from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
import requests
import pytz
import yaml
import math
from tools.final_answer import FinalAnswerTool
from typing import List, Dict, Tuple
from functools import reduce

from Gradio_UI import GradioUI

# Below is an example of a tool that does nothing. Amaze us with your creativity !
@tool
def search_duckduckgo(query: str, num_results: int = 5) -> List[Dict[str, str]]:
    """
    A tool that performs a web search using DuckDuckGo and returns the top results.
    Args:
        query: The search term or question to look up on DuckDuckGo.
        num_results: The number of search results to return. 
                                     Defaults to 5.
    Returns:
        List[Dict[str, str]]: A list of dictionaries, where each dictionary contains:
            - 'title' (str): The title of the search result.
            - 'link' (str): The URL of the search result.
            - 'snippet' (str): A short summary of the content.
    Example:
        >>> results = search_duckduckgo("latest AI advancements 2025", num_results=3)
        >>> for result in results:
        >>>     print(result["title"], result["link"])
    """
    search_tool = DuckDuckGoSearchTool()
    results = search_tool.search(query, num_results=num_results)
    return results
    
@tool
def compute_lcm_gcd(numbers: List[int]) -> str:
    """
    A tool that computes the Least Common Multiple (LCM) and Greatest Common Divisor (GCD) 
    of a given list of integers and returns a formatted string.

    Args:
        numbers: A list of integers.

    Returns:
        str: A formatted string displaying the input numbers, their GCD, and their LCM.

    Example:
        >>> print(compute_lcm_gcd([12, 18, 24]))
        Entered numbers: [12, 18, 24]
        Greatest Common Divisor (GCD): 6
        Least Common Multiple (LCM): 72
    """
    if not numbers:
        raise ValueError("The list of numbers cannot be empty.")

    # Compute GCD of the list
    gcd_result = reduce(math.gcd, numbers)

    # Compute LCM using the formula: lcm(a, b) = abs(a * b) // gcd(a, b)
    def lcm(a, b):
        return abs(a * b) // math.gcd(a, b)

    lcm_result = reduce(lcm, numbers)

    # Format output as a readable string
    output = (
        f"Entered numbers: {numbers}\n"
        f"Greatest Common Divisor (GCD): {gcd_result}\n"
        f"Least Common Multiple (LCM): {lcm_result}"
    )

    return output
    
@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()

# 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='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud',# 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, image_generation_tool, get_current_time_in_timezone,
          compute_lcm_gcd], ## 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()