update app.py

app.py

@@ -16,6 +16,26 @@ client.set_token("zE9NjEOgUMzH9K7mjiGBaPJiNwJLjSM53LevarRK", "token")
 dataset_dir = "dataset_audio"
 os.makedirs(dataset_dir, exist_ok=True)
 
+class AvgRatingTransformer:
+    def __init__(self, est, class_mapping=None):
+        self.est = est
+        if class_mapping is None:
+            self.class_mapping = {0:"MissedInfo", 1:"Low", 2:"Medium", 3:"High"}
+        else:
+            self.class_mapping = class_mapping
+
+    def transform(self, X):
+        X = X.copy()
+        mask_non_zero = X != 0
+        Xt = np.zeros_like(X, dtype=int)
+        if mask_non_zero.any():
+            Xt[mask_non_zero] = self.est.transform(X[mask_non_zero].reshape(-1,1)).flatten() + 1
+        # Appliquer le mapping
+        X_transformed = np.array([self.class_mapping.get(v, "MissedInfo") for v in Xt])
+        return X_transformed
+
+
+
 # -------- Charger les objets sauvegardés --------
 # Music
 scaler_samplerate_music = joblib.load("music/scaler_music_samplerate.joblib")
@@ -40,23 +60,6 @@ glove_model = api.load("glove-wiki-gigaword-100")
 
 # -------- Fonctions --------
 
-class AvgRatingTransformer:
-    def __init__(self, est, class_mapping=None):
-        self.est = est
-        if class_mapping is None:
-            self.class_mapping = {0:"MissedInfo", 1:"Low", 2:"Medium", 3:"High"}
-        else:
-            self.class_mapping = class_mapping
-
-    def transform(self, X):
-        X = X.copy()
-        mask_non_zero = X != 0
-        Xt = np.zeros_like(X, dtype=int)
-        if mask_non_zero.any():
-            Xt[mask_non_zero] = self.est.transform(X[mask_non_zero].reshape(-1,1)).flatten() + 1
-        # Appliquer le mapping
-        X_transformed = np.array([self.class_mapping.get(v, "MissedInfo") for v in Xt])
-        return X_transformed
 
 
 def fetch_sound_metadata(sound_url):