Update app.py

app.py

@@ -312,8 +312,11 @@ def lstm_anomaly_detection(X, feature_columns, num_anomalies=10, epochs=100, bat
 
 def emotion_anomaly_detection(emotion_data, num_anomalies=10, epochs=100, batch_size=64):
     device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    X = torch.FloatTensor(emotion_data.values.reshape(-1, 1)).to(device)
-    X = X.unsqueeze(0)  # Add batch dimension
+    X = torch.FloatTensor(emotion_data.values).to(device)
+    if X.dim() == 1:
+        X = X.unsqueeze(0).unsqueeze(2)  # Add batch and feature dimensions
+    elif X.dim() == 2:
+        X = X.unsqueeze(0)  # Add batch dimension
 
     model = LSTMAutoencoder(input_size=1).to(device)
     criterion = nn.MSELoss()
@@ -495,7 +498,7 @@ def process_video(video_path, num_anomalies, num_components, desired_fps, batch_
             anomaly_scores_all = normalize_scores(anomaly_scores_all)
             anomaly_scores_comp = normalize_scores(anomaly_scores_comp)
 
-            # Perform anomaly detection for each emotion
+            # Perform anomaly detection for each emotion using LSTM autoencoder
             emotion_anomalies = {}
             for emotion in ['fear', 'sad', 'angry', 'happy', 'surprise', 'neutral']:
                 anomalies, scores, indices = emotion_anomaly_detection(df[emotion], num_anomalies=num_anomalies)