revert

gradio_utils.py

@@ -6,7 +6,7 @@ import numpy as np
 import librosa
 
 import gradio as gr
-from datetime import datetime
+#from datetime import datetime
 
 
 def predict_gradio(data, 
@@ -28,7 +28,7 @@ def predict_gradio(data,
     prediction = sklearn_model.predict_proba(y_uniform.reshape(1, -1))
     result = {str(label): float(confidence) for (
         label, confidence) in zip(classes, prediction.flatten())}
-    print(f"{datetime.now()} predict {prediction}")
+    #print(f"{datetime.now()} predict {prediction}")
     return result
 
 def load_as_librosa(y: np.ndarray, sr: int, target_sr: int = 22050) -> np.ndarray: