Update app.py

app.py

@@ -9,7 +9,6 @@ import librosa
 import librosa.display
 
 def transform_data(audio):
-    print("audio: ", audio)
     # Lets transform the dataset so we can apply the predictions
     X, sample_rate = librosa.load(audio
                                 ,res_type='kaiser_fast'
@@ -17,13 +16,10 @@ def transform_data(audio):
                                 ,sr=44100
                                 ,offset=0.5
                                 )
-    
-    print("HEREEEE", sample_rate, X.shape)
-
     sample_rate = np.array(sample_rate)
-    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13),axis=0)
-    newdf = pd.DataFrame(data=mfccs).T
-    return newdf
+    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=30),axis=0)
+    mfccs = np.expand_dims(mfccs, axis=-1)
+    return mfccs
 
 def predict(newdf, loaded_model):
     # Apply predictions