Update core/fusion.py

core/fusion.py

@@ -1,18 +1,18 @@
-from typing import Dict
-
-# judge_score is 1..5 (int). nlp_subscore is 0..1. alpha in [0,1]
+# core/fusion.py (simplified: no fusion, just pass-through)
 
-def fuse_metric(judge_score, nlp_subscore: float, alpha: float) -> float:
-    js = 0.0
-    if judge_score is not None:
-        try:
-            js = max(0.0, min(1.0, float(judge_score) / 5.0))
-        except Exception:
-            js = 0.0
-    ns = max(0.0, min(1.0, float(nlp_subscore)))
-    fused_0_1 = alpha * js + (1 - alpha) * ns
-    return round(fused_0_1 * 10.0, 2)
+from typing import Dict
 
+def normalize_llm_score(judge_score) -> float:
+    """
+    Convert LLM score (1–5) into 0–10 scale.
+    If None, return 0.
+    """
+    if judge_score is None:
+        return 0.0
+    try:
+        return round(max(0.0, min(5.0, float(judge_score))) * 2.0, 2)
+    except Exception:
+        return 0.0
 
 def weighted_total(metric_scores_0_10: Dict[str, float], weights: Dict[str, float]) -> float:
     tot = 0.0