Update app.py

app.py

@@ -1,15 +1,15 @@
 import gradio as gr
 from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import img_to_array, load_img
-import numpy as np
+from tensorflow.keras.preprocessing.image import img_to_array
 from PIL import Image
+import numpy as np
 
-# Load your model (using a relative path)
-model_path = "cats_and_dogs_classifier.h5"
-model = load_model(model_path)
+# Load your model
+model = load_model('cats_and_dogs_classifier.h5')
 
+# Define prediction function
 def predict(image):
-    # Resize image to match model's expected sizing
+    # Resize image to match model's expected sizing (150x150 pixels)
     img = image.resize((150, 150))
     # Convert image to array and expand dimensions to fit model input requirements
     img_array = img_to_array(img)
@@ -21,12 +21,5 @@ def predict(image):
     return class_label
 
 # Create a Gradio interface
-iface = gr.Interface(fn=predict, 
-                     inputs=gr.inputs.Image(type='pil', label="Upload a photo of a cat or dog"), 
-                     outputs="text",
-                     title="Cat or Dog Classifier",
-                     description="Upload an image of a cat or dog to classify it.")
-
-if __name__ == "__main__":
-    iface.launch()
-
+interface = gr.Interface(fn=predict, inputs="image", outputs="text")
+interface.launch()