Update app.py

app.py

@@ -60,18 +60,26 @@ def predict(image):
         # Predictions
         pred_xcp = xcp_model.predict(xcp_tensor, verbose=0).flatten()[0]
         pred_eff = eff_model.predict(eff_tensor, verbose=0).flatten()[0]
-        avg = (pred_xcp + pred_eff) / 2
+        avg = (pred_xcp + pred_eff) / 2  # Real confidence
 
-        label = "Real" if avg > 0.41 else "Fake"
-        color = (0, 255, 0) if label == "Real" else (0, 0, 255)
+        if avg > 0.41:
+            label = "Real"
+            confidence = avg
+            color = (0, 255, 0)
+        else:
+            label = "Fake"
+            confidence = 1 - avg  # Confidence in Fake
 
-        # Annotate image with % instead of raw float
+            color = (0, 0, 255)
+
+        # Annotate image with percentage confidence
         cv2.rectangle(output_image, (x, y), (x2, y2), color, 2)
-        cv2.putText(output_image, f"{label} ({avg * 100:.2f}%)", (x, y - 10),
+        cv2.putText(output_image, f"{label} ({confidence * 100:.2f}%)", (x, y - 10),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
 
+        # Save results
         results.append(
-            f"Face {idx+1}: {label} (Avg: {avg * 100:.2f}%, XCP: {pred_xcp * 100:.2f}%, EFF: {pred_eff * 100:.2f}%)"
+            f"Face {idx+1}: {label} (Confidence: {confidence * 100:.2f}%, Avg Real: {avg * 100:.2f}%, XCP: {pred_xcp * 100:.2f}%, EFF: {pred_eff * 100:.2f}%)"
         )
 
     return "\n".join(results), output_image