Update app.py

app.py

@@ -9,10 +9,10 @@ model = tf.keras.models.load_model('Model_catsVSdogs.h5')
 # Define a function to make predictions
 def predict_image(img):
     # Preprocess the image
-    img = img.resize((224, 224)) 
-    img_array = image.img_to_array(img)  
-    img_array = np.expand_dims(img_array, axis=0)  
-    img_array = img_array / 255.0  
+    img = img.resize((256,256))  # Resize the image to 224x224 pixels
+    img_array = image.img_to_array(img)  # Convert the image to an array
+    img_array = np.expand_dims(img_array, axis=0)  # Add a batch dimension
+    img_array = img_array / 255.0  # Normalize the image
 
     # Make a prediction
     prediction = model.predict(img_array)
@@ -22,14 +22,12 @@ def predict_image(img):
         return "Dog"
 
 # Create the Gradio interface
-iface = gr.Interface(
-    fn=predict_image, 
-    inputs=gr.Image(type="pil"), 
-    outputs="text",
-    title="Cat and Dog Classifier",
-    description="Upload an image of a cat or a dog and the model will classify it.",
-    examples=["cat_example.jpg", "dog_example.jpg"]
-)
+
+interface = gr.Interface(fn=predict_image, 
+                         inputs=gr.Image(type="pil"), 
+                         outputs="text",
+                         title="Cat and Dog Classifier",
+                         description="Upload an image of a cat or dog and the model will predict which one it is.")
 
 # Launch the interface
-iface.launch()
+interface.launch()