Spaces:
Sleeping
Sleeping
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from smolagents import CodeAgent, InferenceClientModel, GradioUI, tool
from huggingface_hub import HfApi
import requests
from typing import List, Dict
@tool
def leaderboard_search(query: str) -> str:
"""
Search Hugging Face Spaces specifically in the model benchmarking category.
Args:
query: The search query to find relevant model benchmarking spaces
Returns:
A formatted string containing search results with space names, descriptions, and additional information
"""
api_url = "https://huggingface.co/api/spaces"
search_words = ["arena", "leaderboard", "benchmark"]
results = []
try:
for word in search_words:
params = {
"search": query + " " + word,
"full": True # Get full information
}
response = requests.get(api_url, params=params)
print(response)
spaces = response.json()
print(spaces)
if not spaces:
continue # Skip if no spaces found for this search word
for space in spaces:
# Extract relevant information
space_id = space.get("id", "Unknown")
author = space_id.split("/")[0] if "/" in space_id else "Unknown"
space_name = space_id.split("/")[1] if "/" in space_id else space_id
likes = space.get("likes", 0)
# Try to get detailed information if available
title = space.get("cardData", {}).get("title") if space.get("cardData") else space_name
description = space.get("cardData", {}).get("short_description", "No description available") if space.get("cardData") else "No description available"
# Create formatted result string
result = f"🚀 **{title}** ({space_id})\n"
result += f" 👤 Author: {author}\n"
result += f" 📝 {description}\n"
result += f" ❤️ Likes: {likes}\n"
result += f" 🔗 URL: https://huggingface.co/spaces/{space_id}\n"
results.append(result)
if not results:
return f"No model benchmarking spaces found for query: '{query}'"
return "\n".join(results)
except requests.exceptions.RequestException as e:
return f"Error searching Hugging Face Spaces: {str(e)}"
except Exception as e:
return f"Unexpected error: {str(e)}"
except requests.exceptions.RequestException as e:
return f"Error searching Hugging Face Spaces: {str(e)}"
except Exception as e:
return f"Unexpected error: {str(e)}"
@tool
def get_space_content(space_id: str) -> str:
"""
Get the content of a Hugging Face Space.
Args:
space_id: The Hugging Face Space ID (e.g., "open-llm-leaderboard/open_llm_leaderboard")
Returns:
The space content or error message
"""
try:
# Get the space's README or main content
readme_url = f"https://huggingface.co/spaces/{space_id}/raw/main/README.md"
response = requests.get(readme_url)
if response.status_code == 200:
return f"Content from {space_id}:\n\n{response.text}"
else:
# Try to get any available file
files_url = f"https://huggingface.co/api/spaces/{space_id}/tree/main"
files_response = requests.get(files_url)
if files_response.status_code == 200:
files = files_response.json()
return f"Available files in {space_id}:\n" + "\n".join([f"- {file['path']}" for file in files])
else:
return f"Space {space_id} exists but couldn't retrieve content"
except Exception as e:
return f"Error accessing space {space_id}: {str(e)}"
@tool
def get_file_from_space(space_id: str, file_path: str) -> str:
"""
Get a specific file from a Hugging Face Space.
Args:
space_id: The Hugging Face Space ID
file_path: Path to the file in the space
Returns:
The file content or error message
"""
try:
url = f"https://huggingface.co/spaces/{space_id}/raw/main/{file_path}"
response = requests.get(url)
if response.status_code == 200:
return f"Content of {file_path} from {space_id}:\n\n{response.text}"
else:
return f"Couldn't retrieve {file_path} from {space_id}"
except Exception as e:
return f"Error: {str(e)}"
# Initialize the agent with the leaderboard search and space content tools
model = InferenceClientModel()
agent = CodeAgent(
tools=[leaderboard_search, get_space_content, get_file_from_space],
additional_authorized_imports=["json", "requests", "pandas"],
model=model,
add_base_tools=False,
description="Your job is to find the best possible model for a given task based on relevant leaderboards or arenas. You will be provided with a task description, and you should use the leaderboard tool to find relevant leaderboards or arenas. If you want to inspect the contents of a particular Space (e.g., README or code), use the space_content_tool. Respond with a list of the top models, including their names, scores, and links to their leaderboard pages.",
)
GradioUI(agent).launch() |