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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)) |