Update prediction.py

prediction.py

@@ -332,18 +332,19 @@ class Prediction:
         )
         prediction_string = f"{prediction} {confidence_deepfake_real:.2f}% Confidence"
     
-        # Create detailed classification results
-        classification_details = (
-            {
-                "Deepfake type": predicted_class,
-                "confidence(%)": f"{confidence_class:.2f}",
-            }
-            if prediction == "Deepfake"
-            else {
-                "Deepfake type": "None (Real video)",
-                "confidence(%)": f"{confidence_class:.2f}",
-            }
-        )
+        # Create detailed classification results as list of (label, confidence) tuples
+        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)
+        else:
+            # For real videos, just show real confidence
+            classification_details = [
+                ("Real", float(class_probs[0]))
+            ]
     
         # Backpropagate for Grad-CAM
         self.model.zero_grad()