Create handler.py

handler.py

@@ -0,0 +1,30 @@
+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.base_model_name = "Salesforce/blip2-opt-2.7b"
+        self.model_name = "sooh-j/blip2-vizwizqa"
+        self.base_model = Blip2ForConditionalGeneration.from_pretrained(self.base_model_name,
+                                                               load_in_8bit=True)
+        self.processor = Blip2Processor.from_pretrained(self.base_model_name)
+        self.model = PeftModel.from_pretrained(self.model_name, self.base_model_name)
+
+        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]]:
+        data = data.pop("inputs", data)
+
+        image = data.image
+        question = data.question
+        
+        prompt = f"Question: {question}, Answer:"
+        processed = self.processor(images=image, prompt, return_tensors="pt").to(self.device)
+
+        out = self.model.generate(**processed)
+
+        return self.processor.decode(out[0], skip_special_tokens=True)