Create handler.py

handler.py

@@ -0,0 +1,26 @@
+from typing import Dict, List, Any
+from PIL import Image
+from io import BytesIO
+from transformers import pipeline
+import base64
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.pipeline=pipeline("Image-to-Text",model=path)
+    
+    def __call__(self, data: Dict[str, Any]) -> str:
+        """
+       data args:
+            images (:obj:`string`)
+      Return:
+            A str containing a caption for the text
+        """
+        inputs = data.pop("inputs", data)
+
+        # decode base64 image to PIL
+        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
+
+        # run prediction one image wit provided candiates
+        prediction = self.pipeline(images=[image])
+        return prediction[0]