app.py updated

app.py

@@ -14,10 +14,6 @@ model = load_model(os.path.join("lstm_all_four_complex.h5"))
 
 
 def convert_class_to_emotion(pred):
-        """
-        Method to convert the predictions (int) into human readable strings.
-        """
-        
         label_conversion = {0: 'neutral',
                             1: 'calm',
                             2: 'happy',
@@ -27,22 +23,10 @@ def convert_class_to_emotion(pred):
                             6: 'disgust',
                             7: 'surprised'}
 
-        # label_conversion = {0: 'very happy',
-        #                     1: 'happy',
-        #                     2: 'very happy',
-        #                     3: 'very unhappy',
-        #                     4: 'very unhappy',
-        #                     5: 'unhappy',
-        #                     6: 'unhappy',
-        #                     7: 'happy'}
-
         return label_conversion[int(pred)]
 
 
 def make_predictions(file, micro=None):
-        """
-        Method to process the files and create your features.
-        """
         if file is not None and micro is None:
             input_audio = file
         elif file is None and micro is not None:
@@ -87,12 +71,9 @@ def make_predictions(file, micro=None):
 
 
 
-# Set the starting state to an empty string
 iface = gr.Interface(
     fn=make_predictions,
-    title="Identify emotion of a chunk of audio speech",
-    description="a simple interface to perform emotion recognition from an audio file",
-    #article="Author: <a href=\"https://huggingface.co/poisso\">Poisso</a>.", 
+    title="Identify emotion of a chunk of audio speech", 
     inputs=[gr.Audio(source="upload", type="filepath", label="File"),
         gr.Audio(source="microphone", type="filepath", streaming=False, label="Microphone")] 
     ,