initial commit

app.py

@@ -7,19 +7,21 @@ import pickle
 import soundfile 
 import librosa
 
-classifier = pickle.load(open('finalized_rf.sav', 'rb'))
+classifier = pickle.load(open('prolove.pkl', '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 == 'happy':
-    return 'Happy 😎'
-  elif rf_prediction == 'neutral':
-    return 'Neutral 😐'
-  elif rf_prediction == 'sad':
-    return 'Sad 😢'
+  if rf_prediction == 'kata_benda':
+    return 'kata_benda'
+  elif rf_prediction == 'kata_kerja':
+    return 'kata_kerja'
+  elif rf_prediction == 'kata_keterangan':
+    return 'kata_keterangan'
+  elif rf_prediction == 'kata_sifat':
+    return 'kata_sifat'      
   else:
-    return 'Angry 😤'
+    return 'LOL😤'
   
 
 def plot_fig(input):
@@ -52,11 +54,11 @@ with gr.Blocks() as app:
   gr.Markdown(
         """
     # PROLOVE 🎵
-    This application classifies inputted audio 🔊 according to the verbal emotion into four categories:
-    1. Happy 😎
-    2. Neutral 😐
-    3. Sad 😢
-    4. Angry 😤
+    This application classifies inputted audio 🔊 according to pronunciation into four categories:
+    1. kata_benda 😎
+    2. kata_kerja 😐
+    3. kata_keterangan 😢
+    4. kata_sifat 😤
     """
   )
   with gr.Tab("Record Audio"):