blip2

handler.py

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+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/blip2-opt-2.7b")
+        self.model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b")
+
+        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)

requirements.txt

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+git+https://github.com/huggingface/transformers
+git+https://github.com/huggingface/accelerate