Diabetic Retinopathy Detection - Python code - Prediction_with_Probabilities

import numpy as np
import keras
from keras import backend as K
from keras.layers.core import Dense, Dropout
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint
from keras.preprocessing.image import img_to_array, array_to_img, load_img
from sklearn.metrics import confusion_matrix
import itertools
import matplotlib.pyplot as plt
import tkinter as tk
from tkinter import filedialog
from tkinter import messagebox
import cv2
import matplotlib.pyplot as plt; plt.rcdefaults()
import matplotlib.pyplot as plt
from itertools import chain


K.tensorflow_backend._get_available_gpus()

# 0: 1.0, # normal
# 1: 10.0, # Mild_NPDR
# 2: 4.0, # Moderate_NPDR
# 3: 29.0, # Severe_NPDR
# 4: 36.0, # PDR

DISEASE_CLASSES ={
0: 'Normal',
1: 'Mild NPDR',
2: 'Moderate NPDR',
3: 'Severe NPDR',
4: 'Proliferative DR'
}

def Load_Training_Model():
# Create a MobileNet model
mobile = keras.applications.mobilenet.MobileNet()
x = mobile.layers[-6].output
x = Dropout(0.25)(x)
predictions = Dense(5, activation='softmax')(x)
model = Model(inputs=mobile.input, outputs=predictions)

for layer in model.layers[:-23]:
layer.trainable = False

model.load_weights('model.h5')

return model

def Predict_Test_Image_File(model):

root = tk.Tk()
root.withdraw()
imageFileName = filedialog.askopenfilename()

image = cv2.imread(imageFileName)

# pre-process the image for classification
image = cv2.resize(image, (224, 224))
image = image.astype("float") / 255.0
image = img_to_array(image)
image = np.expand_dims(image, axis=0)

predictions = model.predict(image)

print(predictions)

final_prediction = predictions.argmax(axis=1)[0]

print(final_prediction)

disease_name = DISEASE_CLASSES[final_prediction]

print(disease_name)

tk.messagebox.showinfo('Test Image Prediction',disease_name)

Show_Bar_Chart(predictions)

def Show_Bar_Chart(predictions_list):

objects = ('normal', 'Mild_NPDR','Moderate_NPDR', 'Severe_NPDR', 'PDR')
y_pos = np.arange(len(objects))

flatten_predictions_list = list(chain.from_iterable(predictions_list))
flatten_predictions_percentages = map(lambda x: round(x * 100, 2), flatten_predictions_list)

performance = list(flatten_predictions_percentages)
print(performance)

plt.bar(y_pos, performance, align='center', alpha=0.5)

for index,data in enumerate(performance):
plt.text(x=index , y =data+1 , s=f"{data}" , fontdict=dict(fontsize=10))

plt.xticks(y_pos, objects)
plt.ylabel('Percentage Probabilities')
plt.title('Disease Predictions')

plt.show()



if __name__ == "__main__":

model_definition = Load_Training_Model()

root= tk.Tk() # create window
root.withdraw()

MsgBox = tk.messagebox.askquestion ('Tensorflow Predictions','Do you want to test Images for Predictions')

while MsgBox == 'yes':
MsgBox = tk.messagebox.askquestion ('Test Image','Do you want to test new Image')
if MsgBox == 'yes':
Predict_Test_Image_File(model_definition)
else:
tk.messagebox.showinfo('EXIT', "Exiting the Application")
break

wheres the dataset