Update app.py

app.py

@@ -19,7 +19,7 @@ def load_emotion_model(model_path):
         print("Error loading emotion prediction model:", e)
         return None
 
-model_path = 'mymodel2_SER_LSTM_AUG_RAVDESS.h5'
+model_path = 'mymodel_SER_LSTM_RAVDESS.h5'
 model = load_emotion_model(model_path)
 
 # Initialize WhisperModel
@@ -46,7 +46,7 @@ def extract_mfcc(wav_file_name):
         return None
 
 # Emotions dictionary
-emotions = {1: 'neutral', 2: 'calm', 3: 'happy', 4: 'sad', 5: 'angry', 6: 'fearful', 7: 'disgust', 8: 'surprised'}
+emotions = {0: 'neutral', 1: 'calm', 2: 'happy', 3: 'sad', 4: 'angry', 5: 'fearful', 6: 'disgust', 7: 'surprised'}
 
 # Function to predict emotion from audio
 def predict_emotion_from_audio(wav_filepath):
@@ -58,7 +58,7 @@ def predict_emotion_from_audio(wav_filepath):
         if test_point is not None:
             test_point = np.reshape(test_point, newshape=(1, 40, 1))
             predictions = model.predict(test_point)
-            predicted_emotion_label = np.argmax(predictions[0]) + 1  # Adding 1 to match your emotion dictionary
+            predicted_emotion_label = np.argmax(predictions[0])   # 
             return emotions.get(predicted_emotion_label, "Unknown emotion")
         else:
             return "Error: Unable to extract features"