Upload handler.py

handler.py

@@ -0,0 +1,36 @@
+from transformers import Blip2Processor, Blip2ForConditionalGeneration
+from typing import Dict, List, Any
+from PIL import Image
+from transformers import pipeline
+import requests
+import torch
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        """
+        path: 
+        """
+        # Preload all the elements you are going to need at inference.
+        # pseudo:
+        # self.model= load_model(path)
+        #self.processor = Blip2Processor.from_pretrained(path)
+        self.pipeline = pipeline(model = path)
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `str` | `PIL.Image` | `np.array`)
+            kwargs
+      Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+        inputs = data.pop("inputs", data)
+        image_url = inputs['image_url']
+        #image = Image.open(requests.get(image_url, stream=True).raw)
+        #processed_image = self.processor(images=image, return_tensors="pt").to(self.device, torch.float16)
+
+        #generated_ids = self.pipeline(**inputs)
+        #generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+
+        return image_url