fixed tabs and spaces

handler.py

@@ -5,40 +5,39 @@ from flask import Flask, Response, request, jsonify
 from segment_anything import SamPredictor, sam_model_registry
 
 class EndpointHandler():
-    def __init__(self, path=""):
-        # Preload all the elements you are going to need at inference.
-        model_type = "vit_b"
-        # prefix = "/opt/ml/model"
-        model_path = "tf_model.h5"
-        # model_checkpoint_path = os.path.join(prefix, "sam_vit_h_4b8939.pth")
-
-        sam = sam_model_registry[model_type](checkpoint=model_path)
-        self.predictor = SamPredictor(sam)
-
-    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
-        """
-
-        inputs = data.pop("inputs", data)
-        image_url = inputs.pop("imageUrl", None)
-
-        if not image_url:
-            return jsonify({"error": "image_url not provided"}), 400
-
-        try:
-            response = requests.get(image_url)
-            response.raise_for_status()
-            image = response.content
-        except requests.RequestException as e:
-            return jsonify({"error": f"Error downloading image: {str(e)}"}), 500
-
-        self.predictor.set_image(image)
-
-        image_embedding = self.predictor.get_image_embedding().cpu().numpy().tolist()
-
-        return jsonify(image_embedding)
+	def __init__(self, path=""):
+		# Preload all the elements you are going to need at inference.
+		model_type = "vit_b"
+		# prefix = "/opt/ml/model"
+		model_path = "tf_model.h5"
+		# model_checkpoint_path = os.path.join(prefix, "sam_vit_h_4b8939.pth")
+		sam = sam_model_registry[model_type](checkpoint=model_path)
+		self.predictor = SamPredictor(sam)
+	
+	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
+		"""
+		
+		inputs = data.pop("inputs", data)
+		image_url = inputs.pop("imageUrl", None)
+		
+		if not image_url:
+			return jsonify({"error": "image_url not provided"}), 400
+			
+		try:
+			response = requests.get(image_url)
+			response.raise_for_status()
+			image = response.content
+		except requests.RequestException as e:
+			return jsonify({"error": f"Error downloading image: {str(e)}"}), 500
+			
+		self.predictor.set_image(image)
+		
+		image_embedding = self.predictor.get_image_embedding().cpu().numpy().tolist()
+		
+		return jsonify(image_embedding)