Update handler.py

handler.py

@@ -3,7 +3,8 @@ from io import BytesIO
 import base64
 import logging
 import uform
-
+from PIL import Image
+import numpy as np
 
 class EndpointHandler():
     def __init__(self, path=""):
@@ -11,9 +12,31 @@ class EndpointHandler():
 
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """
-       data args:
+        data args:
             image (:obj:`string`)
             candidates (:obj:`list`)
-      Return:
-            A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
-        """
+        Return:
+            A :obj:`list`: une liste permettant de passer les embedding 
+        """
+        inputs_request = data.pop("inputs", data)
+
+        # decode base64 image to PIL
+        image = Image.open(BytesIO(base64.b64decode(inputs_request['image'])))
+        text = inputs_request['text']
+
+        image_data = self.processor.preprocess_image(image)
+        text_data = self.processor.preprocess_text(text)
+
+        image_features, image_embedding = self.model.encode_image(image_data)
+        text_features, text_embedding = self.model.encode_text(text_data)
+        joint_embedding = self.model.encode_multimodal(image=image_data, text=text_data)
+
+        # Convert embeddings to lists of floats
+        serializable_results = {
+            'joint_embedding': joint_embedding.tolist() if isinstance(joint_embedding, np.ndarray) else joint_embedding,
+            'text_embedding': text_embedding.tolist() if isinstance(text_embedding, np.ndarray) else text_embedding,
+            'image_embedding': image_embedding.tolist() if isinstance(image_embedding, np.ndarray) else image_embedding
+        }
+
+        return serializable_results
+