Update app.py

app.py

@@ -5,31 +5,48 @@ import time
 from tensorflow.keras.models import load_model
 
 # Load the emotion prediction model
-model = load_model('mymodel_SER_LSTM_RAVDESS.h5')
+def load_emotion_model(model_path):
+    try:
+        model = load_model(model_path)
+        return model
+    except Exception as e:
+        print("Error loading emotion prediction model:", e)
+        return None
+
+model_path = 'mymodel_SER_LSTM_RAVDESS.h5'
+model = load_emotion_model(model_path)
 
 # Function to extract MFCC features from audio
 def extract_mfcc(wav_file_name):
-    y, sr = librosa.load(wav_file_name)
-    mfccs = np.mean(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40).T, axis=0)
-    return mfccs
+    try:
+        y, sr = librosa.load(wav_file_name)
+        mfccs = np.mean(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40).T, axis=0)
+        return mfccs
+    except Exception as e:
+        print("Error extracting MFCC features:", e)
+        return None
 
 # Emotions dictionary
 emotions = {1: 'neutral', 2: 'calm', 3: 'happy', 4: 'sad', 5: 'angry', 6: 'fearful', 7: 'disgust', 8: 'surprised'}
 
 # Function to predict emotion from audio
 def predict_emotion_from_audio(wav_filepath):
-    test_point = extract_mfcc(wav_filepath)
-    test_point = np.reshape(test_point, newshape=(1, 40, 1))
-    predictions = model.predict(test_point)
-    predicted_emotion_label = np.argmax(predictions[0]) + 1
-    return emotions[predicted_emotion_label]
-
-
+    try:
+        test_point = extract_mfcc(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
+            return emotions[predicted_emotion_label]
+        else:
+            return "Error: Unable to extract features"
+    except Exception as e:
+        print("Error predicting emotion:", e)
+        return None
 
 # Create a combined function that calls both models
 def get_predictions(audio_input):
     emotion_prediction = predict_emotion_from_audio(audio_input)
-    
     return [emotion_prediction]
 
 # Create the Gradio interface