Delete predict.py

predict.py

@@ -1,87 +0,0 @@
-import os
-import keyboard
-import cv2
-import numpy as np
-import time
-print("program started!")
-print("Turning off OneDNN operations")
-os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' #Turns off OneDNN operations, which is just something that is automatically turned on to make cpu usage lower. However, as this model is prettly light, we don't need this.
-
-
-
-
-CLASSES = ['Glass', 'Metal', 'Paperboard', 'Plastic-Polystyrene', 'Plastic-Regular']
-import tensorflow as tf
-
-ensemble1 = tf.keras.models.load_model("recyclebot.keras")
-ensemble2 = tf.keras.models.load_model("72-75.keras")
-
-
-
-
-
-# Show the model architecture
-ensemble1.summary()
-ensemble2.summary()
-
-
-index = 1
-
-while True:
-     
-    
-    check = 0
-
-
-
-    test_image = cv2.resize(cv2.imread(input(f'''Path to image #{index} -- Please Replace Backslashes (\\) with forwardslashes (/) please!  
-                                            -->''')),  (240, 240))
-
-    test_image = np.array(test_image).reshape(-1, 240, 240, 3)
-
-    print(test_image.shape)
-
-    # Assign weights to each model
-    weight_1 = 0.70
-    weight_2 = 0.30
-
-
-    # Get predictions (probabilities)
-    preds_1 = ensemble1.predict(test_image)
-    preds_2 = ensemble2.predict(test_image)
-
-
-
-    # Weighted average of probabilities
-    final_preds = (weight_1 * preds_1 + weight_2 * preds_2)
-    print(CLASSES)
-    print(final_preds)
-
-    print()
-
-
-    # Get the class with the highest weighted average probability
-    final_class = (CLASSES[np.argmax(final_preds)])
-    print(final_class)
-
-
-    print(f''' Click space to go on.''')
-    while check == 0:
-    
-        if keyboard.is_pressed(' '): 
-            event = keyboard.read_event(suppress=True)
-            print('--------------------------------------------------')
-            check = 1
-
-
-    
-    print(f'''                                                                           
-          
-          
-          
-          
-          
-          ''')
-
-
-    index = (index + 1)