Update app.py

app.py

@@ -7,8 +7,7 @@ from sklearn.preprocessing import StandardScaler
 from sklearn import preprocessing
 from sklearn.model_selection import train_test_split 
 
-dataset = pd.read_csv('UTIv2.csv')
-dataset = dataset.drop('filename',axis=1)
+dataset = pd.read_csv('125_.csv')
 x = dataset.iloc[:, :-1].values
 y = dataset.iloc[:, -1].values
 encoder = preprocessing.LabelEncoder()
@@ -17,7 +16,7 @@ x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, rando
 sc = StandardScaler()
 sc = sc.fit(x_train)
 
-model = joblib.load('UTI.pkl')
+model = joblib.load('125_.pkl')
 
 def predictor(audio_filename):
     y, sr = librosa.load(audio_filename, mono=True, duration=5)