Upload handler.py

handler.py

@@ -0,0 +1,41 @@
+from transformers import BlipProcessor, BlipForConditionalGeneration
+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: 
+        """
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.processor = BlipProcessor.from_pretrained(path)
+        self.model = BlipForConditionalGeneration.from_pretrained(path, torch_dtype=torch.float16).to(self.device)
+        
+    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
+        """
+        result = {}
+        inputs = data.pop("inputs", data)
+        image_url = inputs['image_url']
+        if "prompt" in inputs:
+            prompt = inputs["prompt"]
+        else:
+            prompt = None
+        image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
+        if prompt:
+            processed_image = self.processor(images=image, text=prompt, return_tensors="pt").to(self.device, torch.float16)
+        else:
+            processed_image = self.processor(images=image, return_tensors="pt").to(self.device, torch.float16)
+        output = self.model.generate(**processed_image)
+        text_output = self.processor.decode(output[0], skip_special_tokens=True)
+        result["text_output"] = text_output
+        feature_vector = output.last_hidden_state[:, 0, :]
+        return result