import os
from typing import Literal

from google.oauth2 import service_account
from langchain_core.messages import AIMessage
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.graph import StateGraph, END, START
from pydantic import BaseModel
from pymongo import MongoClient

from src.core.config import settings, MONGO_URI
from src.server.agent.state import AgentState
from src.server.agent.agents import designer_node, researcher_node, visualizer_node
from langgraph.checkpoint.mongodb import MongoDBSaver


# --- Supervisor (路由逻辑) ---
# 定义路由的输出结构，强制 LLM 选择一个
class RouteResponse(BaseModel):
    next: Literal["Designer", "Researcher", "Visualizer", "FINISH"]


creds = service_account.Credentials.from_service_account_file(
    settings.GOOGLE_GENAI_USE_VERTEXAI,
    scopes=["https://www.googleapis.com/auth/cloud-platform"],
)

llm_supervisor = ChatGoogleGenerativeAI(
    model="gemini-2.0-flash", temperature=0, credentials=creds,
    project="aida-461108", location='us-central1', vertexai=True, api_key=settings.GOOGLE_API_KEY
)


def supervisor_node(state: AgentState):
    messages = state["messages"]
    if not messages:
        return {"next": "FINISH"}

    last_message = messages[-1]

    # --- 改进的拦截逻辑 ---
    # 如果最后一条消息是 AI 产生的（且没有调用工具），说明专家已经回复完了用户
    # 此时我们才拦截并结束，否则会导致专家没机会说话
    if isinstance(last_message, AIMessage) and not last_message.tool_calls:
        return {"next": "FINISH"}

    # 如果最后一条是 HumanMessage，说明用户刚说完，Supervisor 必须派发任务
    system_prompt = """
    你是家具设计团队的主管(Supervisor)。
    请根据用户的意图，选择最合适的专家：
    - Designer: 设计建议、参数细化、闲聊、问候。
    - Visualizer: 绘图、看草图。
    - Researcher: 市场报告、趋势。

    只需输出专家名称。
    """

    chain = llm_supervisor.with_structured_output(RouteResponse)
    decision = chain.invoke([{"role": "system", "content": system_prompt}] + messages)

    return {"next": decision.next}


# --- 构建 Graph ---
workflow = StateGraph(AgentState)

workflow.add_node("Supervisor", supervisor_node)
workflow.add_node("Designer", designer_node)
workflow.add_node("Researcher", researcher_node)
workflow.add_node("Visualizer", visualizer_node)

workflow.add_edge(START, "Supervisor")

# 这里的逻辑是关键：Supervisor 决定去向
workflow.add_conditional_edges(
    "Supervisor",
    lambda state: state["next"],
    {
        "Designer": "Designer",
        "Researcher": "Researcher",
        "Visualizer": "Visualizer",
        "FINISH": END
    }
)

# 重点修改：专家执行完后，必须回到 Supervisor 进行状态检查
# 如果 Supervisor 发现专家刚说完话，它会触发上面的逻辑返回 FINISH
workflow.add_edge("Designer", "Supervisor")
workflow.add_edge("Researcher", "Supervisor")
workflow.add_edge("Visualizer", "Supervisor")

client = MongoClient(MONGO_URI)
checkpointer = MongoDBSaver(
    client=client["furniture_agent_db"],
    db_name="langgraph",
    collection_name="checkpoints"
)
app = workflow.compile(checkpointer=checkpointer)