Update app.py

app.py

@@ -207,7 +207,6 @@ def generate_adversarial(input_image, orig_pred, epsilon=20/255, steps=500):
     # return output
     return output
 
-# Prediction function
 def predict(image, brightness, contrast, hue, overlay_image, alpha, adversarial_switch):
     """Main prediction function"""
     if image is None:
@@ -217,7 +216,7 @@ def predict(image, brightness, contrast, hue, overlay_image, alpha, adversarial_
 
     # Apply preprocessing
     processed = apply_filters(image, brightness, contrast, hue)
-    final_image = superimpose_images(processed, overlay_image, alpha)
+    final_image = superimpose_images(processed, overlay_image, alpha)  # Overlay the image properly
     
     # Generate adversarial if enabled
     if adversarial_switch:
@@ -241,13 +240,13 @@ def predict(image, brightness, contrast, hue, overlay_image, alpha, adversarial_
         output = model(model_input)
         probs = F.softmax(output, dim=1).cpu().numpy()[0]
 
-    # Generate Grad-CAM
+    # Generate Grad-CAM on the properly transformed image with overlay
     heatmap, _ = gradcam.generate(model_input)
     final_np = np.array(final_display)
     heatmap = cv2.resize(heatmap, final_np.shape[:2][::-1])
     heatmap = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET)
     superimposed = cv2.addWeighted(cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB), 0.5, final_np, 0.5, 0)
-    
+
     # Create plot
     fig, ax = plt.subplots(figsize=(6, 4))
     top5_idx = np.argsort(probs)[-5:][::-1]