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, 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 ""
+
+def SwarmyTime(data: List[Dict]) -> Dict:
+    """
+    Aggregates all content from the given data.
+    
+    :param data: List of dictionaries containing the search results
+    :return: Dictionary with aggregated content
+    """
+    aggregated = {
+        "total_items": len(data),
+        "unique_authors": set(),
+        "total_downloads": 0,
+        "item_types": {"Models": 0, "Datasets": 0, "Spaces": 0}
+    }
+
+    for item in data:
+        aggregated["unique_authors"].add(item.get("author", "Unknown"))
+        aggregated["total_downloads"] += item.get("downloads", 0)
+        
+        if "modelId" in item:
+            aggregated["item_types"]["Models"] += 1
+        elif "dataset" in item.get("id", ""):
+            aggregated["item_types"]["Datasets"] += 1
+        else:
+            aggregated["item_types"]["Spaces"] += 1
+
+    aggregated["unique_authors"] = len(aggregated["unique_authors"])
+    
+    return aggregated
+
+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)
+    aggregated_output = gr.JSON(label="Aggregated Content")
+
+    def search_and_aggregate(query, search_type):
+        df = search_hub(query, search_type)
+        aggregated = SwarmyTime(df.to_dict('records'))
+        return df, aggregated
+
+    search_button.click(search_and_aggregate, inputs=[search_query, search_type], outputs=[results_df, aggregated_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)