Update app.py

app.py

@@ -390,7 +390,45 @@ def generate_gradcam_ui_progressive(progress=gr.Progress()):
         import traceback
         traceback.print_exc()
         return None, f"❌ Erreur: {e}"
+        
+def generate_gradcam_ui(progress=gr.Progress()):
+    global current_image, current_predictions
+    if current_image is None or current_predictions is None:
+        return None, "❌ Aucun résultat précédent — lance d'abord l'analyse rapide."
+    
+    try:
+        ensemble_probs = current_predictions["ensemble"]
+        top_class_idx = int(np.argmax(ensemble_probs))
+
+        candidates = []
+        if model_xcept is not None:
+            candidates.append(("xception", model_xcept, current_predictions["xception"][top_class_idx]))
+        if model_resnet50 is not None:
+            candidates.append(("resnet50", model_resnet50, current_predictions["resnet50"][top_class_idx]))
+        if model_densenet is not None:
+            candidates.append(("densenet201", model_densenet, current_predictions["densenet201"][top_class_idx]))
+
+        if not candidates:
+            return None, "❌ Aucun modèle disponible pour Grad-CAM."
+
+        explainer_model_name, explainer_model, conf = max(candidates, key=lambda t: t[2])
+        explainer_layer = LAST_CONV_LAYERS.get(explainer_model_name)
 
+        # Utilisez make_gradcam_with_delays pour une progression fluide
+        gradcam_img = make_gradcam_with_delays(
+            current_image,
+            explainer_model,
+            explainer_layer,
+            class_index=top_class_idx,
+            progress=progress
+        )
+
+        return gradcam_img, f"✅ Grad-CAM généré avec {explainer_model_name} (confiance: {conf:.1%})"
+
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return None, f"❌ Erreur: {e}"
 
 # ---- INTERFACE GRADIO ----
 example_paths = ["ISIC_0024627.jpg", "ISIC_0025539.jpg", "ISIC_0031410.jpg"]