Update app.py

app.py

@@ -1,5 +1,5 @@
 import tensorflow as tf
-from tensorflow.keras import layers, models
+from tensorflow import keras
 import gradio as gr
 import numpy as np
 import cv2
@@ -7,50 +7,9 @@ import os
 
 classes = ["Abyssinian", "Bengal", "Birman", "Bombay", "British Shorthair", "Egyptian Mau", "Maine Coon", "Persian", "Ragdoll", "Russian Blue", "Siamese", "Sphynx"]
 example_images = ["examples/" + f for f in os.listdir("examples")]
-
 img_size = 400
-num_classes = 12
-
-# Create CNN model architecture and apply weights from file
-def create_model():
-    model = models.Sequential()
-    model.add(layers.RandomFlip("horizontal_and_vertical"))
-    model.add(layers.RandomRotation(0.2))
-    model.add(layers.RandomZoom((0, 0.2)))
-    model.add(layers.Rescaling(1./255))
-    
-    model.add(layers.Conv2D(8, 3, activation="relu"))
-    model.add(layers.BatchNormalization())
-    model.add(layers.MaxPooling2D())
-    
-    model.add(layers.Conv2D(16, 3, activation="relu"))
-    model.add(layers.BatchNormalization())
-    model.add(layers.MaxPooling2D())
-    
-    model.add(layers.Conv2D(32, 3, activation="relu"))
-    model.add(layers.BatchNormalization())
-    model.add(layers.MaxPooling2D())
-    
-    model.add(layers.Conv2D(64, 3, activation="relu"))
-    model.add(layers.BatchNormalization())
-    model.add(layers.MaxPooling2D())
-    
-    model.add(layers.Conv2D(92, 3, activation="relu"))
-    model.add(layers.BatchNormalization())
-    model.add(layers.MaxPooling2D())
-    
-    model.add(layers.BatchNormalization())
-    
-    model.add(layers.Flatten())
-    
-    model.add(layers.Dense(1024, activation="relu"))
-    model.add(layers.Dropout(0.5))
-    model.add(layers.Dense(512, activation="relu"))
-    model.add(layers.Dense(num_classes, activation="softmax"))
-    model.load_weights("CatClassifierWeights.h5")
-    return model
 
-model = create_model()
+model = tf.keras.models.load_model("CatClassifier")
 
 def model_predict(image):
     image = cv2.resize(image, (img_size, img_size))