Create client_first.py

nlp/client_first.py

@@ -0,0 +1,15 @@
+from .detectors import personalization_density, fiduciary_flag, product_push_penalty
+
+def score(text: str):
+    pd = personalization_density(text)
+    fid = fiduciary_flag(text)
+    ppush = product_push_penalty(text)
+
+    sub = 0.35*pd + 0.25*fid + 0.25*(1 if fid else 0) - 0.25*ppush
+    sub = max(0.0, min(1.0, sub))
+    return {
+        'subscore': sub,
+        'personalization_density': pd,
+        'fiduciary_flag': fid,
+        'product_push_penalty': ppush,
+    }