app.py

from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import spaces
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
def calculate_vat(price: float, country: str) -> str:
    """
    Calculates the final price including standard VAT for Spain or Portugal.

    Args:
        price: Price before VAT.
        country: Country where VAT should be applied. Use "Spain" or "Portugal".
    """
    
    country = country.lower().strip()

    if country in ["spain", "españa"]:
        vat_rate = 0.21
    elif country in ["portugal"]:
        vat_rate = 0.23
    else:
        return "Country not supported. Please use Spain or Portugal."

    vat_amount = price * vat_rate
    final_price = price + vat_amount

    return (
        f"Base price: €{price:.2f}. "
        f"VAT rate: {vat_rate * 100:.0f}%. "
        f"VAT amount: €{vat_amount:.2f}. "
        f"Final price: €{final_price:.2f}."
    )

@tool
def calculate_field_of_view(
    focal_length: float,
    sensor_format: str
) -> str:
    """
    Calculates the full-frame equivalent focal length and approximate
    diagonal field of view for different camera sensor formats.

    Args:
        focal_length: Actual focal length of the lens in millimeters.
        sensor_format: Sensor format. Supported values include:
            "full frame",
            "canon aps-c",
            "aps-c",
            "micro four thirds",
            "mft",
            "4/3".
    """

    import math

    sensor_format = sensor_format.lower().strip()

    if sensor_format in ["full frame", "full-frame", "ff"]:
        crop_factor = 1.0
        sensor_name = "Full Frame"

    elif sensor_format in [
        "canon aps-c",
        "canon apsc",
        "canon crop"
    ]:
        crop_factor = 1.6
        sensor_name = "Canon APS-C"

    elif sensor_format in [
        "aps-c",
        "apsc",
        "nikon aps-c",
        "nikon dx",
        "sony aps-c",
        "fujifilm aps-c",
        "fuji aps-c",
        "pentax aps-c"
    ]:
        crop_factor = 1.5
        sensor_name = "APS-C (1.5x)"

    elif sensor_format in [
        "micro four thirds",
        "micro 4/3",
        "mft",
        "4/3",
        "four thirds"
    ]:
        crop_factor = 2.0
        sensor_name = "Micro Four Thirds"

    else:
        return (
            "Unsupported sensor format. Try Full Frame, Canon APS-C, "
            "APS-C, or Micro Four Thirds."
        )

    equivalent_focal_length = focal_length * crop_factor

    # Full-frame diagonal is approximately 43.27 mm.
    # Dividing it by the crop factor gives the equivalent sensor diagonal.
    full_frame_diagonal = 43.27
    sensor_diagonal = full_frame_diagonal / crop_factor

    diagonal_fov = math.degrees(
        2 * math.atan(sensor_diagonal / (2 * focal_length))
    )

    return (
        f"Sensor format: {sensor_name}. "
        f"Crop factor: {crop_factor:.1f}x. "
        f"Actual focal length: {focal_length:.1f} mm. "
        f"Full-frame equivalent focal length: "
        f"{equivalent_focal_length:.1f} mm. "
        f"Approximate diagonal field of view: "
        f"{diagonal_fov:.1f} degrees."
    )

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)

#Aquí añado esto para ver si así arranca el app.
@spaces.GPU
def run_image_generation(prompt: str):
    return image_generation_tool(prompt)

@tool
def generate_image(prompt: str) -> str:
    """
    Generates an image from a textual description.

    Args:
        prompt: A detailed description of the image to generate.
    """
    return run_image_generation(prompt)

with open("prompts.yaml", 'r') as stream:
    prompt_templates = yaml.safe_load(stream)
    
agent = CodeAgent(
    model=model,
    tools=[
        generate_image,
        DuckDuckGoSearchTool(),
        get_current_time_in_timezone,
        calculate_vat,
        calculate_field_of_view,
        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()