diff --git a/cnn_classification_service/cnn_classifier.py b/cnn_classification_service/cnn_classifier.py
index df8570b..09a66e4 100644
--- a/cnn_classification_service/cnn_classifier.py
+++ b/cnn_classification_service/cnn_classifier.py
@@ -39,9 +39,11 @@ class Classifier(object):
         librosa.display.specshow(librosa.power_to_db(spectogram, ref=numpy.max))
 
         target_dir = tempfile.mkdtemp()
+        wav_basename = os.path.basename(wav_filename)
 
         # Change extension to jpg... mert 110% biztos vagyok benne hogy a keras nem bírná beolvasni máshogy
-        file_name = os.path.join(target_dir, "unknown", f"{wav_filename[:-4]}.jpg")
+        file_name = os.path.join(target_dir, "unknown", f"{wav_basename[:-4]}.jpg")
+        os.mkdir(os.path.join(target_dir, "unknown"))
 
         matplotlib.pyplot.savefig(file_name, dpi=400, bbox_inches='tight', pad_inches=0)
         matplotlib.pyplot.close()
@@ -49,7 +51,7 @@ class Classifier(object):
         matplotlib.pyplot.close(fig)
         matplotlib.pyplot.close('all')
 
-        return target_dir, file_name
+        return target_dir, file_name  # Az unknown nélkülivel kell visszatérni
 
     def _run_predictor(self, directory: str) -> list:
         predict_generator = self.datagen.flow_from_directory(
diff --git a/cnn_classification_service/magic_doer.py b/cnn_classification_service/magic_doer.py
index d6e20f2..46045e3 100644
--- a/cnn_classification_service/magic_doer.py
+++ b/cnn_classification_service/magic_doer.py
@@ -58,7 +58,7 @@ def run_everything(parameters: dict):
     response = {
         "tag": tag,
         "probability": 1.0 if results[0] == 'sturnus' else 0.0,
-        "model": ...
+        "model": "TODO"
     }
 
     logging.info(f"Classification done!")