Update app.py

app.py

@@ -76,18 +76,22 @@ def predict_image(img, confidence_threshold):
         # Check if either class meets the confidence threshold
         if result_1['artificial'] >= confidence_threshold:
             label_1 = f"AI, Confidence: {result_1['artificial']:.4f}"
+            result_1output += ['AI']
         elif result_1['real'] >= confidence_threshold:
             label_1 = f"Real, Confidence: {result_1['real']:.4f}"
+            result_1output += ['REAL']
         else:
             label_1 = "Uncertain Classification"
+            result_1output += ['UNCERTAIN']
+
     except Exception as e:
         label_1 = f"Error: {str(e)}"
-    
+    print(result_1output)
     # Predict using the second model
     try:
         prediction_2 = clf_2(img_pil)
         result_2 = {pred['label']: pred['score'] for pred in prediction_2}
-        result_2output = [2, result_2['real'], result_2['artificial']]
+        result_2output = [2, result_2['Real Image'], result_2['AI Image']]
         print(result_2output)
         # Ensure the result dictionary contains all class names
         for class_name in class_names_2: