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from typing import Dict, List, Any
from PIL import Image
from io import BytesIO
from transformers import CLIPProcessor, CLIPModel
import base64
import torch
class EndpointHandler():
def __init__(self, path="."):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = CLIPModel.from_pretrained(path).to(self.device).eval()
self.processor = CLIPProcessor.from_pretrained(path)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
images (:obj:`PIL.Image`)
candiates (:obj:`list`)
Return:
A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
"""
inputs = data.pop("inputs", data)
# decode base64 image to PIL
image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
txt = inputs['text']
# preprocess image
txt = self.processor(text=txt, return_tensors="pt",padding=True).to(self.device)
image = self.processor(images=image, return_tensors="pt",padding=True).to(self.device)
with torch.no_grad():
txt_features = self.model.get_text_features(**txt)
image_features = self.model.get_image_features(**image)
img = image_features.tolist()
txt = txt_features.tolist()
pred = {"image": img, "text": txt}
return pred
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