po naciśnieciu "e" sieć sie uczy, po podjeściu do prawego górnego odpalane jest prediction

tiles.py

@@ -13,6 +13,7 @@ import matplotlib.pyplot as plt
 import os
 import cv2
 from tqdm import tqdm
+import keras
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
 from keras.layers import Conv2D, MaxPooling2D
@@ -642,8 +643,8 @@ def prepare_img(filepath):
     return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1) / 255
 
 
-def predict(model):
-    return model.predict([prepare_img('directory')])
+def predict(model,filepath):
+    return model.predict([prepare_img(filepath)])
 
 
 def result(prediction):
@@ -733,6 +734,11 @@ def main():
 
         key = pygame.key.get_pressed()
         left, middle, right = pygame.mouse.get_pressed()
+
+        if key[pygame.K_e]:
+            X, y = create_training_data()
+            model = learn_neural_network(X,y)
+
         if middle:
             waiterGo(mouseToNum())
         elif right:
@@ -800,9 +806,12 @@ def main():
         if waiter.loc == [576, 32]:
             if first_time:
                 number = np.random.randint(20)
-                image = pygame.image.load('test/' + str(number) + '.jpg')
+                path = ('test/' + str(number) + '.jpg')
+                image = pygame.image.load(path)
             first_time = False
             display_img(display, image)
+            prediction = predict(model, path)
+            result(prediction)
         else:
             first_time = True