Update app.py

app.py

@@ -10,7 +10,7 @@ import numpy as np
 
 
 
-model = load_model(os.path.join("model", "Emotion_Voice_Detection_Model_tuned_2.h5"))
+model = load_model(os.path.join("model", "Speech_Recognition_Model.h5"))
 
 
 def convert_class_to_emotion(pred):
@@ -27,14 +27,10 @@ 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'}
+        label_conversion = {0: 'kata_sifat',
+                            1: 'kata_benda',
+                            2: 'kata_kerja',
+                            3: 'kata_keterangan}
 
         return label_conversion[int(pred)]
 
@@ -61,14 +57,10 @@ def make_predictions(file, micro=None):
 
         N = 8
         theta = radar_factory(N, frame='polygon')
-        spoke_labels = np.array(['neutral',
-                            'calm',
-                            'happy',
-                            'sad',
-                            'angry',
-                            'fearful',
-                            'disgust',
-                            'surprised'])
+        spoke_labels = np.array(['kata_sifat',
+                            'kata_benda',
+                            'kata_kerja',
+                            'kata_keterangan'])
         fig_radar, axs = plt.subplots(figsize=(8, 8), nrows=1, ncols=1,
                             subplot_kw=dict(projection='radar'))
         vec = model.predict(x)[0]
@@ -99,4 +91,4 @@ iface = gr.Interface(
     examples=[[os.path.join("examples", filename)] for filename in os.listdir("examples")],
     outputs=[gr.Textbox(label="Text output"), gr.Plot(), gr.Plot()]
     )
-iface.launch(share=True)
+iface.launch(debug=True)