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classification_teath

Image Classification

Image Classification

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classification_teath / app.py

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Update app.py

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	from PIL import Image
	import tensorflow as tf
	import numpy as np
	#from google.colab import files

	#model_path = 'final_teath_classifier.h5'
	# Load the model
	model = tf.keras.models.load_model(model_path)

	def preprocess_image(image: Image.Image) -> np.ndarray:
	# Resize the image to match input size
	image = image.resize((256, 256))
	# Convert image to array and preprocess input
	img_array = np.array(image) / 255.0
	# Add batch dimension
	img_array = np.expand_dims(img_array, axis=0)
	return img_array

	def predict_image(image_path):
	img = Image.open(image_path)
	# Preprocess the image
	img_array = preprocess_image(img)
	predictions = model.predict(img_array)
	predicted_class = np.argmax(predictions)
	if predicted_class == 0:
	predict_label = "Clean"
	else:
	predict_label = "Carries"


	return predict_label,predictions.flatten()
	# Upload the image
	#uploaded = files.upload()

	# Get the uploaded image file name
	#image_path = list(uploaded.keys())[0]

	predict_label, logits = predict_image(image_path)
	print("Predicted class:", predict_label)
	print("Evaluate:", ', '.join(f"{logits*100:.4f}%" for logits in logits))