| import tensorflow as tf | |
| import numpy as np | |
| from PIL import Image | |
| import os | |
| MODEL_PATH = os.path.join( | |
| os.path.dirname(__file__), | |
| "saved_model", | |
| "Inception_V3_Animals_Classification.h5" | |
| ) | |
| model = tf.keras.models.load_model(MODEL_PATH) | |
| CLASS_NAMES = ["Cat", "Dog", "Snake"] | |
| def preprocess_image(img: Image.Image, target_size=(256, 256)): | |
| img = img.convert("RGB") | |
| img = img.resize(target_size) | |
| img = np.array(img).astype("float32") / 255.0 | |
| img = np.expand_dims(img, axis=0) | |
| return img | |
| def predict(img: Image.Image): | |
| input_tensor = preprocess_image(img) | |
| preds = model.predict(input_tensor)[0] | |
| class_idx = int(np.argmax(preds)) | |
| confidence = float(np.max(preds)) | |
| prob_dict = {CLASS_NAMES[i]: float(preds[i]) for i in range(len(CLASS_NAMES))} | |
| return CLASS_NAMES[class_idx], confidence, prob_dict |