Create handler.py

handler.py

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