figured out image issue

handler.py

@@ -22,23 +22,26 @@ class EndpointHandler():
 
             logger.info('data is %s', data)
             text_input = None
-            if isinstance(data, dict):
-                print('data is a dict: ', data)
-                logger.info('data is a dict %s', data)
-                inputs = data.pop("inputs", None)
-                text_input = inputs["text"] if "text" in inputs else None
-                image_data = BytesIO(base64.b64decode(inputs['image'])) if 'image' in inputs else None
-            else:
-                # assuming its an image sent via binary
-                image_data = BytesIO(data)
 
+            logger.info('data is a dict %s', data)
+            inputs = data.pop("inputs", None)
+            text_input = inputs["text"] if "text" in inputs else None
+            image_data = inputs['image'] if 'image' in inputs else None
+
+            if image_data is not None:
+                if isinstance(image_data, Image):
+                    logger.info('image is an image')
+                    image = image_data
+                else:
+                    logger.info('image is encoded')
+                    image = BytesIO(base64.b64decode(image_data))
 
             if text_input:
                 processor = self.processor(text=text_input, return_tensors="pt", padding=True).to(device)
                 with torch.no_grad():
                     return {'embeddings':self.text_model(**processor).pooler_output.tolist()[0]}
-            elif image_data:
-                image = Image.open(image_data)
+            elif image:
+                # image = Image.open(image_data)
                 processor = self.processor(images=image, return_tensors="pt").to(device)
                 with torch.no_grad():
                     return {'embeddings':self.image_model(**processor).image_embeds.tolist()[0]}