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83f24dd | 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 | import tensorflow as tf
import numpy as np
from PIL import Image
# Load your trained CNN model
model = tf.keras.models.load_model(
"saved_model/InceptionV3_Dogs_and_Cats_Classification.h5",
compile=False
)
# Same label order you used when training (from LabelEncoder)
CLASS_NAMES = ["Cat", "Dog"]
def preprocess_image(img: Image.Image, target_size=(256, 256)):
img = img.convert("RGB") # ensure 3 channels
img = img.resize(target_size)
img = np.array(img).astype("float32") / 255.0 # normalize
img = np.expand_dims(img, axis=0) # (1, 256, 256, 3)
return img
def predict(img: Image.Image):
# Apply preprocessing
input_tensor = preprocess_image(img) # (1, 256, 256, 3)
# Model prediction (sigmoid output)
prob = float(model.predict(input_tensor)[0][0]) # probability of class 1 (Dog) or class 0 (Cat)
# Determine label based on 0.5 threshold
if prob >= 0.5:
label = CLASS_NAMES[1] # "Dog"
else:
label = CLASS_NAMES[0] # "Cat"
# Confidence and probability dictionary
confidence = prob if label == CLASS_NAMES[1] else 1 - prob
prob_dict = {
CLASS_NAMES[0]: 1 - prob,
CLASS_NAMES[1]: prob
}
return label, confidence, prob_dict |