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 bs4 import BeautifulSoup

from Gradio_UI import GradioUI

# Below is an example of a tool that does nothing. Amaze us with your creativity !
@tool
def scrape_webpage(url: str, tag: str = "p", class_name: str = None) -> dict:
    """Extrae contenido de una página web según una etiqueta HTML y clase opcional.
    
    Args:
        url: URL de la página a scrapear.
        tag: Etiqueta HTML a extraer (por defecto <p>).
        class_name: Clase CSS opcional para filtrar resultados.

    Returns:
        Un diccionario con el contenido extraído.
    """
    try:
        headers = {'User-Agent': 'Mozilla/5.0'}
        response = requests.get(url, headers=headers)
        response.raise_for_status()

        soup = BeautifulSoup(response.text, 'html.parser')
        
        if class_name:
            elements = soup.find_all(tag, class_=class_name)
        else:
            elements = soup.find_all(tag)

        extracted_data = [element.get_text(strip=True) for element in elements]

        return {"url": url, "scraped_data": extracted_data[:20]}  # Limita a 10 resultados

    except requests.exceptions.RequestException as e:
        return {"error": f"Error al acceder a la URL: {str(e)}"}
    except Exception as e:
        return {"error": f"Error inesperado: {str(e)}"}


@tool
def extract_metadata_from_url(url: str) -> dict:
    """Extrae todos los metadatos de una página web.
    
    Args:
        url: La URL de la página web a analizar.

    Returns:
        Un diccionario con los metadatos encontrados.
    """
    try:
        # Obtener el contenido de la página
        headers = {'User-Agent': 'Mozilla/5.0'}
        response = requests.get(url, headers=headers)
        response.raise_for_status()  # Lanza un error si el request falla

        # Parsear el contenido HTML con BeautifulSoup
        soup = BeautifulSoup(response.text, 'html.parser')

        # Extraer los metadatos de la página
        metadata = {}
        for meta in soup.find_all('meta'):
            if 'name' in meta.attrs and 'content' in meta.attrs:
                metadata[meta['name']] = meta['content']
            elif 'property' in meta.attrs and 'content' in meta.attrs:
                metadata[meta['property']] = meta['content']

        return metadata if metadata else {"error": "No se encontraron metadatos en la página."}

    except requests.exceptions.RequestException as e:
        return {"error": f"Error al acceder a la URL: {str(e)}"}

@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, extract_metadata_from_url, scrape_webpage], ## 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()