app.py

from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
import requests
import pytz
import yaml
from dateutil import parser
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 calculate_age(birthdate: str) -> int:
    """
    Parses a natural language birthdate string and calculates the age.
    Args:
        birthdate (str): A string representing the birthdate in natural language (e.g., "I was born on March 17, 1993, by evening 6").
    Returns:
        int: The calculated age, or -1 if parsing fails.
    """
    if not birthdate or not isinstance(birthdate, str):
        print("Invalid input: birthdate must be a non-empty string.")
        return -1
    try:
        birth_date = parser.parse(birthdate, fuzzy=True)
        today = datetime.now(pytz.UTC).date()
        age = today.year - birth_date.year
        
        if (today.month, today.day) < (birth_date.month, birth_date.day):
            age -= 1
        
        return age
    except Exception as e:
        print(f"Error parsing birthdate: {e}")
        return -1

final_answer = FinalAnswerTool()
search_tool = DuckDuckGoSearchTool()

@tool
def get_lifestyle_recommendations_web(age: int) -> str:
    """Fetches lifestyle recommendations based on age using web search.
    Args:
        age: The calculated age of the user.
    Returns:
        A summary of lifestyle recommendations.
    """
    search_query = f"Lifestyle health and wellness tips for {age} years old"
    return search_tool(search_query) 

@tool
def get_lifestyle_recommendations_offline(age: int) -> str:
    """Provides daily lifestyle habits based on age with the following dataset.
    Args:
        age: The age of the user.
    """
    habits = {
        (10, 20): ["Stretching or Yoga", "Learn a new skill daily", "Keep a journal"],
        (20, 30): ["Strength training", "Time-block work/study", "Socialize daily"],
        (30, 40): ["Regular cardio", "Reduce screen time", "Practice gratitude"],
        (40, 50): ["Mindful eating", "Prioritize deep work", "Spend time with family"],
        (50, 60): ["Daily walks", "Mentor someone", "Explore a hobby"],
        (60, 100): ["Light exercise", "Read daily", "Stay socially engaged"],
    }
    
    for age_range, habit_list in habits.items():
        if age_range[0] <= age <= age_range[1]:
            return f"Based on your age ({age}), here are 3 habits to follow daily:\n1️⃣ {habit_list[0]}\n2️⃣ {habit_list[1]}\n3️⃣ {habit_list[2]}"
    
    return "Age not in expected range. Try again."
@tool
def get_lifestyle_recommendations(age: int, user_input: str) -> str:
    """Fetches lifestyle recommendations based on age.
    Args:
        age: The calculated age of the user.
        user_input: The user's input as natural language.
    Returns:
        A summary of lifestyle recommendations as a string.
    """
    try:
        if determine_user_preference(user_input): 
            web_recommendations = get_lifestyle_recommendations_web(age)
            return web_recommendations  # Web search
        else:
            offline_recommendations = get_lifestyle_recommendations_offline(age)
            return offline_recommendations  # Offline dataset
    except Exception as e:
        return "An error occurred while fetching recommendations."
@tool
def determine_user_preference(user_input: str) -> bool:
    """
    Determines if the user prefers web-based recommendations based on input.
    Args:
        user_input: The user's natural language input.
    Returns:
        True if web-based recommendations should be used, False otherwise.
    """
    prompt = f"""
    The user has provided the following request: "{user_input}".
    Determine if the user is explicitly asking for real-time or external data-based insights. 
    Respond with ONLY 'yes' or 'no'. Do not include any additional text or explanation.
    - 'yes' if the user wants real-time or web-based recommendations.
    - 'no' if the user does not explicitly ask for real-time or web-based recommendations.
    """
    
    try:
        response = model(prompt)
        
        response = response.strip().lower()
        if response == "yes":
            return True
        elif response == "no":
            return False
        else:
            return False
    except Exception as e:
        return False
        
# 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_lifestyle_recommendations,calculate_age], ## add your tools here (don't remove final answer)
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name='LifestyleAdvisor',
    description='Provides lifestyle recommendations based on age',
    prompt_templates=prompt_templates
)


GradioUI(agent).launch()