Update app.py

app.py

@@ -5,27 +5,28 @@ import tensorflow as tf
 import tensorflow_hub as hub
 import cv2
 
-# Define a dictionary to map the custom layer to its implementation
-custom_objects = {'KerasLayer': hub.KerasLayer}
+# Ensure compatibility between TensorFlow and TensorFlow Hub
+tf.keras.utils.get_custom_objects()['KerasLayer'] = hub.KerasLayer
 
-# Load your model (ensure the path to the model file is correct)
-model = tf.keras.models.load_model('bird_model4.h5', custom_objects=custom_objects)
+# Load the model with the correct path and custom objects
+model = tf.keras.models.load_model('bird_model4.h5', custom_objects={'KerasLayer': hub.KerasLayer})
 
-# Load the class labels from the text file
+# 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()]
 
 # Function to preprocess the image and make predictions
 def predict_image(image):
-    # Resize the image to the model's input size
+    # Resize the image to the expected input shape
     img = cv2.resize(image, (224, 224))
-    img = img / 255.0  # Normalize the image
-    # Make predictions using the model
+    img = img / 255.0  # Normalize the image to the range [0, 1]
+    
+    # Make predictions using the loaded model
     predictions = model.predict(img[np.newaxis, ...])[0]
     top_classes = np.argsort(predictions)[-3:][::-1]  # Get indices of top 3 predictions
-    top_class = top_classes[0]  # Index of the top prediction
-    label = train_info[top_class]  # Retrieve the label using the index
+    top_class = top_classes[0]  # Index of the highest prediction
+    label = train_info[top_class]  # Retrieve the class label using the index
     return label
 
 # Define the Gradio interface