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2f34ba3 22a70b4 2f34ba3 22a70b4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | # predict.py
from tensorflow.keras.preprocessing import image
import numpy as np
import tensorflow as tf
from model_loader import load_model
model = load_model()
# Pre-calculated optimal temperature value
TEMPERATURE = 2.77
class_names_mapping = {
0: "AKIEC",
1: "BCC",
2: "BKL",
3: "DF",
4: "MEL",
5: "NV",
6: "VASC"
}
full_names = {
"AKIEC": "Actinic Keratoses and Intraepithelial Carcinoma (AKIEC)",
"BCC": "Basal Cell Carcinoma (BCC)",
"BKL": "Benign Keratosis-like Lesions (BKL)",
"DF": "Dermatofibroma (DF)",
"MEL": "Melanoma (MEL)",
"NV": "Melanocytic Nevi (NV)",
"VASC": "Vascular Lesions (VASC)"
}
def preprocess_image(file):
img = image.load_img(file, target_size=(224, 224))
img_array = image.img_to_array(img) / 255.0
img_array = (img_array - 0.5) * 2
return np.expand_dims(img_array, axis=0)
def predict_image(file):
processed = preprocess_image(file)
# Obtaining logits by disabling the final softmax
logits_model = tf.keras.Model(inputs=model.input, outputs=model.layers[-2].output)
logits = logits_model(processed)
# Apply final layer without softmax (if the last layer is Dense with softmax)
final_dense = model.layers[-1]
logits = final_dense(logits)
# Apply T-scaling: logits / T, then softmax
scaled_logits = logits / TEMPERATURE
scaled_probs = tf.nn.softmax(scaled_logits).numpy()[0]
class_idx = int(np.argmax(scaled_probs))
top_label = full_names[class_names_mapping[class_idx]]
top_confidence = float(scaled_probs[class_idx])
all_predictions = [
{"label": class_names_mapping[i], "confidence": float(pred)}
for i, pred in enumerate(scaled_probs)
]
return top_label, top_confidence, all_predictions
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