Tensorflow Lite gives me 0% accuracy for any image - tensorflow

Question

Here is my Colab notebook which anyone should be able to run in 5 seconds. The predictions are always 0. I suspect the problem is in these two lines. Or perhaps my tflite model is corrupt or wrong?
label_id, prob = classify_image(interpreter, np.expand_dims(img_array, axis=0)/255 )
#label_id, prob = classify_image(interpreter, img )

Answer 1

Think I figured it out. Basically the output from the classify_image() function needs furthur processing to be useable. So I need to do this on the output:
label_index= np.argmax(probs_lite)
score = tf.nn.softmax(probs_lite)
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[label_index], 100 * np.max(score))
)
And here's my full script in case anyone has similiar issue in the future:
import itertools, time, os, numpy as np,sys
from PIL import Image
import tensorflow as tf
def lite_model(images):
interpreter.allocate_tensors()
interpreter.set_tensor(interpreter.get_input_details()[0]['index'], images)
interpreter.invoke()
return interpreter.get_tensor(interpreter.get_output_details()[0]['index'])
class_names = ['empty_court', 'occupied_court' ]
path_to_tflite = tf.keras.utils.get_file(
"court.tflite",
"https://mysite.site/AI/court.tflite",
untar=False)
interpreter = tf.lite.Interpreter( path_to_tflite )
interpreter.allocate_tensors()
_, height, width, _ = interpreter.get_input_details()[0]['shape']
print("Image Shape (", width, ",", height, ")")
image_url = tf.keras.utils.get_file('Court', origin='https://mysite.site/share/court4.jpg' )
img = tf.keras.preprocessing.image.load_img(image_url, target_size=( width, height ) )
os.remove(image_url) # Remove the cached file
img_array = tf.keras.preprocessing.image.img_to_array(img)
probs_lite = lite_model( np.expand_dims(img_array, axis=0)/255 )[0]
print ( probs_lite )
label_index= np.argmax(probs_lite)
score = tf.nn.softmax(probs_lite)
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[label_index], 100 * np.max(score))
)