Update app.py

app.py

@@ -17,24 +17,17 @@ def classify_image(input_image):
 
     # Assuming your model outputs probabilities for two classes, you can return the class with the highest probability
     class_index = np.argmax(prediction)
-    class_labels = ["Class 0", "Class 1"]  # Replace with your actual class labels
+    class_labels = ["Normal", "Cataract"]  # Replace with your actual class labels
     predicted_class = class_labels[class_index]
 
     return predicted_class
 
 # Create a Gradio interface
-input_interface = gr.inputs.Image()  # Gradio input component for image
-output_interface = gr.outputs.Text()  # Gradio output component for text
-
-# Create the Gradio app
-app = gr.Interface(
+input_interface = gr.Interface(
     fn=classify_image,
-    inputs=input_interface,
-    outputs=output_interface,
-    live=True,
-    title="Image Classifier",
-    description="Classify images using a trained model."
+    inputs="image",  # Specify input type as "image"
+    outputs="text"   # Specify output type as "text"
 )
 
-# Start the Gradio app
-app.launch()
+# Launch the Gradio app
+input_interface.launch()