Update prediction.py

prediction.py

@@ -332,19 +332,17 @@ class Prediction:
         )
         prediction_string = f"{prediction} {confidence_deepfake_real:.2f}% Confidence"
     
-        # Create detailed classification results as list of (label, confidence) tuples
+        # Create detailed classification results as a dictionary
         if prediction == "Deepfake":
             # For deepfakes, show probabilities for each deepfake type
-            classification_details = [
-                (self.classes[i], float(class_probs[i])) for i in range(1, len(self.classes))
-            ]
-            # Sort by confidence (highest first)
-            classification_details.sort(key=lambda x: x[1], reverse=True)
+            classification_details = {
+                self.classes[i]: float(class_probs[i]) for i in range(1, len(self.classes))
+            }
         else:
             # For real videos, just show real confidence
-            classification_details = [
-                ("Real", float(class_probs[0]))
-            ]
+            classification_details = {
+                "Real": float(class_probs[0])
+            }
     
         # Backpropagate for Grad-CAM
         self.model.zero_grad()