PredictSupplierSumTop10

Co-authored-by: Copilot <copilot@github.com>

expense_predictor.py

@@ -189,26 +189,15 @@ def predict_expenses(expenses: list[dict], target_user_id, debug: bool = False)
             pct = count / total_records * 100
             print(f"[PREDICT]   supplier_id={supplier_id} -> {count} records ({pct:.1f}%)")
 
-    # Select suppliers whose frequency is strictly greater than 50% of the top supplier frequency
-    max_freq = max(supplier_freq.values()) if supplier_freq else 0
-    freq_threshold = 0.5 * max_freq
-    candidate_items = [
-        item for item in supplier_history.items()
-        if supplier_freq[item[0]] > freq_threshold
-    ]
-
-    # Keep candidates sorted by supplier usage frequency (desc).
+    # Select top 10 most popular suppliers by frequency
     candidate_items = sorted(
-        candidate_items,
+        supplier_history.items(),
         key=lambda item: supplier_freq[item[0]],
         reverse=True,
-    )
+    )[:10]
 
     if debug:
-        print(
-            f"[PREDICT] Processing {len(candidate_items)} suppliers "
-            f"with freq > 50% of max ({freq_threshold:.2f})"
-        )
+        print(f"[PREDICT] Processing top {len(candidate_items)} suppliers")
 
     if not candidate_items:
         if debug: