commit

app.py

@@ -12,14 +12,14 @@ classifier = pickle.load(open('finalized_rf.sav', 'rb'))
 def emotion_predict(input):
   input_features = extract_feature(input, mfcc=True, chroma=True, mel=True, contrast=True, tonnetz=True)
   rf_prediction = classifier.predict(input_features.reshape(1,-1))
-  if rf_prediction == 'kata-benda':
-    return 'kata-benda'
-  elif rf_prediction == 'kata-kerja':
-    return 'kata-kerja'
-  elif rf_prediction == 'kata-keterangan':
-    return 'kata-keterangan'      
+  if rf_prediction == 'happy':
+    return 'Happy 😎'
+  elif rf_prediction == 'neutral':
+    return 'Neutral 😐'
+  elif rf_prediction == 'sad':
+    return 'Sad 😢'
   else:
-    return 'kata-sifat'
+    return 'Angry 😤'
   
 
 def plot_fig(input):
@@ -51,13 +51,12 @@ def plot_fig(input):
 with gr.Blocks() as app:
   gr.Markdown(
         """
-    #  💞PROLOVE 🎵🎸🎼
-    
-    This application classifies inputted audio according to pronunciation into four categories:
-    1. kata benda 
-    2. kata kerja 
-    3. kata keterangan 
-    4. kata sifat 
+    # Speech Emotion Detector 🎵😍
+    This application classifies inputted audio 🔊 according to the verbal emotion into four categories:
+    1. Happy 😎
+    2. Neutral 😐
+    3. Sad 😢
+    4. Angry 😤
     """
   )
   with gr.Tab("Record Audio"):
@@ -72,8 +71,8 @@ with gr.Blocks() as app:
       plot_record = gr.Button("Display Audio Signal")
       plot_record_c = gr.Plot(label='Waveform Of the Audio')
     
-    record_button = gr.Button("Detection Parts of Speech")
-    record_output = gr.Text(label = 'detected')
+    record_button = gr.Button("Detect Emotion")
+    record_output = gr.Text(label = 'Emotion Detected')
 
   with gr.Tab("Upload Audio File"):
     gr.Markdown(
@@ -93,12 +92,12 @@ with gr.Blocks() as app:
       plot_upload = gr.Button("Display Audio Signal")
       plot_upload_c = gr.Plot(label='Waveform Of the Audio')
 
-    upload_button = gr.Button("Detection Parts of Speech")
-    upload_output = gr.Text(label = 'detected')          
-
+    upload_button = gr.Button("Detect Emotion")
+    upload_output = gr.Text(label = 'Emotion Detected')
+    
   record_button.click(emotion_predict, inputs=record_input, outputs=record_output)
   upload_button.click(emotion_predict, inputs=upload_input, outputs=upload_output)
   plot_record.click(plot_fig, inputs=record_input, outputs=plot_record_c)
-  plot_upload.click(plot_fig, inputs=upload_input, outputs=plot_upload_c) 
+  plot_upload.click(plot_fig, inputs=upload_input, outputs=plot_upload_c)
 
 app.launch()