Create app.py

app.py

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+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)