diff --git a/nlp/app/routes.py b/nlp/app/routes.py
index 54ca5fa..5259d8e 100644
--- a/nlp/app/routes.py
+++ b/nlp/app/routes.py
@@ -14,8 +14,6 @@
 cnn = pickle.load(open(lr_path, 'rb'))
 
 
-labels = ['loss', 'win']
-
 @app.route('/', methods=['GET', 'POST'])
 @app.route('/index', methods=['GET', 'POST'])
 def index():
@@ -28,27 +26,40 @@ def index():
             model = bnb
             text = process_text(input_field)
             text = bnb_vectorizer.transform([text])
-            pred_labels = bnb.predict(text)
             probas = bnb.predict_proba(text)
-            pred = pred_labels[0]
-            proba = probas[0, pred]
+            positive_proba = probas[:, 1]
+            if positive_proba > 0.5:
+                prediction == "WIN":
+                proba = positive_proba
+            else:
+                prediction == "LOSS":
+                proba = 1 - positive_proba
         elif model_choice == 'lr':
             model = lr
             text = process_text(input_field)
             text = lr_vectorizer.transform([text])
-            pred_labels = lr.predict(text)
             probas = lr.predict_proba(text)
-            pred = pred_labels[0]
-            proba = probas[0, pred]
+            positive_proba = probas[:, 1]
+            if positive_proba > 0.5:
+                prediction == "WIN":
+                proba = positive_proba
+            else:
+                prediction == "LOSS":
+                proba = 1 - positive_proba
         elif model_choice == 'cnn':
             # For CNN, assuming preprocessing is handled differently or is built-in
             model_path = '/Users/jackiecollopy/Downloads/project-reddit/nlp/cnn_model.h5'
             model = load_model(model_path, compile=False)
             text = basic_process(input_field)
             text = cnn_process(text)
-            predictions_proba = model.predict(text)
-            pred = np.argmax(predictions_proba)
-            proba = predictions_proba[0]
+            probas = model.predict(text)
+            positive_proba = probas[:, 1]
+            if positive_proba > 0.5:
+                prediction == "WIN":
+                proba = positive_proba
+            else:
+                prediction == "LOSS":
+                proba = 1 - positive_proba
         elif model_choice == 'bert':
             tokenizer = BertTokenizerFast.from_pretrained('prajjwal1/bert-mini')
             model = AutoModelForSequenceClassification.from_pretrained('/Users/jackiecollopy/Downloads/project-reddit/notebooks/bert.pth')
@@ -58,5 +69,5 @@ def index():
             predicted_class = torch.argmax(outputs.logits).item()
 
         return render_template('myform.html', title='', form=form, 
-                               prediction=labels[pred], confidence='%.2f' % (proba * 100))
+                               prediction=prediction, confidence='%.2f' % (proba * 100))
     return render_template('myform.html', title='', form=form, prediction=None, confidence=None)