Create handler.py
Browse files- handler.py +42 -0
handler.py
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import base64
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from io import BytesIO
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from PIL import Image
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import torch
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from transformers import CLIPProcessor, CLIPModel
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from typing import Dict, Any
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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class EndpointHandler():
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def __init__(self, path=""):
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self.processor = CLIPProcessor.from_pretrained("openai/openai/clip-vit-large-patch14")
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self.model = CLIPModel.from_pretrained("openai/openai/clip-vit-large-patch14").to(device)
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self.model.eval()
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def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
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input_data = data.get("inputs", {})
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encoded_images = input_data.get("images")
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texts = input_data.get("texts", [])
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if not encoded_images or not texts:
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return {"error": "Both images and texts must be provided"}
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try:
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images = [Image.open(BytesIO(base64.b64decode(img))).convert("RGB") for img in encoded_images]
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inputs = self.processor(text=texts, images=images, return_tensors="pt", padding=True)
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# Move tensors to the same device as model
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inputs = {k: v.to(device) for k, v in inputs.items()}
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with torch.no_grad():
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outputs = self.model(**inputs)
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logits_per_image = outputs.logits_per_image # this is the image-text similarity score
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logits_per_text = outputs.logits_per_text # this is the text-image similarity score
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return {
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"logits_per_image": logits_per_image.cpu().numpy().tolist(),
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"logits_per_text": logits_per_text.cpu().numpy().tolist()
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}
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except Exception as e:
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print(f"Error during processing: {str(e)}")
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return {"error": str(e)}
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