Updated app.py and fixed the error

app.py

@@ -45,22 +45,23 @@ class MyModel(nn.Module):
     def forward(self, x):
         return self.model(x)
 
-    def test(self, video_paths):
+    def predict(self, video_path):
         """Test the model on the given videos and compute accuracy."""
+        print(video_path)
+
         self.model.eval()
 
         predictions = []
 
         with torch.no_grad():
-            for i, video_path in enumerate(video_paths):
 
-                X = self.preprocess_video(video_path)
+              X = self.preprocess_video(video_path)
 
-                output = self.model(X)
-                pred = torch.sigmoid(output)  # Apply sigmoid for binary classification
+              output = self.model(X)
+              pred = torch.sigmoid(output)  # Apply sigmoid for binary classification
 
-                # Track predictions
-                predictions.append(pred.item())
+              # Track predictions
+              predictions.append(pred.item())
 
 
         return predictions
@@ -83,12 +84,13 @@ class MyModel(nn.Module):
 
 model = MyModel().load_model('pre_3D_model.h5')
 
+
+
 def classify_video(video):
-    prob = model.test(video)
+    prob = model.predict(video)
 
-    label = "Non-violent" if prob >= 0.5 else "Violent"
-    
-    violent_prob_percentage = f"{round((1 - prob) * 100, 2)}% chance of being violent"
+    label = "Non-violent" if prob[0] >= 0.5 else "Violent"
+    violent_prob_percentage = f"{round((1 - prob[0]) * 100, 2)}% chance of being violent"
 
     return label, violent_prob_percentage