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| from smolagents import DuckDuckGoSearchTool | |
| from smolagents import Tool | |
| import random | |
| # from smolagents import Tool | |
| from huggingface_hub import list_models | |
| from dotenv import load_dotenv | |
| import os | |
| load_dotenv() | |
| from smolagents import CodeAgent, InferenceClientModel | |
| # Load the Hugging Face API key from environment variables | |
| api_key = os.getenv("HUGGINGFACE_API_KEY") | |
| # Initialize the DuckDuckGo search tool | |
| search_tool = DuckDuckGoSearchTool() | |
| # Example usage | |
| # results = search_tool("Who's the current President of France?") | |
| # print(results) | |
| 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() | |
| 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 | |
| # Initialize the Hugging Face model | |
| model = InferenceClientModel(token=api_key) | |
| # 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("I'am planning a trip to Paris. What is the weathere there, and can you tell me who the current mayor is? Also, what's the most popular machine learning model from French researchers?") | |
| # print("🎩 Alfred's Response:") | |
| # print(response) |