handler.py

from typing import Dict, List, Any
from tangoflux import TangoFluxInference
import torchaudio
#from huggingface_inference_toolkit.logging import logger

class EndpointHandler():
    def __init__(self, path=""):
        # Preload all the elements you are going to need at inference.
        # pseudo:
        # self.model= load_model(path)
        self.model = TangoFluxInference(name='declare-lab/TangoFlux',device='cuda')


    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
       data args:
            inputs (:obj: `str` | `PIL.Image` | `np.array`)
            kwargs
      Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """

        logger.info(f"Received incoming request with {data=}")

        if "inputs" in data and isinstance(data["inputs"], str):
            prompt = data.pop("inputs")
        elif "prompt" in data and isinstance(data["prompt"], str):
            prompt = data.pop("prompt")
        else:
            raise ValueError(
                "Provided input body must contain either the key `inputs` or `prompt` with the"
                " prompt to use for the audio generation, and it needs to be a non-empty string."
            )

        parameters = data.pop("parameters", {})

        num_inference_steps = parameters.get("num_inference_steps", 50)
        duration = parameters.get("duration", 10)
        guidance_scale = parameters.get("guidance_scale", 3.5)

        
        return self.model.generate(prompt,steps=num_inference_steps,
        duration=duration,
        guidance_scale=guidance_scale)