True)