app.py

import yaml
import os
import gradio as gr
from huggingface_hub import login
from smolagents import tool, CodeAgent, InferenceClientModel
from typing import Dict, List

# Activity from the second part of the Hugging Face Smolagents course
# student BettoEsteves

CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))

# Fixed calculation settings (can be adjusted)
MEAT_PER_PERSON = {
    "Picanha": 0.3, # grams per person
    "Ribs": 0.2,
    "Sausage": 0.1,
    "Chicken": 0.2,
    "Garlic bread": 0.1
}

DRINKS_PER_PERSON = {
    "Beer": 3, # cans
    "Soda": 1, # 2L bottles
    "Water": 1, # 500ml bottles
    "Juice": 0.5 # liters
}

@tool
def calcular_carnes(persons: int) -> float:
    """
    Calculates the amount of meat needed for the barbecue.

    Args:
        persons (int): Number of people attending the barbecue.

    Returns:
        float: Total amount of meat in kg.
    """
    return sum(MEAT_PER_PERSON.values()) * persons

@tool
def calcular_bebidas(persons: int) -> Dict[str, int]:
    """
    Calculates the amount of drinks for the given number of people.

    Args:
        persons (int): Number of people attending the barbecue.

    Returns:
        Dict[str, int]: Amount of each drink needed.
    """
    return {drink: round(qt * persons) for drink, qt in DRINKS_PER_PERSON.items()}

@tool
def sugerir_playlist(persons: int) -> List[str]:
    """
    Suggests a barbecue playlist based on the number of people.

    Args:
        persons (int): Number of people attending the barbecue.

    Returns:
        List[str]: Suggested list of songs.
    """
    try:
        from ddgs import DDGS
        query = "Brazilian barbecue songs samba pagode mpb funk sertanejo"
        with DDGS() as ddgs:
            resultados = [r['title'] for r in ddgs.text(query, max_results=10) if 'title' in r]
        if resultados:
            return resultados
    except Exception:
        pass
    return [
        "Cheia de Manias – Raça Negra",
        "Que Se Chama Amor – Só Pra Contrariar.",
        "Pimpolho – Art Popular.",
        "Deixa Acontecer – Grupo Revelação,",
        "Derê – Belo", 
        "O Show Tem Que Continuar – Grupo Fundo de Quintal.",
        "Foi Um Rio Que Passou Em Minha Vida – Paulinho da Viola.",
        "Verdade – Zeca Pagodinho",
        "É Tarde Demais – Grupo Raça.", 
        "Fio de Cabelo - Bruno & Marrone"
    ]

model = InferenceClientModel(
    model_id='Qwen/Qwen2.5-Coder-32B-Instruct',
    provider=None,
)

with open(os.path.join(CURRENT_DIR, "prompts.yaml"), 'r') as stream:
    prompt_templates = yaml.safe_load(stream)

token = os.getenv("HUGGINGFACE_TOKEN")
if token:
    login(token=token)

agent = CodeAgent(
    model=model,
    tools=[calcular_bebidas, calcular_carnes, sugerir_playlist],
    managed_agents=[],
    max_steps=10,
    verbosity_level=2,
    grammar=None,
    planning_interval=None,
    name=None,
    description=None,
    prompt_templates=prompt_templates
)


def greet(user_input, history=[]):
    # Se for a primeira pergunta, espera um número de pessoas
    if not history:
        try:
            persons = int(user_input)
            if persons <= 0:
                return [(user_input, "❌ Please enter a number greater than zero.")]
            carnes_total = calcular_carnes(persons)
            bebidas = calcular_bebidas(persons)
            playlist = sugerir_playlist(persons)
            # Lista de carnes detalhada
            carnes_lista = [f"🥩 {meat}: {qt*persons:.2f} kg" for meat, qt in MEAT_PER_PERSON.items()]
            carnes_str = '\n'.join(carnes_lista)
            # Lista de bebidas detalhada
            bebidas_lista = [f"🍺 {drink}: {qt}" if drink=="Beer" else f"🥤 {drink}: {qt}" for drink, qt in bebidas.items()]
            bebidas_str = '\n'.join(bebidas_lista)
            # Playlist formatada
            playlist_formatada = '\n'.join([f"🎶 {song}" for song in playlist])
            resposta = (
                f"🛒 Shopping List for {persons} people:\n"
                f"{carnes_str}\n"
                f"{bebidas_str}\n\n"
                f"Total Meat: {carnes_total:.2f} kg\n\n"
                f"🎵 Playlist:\n{playlist_formatada}"
            )
            history.append((user_input, resposta))
            return history
        except ValueError:
            return [(user_input, "❌ Please enter a valid number of people.")]
    # Para perguntas subsequentes, chama o agente
    resposta = agent.run(user_input)
    history.append((user_input, resposta))
    return history

demo = gr.Interface(
    fn=greet,
    inputs=gr.Textbox(label="How many people for the barbecue?"),
    outputs=gr.Chatbot(label="Conversation History"),
    title="Barbecue Agent",
    description="Enter the number of people and get a shopping list and playlist!"
)
demo.launch()

#if __name__ == "__main__":
#    main()