added custom handler

handler.py

@@ -0,0 +1,22 @@
+import requests
+from PIL import Image
+from transformers import Blip2Processor, Blip2ForConditionalGeneration
+from typing import Dict, List, Any
+import torch
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.processor = Blip2Processor.from_pretrained("Salesforce/blip-image-captioning-large")
+        self.model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.model.to(self.device)
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        image = data.pop("inputs", data)
+
+        processed = self.processor(images=image, return_tensors="pt").to(self.device)
+
+        out = self.model.generate(**processed)
+
+        return self.processor.decode(out[0], skip_special_tokens=True)