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from typing import Dict, List, Any
from PIL import Image
import torch
import base64
from io import BytesIO
from transformers import AutoProcessor, BlipForConditionalGeneration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class EndpointHandler():
def __init__(self, path=""):
self.processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device)
def __call__(self, data: Any) -> List[float]:
inputs = data.pop("inputs", data)
image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
inputs = self.processor(image, inputs['question'], return_tensors="pt").to(device)
outputs = self.model.generate(**inputs)
return processor.decode(out[0], skip_special_tokens=True)
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