import os
import asyncio
from typing import TypedDict, Annotated

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages

# ---------- LLM ----------
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.0,
)

# ---------- Backend for deepagents ----------
backend = CompositeBackend([
    LocalShellBackend(workspace_dir="./workspace"),
    FilesystemBackend(),
])

# ---------- State definition ----------
class ReflectState(TypedDict):
    question: str
    draft: str
    critique: str
    verdict: str  # ok | needs_revision
    round: int
    max_rounds: int

# ---------- Nodes ----------
async def draft_answer(state: ReflectState) -> ReflectState:
    prompt = f"Write a concise answer (5–10 sentences) to the following question: {state['question']}"
    response = await llm.ainvoke([HumanMessage(content=prompt)])
    state['draft'] = response.content.strip()
    return state

async def reflect(state: ReflectState) -> ReflectState:
    prompt = (
        f"You are a critic. Evaluate the following draft answer for completeness, specificity, and lack of filler.\n\nDraft: {state['draft']}\n\nProvide a verdict (ok or needs_revision) and 2–3 bullet points of critique."
    )
    response = await llm.ainvoke([HumanMessage(content=prompt)])
    text = response.content.strip()
    # Simple parsing: first line verdict, rest critique
    lines = text.splitlines()
    verdict_line = lines[0].lower()
    verdict = "ok" if "ok" in verdict_line else "needs_revision"
    critique = "\n".join(lines[1:]).strip()
    state['verdict'] = verdict
    state['critique'] = critique
    return state

async def rewrite(state: ReflectState) -> ReflectState:
    prompt = (
        f"Rewrite the draft answer taking into account the following critique: {state['critique']}\n\nOriginal draft: {state['draft']}"
    )
    response = await llm.ainvoke([HumanMessage(content=prompt)])
    state['draft'] = response.content.strip()
    state['round'] += 1
    return state

# ---------- Graph ----------
async def run_graph(question: str, max_rounds: int = 2) -> str:
    graph = StateGraph(ReflectState)
    graph.add_node("draft_answer", draft_answer)
    graph.add_node("reflect", reflect)
    graph.add_node("rewrite", rewrite)

    graph.set_entry_point("draft_answer")
    graph.add_edge("draft_answer", "reflect")
    graph.add_conditional_edges(
        "reflect",
        lambda x: "rewrite" if x["verdict"] == "needs_revision" and x["round"] < x["max_rounds"] else "END",
    )
    graph.add_edge("rewrite", "reflect")

    graph.add_edge("END", END)

    app = graph.compile()
    initial_state: ReflectState = {
        "question": question,
        "draft": "",
        "critique": "",
        "verdict": "",
        "round": 0,
        "max_rounds": max_rounds,
    }
    final_state = await app.ainvoke(initial_state)
    return final_state["draft"]

# ---------- DeepAgent tool ----------
@tool
async def answer_question(query: str) -> str:
    """Generate a refined answer using self‑reflection graph."""
    return await run_graph(query)

# ---------- DeepAgent ----------
agent = create_deep_agent(
    model=llm,
    tools=[answer_question],
    backend=backend,
    system_prompt="You are an AI assistant that answers questions. Use the provided tool to generate answers.",
)

# ---------- CLI ----------
async def main():
    question = "Объясни студенту разницу между tool и resource в MCP"
    result = await agent.ainvoke(
        {"messages": [HumanMessage(content=question)]},
        {"configurable": {"thread_id": "session-1"}},
    )
    print("\nFinal answer:\n", result["messages"][-1].content)

if __name__ == "__main__":
    asyncio.run(main())