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from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import smolagents # Make sure to import smolagents
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
# Tool 1: Ask for favorite weather and return a place with that weather
def weather_tool(agent_input: str) -> str:
"""
This tool asks for the user's favorite weather and returns a place where that weather is currently happening.
Args:
agent_input (str): The favorite weather type input by the user (e.g., 'sunny', 'rainy').
Returns:
str: A message indicating a place with the current weather type.
"""
# Predefined weather types and corresponding places (this can be expanded)
weather_to_places = {
"sunny": "Los Angeles, USA",
"rainy": "London, UK",
"snowy": "Moscow, Russia",
"cloudy": "Vancouver, Canada",
"stormy": "Miami, USA"
}
# Find the corresponding place for the favorite weather
weather = agent_input.lower()
if weather in weather_to_places:
return f"Your favorite weather is {weather}, and a place with such weather is {weather_to_places[weather]}."
else:
return "Sorry, I couldn't find a place with that weather type."
# Register the tool in Smolagents
#tool_1 = smolagents.Tool(name="FavoriteWeather", function=weather_tool)
# Register the tool
tool_1 = smolagents.tools.Tool(name="FavoriteWeather", function=weather_tool)
# Example of running the agent directly with the tool
#agent = smolagents.agents.Agent() # Use this if `Agent` is the correct class
#agent.add_tool(tool_1) # Add tool to agent
#response = agent.run("sunny") # Run the tool with an example input
#print(response) # Should print the response for "sunny" weather
# Tool 2: Ask for favorite color and return a shape associated with it
# def color_shape_tool(agent_input: str):
# Predefined colors and shapes (this can be expanded)
# color_to_shape = {
# "red": "circle",
# "blue": "square",
# "green": "triangle",
# "yellow": "rectangle",
# "purple": "pentagon"
# }
# Find the corresponding shape for the favorite color
# color = agent_input.lower()
# if color in color_to_shape:
# return f"Your favorite color is {color}, and the shape associated with it is a {color_to_shape[color]}."
# else:
# return "Sorry, I don't know a shape for that color."
# Register the tool in Smolagents
#tool_2 = smolagents.Tool(name="FavoriteColorShape", function=color_shape_tool)
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()