Update main.py

main.py

@@ -249,21 +249,44 @@ def gemini_explain(prompt: str, sys: str = None, model: str = EXPLAIN_MODEL) ->
     return getattr(resp, "text", "").strip() or ""
 
 # ───────────────────────────────────────────────────────────────────────────────
-# UTIL: Build graph & timeline from events (+ risk overlays)
+# UTIL: Build risk scores, graph & timeline from events (+ risk overlays)
 # ───────────────────────────────────────────────────────────────────────────────
+# 
+# Targets:
+#   raw 100  -> ~55
+#   raw 200  -> ~80
+#   raw 2000 -> ~99 (slow approach to 99 beyond this)
 import math
 
 def normalize_risk(score: float) -> float:
-    """
-    Normalize a raw risk score to a 0–99 scale.
-    Quick jump from 100–200, slow flattening to 99 by 2000+.
-    """
-    score = max(score, 1)
-    offset = 100
-    scale = 10
-    max_score = 99
-    norm = math.log(score + offset) * scale
-    return min(round(norm, 2), max_score)
+    if score is None:
+        return None
+    s = max(float(score), 0.0)
+
+    # 0–100: quick lift to ~55 by 100
+    if s <= 100.0:
+        return round(55.0 * ((s / 100.0) ** 0.7), 2)
+
+    # 100–200: fast jump 55 -> 80
+    if s <= 200.0:
+        return round(55.0 + 25.0 * (((s - 100.0) / 100.0) ** 0.8), 2)
+
+    # 200+: slow, asymptotic approach to 99
+    # Choose k so that at s=2000, value ≈ 98.8 (renders 99%)
+    k = math.log(100.0) / 1800.0
+    return round(99.0 - 19.0 * math.exp(-k * (s - 200.0)), 2)
+
+#  Backend-only compatibility shim
+# Preserves the original raw value, overwrites "risk_score" for the UI,
+# and also exposes an explicit "risk_score_normalized" key.
+def _apply_normalized_risk_inplace(row: dict):
+    if not isinstance(row, dict):
+        return
+    rs = row.get("risk_score")
+    if isinstance(rs, (int, float)):
+        row["risk_score_raw"] = rs                 # keep original server-side number
+        row["risk_score"] = normalize_risk(rs)     # UI keeps working without changes
+        row["risk_score_normalized"] = row["risk_score"]
 
 
 
@@ -553,10 +576,8 @@ def get_leads():
         cur.execute(sql, args)
         rows = cur.fetchall()
 
-    # Add normalized risk score
     for row in rows:
-        if "risk_score" in row and isinstance(row["risk_score"], (int, float)):
-            row["risk_score_normalized"] = normalize_risk(row["risk_score"])
+        _apply_normalized_risk_inplace(row)
 
     return jsonify({"ok": True, "data": rows})
 
@@ -570,9 +591,7 @@ def object_detail(object_id: int):
         if not obj:
             return jsonify({"ok": False, "error": "not_found"}), 404
 
-        # Normalize risk score
-        if "risk_score" in obj and isinstance(obj["risk_score"], (int, float)):
-            obj["risk_score_normalized"] = normalize_risk(obj["risk_score"])
+        _apply_normalized_risk_inplace(obj)
 
         cur.execute("SELECT seq, sentence FROM provenance_sentences WHERE object_id=%s ORDER BY seq", (object_id,))
         sents = cur.fetchall()
@@ -588,6 +607,7 @@ def object_detail(object_id: int):
     return jsonify({"ok": True, "object": obj, "sentences": sents, "events": events, "risks": risks})
 
 
+
 @app.get("/api/graph/<int:object_id>")
 @with_db_retry
 def graph(object_id: int):