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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 |