added custom handler

handler.py

@@ -0,0 +1,38 @@
+from typing import Dict, List, Any
+from PIL import Image
+from io import BytesIO
+from transformers import CLIPProcessor, CLIPModel
+import base64
+import torch
+
+class EndpointHandler():
+    def __init__(self, path="."):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+     
+        self.model = CLIPModel.from_pretrained(path).to(self.device).eval()
+        self.processor = CLIPProcessor.from_pretrained(path)
+    
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            images (:obj:`PIL.Image`)
+            candiates (:obj:`list`)
+      Return:
+            A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
+        """
+        inputs = data.pop("inputs", data)
+
+        # decode base64 image to PIL
+        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
+        txt = inputs['text']
+        # preprocess image
+        txt = self.processor(text=txt, return_tensors="pt",padding=True).to(self.device)
+        image = self.processor(images=image, return_tensors="pt",padding=True).to(self.device)
+        with torch.no_grad():
+            txt_features = self.model.get_text_features(**txt)
+            image_features = self.model.get_image_features(**image)
+        img = image_features.tolist()
+        txt = txt_features.tolist()
+        pred = {"image": img, "text": txt}
+
+        return pred