feat: add custom handler

handler.py

@@ -0,0 +1,49 @@
+# +
+from typing import  Dict, List, Any
+from PIL import Image
+import torch
+import os
+from io import BytesIO
+from transformers import BlipForConditionalGeneration, BlipProcessor
+# -
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load the optimized model
+        
+        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") 
+        self.model = BlipForConditionalGeneration.from_pretrained(
+            "Salesforce/blip-image-captioning-base"
+        ).to(device)
+        self.model.eval()
+        self.model = self.model.to(device)
+        
+
+
+    def __call__(self, data: Any) -> Dict[str, Any]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`dict`:. The object returned should be a dict of one list like {"captions": ["A hugging face at the office"]} containing :
+                - "caption": A string corresponding to the generated caption.
+        """
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", {})
+ 
+        raw_images = [Image.open(BytesIO(_img)) for _img in inputs]
+                                     
+        processed_image = self.processor(images=raw_images, return_tensors="pt") 
+        processed_image["pixel_values"] = processed_image["pixel_values"].to(device)
+        processed_image = {**processed_image, **parameters}
+        
+        with torch.no_grad():
+            out = self.model.generate(
+                **processed_image
+            )
+        captions = self.processor.batch_decode(out, skip_special_tokens=True)
+        # postprocess the prediction
+        return {"captions": captions}