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added handler for inference endpoints

handler.py

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+from typing import Any, Dict
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from PIL import Image
+from io import BytesIO
+import torch
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.model = BlipForConditionalGeneration.from_pretrained("quadranttechnologies/qhub-blip-image-captioning-finetuned").to(device)
+        self.processor = BlipProcessor.from_pretrained("quadranttechnologies/qhub-blip-image-captioning-finetuned")
+        self.model.eval()
+        self.model = self.model.to(device).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 {"descriptions": ["Description of the image"]} containing :
+                - "description": A string corresponding to the generated description.
+        """
+
+        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
+            )
+        description = self.processor.batch_decode(out, skip_special_tokens=True)
+
+        return {"description": description}

requirements.txt

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+pillow
+transformers