app.py

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 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 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], ## 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()