Update app.py

app.py

@@ -6,33 +6,29 @@ from huggingface_hub import HfApi, ModelCard
 
 def search_hub(query: str, search_type: str) -> pd.DataFrame:
     api = HfApi()
-    data = []
-    
     if search_type == "Models":
         results = api.list_models(search=query)
-        data = [{"id": model.modelId, "author": model.author, "downloads": model.downloads, 
-                 "link": f"https://huggingface.co/{model.modelId}"} for model in results]
+        data = [{"id": model.modelId, "author": model.author, "downloads": model.downloads, "link": f"https://huggingface.co/{model.modelId}"} for model in results]
     elif search_type == "Datasets":
         results = api.list_datasets(search=query)
-        data = [{"id": dataset.id, "author": dataset.author, "downloads": dataset.downloads, 
-                 "link": f"https://huggingface.co/datasets/{dataset.id}"} for dataset in results]
+        data = [{"id": dataset.id, "author": dataset.author, "downloads": dataset.downloads, "link": f"https://huggingface.co/datasets/{dataset.id}"} for dataset in results]
     elif search_type == "Spaces":
         results = api.list_spaces(search=query)
-        data = [{"id": space.id, "author": space.author, 
-                 "link": f"https://huggingface.co/spaces/{space.id}"} for space in results]
-    
+        data = [{"id": space.id, "author": space.author, "link": f"https://huggingface.co/spaces/{space.id}"} for space in results]
+    else:
+        data = []
     return pd.DataFrame(data)
 
 def open_url(row):
     if row is not None and not row.empty:
-        url = row['link']
+        url = row.iloc[0]['link']
         return f'<iframe src="{url}" width="100%" height="600px"></iframe>'
     else:
         return ""
 
 def load_metadata(row, search_type):
     if row is not None and not row.empty:
-        item_id = row['id']
+        item_id = row.iloc[0]['id']
         
         if search_type == "Models":
             try:
@@ -89,21 +85,17 @@ with gr.Blocks() as demo:
         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")
+    web_view = gr.HTML(label="Web View")
     metadata_output = gr.Textbox(label="Metadata", lines=10)
     aggregated_output = gr.JSON(label="Aggregated Content")
-    iframe_output = gr.HTML(label="Web Page")
 
     def search_and_aggregate(query, search_type):
         df = search_hub(query, search_type)
         aggregated = SwarmyTime(df.to_dict('records'))
         return df, aggregated
 
-    def display_iframe(row):
-        return open_url(row)
-
     search_button.click(search_and_aggregate, inputs=[search_query, search_type], outputs=[results_df, aggregated_output])
-    results_df.select(display_iframe, outputs=[iframe_output])
+    results_df.select(open_url, outputs=[web_view])
     results_df.select(load_metadata, inputs=[results_df, search_type], outputs=[metadata_output])
 
-demo.launch(debug=True)
+demo.launch(debug=True)