import json import logging import traceback import uuid from typing import Any, Dict, List import networkx as nx import matplotlib.pyplot as plt import requests logger = logging.getLogger(__name__) class SimpleGraph: """简单的有向图实现，用于构建工作流图""" def __init__(self): self.nodes = {} self.edges = [] def add_node(self, node_id, **attrs): """添加节点""" self.nodes[node_id] = attrs def add_edge(self, source, target, **attrs): """添加边""" # edge = {"source": source, "target": target, **attrs} edge = { "source": source, "target": target, "source_node_uuid": source, "target_node_uuid": target, "source_handle_io": "source", "target_handle_io": "target", **attrs } self.edges.append(edge) def to_dict(self): """转换为工作流图格式""" nodes_list = [] for node_id, attrs in self.nodes.items(): node_attrs = attrs.copy() params = node_attrs.pop("parameters", {}) or {} node_attrs.update(params) nodes_list.append({"id": node_id, **node_attrs}) return { "directed": True, "multigraph": False, "graph": {}, "nodes": nodes_list, "edges": self.edges, "links": self.edges, } def extract_json_from_markdown(text: str) -> str: """从markdown代码块中提取JSON""" text = text.strip() if text.startswith("```json\n"): text = text[8:] if text.startswith("```\n"): text = text[4:] if text.endswith("\n```"): text = text[:-4] return text def create_workflow( steps_info: str, labware_info: str, workflow_name: str = "Generated Workflow", workstation_name: str = "workstation", workflow_description: str = "Auto-generated workflow from protocol", ) -> Dict[str, Any]: """ 创建工作流，输入数据已经是统一的字典格式 Args: steps_info: 步骤信息 (JSON字符串，已经是list of dict格式) labware_info: 实验器材和试剂信息 (JSON字符串，已经是list of dict格式) workflow_name: 工作流名称 workflow_description: 工作流描述 Returns: 创建结果，包含工作流UUID和详细信息 """ try: # 直接解析JSON数据 steps_info_clean = extract_json_from_markdown(steps_info) labware_info_clean = extract_json_from_markdown(labware_info) steps_data = json.loads(steps_info_clean) labware_data = json.loads(labware_info_clean) # 统一处理所有数据 protocol_graph = build_protocol_graph(labware_data, steps_data, workstation_name=workstation_name) # 检测协议类型（用于标签） protocol_type = "bio" if any("biomek" in step.get("template", "") for step in refactored_steps) else "organic" # 转换为工作流格式 data = protocol_graph.to_dict() # 转换节点格式 for i, node in enumerate(data["nodes"]): description = node.get("description", "") onode = { "template": node.pop("template"), "id": node["id"], "lab_node_type": node.get("lab_node_type", "Device"), "name": description or f"Node {i + 1}", "params": {"default": node}, "handles": {}, } # 处理边连接 for edge in data["links"]: if edge["source"] == node["id"]: source_port = edge.get("source_port", "output") if source_port not in onode["handles"]: onode["handles"][source_port] = {"type": "source"} if edge["target"] == node["id"]: target_port = edge.get("target_port", "input") if target_port not in onode["handles"]: onode["handles"][target_port] = {"type": "target"} data["nodes"][i] = onode # 发送到API创建工作流 api_secret = configs.Lab.Key if not api_secret: return {"error": "API SecretKey is not configured", "success": False} # Step 1: 创建工作流 workflow_url = f"{configs.Lab.Api}/api/v1/workflow/" headers = { "Content-Type": "application/json", } params = {"secret_key": api_secret} graph_data = {"name": workflow_name, **data} logger.info(f"Creating workflow: {workflow_name}") response = requests.post( workflow_url, params=params, json=graph_data, headers=headers, timeout=configs.Lab.Timeout ) response.raise_for_status() workflow_info = response.json() if workflow_info.get("code") != 0: error_msg = f"API returned an error: {workflow_info.get('msg', 'Unknown Error')}" logger.error(error_msg) return {"error": error_msg, "success": False} workflow_uuid = workflow_info.get("data", {}).get("uuid") if not workflow_uuid: return {"error": "Failed to get workflow UUID from response", "success": False} # Step 2: 添加到模板库（可选） try: library_url = f"{configs.Lab.Api}/api/flociety/vs/workflows/library/" lib_payload = { "workflow_uuid": workflow_uuid, "title": workflow_name, "description": workflow_description, "labels": [protocol_type.title(), "Auto-generated"], } library_response = requests.post( library_url, params=params, json=lib_payload, headers=headers, timeout=configs.Lab.Timeout ) library_response.raise_for_status() library_info = library_response.json() logger.info(f"Workflow added to library: {library_info}") return { "success": True, "workflow_uuid": workflow_uuid, "workflow_info": workflow_info.get("data"), "library_info": library_info.get("data"), "protocol_type": protocol_type, "message": f"Workflow '{workflow_name}' created successfully", } except Exception as e: # 即使添加到库失败，工作流创建仍然成功 logger.warning(f"Failed to add workflow to library: {str(e)}") return { "success": True, "workflow_uuid": workflow_uuid, "workflow_info": workflow_info.get("data"), "protocol_type": protocol_type, "message": f"Workflow '{workflow_name}' created successfully (library addition failed)", } except requests.exceptions.RequestException as e: error_msg = f"Network error when calling API: {str(e)}" logger.error(error_msg) return {"error": error_msg, "success": False} except json.JSONDecodeError as e: error_msg = f"JSON parsing error: {str(e)}" logger.error(error_msg) return {"error": error_msg, "success": False} except Exception as e: error_msg = f"An unexpected error occurred: {str(e)}" logger.error(error_msg) logger.error(traceback.format_exc()) return {"error": error_msg, "success": False}