Update app.py

app.py

@@ -3,10 +3,10 @@ import tensorflow as tf
 import numpy as np
 from PIL import Image
 
-# Load your custom classification models 
-transfer_learning_model = tf.keras.models.load_model('model_mobilenet.keras')
+# Load the custom classification models 
+transfer_learning_model = tf.keras.models.load_model('model_vgg16.keras')
 
-# Class names, should match your dataset
+# Class names
 class_names = ['butterfly', 'cat', 'elephant', 'horse', 'squirrel']
 
 def classify_image(image, model):
@@ -15,7 +15,7 @@ def classify_image(image, model):
         image = Image.fromarray(image.astype('uint8'), 'RGB')
     
     # Resize the image using np.resize
-    image = np.resize(image, (224, 224, 3))  # Add the channel dimension
+    image = np.resize(image, (300, 300, 3))  # Add the channel dimension
     
     image = image / 255.0  # Normalize the image
     image = np.expand_dims(image, axis=0)  # Add batch dimension
@@ -40,7 +40,7 @@ transfer_learning_interface = gr.Interface(
     inputs=image_input, 
     outputs=label,
     title='Animal Classifier',
-    description='Upload an image of an animal, and the classifier will tell you which animal it is, along with the confidence level of the prediction.'
+    description='Upload an image of a butterfly, a cat, an elephant, a horse or a squirrel, and the classifier will tell you which animal it is, along with the confidence level of the prediction.'
 )
 
 transfer_learning_interface.launch()