Added handler.py

handler.py

@@ -0,0 +1,27 @@
+import torch
+from typing import Dict, List, Any
+from transformers import pipeline
+
+
+# check for GPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Preload all the elements you are going to need at inference.
+        # pseudo:
+        self.pipeline= pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning", device=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
+        """
+        
+        inputs = data.pop("inputs", data)
+        return self.pipeline(inputs)
+