app.py

from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
import requests
import pytz
import yaml
import yfinance as yf
from ta.momentum import RSIIndicator, StochasticOscillator
from ta.trend import MACD
from ta.volume import volume_weighted_average_price
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_stock_price(ticker: str) -> Union[Dict, str]:
    """
    A tool that fetches the historical stock price data and technical indicators for a given ticker.
    Args:
        ticker: A string representing a stocke ticker name (e.g AAPL)
    """

    try:
        data = yf.download(
            ticker,
            start=dt.datetime.now() - dt.timedelta(weeks=24 * 3),
            end=dt.datetime.now(),
            interval="1wk",
        )
        df = data.copy()
        data.reset_index(inplace=True)
        data.Date = data.Date.astype(str)

        indicators = {}

        rsi_series = RSIIndicator(df["Close"], window=14).rsi().iloc[-12:]
        indicators["RSI"] = {
            date.strftime("%Y-%m-%d"): int(value)
            for date, value in rsi_series.dropna().to_dict().items()
        }

        stochastic_series = (
            StochasticOscillator(df["High"], df["Low"], df["Close"], window=14)
            .stoch()
            .iloc[-12:]
        )
        indicators["Stochastic Oscillator"] = {
            date.strftime("%Y-%m-%d"): int(value)
            for date, value in stochastic_series.dropna().to_dict().items()
        }

        macd = MACD(df["Close"])
        macd_series = macd.macd().iloc[-12:]
        indicators["MACD"] = {
            date.strftime("%Y-%m-%d"): int(value)
            for date, value in macd_series.to_dict().items()
        }

        macd_signal_series = macd.macd_signal().iloc[-12:]
        indicators["MACD Signal"] = {
            date.strftime("%Y-%m-%d"): int(value)
            for date, value in macd_signal_series.to_dict().items()
        }

        vwap_series = volume_weighted_average_price(
            df["High"], df["Low"], df["Close"], df["Volume"]
        ).iloc[-12:]
        indicators["vwap"] = {
            date.strftime("%Y-%m-%d"): int(value)
            for date, value in vwap_series.to_dict().items()
        }

        return {"stock_price": data.to_dict(orient="records"), "indicators": indicators}
    except Exception as e:
        return f"Error fetching price data: {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:
        # 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='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], ## 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()