Upload handler.py with huggingface_hub
Browse files- handler.py +43 -0
handler.py
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from typing import Dict, List, Any
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from transformers import AutoFeatureExtractor, EfficientNetForImageClassification
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import torch
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from PIL import Image
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import io
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import base64
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class EndpointHandler:
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def __init__(self, path=""):
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Load feature extractor
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self.feature_extractor = AutoFeatureExtractor.from_pretrained(path)
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# Load model
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self.model = EfficientNetForImageClassification.from_pretrained(path)
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# Replace the classification head with a regression head
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self.model.classifier = torch.nn.Linear(self.model.classifier.in_features, 1)
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# Load custom weights
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self.model.load_state_dict(torch.load(f"{path}/model.pt", map_location=self.device))
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self.model.to(self.device)
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self.model.eval()
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
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# Get the image data from the request
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image_bytes = data.get("inputs", "")
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# Decode and open the image
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image = Image.open(io.BytesIO(base64.b64decode(image_bytes))).convert('RGB')
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# Prepare the image for the model
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inputs = self.feature_extractor(images=image, return_tensors="pt")
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inputs = {k: v.to(self.device) for k, v in inputs.items()}
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# Make prediction
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with torch.no_grad():
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outputs = self.model(**inputs)
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prediction = outputs.logits.item() # For regression, we directly use the output
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return [{"prediction": float(prediction)}]
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