"""输入解析与校验，可直接运行本文件修改示例进行调试"""

from app.processor.base import BaseNode
from app.processor.state import QueryState
from app.schemas.input_context import InputContext


class InputNode(BaseNode):
    """纯输入节点，不依赖模型配置；校验失败保留 ValidationError"""

    name = "input_node"

    def process(self, state: QueryState) -> QueryState:
        # 浅拷贝状态
        updated_state: QueryState = state.copy()
        # 获取待合并的用户澄清回复
        reply = state.get("clarification_reply")

        # 如果有澄清回复，将补充信息合并到已有上下文后重新处理
        if reply is not None:
            # 将已有上下文转成独立的字典，避免直接修改原上下文中的嵌套数据
            data = state["input_context"].model_dump(mode="json")
            # 记录本轮澄清问题和用户回答，供后续节点理解补充需求
            turn = {
                "question": state["clarification"].question,
                "answer": reply.answer,
            }

            # 如果用户选择了候选项，按选项 ID 查找并保存完整选项内容
            if reply.option_id is not None:
                option = next(
                    option
                    for option in state["clarification"].options
                    if option.id == reply.option_id
                )
                turn["selected_option"] = option.model_dump(mode="json")

            # 将本轮问答追加到历史中，保留多轮澄清信息
            data["clarification_history"].append(turn)

            # 如果回复携带最新大屏快照，用它替换旧快照
            if reply.screen is not None:
                data["screen"] = reply.screen.model_dump(mode="json")
                # 如果没有同时提供交互上下文，清空可能已失效的选中节点和版本号
                if reply.context is None:
                    data["context"]["selected_node_ids"] = []
                    data["context"]["screen_revision"] = None

            # 如果回复携带交互上下文，使用本次提交的选中节点和版本等信息
            if reply.context is not None:
                data["context"] = reply.context.model_dump(mode="json")
            # 重新校验合并后的数据并构建上下文，校验失败时向上抛出 ValidationError
            context = InputContext.model_validate(data)

            # 保存新上下文并清空已消费的回复，避免后续重复合并
            updated_state["input_context"] = context
            updated_state["clarification_reply"] = None
            # 清空旧意图、需求和规划，让后续节点根据补充信息重新推导
            # LangGraph 的状态合并不会删除省略字段，因此需要显式赋空值
            updated_state["intent_decision"] = None
            updated_state["intent"] = None
            updated_state["requirement"] = None
            updated_state["requirement_outcome"] = None
            updated_state["notice"] = None
            updated_state["offer"] = None
            updated_state["accepted_offer"] = None
            updated_state["change_scope"] = None
            updated_state["screen_plan"] = None
            updated_state["component_resources"] = None
            # 清空旧动作及其校验、模拟结果，避免复用补充需求前的处理结果
            updated_state["actions"] = None
            updated_state["validation_passed"] = None
            updated_state["validation_errors"] = []
            updated_state["simulated_screen"] = None
            # 重置自动修正次数和失败信息，让本轮处理重新计算重试次数
            updated_state["retry_count"] = 0
            updated_state["repair_plan"] = None
            updated_state["failure_reason"] = None
            # 清空上一轮澄清问题和回答，恢复任务处理状态
            updated_state["clarification"] = None
            updated_state["answer"] = None
            updated_state["status"] = "running"

            # 返回澄清恢复后的状态，不再执行下方首次请求的初始化逻辑
            return updated_state

        # 没有澄清回复时，按首次请求校验原始输入，并深拷贝隔离提交时的快照
        context = InputContext.model_validate(state["request"]).model_copy(deep=True)

        # 保存校验后的上下文，供后续节点统一读取
        updated_state["input_context"] = context
        # 初始化自动修正次数，并清空修正建议和失败原因
        updated_state["retry_count"] = 0
        updated_state["repair_plan"] = None
        updated_state["failure_reason"] = None
        # 首次请求进入处理状态，恢复澄清时也使用同一状态
        updated_state["status"] = "running"
        # 返回初始化后的状态，由图接续执行后续节点
        return updated_state


if __name__ == "__main__":
    import json

    # 1. 创建 state：可将 screen 替换成前端的完整大屏 JSON
    state: QueryState = {
        "request": {
            "prompt": "把销售额统计向右移动 50px",
            "screen": {
                "canvas": {
                    "width": 1920,
                    "height": 1080,
                    "backgroundColor": "#ffffff10",
                },
                "nodes": [
                    {
                        "id": "sales-chart",
                        "type": "bar-chart",
                        "name": "销售额统计",
                        "layout": {"x": 26, "y": 34.8, "width": 400, "height": 260},
                        "props": {"option": {"title": {"text": "销售额统计"}}},
                    }
                ],
                "dataSource": [],
            },
            "context": {"selected_node_ids": ["sales-chart"]},
            "attachments": [],
        }
    }

    # 2. 执行节点
    node = InputNode()
    final_state = node.process(state)

    # 3. 将上下文模型转成可序列化的字典，再打印完整状态
    printable_state = {
        **final_state,
        "input_context": final_state["input_context"].model_dump(
            mode="json", exclude_unset=True
        ),
    }
    print(json.dumps(printable_state, ensure_ascii=False, indent=2))