Update handler.py

handler.py

@@ -57,6 +57,10 @@ class EndpointHandler():
 
         # Extract MFCC features
             mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=30)
+            if mfcc.shape[1] < 216:
+                mfcc = np.pad(mfcc, ((0, 0), (0, 216 - mfcc.shape[1])), mode='constant')
+            elif mfcc.shape[1] > 216:
+                mfcc = mfcc[:, :216]
 
             return mfcc
 
@@ -65,10 +69,17 @@ class EndpointHandler():
             raise
     
     def perform_emotion_analysis(self, features, emotion_padding=216, depression_padding=2584):
-        emotion_features = self.get_mfcc_features(features, emotion_padding)
+        emotion_features = features[:, :emotion_padding]
+        emotion_features = np.expand_dims(emotion_features, axis=-1)  # Add channel dimension
+        emotion_features = np.expand_dims(emotion_features, axis=0)  # Add batch dimension
+
         depression_features = self.get_mfcc_features(features, depression_padding)
+    
+        print("Emotion model input shape:", self.emotion_model.input_shape)
+        print("Emotion features shape:", emotion_features.shape)
+    
         emotion_prediction = self.emotion_model.predict(emotion_features)[0]
         emotion_prediction = self.emotion_labels[np.argmax(emotion_prediction)]
+    
         depression_prediction = self.depression_model.predict(depression_features)[0]
-        # depression_prediction = "Depressed" if depression_prediction >= 0.5 else "Not Depressed"
         return emotion_prediction, depression_prediction