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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, "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]
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]
else:
data = []
# Add numbering and format the link
for i, item in enumerate(data, 1):
item['number'] = i
item['formatted_link'] = format_link(item, i, search_type)
return pd.DataFrame(data)
def format_link(item: Dict, number: int, search_type: str) -> str:
link = item['link']
readme_link = f"{link}/blob/main/README.md"
title = f"{number}. {item['id']}"
metadata = f"Author: {item['author']}"
if 'downloads' in item:
metadata += f", Downloads: {item['downloads']}"
html = f"""
<div style="margin-bottom: 10px;">
<strong>{title}</strong><br>
<a href="{link}" target="_blank" style="color: #4a90e2; text-decoration: none;">View {search_type[:-1]}</a> |
<a href="{readme_link}" target="_blank" style="color: #4a90e2; text-decoration: none;">View README</a><br>
<small>{metadata}</small>
</div>
"""
return html
def display_results(df: pd.DataFrame):
if df is not None and not df.empty:
html = "<div style='max-height: 400px; overflow-y: auto;'>"
for _, row in df.iterrows():
html += row['formatted_link']
html += "</div>"
return html
else:
return "<p>No results found.</p>"
def load_metadata(evt: gr.SelectData, df: pd.DataFrame, search_type: str):
if df is not None and not df.empty and evt.index[0] < len(df):
item_id = df.iloc[evt.index[0]]['id']
if search_type == "Models":
try:
card = ModelCard.load(item_id)
return str(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", value="awacke1")
search_type = gr.Radio(["Models", "Datasets", "Spaces"], label="Search Type", value="Models")
search_button = gr.Button("Search")
results_html = gr.HTML(label="Search Results")
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'))
html_results = display_results(df)
return html_results, aggregated
search_button.click(search_and_aggregate, inputs=[search_query, search_type], outputs=[results_html, aggregated_output])
demo.launch(debug=True) |