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| 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 ! | |
| 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 ?" | |
| 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() | |
| 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() |