修改预测界面不显示best版本和不能预测
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@ -116,7 +116,7 @@ const filters = reactive({
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const pagination = reactive({
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currentPage: 1,
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pageSize: 8
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pageSize: 12
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})
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const filteredModelList = computed(() => {
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@ -124,7 +124,7 @@ const filters = reactive({
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const pagination = reactive({
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currentPage: 1,
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pageSize: 8
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pageSize: 12
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})
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const filteredModelList = computed(() => {
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@ -126,7 +126,7 @@ const filters = reactive({
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const pagination = reactive({
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currentPage: 1,
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pageSize: 8
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pageSize: 12
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})
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const filteredModelList = computed(() => {
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@ -1329,10 +1329,17 @@ def predict():
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if not model_uid:
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return jsonify({"status": "error", "message": "缺少 'model_uid' 参数"}), 400
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# 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)
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is_best_request = model_uid.endswith('_best')
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if is_best_request:
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db_query_uid = model_uid[:-len('_best')]
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else:
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db_query_uid = model_uid
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# 从数据库查找模型记录
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model_record = find_model_by_uid(model_uid)
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model_record = find_model_by_uid(db_query_uid)
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if not model_record:
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return jsonify({"status": "error", "message": f"模型UID '{model_uid}' 不存在"}), 404
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return jsonify({"status": "error", "message": f"找不到基础模型UID '{db_query_uid}'"}), 404
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# 解析必要的模型元数据
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model_type = model_record.get('model_type')
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@ -1341,6 +1348,16 @@ def predict():
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# 解析 artifacts 找到模型文件路径
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artifacts = json.loads(model_record.get('artifacts', '{}'))
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# 根据请求类型选择正确的模型路径
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if is_best_request:
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model_file_path = artifacts.get('best_model')
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else:
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# 对于 v1, v2 等版本，使用 versioned_model
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model_file_path = artifacts.get('versioned_model')
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# 如果特定版本路径不存在，提供一个后备方案，增加鲁棒性
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if not model_file_path:
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model_file_path = artifacts.get('best_model') or artifacts.get('versioned_model')
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# 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
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@ -1412,10 +1429,13 @@ def predict():
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analysis_result = prediction_result.get('analysis', {}) if prediction_result else {}
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metrics_result = analysis_result.get('metrics', {}) if analysis_result else {}
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# 准备要保存到历史记录的model_type，确保与前端显示一致
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history_model_type = f"{model_type}(best)" if is_best_request else model_type
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db_payload = {
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"prediction_uid": prediction_uid,
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"model_id": model_uid,
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"model_type": model_type,
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"model_type": history_model_type, # 使用处理后的一致性名称
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"product_name": product_name_to_save, # 使用修正后的名称
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"model_version": model_record.get('display_name'), # 将模型信息保存到新字段
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"prediction_scope": {"product_id": product_id, "store_id": store_id},
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@ -86,6 +86,11 @@ register_predictor('default', default_pytorch_predictor)
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register_predictor('xgboost', default_pytorch_predictor)
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# 将新模型也注册给默认预测器
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register_predictor('cnn_bilstm_attention', default_pytorch_predictor)
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register_predictor('transformer', default_pytorch_predictor)
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register_predictor('mlstm', default_pytorch_predictor)
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register_predictor('kan', default_pytorch_predictor)
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register_predictor('optimized_kan', default_pytorch_predictor)
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register_predictor('tcn', default_pytorch_predictor)
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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):
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@ -84,26 +84,48 @@ def query_models_from_db(filters: dict, page: int = 1, page_size: int = 10):
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if conditions:
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query += " WHERE " + " AND ".join(conditions)
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# 获取总数
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count_query = query.replace("*", "COUNT(*)")
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cursor.execute(count_query, params)
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total_count = cursor.fetchone()[0]
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# 添加分页
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offset = (page - 1) * page_size
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query += " ORDER BY created_at DESC LIMIT ? OFFSET ?"
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params.extend([page_size, offset])
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# 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
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query += " ORDER BY created_at DESC"
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cursor.execute(query, params)
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models = [dict(row) for row in cursor.fetchall()]
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all_models = [dict(row) for row in cursor.fetchall()]
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# 增强逻辑：如果模型同时有best版本，则额外生成一条记录
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enhanced_models = []
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for model in all_models:
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# 首先添加原始的版本记录
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enhanced_models.append(model)
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try:
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artifacts = json.loads(model.get('artifacts', '{}'))
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if artifacts and artifacts.get('best_model'):
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original_version = model.get('version', 'N/A')
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best_model_record = model.copy()
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best_model_record['version'] = f"best({original_version})"
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best_model_record['model_uid'] = f"{model['model_uid']}_best"
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best_model_record['model_type'] = f"{model['model_type']}(best)"
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new_artifacts = {
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'best_model': artifacts.get('best_model'),
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'scaler_X': artifacts.get('scaler_X'),
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'scaler_y': artifacts.get('scaler_y')
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}
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best_model_record['artifacts'] = json.dumps(new_artifacts)
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enhanced_models.append(best_model_record)
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except (json.JSONDecodeError, TypeError):
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continue
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# 在内存中进行分页
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total_count = len(enhanced_models)
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offset = (page - 1) * page_size
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paginated_models = enhanced_models[offset : offset + page_size]
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return {
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"models": models,
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"models": paginated_models,
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"pagination": {
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"total": total_count,
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"page": page,
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"page_size": page_size,
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"total_pages": (total_count + page_size - 1) // page_size
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"total_pages": (total_count + page_size - 1) // page_size if page_size > 0 else 0
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}
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}
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