Update app.py

app.py

@@ -2,7 +2,7 @@ from typing import List, Dict
 import httpx
 import gradio as gr
 import pandas as pd
-from huggingface_hub import HfApi
+from huggingface_hub import HfApi, ModelCard
 
 def search_hub(query: str, search_type: str) -> pd.DataFrame:
     api = HfApi()
@@ -21,22 +21,48 @@ def search_hub(query: str, search_type: str) -> pd.DataFrame:
 
     return pd.DataFrame(data)
 
-def open_url(url):
-    if url:
+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=False)
+    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(lambda row: open_url(f"https://huggingface.co/{row.iloc[0]}"), outputs=[url_output])
+    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)