Create handler.py

handler.py

@@ -0,0 +1,23 @@
+from typing import Dict, List, Any
+from PIL import Image
+import torch
+import base64
+from io import BytesIO
+from transformers import AutoProcessor, BlipForConditionalGeneration
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+        self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device)
+
+    def __call__(self, data: Any) -> List[float]:
+        inputs = data.pop("inputs", data)
+
+        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
+        inputs = self.processor(image, return_tensors="pt").to(device)
+        outputs = self.model(**inputs)
+        
+        return outputs.toList()
+