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from huggingface_hub import HfApi, InferenceClient
from smolagents import Tool
class SearchHfSpacesTool(Tool):
name = "search_hf_spaces"
description = "Searches for Hugging Face Spaces that match the given query."
inputs = {
"query": {
"type": "string",
"description": "The search query.",
}
}
output_type = "string"
def forward(self, query: str) -> str:
"""
Searches for Hugging Face Spaces that match the given query.
Args:
query: The search query.
Returns:
A string containing a list of matching Spaces, including their ID and description.
"""
api = HfApi()
spaces = api.list_spaces(search=query)
return "\n".join([f"- {space.id}: {space.card_data.get('title', '')}" for space in spaces])
class CallHfSpaceApiTool(Tool):
name = "call_hf_space_api"
description = "Calls the API of a Hugging Face Space."
inputs = {
"space_id": {
"type": "string",
"description": "The ID of the Space to call.",
},
"args": {
"type": "array",
"description": "Positional arguments to pass to the Space's API.",
},
"kwargs": {
"type": "object",
"description": "Keyword arguments to pass to the Space's API.",
},
}
output_type = "string"
def forward(self, space_id: str, *args, **kwargs) -> str:
"""
Calls the API of a Hugging Face Space.
Args:
space_id: The ID of the Space to call.
*args: Positional arguments to pass to the Space's API.
**kwargs: Keyword arguments to pass to the Space's API.
Returns:
The result of the API call.
"""
client = InferenceClient()
try:
# This is a generic way to call a Gradio API.
# The exact parameters will depend on the specific Space.
result = client.predict(repo_id=space_id, *args, **kwargs)
return str(result)
except Exception as e:
return f"Error calling Space API: {e}" |