Update app.py

app.py

@@ -294,35 +294,49 @@ def quick_predict_ui(image_pil):
     except Exception as e:
         return f"Erreur: {e}", None, "❌ Erreur lors de l'analyse."
 
-def generate_gradcam_ui():
+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:
-        #_update_progress(progress, 0, desc="Début de la génération Grad-CAM...")
+        # On ne fait plus de mise à jour ici, make_gradcam gère tout
         ensemble_probs = current_predictions["ensemble"]
         top_class_idx = int(np.argmax(ensemble_probs))
 
+        # Sélection des modèles disponibles
         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 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."
 
+        # Choix du meilleur modèle
         explainer_model_name, explainer_model, conf = max(candidates, key=lambda t: t[2])
         explainer_layer = LAST_CONV_LAYERS.get(explainer_model_name)
-        #_update_progress(progress, 5, desc=f"Génération Grad-CAM avec {explainer_model_name}...")
 
-        gradcam_img = make_gradcam(current_image, explainer_model, explainer_layer, class_index=top_class_idx)
+        # Génération Grad-CAM avec la progression gérée en interne
+        gradcam_img = make_gradcam(
+            current_image,
+            explainer_model,
+            explainer_layer,
+            class_index=top_class_idx,
+            progress=progress
+        )
 
-        #_update_progress(progress, 100, desc="✅ Grad-CAM généré !")
         return gradcam_img, f"✅ Grad-CAM généré avec {explainer_model_name} (confiance: {conf:.1%})"
+
     except Exception as e:
-        import traceback; traceback.print_exc()
+        import traceback
+        traceback.print_exc()
         return None, f"❌ Erreur: {e}"
 
+
 # ---- INTERFACE GRADIO ----
 example_paths = ["ISIC_0024627.jpg", "ISIC_0025539.jpg", "ISIC_0031410.jpg"]