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from transformers import Blip2Processor, Blip2Model
from typing import Dict, List, Any
from PIL import Image
from transformers import pipeline
import requests
import torch
class EndpointHandler():
def __init__(self, path=""):
"""
path:
"""
# Preload all the elements you are going to need at inference.
# pseudo:
# self.model= load_model(path)
#self.processor = Blip2Processor.from_pretrained(path)
#self.pipeline = pipeline(model = path)
self.path = path
self.device = "cuda" if torch.cuda.is_available() else "cpu"
#self.processor = Blip2Processor.from_pretrained(path)
#self.model = Blip2Model.from_pretrained(path, torch_dtype=torch.float16)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
inputs = data.pop("inputs", data)
image_url = inputs['image_url']
#image = Image.open(requests.get(image_url, stream=True).raw)
#processed_image = self.processor(images=image, return_tensors="pt").to(self.device, torch.float16)
#generated_ids = self.pipeline(**inputs)
#generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
return image_url, self.path, self.device
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