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| from smolagents import DuckDuckGoSearchTool | |
| from smolagents import Tool | |
| import random | |
| from huggingface_hub import list_models | |
| import requests | |
| from bs4 import BeautifulSoup | |
| import openpyxl | |
| import wikipedia | |
| class WikipediaTool(Tool): | |
| name = "wikipedia_api" | |
| description = "Returns the contents of a wikipedia article through a direct API call." | |
| inputs = { | |
| "title": { | |
| "type": "string", | |
| "description": "The title of the wikipedia article to retrieve." | |
| } | |
| } | |
| output_type = "string" | |
| def forward(self, title: str): | |
| page = wikipedia.page(title) | |
| return page.content | |
| class ExcelTool(Tool): | |
| name = "read_excel" | |
| description = "Returns the contents of an Excel file as a Pandas dataframe." | |
| inputs = { | |
| "filename": { | |
| "type": "string", | |
| "description": "The name of the Excel file to read." | |
| } | |
| } | |
| output_type = "string" | |
| def forward(self, question: str): | |
| search_tool = DuckDuckGoSearchTool() | |
| result = search_tool(question) | |
| return result | |
| class WebscraperTool(Tool): | |
| name = "webscraper" | |
| description = "Returns the page's html content from the input url." | |
| inputs = { | |
| "url": { | |
| "type": "string", | |
| "description": "The link of the web page to scrape." | |
| } | |
| } | |
| output_type = "string" | |
| def forward(self, url: str): | |
| response = requests.get(url, stream=True) | |
| if response.status_code == 200: | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| html_text = soup.get_text() | |
| return html_text | |
| else: | |
| raise Exception(f"Failed to retrieve the webpage. Status code: {response.status_code}") | |
| # Initialize the DuckDuckGo search tool | |
| class InternetSearchTool(Tool): | |
| name = "internet_search" | |
| description = "Searches the internet for the answer to any question input. Returns a long batch of textual information related to the query." | |
| inputs = { | |
| "question": { | |
| "type": "string", | |
| "description": "The question to retrieve an answer for." | |
| } | |
| } | |
| output_type = "string" | |
| def forward(self, question: str): | |
| search_tool = DuckDuckGoSearchTool() | |
| result = search_tool(question) | |
| return result | |
| 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)}" | |