from smolagents import DuckDuckGoSearchTool import requests from bs4 import BeautifulSoup from urllib.parse import urljoin # Initialize the DuckDuckGo search tool search_tool = DuckDuckGoSearchTool() # Example usage # results = search_tool("Who's the current President of France?") # print(results) from smolagents import Tool import random class WeatherInfoTool(Tool): name = "weather_info" description = "Fetches dummy weather information for a given location." inputs = { "location": { "type": "string", "description": "The location to get weather information for." } } output_type = "string" def forward(self, location: str): # Dummy weather data weather_conditions = [ {"condition": "Rainy", "temp_c": 15}, {"condition": "Clear", "temp_c": 25}, {"condition": "Windy", "temp_c": 20} ] # Randomly select a weather condition data = random.choice(weather_conditions) return f"Weather in {location}: {data['condition']}, {data['temp_c']}°C" # # Initialize the tool # weather_info_tool = WeatherInfoTool() from smolagents import Tool from huggingface_hub import list_models class HubStatsTool(Tool): name = "hub_stats" description = "Fetches the most downloaded model from a specific author on the Hugging Face Hub." inputs = { "author": { "type": "string", "description": "The username of the model author/organization to find models from." } } output_type = "string" def forward(self, author: str): try: # List models from the specified author, sorted by downloads models = list(list_models(author=author, sort="downloads", direction=-1, limit=1)) if models: model = models[0] return f"The most downloaded model by {author} is {model.id} with {model.downloads:,} downloads." else: return f"No models found for author {author}." except Exception as e: return f"Error fetching models for {author}: {str(e)}" # # Initialize the tool # hub_stats_tool = HubStatsTool() # # Example usage # print(hub_stats_tool("facebook")) # Example: Get the most downloaded model by Facebook # from smolagents import CodeAgent, InferenceClientModel # # Initialize the Hugging Face model # model = InferenceClientModel() # # Create Alfred with all the tools # alfred = CodeAgent( # tools=[search_tool, weather_info_tool, hub_stats_tool], # model=model # ) # # Example query Alfred might receive during the gala # response = alfred.run("What is Facebook and what's their most popular model?") # print("🎩 Alfred's Response:") # print(response) class ScrapingWeb(Tool): name = "scraping_web" description = "Scraping the website to extract all text information available" inputs = { "link": { "type": "string", "description": "The link of the website to scrape" } } output_type = "string" def forward(self, link: str): try: # 1. Fetch the page response = requests.get(link, timeout=10) response.raise_for_status() # raise HTTPError for bad status codes except (requests.RequestException) as e: print(f"Error fetching {link}: {e}") return None # 2. Parse with BeautifulSoup soup = BeautifulSoup(response.text, "html.parser") # 3. Extract the page title title_tag = soup.find("title") title = title_tag.get_text(strip=True) if title_tag else None # 4. Extract all visible text # We’ll grab the text of each
,