Delete handler.py

handler.py

@@ -1,44 +0,0 @@
-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)