from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool import datetime import requests import pytz import yaml from datetime import datetime import re from tools.final_answer import FinalAnswerTool import os from Gradio_UI import GradioUI HF_TOKEN = os.environ['HF_TOKEN'] # Below is an example of a tool that does nothing. Amaze us with your creativity ! @tool def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type #Keep this format for the description / args / args description but feel free to modify the tool """A tool that does nothing yet Args: arg1: the first argument arg2: the second argument """ return "What magic will you build ?" @tool def get_current_time_in_timezone(timezone: str) -> str: """A tool that fetches the current local time in a specified timezone. Args: timezone: A string representing a valid timezone (e.g., 'America/New_York'). """ try: # Create timezone object tz = pytz.timezone(timezone) # Get current time in that timezone local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S") return f"The current local time in {timezone} is: {local_time}" except Exception as e: return f"Error fetching time for timezone '{timezone}': {str(e)}" def sentiment_analysis(text: str) -> str: """Basic economic sentiment analysis based on keywords""" positive_keywords = ["growth", "profit", "increase", "investment", "record revenue", "expansion", "gain"] negative_keywords = ["loss", "decline", "layoff", "decrease", "cut", "lawsuit", "scandal", "drop"] text_lower = text.lower() pos_hits = sum(1 for kw in positive_keywords if kw in text_lower) neg_hits = sum(1 for kw in negative_keywords if kw in text_lower) if pos_hits > neg_hits: return "positive" elif neg_hits > pos_hits: return "negative" else: return "neutral" def news_date_is_today(text: str) -> bool: """Check if the news mention today's date (very naive)""" today = datetime.now().strftime("%B %d, %Y") # e.g., "April 30, 2025" return today.lower() in text.lower() @tool def company_news_sentiment(company_name: str, top_k: int) -> str: """A tool that searches for the latest company news and assesses daily relevance and sentiment. Args: company_name: the name of the company to search news for. top_k: the number of top search results to analyze. Returns: A string summarizing if there are daily news and whether the sentiment is positive or negative. """ search_tool = DuckDuckGoSearchTool() results = search_tool.run(f"{company_name} latest news") if not results: return f"No news found for {company_name}." selected_results = results[:top_k] combined_text = " ".join(res["body"] for res in selected_results if "body" in res) has_today_news = any(news_date_is_today(res.get("body", "")) for res in selected_results) sentiment = sentiment_analysis(combined_text) daily_news_str = "There is news today" if has_today_news else "There is no news specifically from today" return f"{daily_news_str} about {company_name}. Overall sentiment appears {sentiment} from an economic perspective." final_answer = FinalAnswerTool() # If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder: # model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' # original model : model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded model = HfApiModel( max_tokens=2096, temperature=0.5, model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded custom_role_conversions=None, token=HF_TOKEN ) # Import tool from Hub image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True) with open("prompts.yaml", 'r') as stream: prompt_templates = yaml.safe_load(stream) agent = CodeAgent( model=model, tools=[image_generation_tool,company_news_sentiment,get_current_time_in_timezone,final_answer], ## add your tools here (don't remove final answer) max_steps=6, verbosity_level=1, grammar=None, planning_interval=None, name=None, description=None, prompt_templates=prompt_templates ) GradioUI(agent).launch()