Update app.py

app.py

@@ -177,13 +177,19 @@ def predict_single(img_input, weights=(0.45, 0.25, 0.3), normalize=True):
     preds = {}
     if model_xcept is not None:
         preds['xception'] = model_xcept.predict(img_x, verbose=0)[0]
-        print("Xception (Gradio):", preds['xception'])
+        print("\n--- Xception (Gradio) ---")
+        for i, (class_name, prob) in enumerate(zip(CLASS_NAMES, preds['xception'])):
+            print(f"{class_name}: {prob*100:.2f}%")
     if model_resnet50 is not None:
         preds['resnet50'] = model_resnet50.predict(img_r, verbose=0)[0]
-        print("ResNet (Gradio):", preds['resnet50'])
+        print("\n--- ResNet (Gradio) ---")
+        for i, (class_name, prob) in enumerate(zip(CLASS_NAMES, preds['resnet50'])):
+            print(f"{class_name}: {prob*100:.2f}%")
     if model_densenet is not None:
         preds['densenet201'] = model_densenet.predict(img_d, verbose=0)[0]
-        print("DenseNet (Gradio):", preds['densenet201'])
+        print("\n--- DenseNet (Gradio) ---")
+        for i, (class_name, prob) in enumerate(zip(CLASS_NAMES, preds['densenet201'])):
+            print(f"{class_name}: {prob*100:.2f}%")
 
     # Combinaison pondérée
     ensemble = np.zeros(len(CLASS_NAMES), dtype=np.float32)
@@ -191,24 +197,40 @@ def predict_single(img_input, weights=(0.45, 0.25, 0.3), normalize=True):
     if 'resnet50' in preds: ensemble += weights[1] * preds['resnet50']
     if 'densenet201' in preds: ensemble += weights[2] * preds['densenet201']
 
-    print("Ensemble avant mel boost (Gradio):", ensemble)
+    print("\n--- Ensemble avant mel boost (Gradio) ---")
+    for i, (class_name, prob) in enumerate(zip(CLASS_NAMES, ensemble)):
+        print(f"{class_name}: {prob*100:.2f}%")
     print("Ensemble sum avant mel boost (Gradio):", np.sum(ensemble))
 
     # Ajustement pour "mel"
     mel_idx = label_to_index['mel']
     if 'densenet201' in preds:
+        old_mel_prob = ensemble[mel_idx]
         ensemble[mel_idx] = 0.5 * ensemble[mel_idx] + 0.5 * preds['densenet201'][mel_idx]
-        print(f"Mel boost (Gradio): {0.5 * ensemble[mel_idx] + 0.5 * preds['densenet201'][mel_idx]}")
+        print(f"\nMel boost (Gradio): {old_mel_prob*100:.2f}% -> {ensemble[mel_idx]*100:.2f}%")
+
+    print("\n--- Ensemble après mel boost (Gradio) ---")
+    for i, (class_name, prob) in enumerate(zip(CLASS_NAMES, ensemble)):
+        print(f"{class_name}: {prob*100:.2f}%")
 
     if normalize:
+        ensemble_before_norm = ensemble.copy()
         ensemble = _renorm_safe(ensemble)
-        print("Ensemble final (Gradio):", ensemble)
+        print("\n--- Ensemble final après normalisation (Gradio) ---")
+        for i, (class_name, prob) in enumerate(zip(CLASS_NAMES, ensemble)):
+            print(f"{class_name}: {prob*100:.2f}%")
         print("Ensemble sum final (Gradio):", np.sum(ensemble))
 
+        # Afficher l'impact de la normalisation
+        print("\n--- Impact de la normalisation ---")
+        for i, (class_name, prob_before, prob_after) in enumerate(zip(CLASS_NAMES, ensemble_before_norm, ensemble)):
+            print(f"{class_name}: {prob_before*100:.2f}% -> {prob_after*100:.2f}%")
+
     preds['ensemble'] = ensemble
     return preds
 
 
+
 # ---- Helpers Grad-CAM ----
 LAST_CONV_LAYERS = {
     "xception": "block14_sepconv2_act",