Update app.py

app.py

@@ -4,8 +4,6 @@ import numpy as np
 import os
 import gradio as gr
 loaded_model = load_model('diabetic_retinopathy_model.h5')
-import gradio as gr
-import numpy as np
 from tensorflow.keras.preprocessing import image
 
 # Class mapping
@@ -17,19 +15,19 @@ class_mapping = {
     4: 'Proliferative DR'
 }
 
-# URL of the fixed example image to display
-example_image_url = "1.jpg"  # Replace with the actual URL
+#  fixed  image 
+example_image_url = "1.jpg"  
 
 def predict_diabetic_retinopathy(test_image, loaded_model, height=512, width=512):
-    # Always return the example image
+    # Always return the image
     try:
         if test_image is None:
             return "No image uploaded. Please upload an image.", example_image_url
 
-        # Ensure the image is in the correct format
+        
         img = image.img_to_array(test_image)
         
-        # Resize the image while maintaining the aspect ratio
+        # Resize the image 
         img = np.array(image.smart_resize(img, (height, width)))
         
         img_array = np.expand_dims(img, axis=0)
@@ -41,13 +39,13 @@ def predict_diabetic_retinopathy(test_image, loaded_model, height=512, width=512
         # Convert predictions to the corresponding class
         predicted_class = np.argmax(predictions)
 
-        # Return the predicted class and the example image URL
+        # Return the predicted class and the  image 
         return f"**Predicted Diabetic Retinopathy Stage:** {class_mapping[predicted_class]}", example_image_url
     
     except Exception as e:
         return f"An error occurred: {str(e)}", example_image_url
 
-# Create the Gradio interface with fixed example images
+# Create the Gradio interface 
 example_images = [
     "No_DR.png",
     "Mild.png",