修改预测界面不显示best版本和不能预测

UI/src/views/prediction/GlobalPredictionView.vue

@@ -116,7 +116,7 @@ const filters = reactive({
 
 const pagination = reactive({
   currentPage: 1,
-  pageSize: 8
+  pageSize: 12
 })
 
 const filteredModelList = computed(() => {

UI/src/views/prediction/ProductPredictionView.vue

@@ -124,7 +124,7 @@ const filters = reactive({
 
 const pagination = reactive({
   currentPage: 1,
-  pageSize: 8
+  pageSize: 12
 })
 
 const filteredModelList = computed(() => {

UI/src/views/prediction/StorePredictionView.vue

@@ -126,7 +126,7 @@ const filters = reactive({
 
 const pagination = reactive({
   currentPage: 1,
-  pageSize: 8
+  pageSize: 12
 })
 
 const filteredModelList = computed(() => {

server/api.py

@@ -1329,10 +1329,17 @@ def predict():
         if not model_uid:
             return jsonify({"status": "error", "message": "缺少 'model_uid' 参数"}), 400
 
+        # 智能处理带 '_best' 后缀的UID，以解决前端key冲突问题 (兼容旧版Python)
+        is_best_request = model_uid.endswith('_best')
+        if is_best_request:
+            db_query_uid = model_uid[:-len('_best')]
+        else:
+            db_query_uid = model_uid
+
         # 从数据库查找模型记录
-        model_record = find_model_by_uid(model_uid)
+        model_record = find_model_by_uid(db_query_uid)
         if not model_record:
-            return jsonify({"status": "error", "message": f"模型UID '{model_uid}' 不存在"}), 404
+            return jsonify({"status": "error", "message": f"找不到基础模型UID '{db_query_uid}'"}), 404
 
         # 解析必要的模型元数据
         model_type = model_record.get('model_type')
@@ -1341,7 +1348,17 @@ def predict():
         
         # 解析 artifacts 找到模型文件路径
         artifacts = json.loads(model_record.get('artifacts', '{}'))
-        model_file_path = artifacts.get('best_model') or artifacts.get('versioned_model')
+        
+        # 根据请求类型选择正确的模型路径
+        if is_best_request:
+            model_file_path = artifacts.get('best_model')
+        else:
+            # 对于 v1, v2 等版本，使用 versioned_model
+            model_file_path = artifacts.get('versioned_model')
+        
+        # 如果特定版本路径不存在，提供一个后备方案，增加鲁棒性
+        if not model_file_path:
+            model_file_path = artifacts.get('best_model') or artifacts.get('versioned_model')
         
         # 修正路径问题：将相对路径转换为绝对路径以进行可靠的文件检查
         project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
@@ -1412,10 +1429,13 @@ def predict():
         analysis_result = prediction_result.get('analysis', {}) if prediction_result else {}
         metrics_result = analysis_result.get('metrics', {}) if analysis_result else {}
 
+        # 准备要保存到历史记录的model_type，确保与前端显示一致
+        history_model_type = f"{model_type}(best)" if is_best_request else model_type
+
         db_payload = {
             "prediction_uid": prediction_uid,
             "model_id": model_uid,
-            "model_type": model_type,
+            "model_type": history_model_type, # 使用处理后的一致性名称
             "product_name": product_name_to_save, # 使用修正后的名称
             "model_version": model_record.get('display_name'), # 将模型信息保存到新字段
             "prediction_scope": {"product_id": product_id, "store_id": store_id},

server/predictors/model_predictor.py

@@ -86,6 +86,11 @@ register_predictor('default', default_pytorch_predictor)
 register_predictor('xgboost', default_pytorch_predictor)
 # 将新模型也注册给默认预测器
 register_predictor('cnn_bilstm_attention', default_pytorch_predictor)
+register_predictor('transformer', default_pytorch_predictor)
+register_predictor('mlstm', default_pytorch_predictor)
+register_predictor('kan', default_pytorch_predictor)
+register_predictor('optimized_kan', default_pytorch_predictor)
+register_predictor('tcn', default_pytorch_predictor)
 
 
 def load_model_and_predict(model_path: str, product_id: str, model_type: str, store_id: Optional[str] = None, future_days: int = 7, start_date: Optional[str] = None, analyze_result: bool = False, version: Optional[str] = None, training_mode: str = 'product', history_lookback_days: int = 30):

server/utils/database_utils.py

@@ -84,26 +84,48 @@ def query_models_from_db(filters: dict, page: int = 1, page_size: int = 10):
         if conditions:
             query += " WHERE " + " AND ".join(conditions)
             
-        # 获取总数
+        # 修复分页逻辑：先查询所有符合条件的模型，再在内存中进行增强和分页
-        count_query = query.replace("*", "COUNT(*)")
+        query += " ORDER BY created_at DESC"
-        cursor.execute(count_query, params)
-        total_count = cursor.fetchone()[0]
-        
-        # 添加分页
-        offset = (page - 1) * page_size
-        query += " ORDER BY created_at DESC LIMIT ? OFFSET ?"
-        params.extend([page_size, offset])
-        
         cursor.execute(query, params)
-        models = [dict(row) for row in cursor.fetchall()]
+        all_models = [dict(row) for row in cursor.fetchall()]
         
+        # 增强逻辑：如果模型同时有best版本，则额外生成一条记录
+        enhanced_models = []
+        for model in all_models:
+            # 首先添加原始的版本记录
+            enhanced_models.append(model)
+            
+            try:
+                artifacts = json.loads(model.get('artifacts', '{}'))
+                if artifacts and artifacts.get('best_model'):
+                    original_version = model.get('version', 'N/A')
+                    best_model_record = model.copy()
+                    best_model_record['version'] = f"best({original_version})"
+                    best_model_record['model_uid'] = f"{model['model_uid']}_best"
+                    best_model_record['model_type'] = f"{model['model_type']}(best)"
+                    
+                    new_artifacts = {
+                        'best_model': artifacts.get('best_model'),
+                        'scaler_X': artifacts.get('scaler_X'),
+                        'scaler_y': artifacts.get('scaler_y')
+                    }
+                    best_model_record['artifacts'] = json.dumps(new_artifacts)
+                    enhanced_models.append(best_model_record)
+            except (json.JSONDecodeError, TypeError):
+                continue
+        
+        # 在内存中进行分页
+        total_count = len(enhanced_models)
+        offset = (page - 1) * page_size
+        paginated_models = enhanced_models[offset : offset + page_size]
+
         return {
-            "models": models,
+            "models": paginated_models,
             "pagination": {
                 "total": total_count,
                 "page": page,
                 "page_size": page_size,
-                "total_pages": (total_count + page_size - 1) // page_size
+                "total_pages": (total_count + page_size - 1) // page_size if page_size > 0 else 0
             }
         }