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import requests |
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from PIL import Image |
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from transformers import Blip2Processor, Blip2ForConditionalGeneration |
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from typing import Dict, List, Any |
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import torch |
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import base64 |
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from io import BytesIO |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-6.7b") |
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self.model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-6.7b") |
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self.device = "cuda" if torch.cuda.is_available() else "cpu" |
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self.model.to(self.device) |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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image_encoded = data.pop("inputs", data) |
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text = data["text"] |
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image = self.decode_base64_image(image_encoded) |
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processed = self.processor(images=image, text=text, return_tensors="pt").to(self.device) |
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out = self.model.generate(**processed) |
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return self.processor.decode(out[0], skip_special_tokens=True) |
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def decode_base64_image(self, image_string): |
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base64_image = base64.b64decode(image_string) |
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buffer = BytesIO(base64_image) |
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image = Image.open(buffer) |
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return image |
