Update app.py

app.py

@@ -1,6 +1,6 @@
 import tensorflow as tf
 from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense,RandomRotation,RandomZoom,RandomFlip,RandomBrightness,Dropout
+from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense,RandomRotation,RandomZoom,RandomFlip,RandomBrightness,Dropout,Input
 import pandas as pd
 import numpy as np
 import cv2
@@ -17,23 +17,24 @@ data_aug_layer = tf.keras.Sequential([
     RandomZoom(0.2),
     RandomRotation(0.1)
 ])
+model = Sequential()
+num_classes = 2
+model.add(Input(shape=(180, 180, 3)))
+# Add the pre-trained base model
 model.add(data_aug_layer)
 model.add(model_imagenet)
-
-model.add(Flatten())
+# Add custom layers on top
 model.add(Flatten())
 model.add(Dense(1024, activation='relu'))
 model.add(Dense(512, activation='relu'))
 model.add(Dense(32, activation='relu'))
-model.add(Dense(num_classes)) 
-
-
+model.add(Dense(num_classes))
 model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
               metrics=['accuracy'])
 
 # Using saved weights
-model.load_weights('model_weights2.h5')
+model.load_weights('model_weights.h5')
 def classify_image(image):
     # Convert Gradio Image to numpy array
     image = np.array(image)