from typing import Optional
import requests

from smolagents.agents import ToolCallingAgent
from smolagents import CodeAgent, HfApiModel, tool
from huggingface_hub import login
from smolagents import LiteLLMModel

from dotenv import load_dotenv
import os

# load .env file
load_dotenv()

api_key = os.environ.get('API_KEY')

#print(api_key)
login(api_key)


# Select LLM engine to use!
model = HfApiModel()
# model = LiteLLMModel(
#         model_id="ollama_chat/llama3.1",
#         api_base="http://localhost:11434",  # replace with remote open-ai compatible server if necessary
#         #api_key="your-api-key",  # replace with API key if necessary
#         #num_ctx=8192,  # ollama default is 2048 which will often fail horribly. 8192 works for easy tasks, more is better. Check https://huggingface.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator to calculate how much VRAM this will need for the selected model.
#     )


@tool
def get_random_fact() -> str:
    """
    Fetches a random fact from the "uselessfacts.jsph.pl" API.
    Returns:
        str: A string containing the random fact or an error message if the request fails.
    """
    url = "https://uselessfacts.jsph.pl/random.json?language=en"

    try:
        response = requests.get(url)
        response.raise_for_status()

        data = response.json()

        return f"Random Fact: {data['text']}"
    except requests.exceptions.RequestException as e:
        return f"Error fetching random fact: {str(e)}"

agent = ToolCallingAgent(tools=[get_random_fact], model=model)
# agent = CodeAgent(tools=[get_weather], model=model)

agent.run("Tell me a random fact!")