fix: 修正bug

app/app.py

@@ -630,9 +630,7 @@ def main():
                     )
                     all_results.append({"smiles": smiles, "results": results})
                     
-                    # 为多 SMILES 模式添加 SMILES 标签
-                    smiles_label = smiles[:30] + "..." if len(smiles) > 30 else smiles
-                    df = format_results_dataframe(results, smiles_label if is_multi_smiles else None)
+                    df = format_results_dataframe(results, smiles if is_multi_smiles else None)
                     all_dfs.append(df)
                     
                 except httpx.HTTPStatusError as e:
@@ -727,7 +725,7 @@ def main():
         with col_export:
             smiles_used = st.session_state.get("smiles_used", "")
             if isinstance(smiles_used, list):
-                smiles_used = ",".join(smiles_used)
+                smiles_used = " | ".join(smiles_used)
             
             csv_content = create_export_csv(
                 df,
@@ -754,6 +752,13 @@ def main():
             use_container_width=True,
             hide_index=True,
             height=600,
+            column_config={
+                "SMILES": st.column_config.TextColumn(
+                    "SMILES",
+                    width="medium",
+                    help="界面上按列宽省略显示，可拖动列头加宽；导出的 CSV 中为完整字符串",
+                ),
+            },
         )
         
         # 详细信息

app/optimize.py

@@ -897,6 +897,7 @@ def optimize(
     logger.info(f"Comp ranges: {comp_ranges.to_dict()}")
     
     seeds = None
+    global_pool: List[Formulation] = []
 
     _has_llm = getattr(model, "llm_prompt", None) is not None
     _do_rerank = bool(_has_llm and rerank_top_n and rerank_top_n > 0)
@@ -934,7 +935,12 @@ def optimize(
             # 选择 top num_seeds 个种子点
             seeds = select_top_k(df, organ, num_seeds, scoring_weights)
 
-            logger.info(f"Selected {len(seeds)} seeds for next iteration")
+            global_pool = select_top_k(
+                df, organ, max(num_seeds, top_k * 10), scoring_weights
+            )
+
+            logger.info(f"Selected {len(seeds)} seeds for next iteration "
+                        f"(global pool: {len(global_pool)})")
             
         else:
             # ==================== 后续迭代：层级局部搜索 ====================
@@ -999,7 +1005,20 @@ def optimize(
 
     logger.info(f"Stage-1: {len(unique_results)} unique formulations (from {len(seeds)} candidates)")
 
-    if not _do_rerank:
+    if len(unique_results) < top_k and global_pool:
+        n_before = len(unique_results)
+        for f in global_pool:
+            if len(unique_results) >= top_k:
+                break
+            key = f.unique_key()
+            if key not in seen_keys:
+                seen_keys.add(key)
+                unique_results.append(f)
+        if len(unique_results) > n_before:
+            logger.warning(
+                f"局部搜索收敛至 {n_before} 个不同配方（请求 {top_k} 个），"
+                f"已从粗筛池回填 {len(unique_results) - n_before} 个未细化候选"
+            )
         return unique_results[:top_k]
 
     # ==================== 第二阶段：完整模型重排 ====================