from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
import re
import requests
import pytz
import yaml
import numpy as np
from tools.final_answer import FinalAnswerTool

from Gradio_UI import GradioUI

# Below is an example of a tool that does nothing. Amaze us with your creativity !
@tool
def random_normal_draws(arg1: float, arg2: float)-> 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 generates 10 draws from a normal distribution with mean and standard deviation given.
    Args:
        arg1: the mean of the distribution
        arg2: the standard deviation of the distribution (must be positive)
    """
    try:
        if arg2 <= 0:
            return "Error: Standard deviation must be positive."
        rand_norm = np.random.normal(loc=arg1, scale=arg2, size=10)
        return ", ".join(f"{x:.2f}" for x in rand_norm)
    except Exception as e:
        return f"Error generating distribution: {e}"

@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)}"


@tool
def extract_fnol_info(email_text: str) -> str:
    """Extracts FNOL details (car make, injury type, date of incident) from a natural-language claim email.
    
    Args:
        email_text: The body of the claim email.
    
    Returns:
        A structured summary with the car make, injury type, and date of incident, or 'missing' if not found.
    """
    prompt = f"""
You are an assistant extracting First Notification of Loss (FNOL) information from an email. 
Extract the following from the email below:

- Car make and model
- Date of incident (in YYYY-MM-DD format if possible)
- Injury type

If any information is missing, say 'Missing'.

Email:
\"\"\"
{email_text}
\"\"\"

Return in this format:
Car: <car>
Date: <date>
Injury: <injury>
"""
    return prompt


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='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,
        image_generation_tool,
        extract_fnol_info,
        get_current_time_in_timezone,  # even though it's decorated
        random_normal_draws                 # same here
    ],
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name=None,
    description=None,
    prompt_templates=prompt_templates
)


GradioUI(agent).launch()