add custom handler

handler.py

@@ -0,0 +1,37 @@
+from typing import Dict, List, Any
+from transformers import CLIPTokenizer, CLIPModel
+import numpy as np
+import os
+import torch
+
+
+class EndpointHandler:
+    def __init__(self, path="."):
+        # load the model
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = CLIPModel.from_pretrained(path).to(self.device).eval()
+        self.tokenizer = CLIPTokenizer.from_pretrained(path)
+
+    def __call__(self, data: Dict[str, Any]) -> List[float]:
+        """
+         data args:
+              inputs (:obj: `str` | `PIL.Image` | `np.array`)
+              kwargs
+        Return:
+              A :obj:`list` | `dict`: will be serialized and returned
+        """
+        # compute the embedding of the input
+        query = data["inputs"]
+        inputs = self.tokenizer(query, padding=True, return_tensors="pt").to(
+            self.device
+        )
+        with torch.no_grad():
+            text_features = self.model.get_text_features(**inputs)
+
+        text_features = text_features.cpu().detach().numpy()
+        input_embedding = text_features[0]
+
+        # normalize the embedding
+        input_embedding /= np.linalg.norm(input_embedding)
+
+        return input_embedding.tolist()