import json from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool import datetime import requests import pytz import yaml import numpy as np from tools.final_answer import FinalAnswerTool from tools.visit_webpage import VisitWebpageTool from tools.web_search import DuckDuckGoSearchTool from typing import Optional, Tuple from Gradio_UI import GradioUI # Below is an example of a tool that does nothing. Amaze us with your creativity ! @tool def calculate_risk_metrics( returns: np.ndarray, var_level: float = 0.95, n_simulations: int = 10000, bootstrap: bool = False, random_seed: Optional[int] = None ) -> Tuple[float, float]: """ Calculate Value at Risk (VaR) and Expected Shortfall (ES) using the historical method, with an option for bootstrapped historical simulation. Args: returns: Array of daily returns. Each value represents the percentage return for a single day. var_level: VaR level (e.g., 0.95 for 95% confidence). Defaults to 0.95. n_simulations: Number of bootstrap simulations. Defaults to 10000. bootstrap: If True, use bootstrapped historical simulation. Defaults to False. random_seed: Seed for random number generation to ensure reproducibility. Defaults to None. Returns: Tuple[float, float]: A tuple containing the VaR and Expected Shortfall (ES) values. """ if random_seed is not None: np.random.seed(random_seed) if bootstrap: # Perform bootstrapped historical simulation simulated_var = np.zeros(n_simulations) simulated_es = np.zeros(n_simulations) for i in range(n_simulations): # Resample returns with replacement resampled_returns = np.random.choice(returns, size=len(returns), replace=True) # Sort the resampled returns sorted_returns = np.sort(resampled_returns) # Determine the index for the VaR level index = int((1 - var_level) * len(sorted_returns)) # Calculate VaR for this simulation simulated_var[i] = sorted_returns[index] # Calculate ES for this simulation (average of returns below VaR) simulated_es[i] = np.mean(sorted_returns[:index]) # Calculate the average VaR and ES across all simulations var_value = np.mean(simulated_var) es_value = np.mean(simulated_es) else: # Use the standard historical method sorted_returns = np.sort(returns) index = int((1 - var_level) * len(returns)) # Calculate VaR var_value = sorted_returns[index] # Calculate ES (average of returns below VaR) es_value = np.mean(sorted_returns[:index]) return var_value, es_value @tool def provide_my_information(query: str) -> str: """ Provide information about me (Tianqing LIU)based on the user's query. Args: query: The user's question or request for information. Returns: str: A response containing the requested information. """ # Convert the query to lowercase for case-insensitive matching query = query.lower() my_info = None with open("info/info.json", 'r') as file: my_info = json.load(file) # Check for specific keywords in the query and return the corresponding information if "who" in query or "about" in query or "introduce" in query or "presentation" in query: return f" {my_info['introduction']}." if "name" in query: return f"My name is {my_info['name']}." elif "location" in query: return f"I am located in {my_info['location']}." elif "occupation" in query or "job" in query or "work" in query: return f"I work as a {my_info['occupation']}." elif "education" in query or "degree" in query: return f"I have a {my_info['education']}." elif "skills" in query or "what can you do" in query: return f"My skills include: {', '.join(my_info['skills'])}." elif "hobbies" in query or "interests" in query: return f"My hobbies are: {', '.join(my_info['hobbies'])}." elif "contact" in query or "email" in query or "linkedin" in query or "github" in query or "cv" in query or "resume" in query: contact_info = my_info["contact"] return ( f"You can contact me via email at {contact_info['email']}, " f"connect with me on LinkedIn at {contact_info['linkedin']}, " f"or check out my GitHub profile at {contact_info['github']}." f"or check out my website at {contact_info['website']}." ) else: return "I'm sorry, I don't have information on that. Please ask about my name, location, occupation, education, skills, hobbies, or contact details." @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)}" final_answer = FinalAnswerTool() visit_webpage = VisitWebpageTool() web_search = DuckDuckGoSearchTool() # 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' 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, ) # 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=[final_answer,calculate_risk_metrics,get_current_time_in_timezone,visit_webpage,web_search,provide_my_information], ## 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()