| 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)}] | |