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("controlnet_qrcode-control_v11p_sd21",model=path)
+    
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            images (:obj:`string`)
+            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)
+        prompts = data.pop("prompt", data)
+
+        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
+
+        # run prediction one image wit provided candiates
+        prediction = self.pipeline(images=[image], prompt=[prompts])
+        return prediction[0]