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handler.py

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+from typing import Dict, List, Any
+import numpy as np
+from transformers import CLIPProcessor, CLIPModel
+from PIL import Image
+from io import BytesIO
+import base64
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Preload all the elements you we need at inference.
+        self.model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+        self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        inputs = data.get("inputs")
+        text = inputs.get("text")
+        imageData = inputs.get("image")
+        image = Image.open(BytesIO(base64.b64decode(imageData)))
+        inputs = self.processor(text=text, images=image, return_tensors="pt", padding=True)
+        outputs = self.model(**inputs)
+        embeddings = outputs.image_embeds.detach().numpy().flatten().tolist()
+        return { "embeddings": embeddings }

requirements.txt

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+pillow
+numpy