Create handler and requirements.txt

handler.py

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+from typing import Dict, List, Any
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from PIL import Image
+import requests
+
+class EndpointHandler():
+  def __init__(self, path=""):
+    self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+    self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+
+  def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """
+    data args:
+        image_url (:obj: `str`): URL of the image to be captioned
+    Return:
+        A :obj:`list` | `dict`: will be serialized and returned
+    """
+    # get inputs
+    image_url = data.pop("image_url", None)
+
+    # check if image_url exists
+    if image_url is None:
+        return [{"error": "image_url not provided"}]
+
+    # get image from URL
+    try:
+        raw_image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
+    except:
+        return [{"error": "unable to load image from the provided URL"}]
+
+    # unconditional image captioning
+    inputs = self.processor(raw_image, return_tensors="pt")
+
+    # generate captions
+    out = self.model.generate(**inputs)
+
+    # return the generated captions
+    return [{"caption": self.processor.decode(out[0], skip_special_tokens=True)}]

requirements.txt

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+torch
+transformers
+pillow
+requests