deployNeuralNetwork.py

import tensorflow.keras
from PIL import Image, ImageOps
import numpy as np

# Disable scientific notation for clarity
np.set_printoptions(suppress=True)


# Load the model
model = tensorflow.keras.models.load_model('keras_model.h5')

# Load the labels
with open('labels.txt', 'r') as f:
   class_names = f.read().split('\n')

# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1.
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)

# Replace this with the path to your image
#image = Image.open('thumbsUp.jpg')
#image = Image.open('wave.jpg')
image = Image.open('victory.jpg')

#resize the image to a 224x224 with the same strategy as in TM2:
#resizing the image to be at least 224x224 and then cropping from the center
size = (224, 224)
image = ImageOps.fit(image, size, Image.ANTIALIAS)

#turn the image into a numpy array
image_array = np.asarray(image)

# display the resized image
image.show()

# Normalize the image
normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1

# Load the image into the array
data[0] = normalized_image_array

# run the inference
prediction = model.predict(data)
print(prediction)

index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]

print("Class: ", class_name)
print("Confidence score: ", confidence_score)