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from smolagents import CodeAgent, load_tool, tool, LiteLLMModel
import datetime
import os
import pytz
import yaml
from tools.final_answer import FinalAnswerTool
from tools.calories_checker import CaloriesCheckerTool
import gradio as gr
from Gradio_UI import GradioUI
@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()
calories_checker = CaloriesCheckerTool()
# 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'
gemini_api_key = os.environ.get("GEMINI_API_KEY", None)
model = LiteLLMModel(
model_id="gemini/gemini-2.0-flash-lite",
api_key=gemini_api_key,
)
# 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)
prompt_templates["final_answer"] = {
"pre_messages": "",
"final_answer": "Here is the final answer from your managed agent '{{name}}':\n{{final_answer}}",
"post_messages": "",
}
# print("Loaded prompt templates:", prompt_templates)
agent = CodeAgent(
model=model,
tools=[
final_answer,
calories_checker,
],
max_steps=6,
verbosity_level=1,
grammar=None,
planning_interval=None,
name="NutriCoach",
description="Assistant to help you with nutritional information, meal planning, and food-related queries.",
prompt_templates=prompt_templates,
)
GradioUI(agent).launch()
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