"""
NeuroScan AI Version Alpha
Created By Ishan Leung

Built Using Tensorflow and Python 3

Instructions:
(1) Drag keras_model.h5, labels.txt, and testing data (image files)
to files tab. Note: Drag files directly and DO NOT put the files into a
subfolder and drag that subfolder over. Ensure the files follow the naming
convention: image(num).jpg. ie: image(37).jpg

(2) Click Runtime --> Run All

(3) Enter file number for the file you want to test

(4) Interpret Results

TODO:
* Batch Upload
* Accuracy as a Percent
"""

from keras.models import load_model # TensorFlow is required for Keras to work
from PIL import Image, ImageOps  # Install pillow instead of PIL
import numpy as np

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

# Load the model
model = load_model("Tumor Data\Keras Model 3 - Trained on 800 imgs\keras_model.h5", compile=False)

# Load the labels
class_names = open("Tumor Data\Keras Model 3 - Trained on 800 imgs\labels.txt", "r").readlines()

# 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("Tumor Data\Testing\meningioma_tumor\image(28).jpg").convert("RGB")

# resizing the image to be at least 224x224 and then cropping from the center
size = (224, 224)
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)

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

# Normalize the image
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1

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

# Predicts the model
prediction = model.predict(data)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]

# Print prediction and confidence score
print("Class:", class_name[2:], end="")
print("Confidence Score:", confidence_score)