app.py

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

import openmeteo_requests

import pandas as pd
import requests_cache
from retry_requests import retry

# 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_today_average_temperature(latitude:float, longitude:float)-> float:
    """A tool that fetches today average temperature for given latitude and longitude
    Args:
        latitude: A coordinate representing latitude
        longitude: A coordinate representing longitude
    """
    # Setup the Open-Meteo API client with cache and retry on error
    cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)
    retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
    openmeteo = openmeteo_requests.Client(session = retry_session)

    # Make sure all required weather variables are listed here
    # The order of variables in hourly or daily is important to assign them correctly below
    url = "https://api.open-meteo.com/v1/forecast"
    params = {
    	"latitude": latitude,
    	"longitude": longitude,
    	"hourly": "temperature_2m",
    	"models": "ecmwf_ifs025"
    }
    responses = openmeteo.weather_api(url, params=params)

    # Process first location. Add a for-loop for multiple locations or weather models
    response = responses[0]

    # Process hourly data. The order of variables needs to be the same as requested.
    hourly = response.Hourly()
    hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()

    hourly_data = {"date": pd.date_range(
    	start = pd.to_datetime(hourly.Time(), unit = "s", utc = True),
    	end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True),
    	freq = pd.Timedelta(seconds = hourly.Interval()),
    	inclusive = "left"
    )}

    hourly_data["temperature_2m"] = hourly_temperature_2m

    hourly_dataframe = pd.DataFrame(data = hourly_data)
    return float(hourly_dataframe["temperature_2m"].mean())

@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], ## 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()