Upload handler.py

handler.py

@@ -0,0 +1,32 @@
+from transformers import CLIPModel, CLIPProcessor
+from typing import Dict, List, Any
+from PIL import Image
+from transformers import pipeline
+import requests
+import torch
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        """
+        path: 
+        """
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.processor = CLIPProcessor.from_pretrained(path)
+        self.model = CLIPModel.from_pretrained(path).to(self.device)
+        
+    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
+        """
+        result = {}
+        inputs = data.pop("inputs", data)
+        image_url = inputs['image_url']
+        image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
+        processed_image = self.processor(images=image, return_tensors="pt").to(self.device)
+        output = self.model.get_image_features(processed_image["pixel_values"])[0].tolist()
+        result["embedding"] = output
+        return result