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| from smolagents import DuckDuckGoSearchTool | |
| from smolagents import Tool | |
| import random | |
| from huggingface_hub import list_models | |
| # Initialize the DuckDuckGo search tool | |
| #search_tool = DuckDuckGoSearchTool() | |
| 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" | |
| 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)}" | |
| class GetLatestNewsTool(Tool): | |
| name = "latest_news" | |
| description = "Fetches the latest news headlines." | |
| inputs = { | |
| "topic": { | |
| "type": "string", | |
| "description": "The topic to get news headlines for." | |
| } | |
| } | |
| output_type = "string" | |
| def forward(self, topic: str): | |
| # Dummy news data | |
| news_headlines = [ | |
| f"Latest news on {topic}: Major breakthrough in AI technology!", | |
| f"Breaking: {topic} market sees unprecedented events.", | |
| f"Update: New developments in {topic} sector." | |
| ] | |
| # Randomly select a news headline | |
| return random.choice(news_headlines) | |