from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
import requests
import pytz
import yaml
from tools.final_answer import FinalAnswerTool
import yfinance as yf   
from textblob import TextBlob 
from Gradio_UI import GradioUI
import os

# from huggingface_hub import login

# login(token = os.getenv("AgentToken"))


@tool
def get_stock_price(symbol: str) -> str:
    """Fetches the latest stock price and historical trend for a given stock symbol.
    
    Args:
        symbol: The stock ticker symbol (e.g., 'AAPL' for Apple, 'TSLA' for Tesla).
    """
    try:
        stock = yf.Ticker(symbol)
        hist = stock.history(period="1mo") 
        latest_price = stock.history(period="1d")['Close'].iloc[-1]
        if hist['Close'].iloc[-1] > hist['Close'].iloc[0]:
            trend = "increasing"
        else:
            trend = "decreasing"

        return f"The latest price of {symbol} is ${latest_price:.2f}. The trend over the last month is {trend}."
    except Exception as e:
        return f"Error fetching stock data for {symbol}: {str(e)}"
@tool
def stock_news_sentiment(symbol: str) -> str:
    """Fetches recent news articles on a stock and performs sentiment analysis.
    
    Args:
        symbol: The stock ticker symbol (e.g., 'AAPL' for Apple, 'TSLA' for Tesla).
    """
    try:
        search_tool = DuckDuckGoSearchTool()
        query = f"{symbol} stock market news"
        news_results = search_tool.forward(query)
        
        if not news_results:
            return f"No recent news found for {symbol}."
        
        articles = news_results.split("\n\n")[:10]  
        sentiment_scores = []
        for article in articles:
            try:
                lines = article.split("\n")
                if len(lines) > 1:
                    snippet = lines[1] 
                else:
                    snippet = article  

                analysis = TextBlob(snippet)
                sentiment_scores.append(analysis.sentiment.polarity)
            except Exception as inner_e:
                print(f"Skipping an article due to error: {inner_e}")

        if not sentiment_scores:
            return f"No valid articles found for sentiment analysis of {symbol}."

        avg_sentiment = sum(sentiment_scores) / len(sentiment_scores)
        sentiment = "positive" if avg_sentiment > 0 else "negative" if avg_sentiment < 0 else "neutral"

        return f"The sentiment for {symbol} based on recent news is {sentiment}."
    
    except Exception as e:
        return f"Error analyzing sentiment for {symbol}: {str(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:
        tz = pytz.timezone(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='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, get_stock_price, stock_news_sentiment], ## add your tools here (don't remove final answer)
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name="Stock Investment Advisor",
    description="Analyzes stock trends and news sentiment to provide investment advice.",
    prompt_templates=prompt_templates
)


GradioUI(agent).launch()