import os
from typing import Annotated, Literal, TypedDict
from pydantic import BaseModel

from langchain_core.messages import BaseMessage, SystemMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import create_react_agent

# Импорты из соседних файлов
from api.ads import ads_tools
from api.analytics import analytics_tools
from api.finance import finance_tools
from api.logistics import logistics_tools
from prompts import SUPERVISOR_PROMPT, ADS_ANALYST_PROMPT, ANALYTICS_ANALYST_PROMPT, FINANCE_ANALYST_PROMPT, LOGISTICS_ANALYST_PROMPT

# ==========================================
# 1. СТРУКТУРА СОСТОЯНИЯ (STATE)
# ==========================================
class AgentState(TypedDict):
    messages: Annotated[list[BaseMessage], add_messages]
    next_node: str
    agent_responded: bool

# ==========================================
# 2. ИНИЦИАЛИЗАЦИЯ LLM (OpenRouter)
# ==========================================
llm = ChatOpenAI(
    model="google/gemini-2.0-flash-001", 
    openai_api_key=os.getenv("OPENROUTER_API_KEY"),
    openai_api_base="https://openrouter.ai/api/v1",
    temperature=0.2 
)

# ==========================================
# 3. УЗЕЛ: АНАЛИТИК РЕКЛАМЫ (Суб-агент)
# ==========================================
# Оставляем ТОЛЬКО модель и инструменты. Никаких модификаторов промпта.
ads_agent_runnable = create_react_agent(
    model=llm,
    tools=ads_tools
)

async def ads_node(state: AgentState):
    # Промпт склеиваем с сообщениями вручную перед вызовом агента
    messages_with_prompt = [SystemMessage(content=ADS_ANALYST_PROMPT)] + state["messages"]
    
    # Отдаем агенту готовый массив
    result = await ads_agent_runnable.ainvoke({"messages": messages_with_prompt})
    
    return {"messages": [result["messages"][-1]], "agent_responded": True}

# ==========================================
# 4. УЗЕЛ: АНАЛИТИК ПРОДАЖ (Суб-агент)
# ==========================================
analytics_agent_runnable = create_react_agent(
    model=llm,
    tools=analytics_tools
)

async def analytics_node(state: AgentState):
    messages_with_prompt = [SystemMessage(content=ANALYTICS_ANALYST_PROMPT)] + state["messages"]
    result = await analytics_agent_runnable.ainvoke({"messages": messages_with_prompt})
    return {"messages": [result["messages"][-1]], "agent_responded": True}

# ==========================================
# 5. УЗЕЛ: ФИНАНСОВЫЙ АНАЛИТИК (Суб-агент)
# ==========================================
finance_agent_runnable = create_react_agent(
    model=llm,
    tools=finance_tools
)

async def finance_node(state: AgentState):
    messages_with_prompt = [SystemMessage(content=FINANCE_ANALYST_PROMPT)] + state["messages"]
    result = await finance_agent_runnable.ainvoke({"messages": messages_with_prompt})
    return {"messages": [result["messages"][-1]], "agent_responded": True}

# ==========================================
# 6. УЗЕЛ: АНАЛИТИК ЛОГИСТИКИ (Суб-агент)
# ==========================================
logistics_agent_runnable = create_react_agent(
    model=llm,
    tools=logistics_tools
)

async def logistics_node(state: AgentState):
    messages_with_prompt = [SystemMessage(content=LOGISTICS_ANALYST_PROMPT)] + state["messages"]
    result = await logistics_agent_runnable.ainvoke({"messages": messages_with_prompt})
    return {"messages": [result["messages"][-1]], "agent_responded": True}

# ==========================================
# 7. УЗЕЛ: СУПЕРВАЙЗЕР (Оркестратор)
# ==========================================
class RouterOutput(BaseModel):
    next_node: Literal["Ads_Analyst", "Analytics_Analyst", "Finance_Analyst", "Logistics_Analyst", "FINISH"]

async def supervisor_node(state: AgentState):
    # Если агент уже ответил — детерминированно завершаем, без вызова LLM
    if state.get("agent_responded"):
        return {"next_node": "FINISH", "agent_responded": False}
    messages = [SystemMessage(content=SUPERVISOR_PROMPT)] + state["messages"]
    response = await llm.with_structured_output(RouterOutput).ainvoke(messages)
    return {"next_node": response.next_node, "agent_responded": False}

# ==========================================
# 8. СБОРКА ГРАФА СОСТОЯНИЙ
# ==========================================
workflow = StateGraph(AgentState)

workflow.add_node("Supervisor", supervisor_node)
workflow.add_node("Ads_Analyst", ads_node)
workflow.add_node("Analytics_Analyst", analytics_node)
workflow.add_node("Finance_Analyst", finance_node)
workflow.add_node("Logistics_Analyst", logistics_node)

def router(state: AgentState) -> str:
    if state["next_node"] == "FINISH":
        return END
    return state["next_node"]

workflow.add_edge(START, "Supervisor")
workflow.add_conditional_edges("Supervisor", router)
workflow.add_edge("Ads_Analyst", "Supervisor")
workflow.add_edge("Analytics_Analyst", "Supervisor")
workflow.add_edge("Finance_Analyst", "Supervisor")
workflow.add_edge("Logistics_Analyst", "Supervisor")

# Граф компилируется в app.py после инициализации checkpointer