Create handler.py

handler.py

@@ -0,0 +1,22 @@
+from transformers import AutoImageProcessor, Swinv2Model
+import torch
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.model = Swinv2Model.from_pretrained("microsoft/moooji/swinv2-large-patch4-window12to24-192to384-22kto1k-ft").to(device)
+        self.processor = AutoImageProcessor.from_pretrained("microsoft/moooji/swinv2-large-patch4-window12to24-192to384-22kto1k-ft")
+
+    def __call__(self, data: Any) -> List[float]:
+        inputs = data.pop("inputs", data)
+
+        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))  
+        inputs = self.processor(image, return_tensors="pt").to(device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+        
+        last_hidden_states = outputs.last_hidden_state
+        return last_hidden_states[2].tolist()
+