[Feature Updated] > Tweaked aggregator for video processing a bit to identify videos more effectively

backend/app/ai/video/aggregator.py

@@ -52,6 +52,16 @@ def aggregate_scores(
             (md_score * md_weight)
         ) / total_weight
 
+        # --- NEURAL OVERRIDE FOR SORA-CLASS MODELS ---
+        # SORA and modern generators have near-perfect optical flow and noise consistency.
+        # They will score 0.0 on the physics modules, dragging the final score down by 50%.
+        # If the SigLIP neural module is highly confident it found visual anomalies,
+        # we do not let the analog physics modules drag the verdict down to "Real".
+        if avg_frame_score >= 0.82:
+            final_probability = max(final_probability, 0.80) # Auto-AI
+        elif avg_frame_score >= 0.72:
+            final_probability = max(final_probability, 0.55) # Auto-Suspicious
+
         if final_probability >= THRESHOLDS["ai_certainty"]:
             label = "AI"
         elif final_probability >= THRESHOLDS["suspicious_range"]:

backend/app/api/file_routes.py

@@ -26,6 +26,7 @@ def format_file_response(f):
     if f.filetype and f.filetype.startswith("video/"):
         base_dict["confidence"] = f.confidence
         base_dict["ai_explanation"] = f.ai_explanation
+        base_dict["size"] = f"{(f.filesize/(1024**2)):.2f} MB"
     return base_dict
 
 @router.get("/my-files")