fix(step8): 修复外部模型字典透传断链 + 规范化 loaded_model_data 防 Ridge subscriptable 崩溃

src/core/prediction/inference_batch.py

@@ -41,10 +41,17 @@ class WaterQualityInference:
             print(f"警告: 模型目录不存在: {artifacts_dir}，将在需要时创建")
 
         self.best_model_info = None
-        self.loaded_model_data = None
         self.external_model = external_model
         self.external_model_path = external_model_path
 
+        # 规范化 loaded_model_data：始终为 dict，确保 ['model'] 访问不崩溃
+        if external_model is not None:
+            # 外部传入的是裸模型对象 → 包装为 dict，统一后续 .get('model') 访问
+            self.loaded_model_data = {'model': external_model, 'preprocess_method': 'None'}
+            print(f"  外部模型已规范化: type={type(external_model).__name__}")
+        else:
+            self.loaded_model_data = None
+
     def load_sampling_data(self, csv_path: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
         """
         加载sampling生成的CSV数据（兼容 WQI 增强版 CSV）
@@ -751,8 +758,7 @@ class WaterQualityInference:
             print("\n步骤1: 加载模型")
             print("-" * 40)
             if self.external_model is not None:
-                # 外部预训练模型已注入，直接使用，跳过磁盘加载
-                self.loaded_model_data = self.external_model
+                # 已在 __init__ 中规范化，无需重复赋值
                 print(f"  使用外部预训练模型: type={type(self.external_model).__name__}")
             elif model_file_path:
                 self.load_specific_model(model_file_path)
@@ -802,8 +808,8 @@ class WaterQualityInference:
 
         info = {
             "status": "model_loaded",
-            "preprocess_method": self.loaded_model_data['preprocess_method'],
-            "model_name": self.loaded_model_data['model_name'],
+            "preprocess_method": self.loaded_model_data.get('preprocess_method', 'Unknown'),
+            "model_name": self.loaded_model_data.get('model_name', type(self.external_model).__name__ if self.external_model else 'Unknown'),
             "model_type": str(type(self.loaded_model_data['model'])),
             "metadata": self.loaded_model_data.get('metadata', {})
         }
@@ -877,7 +883,8 @@ class WaterQualityInference:
                                    prediction_column: str = 'prediction',
                                    output_format: str = 'csv',
                                    external_model=None,
-                                   external_model_path=None):
+                                   external_model_path=None,
+                                   external_models_dict=None):
         """
         使用多个子文件夹中的模型进行批量推理
         
@@ -893,45 +900,61 @@ class WaterQualityInference:
         output_path = Path(output_dir)
         output_path.mkdir(parents=True, exist_ok=True)
 
-        # 优先级：外部预训练模型 > 从磁盘加载
-        if external_model is not None:
-            effective_model = external_model
-            model_desc = (
-                f"外部导入模型 ({external_model_path or 'unknown'}), "
-                f"type={type(external_model).__name__}"
-            )
-            print(f"\n使用外部预训练模型: {model_desc}")
-        else:
-            effective_model = None
-
-        # 查找所有子文件夹
-        subdirs = [d for d in models_root.iterdir() if d.is_dir()]
-        
-        if not subdirs:
-            print(f"在目录 {models_root_dir} 中未找到子文件夹")
-            return
-            
-        print(f"找到 {len(subdirs)} 个模型子文件夹进行批量推理")
-        print(f"输出格式: {output_format.upper()}")
-        
         all_results = {}
-        
-        for subdir in subdirs:
+
+        # 优先级 1：_external_models_dict 非空 → 直接用字典的 keys 作为 targets，不扫描磁盘
+        print(f"[BatchInference] 终于收到字典啦！包含模型: {list(external_models_dict.keys()) if external_models_dict else 'None'}")
+        if external_models_dict is not None and len(external_models_dict) > 0:
+            targets = list(external_models_dict.keys())
+            print(f"\n使用外部导入模型字典（{len(targets)} 个模型）")
+            print(f"检测到外部导入模型，将预测以下参数: {targets}")
+        elif external_model is not None:
+            print(f"\n使用外部预训练模型: {external_model_path or 'unknown'}")
+            subdirs = [d for d in models_root.iterdir() if d.is_dir()]
+            if not subdirs:
+                print(f"在目录 {models_root_dir} 中未找到子文件夹")
+                return {}
+            print(f"找到 {len(subdirs)} 个模型子文件夹进行批量推理")
+            targets = [d.name for d in subdirs]
+        else:
+            subdirs = [d for d in models_root.iterdir() if d.is_dir()]
+            if not subdirs:
+                print(f"在目录 {models_root_dir} 中未找到子文件夹")
+                return {}
+            print(f"找到 {len(subdirs)} 个模型子文件夹进行批量推理")
+            targets = [d.name for d in subdirs]
+
+        print(f"输出格式: {output_format.upper()}")
+
+        for subdir_name in targets:
             try:
-                subdir_name = subdir.name
                 print(f"\n{'='*60}")
-                print(f"处理模型文件夹: {subdir_name}")
+                print(f"处理模型: {subdir_name}")
                 print(f"{'='*60}")
 
-                # 创建推理实例：外部模型优先注入，跳过磁盘查找
+                # 优先级：字典中该 target 的模型 > 共享单模型 > 磁盘加载
+                effective_model = None
+                if external_models_dict and subdir_name in external_models_dict:
+                    effective_model = external_models_dict[subdir_name]
+                    print(f"  → 使用字典中模型: {type(effective_model).__name__}")
+                elif external_model is not None:
+                    effective_model = external_model
+                    print(f"  → 使用共享外部模型: {type(effective_model).__name__}")
+
+                # artifacts_dir：字典模式优先用 placeholder "./"，否则用真实子目录
+                artifacts_dir = (
+                    str(models_root / subdir_name)
+                    if (models_root / subdir_name).is_dir()
+                    else str(models_root)
+                )
                 if effective_model is not None:
                     model_inferencer = WaterQualityInference(
-                        str(subdir),
+                        artifacts_dir,
                         external_model=effective_model,
-                        external_model_path=external_model_path,
+                        external_model_path=external_model_path or "",
                     )
                 else:
-                    model_inferencer = WaterQualityInference(str(subdir))
+                    model_inferencer = WaterQualityInference(artifacts_dir)
                 
                 # 根据输出格式设置文件扩展名
                 file_ext = f".{output_format}"
@@ -960,10 +983,10 @@ class WaterQualityInference:
                     }
                 }
                 
-                print(f"子文件夹 {subdir_name} 处理完成")
+                print(f"模型 {subdir_name} 处理完成")
                 
             except Exception as e:
-                print(f"处理子文件夹 {subdir_name} 失败: {e}")
+                print(f"处理模型 {subdir_name} 失败: {e}")
                 all_results[subdir_name] = {
                     'status': 'error',
                     'error': str(e)