Update app.py

app.py

@@ -5,29 +5,32 @@ import tensorflow as tf
 import tensorflow_hub as hub
 import cv2
 
-# Register KerasLayer from TensorFlow Hub in a custom object scope
-custom_objects = {'KerasLayer': hub.KerasLayer}
-
-# Load the model and ensure KerasLayer is registered
-try:
-    with tf.keras.utils.custom_object_scope(custom_objects):
-        model = tf.keras.models.load_model('bird_model4.h5')
-except Exception as e:
-    print("Error loading the model:", e)
-    exit()
-
 # Load class labels from the text file
 train_info = []
 with open('labelwithspace.txt', 'r') as file:
     train_info = [line.strip() for line in file.readlines()]
 
+# Construct a simple mock model using the Functional API
+def create_mock_model():
+    inputs = tf.keras.Input(shape=(224, 224, 3))
+    # A simple convolutional base (for demonstration purposes)
+    x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu')(inputs)
+    x = tf.keras.layers.MaxPooling2D((2, 2))(x)
+    x = tf.keras.layers.Flatten()(x)
+    outputs = tf.keras.layers.Dense(len(train_info), activation='softmax')(x)
+    model = tf.keras.Model(inputs, outputs)
+    return model
+
+# Initialize the mock model
+model = create_mock_model()
+
 # Function to preprocess the image and make predictions
 def predict_image(image):
-    # Resize the image to 224x224 and normalize pixel values
+    # Resize the image to 224x224 and normalize
     img = cv2.resize(image, (224, 224))
     img = img / 255.0  # Normalize to [0, 1] range
 
-    # Make predictions
+    # Make predictions using the mock model
     predictions = model.predict(img[np.newaxis, ...])[0]
     top_classes = np.argsort(predictions)[-3:][::-1]  # Indices of top 3 predictions
     top_class = top_classes[0]  # Index of the highest probability class