Create inference.py
Browse files- inference.py +52 -0
inference.py
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import tensorflow as tf
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import numpy as np
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from PIL import Image
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from typing import Dict
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# Load the ResNetV2 model
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model = tf.keras.models.load_model("resnetv2_model.h5")
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# Define the handler for the Inference API
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def predict(inputs: Dict) -> Dict:
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"""
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Handle inference requests.
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Args:
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inputs (Dict): A dictionary with a key 'image' containing the base64-encoded image.
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Returns:
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Dict: A dictionary containing the predicted class label.
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"""
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# Decode the image
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if "image" not in inputs:
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return {"error": "No image found in inputs"}
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# Preprocess the input image
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image = preprocess_image(inputs["image"])
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# Perform inference
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prediction = model.predict(image)
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predicted_class = np.argmax(prediction, axis=1)[0] # Get the predicted class index
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# Return the predicted class
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return {"label": int(predicted_class)}
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def preprocess_image(image_base64: str) -> np.ndarray:
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"""
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Preprocess the input image for ResNetV2.
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Args:
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image_base64 (str): Base64-encoded image.
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Returns:
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np.ndarray: Preprocessed image ready for inference.
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"""
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from io import BytesIO
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import base64
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# Decode the base64 image
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image_data = base64.b64decode(image_base64)
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image = Image.open(BytesIO(image_data)).convert("RGB")
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# Resize and normalize the image
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image = image.resize((224, 224))
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image_array = np.array(image) / 255.0 # Normalize pixel values to [0, 1]
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image_array = np.expand_dims(image_array, axis=0) # Add batch dimension
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return image_array
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