Spaces:
Running
Running
| from smolagents import CodeAgent, InferenceClientModel, GradioUI, tool | |
| from huggingface_hub import HfApi | |
| import requests | |
| from typing import List, Dict | |
| 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)}" | |
| 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)}" | |
| 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() |