from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
import requests
import pytz
import yaml
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 my_custom_tool(arg1:str, arg2:int)-> 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 does nothing yet 
    Args:
        arg1: the first argument
        arg2: the second argument
    """
    return "What magic will you build ?"

@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 analyze_stock(ticker: str) -> dict:  # type: ignore[type-arg]
    """
    A tool that analyze stock data.
    Args:
        ticker: A string representing ticker
    """
    import os
    from datetime import datetime, timedelta

    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    import yfinance as yf
    from pytz import timezone  # type: ignore

    stock = yf.Ticker(ticker)

    # Get historical data (1 year of data to ensure we have enough for 200-day MA)
    end_date = datetime.now(timezone("UTC"))
    start_date = end_date - timedelta(days=365)
    hist = stock.history(start=start_date, end=end_date)

    # Ensure we have data
    if hist.empty:
        return {"error": "No historical data available for the specified ticker."}

    # Compute basic statistics and additional metrics
    current_price = stock.info.get("currentPrice", hist["Close"].iloc[-1])
    year_high = stock.info.get("fiftyTwoWeekHigh", hist["High"].max())
    year_low = stock.info.get("fiftyTwoWeekLow", hist["Low"].min())

    # Calculate 50-day and 200-day moving averages
    ma_50 = hist["Close"].rolling(window=50).mean().iloc[-1]
    ma_200 = hist["Close"].rolling(window=200).mean().iloc[-1]

    # Calculate YTD price change and percent change
    ytd_start = datetime(end_date.year, 1, 1, tzinfo=timezone("UTC"))
    ytd_data = hist.loc[ytd_start:]  # type: ignore[misc]
    if not ytd_data.empty:
        price_change = ytd_data["Close"].iloc[-1] - ytd_data["Close"].iloc[0]
        percent_change = (price_change / ytd_data["Close"].iloc[0]) * 100
    else:
        price_change = percent_change = np.nan

    # Determine trend
    if pd.notna(ma_50) and pd.notna(ma_200):
        if ma_50 > ma_200:
            trend = "Upward"
        elif ma_50 < ma_200:
            trend = "Downward"
        else:
            trend = "Neutral"
    else:
        trend = "Insufficient data for trend analysis"

    # Calculate volatility (standard deviation of daily returns)
    daily_returns = hist["Close"].pct_change().dropna()
    volatility = daily_returns.std() * np.sqrt(252)  # Annualized volatility

    # Create result dictionary
    result = {
        "ticker": ticker,
        "current_price": current_price,
        "52_week_high": year_high,
        "52_week_low": year_low,
        "50_day_ma": ma_50,
        "200_day_ma": ma_200,
        "ytd_price_change": price_change,
        "ytd_percent_change": percent_change,
        "trend": trend,
        "volatility": volatility,
    }

    # Convert numpy types to Python native types for better JSON serialization
    for key, value in result.items():
        if isinstance(value, np.generic):
            result[key] = value.item()

    return result


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], ## 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()