feat: 风险关键词红色标记 + 点击显示权重贡献

- 后端: _extract_reason_tokens 返回 [{token, weight}] 格式
- 前端: detect/batch 页面风险关键词使用红色标签样式
- 点击关键词弹窗显示权重值及判定倾向

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

backend/app/ml/naive_bayes_classifier.py

@@ -119,7 +119,7 @@ class NaiveBayesSpamClassifier:
             "trained_at": self.metadata.get("trained_at"),
         }
 
-    def _extract_reason_tokens(self, text: str, classes: list[str], x_row) -> list[str]:
+    def _extract_reason_tokens(self, text: str, classes: list[str], x_row) -> list[dict]:
         try:
             vocab = self.vectorizer.vocabulary_
             feature_names = self.vectorizer.get_feature_names_out()
@@ -138,12 +138,12 @@ class NaiveBayesSpamClassifier:
                 if idx is None:
                     continue
                 delta = class_log_prob[spam_idx][idx] - class_log_prob[ham_idx][idx]
-                scored.append((token, delta))
+                scored.append({"token": token, "weight": round(delta, 4)})
 
-            scored.sort(key=lambda row: abs(row[1]), reverse=True)
-            return [token for token, _ in scored[:5]]
+            scored.sort(key=lambda row: abs(row["weight"]), reverse=True)
+            return scored[:5]
         except Exception:
-            return list(text[:5])
+            return [{"token": ch, "weight": 0.0} for ch in list(text[:5])]
 
     def model_info(self) -> dict:
         return {