| import tensorflow as tf | |
| import numpy as np | |
| # Load trained model | |
| model = tf.keras.models.load_model("saved_model/Audio_Model_Classification.h5") | |
| # IMPORTANT: Keep same order as training (alphabetical default in TF) | |
| CLASS_NAMES = ["Baby Cry", "Chainsaw", "Clock Tick", "Cow", "Dog", "Fire Crackling", "Frog", "Helicopter", "Person Sneeze", "Pig", "Rain", "Rooster", "Sea Waves"] | |
| def predict(img): | |
| # Convert PIL image → numpy array | |
| img = np.array(img).astype("float32") / 255.0 # normalize [0,1] | |
| # Resize to match training target (231x232) | |
| img = tf.image.resize(img, (231, 232)) # (231, 232, 4) | |
| # Add batch dimension | |
| img = np.expand_dims(img, axis=0) # (1, 231, 232, 4) | |
| # Predict | |
| preds = model.predict(img) | |
| probs = preds[0] | |
| class_idx = int(np.argmax(probs)) | |
| confidence = float(np.max(probs)) | |
| prob_dict = {CLASS_NAMES[i]: float(probs[i]) for i in range(len(CLASS_NAMES))} | |
| return CLASS_NAMES[class_idx], confidence, prob_dict | |
| # THIS OUR PAST VERSION OF... IT WAS OUT RESIZING BUT EVEN THOUGH THE MODEL WAS WORKING FINE, SO, MAYBE TF COULD ACCEPT DYNAMIC SIZES OF INPUT IMAGES | |
| # def predict(img): | |
| # # Convert to numpy array (RGBA) | |
| # img = np.array(img) / 255.0 # shape (H, W, 4) | |
| # img = np.expand_dims(img, axis=0) # (1, H, W, 4) | |
| # | |
| # # Predict | |
| # preds = model.predict(img) | |
| # probs = preds[0] | |
| # | |
| # class_idx = int(np.argmax(probs)) | |
| # confidence = float(np.max(probs)) | |
| # prob_dict = {CLASS_NAMES[i]: float(probs[i]) for i in range(len(CLASS_NAMES))} | |
| # | |
| # return CLASS_NAMES[class_idx], confidence, prob_dict |