Update app.py

app.py

@@ -475,13 +475,13 @@ def auto_ensemble_process(audio, model_keys, seg_size=64, overlap=0.1, out_forma
             scipy.io.wavfile.write(temp_audio_path, sample_rate, data)
             audio = temp_audio_path
 
-        # Aynı ses dosyası mı kontrolü
-        if ensemble_state["current_audio"] != audio:
+        # State yönetimini kontrol et ve sıfırlama
+        if ensemble_state["current_audio"] != audio or ensemble_state["current_model_idx"] >= len(model_keys):
             ensemble_state["current_audio"] = audio
             ensemble_state["current_model_idx"] = 0
             ensemble_state["processed_stems"] = []
             ensemble_state["model_outputs"] = {model_key: {"vocals": [], "other": []} for model_key in model_keys}
-            logger.info("New audio file detected, resetting ensemble state.")
+            logger.info("New audio or completed cycle detected, resetting ensemble state.")
 
         use_tta = use_tta == "True"
         base_name = os.path.splitext(os.path.basename(audio))[0]
@@ -510,7 +510,6 @@ def auto_ensemble_process(audio, model_keys, seg_size=64, overlap=0.1, out_forma
             for model_key, stems_dict in ensemble_state["model_outputs"].items():
                 for stem_type in ["vocals", "other"]:
                     if stems_dict[stem_type]:
-                        # Stem tipini kontrol et, excluded listesinde varsa atla
                         if stem_type.lower() in excluded_stems_list:
                             logger.info(f"Excluding {stem_type} for {model_key} from ensemble")
                             continue
@@ -670,7 +669,7 @@ def auto_ensemble_process(audio, model_keys, seg_size=64, overlap=0.1, out_forma
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
             logger.info("GPU memory cleared")
-
+            
 def update_roformer_models(category):
     """Update Roformer model dropdown based on selected category."""
     choices = list(ROFORMER_MODELS.get(category, {}).keys()) or []