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 (not used directly in graph but required by create_deep_agent) ----------
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:\n\n{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:\n{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. Keep the answer concise (5–10 sentences).\n\nCritique:\n{state['critique']}\n\nOriginal Draft:\n{state['draft']}"
    )
    response = await llm.ainvoke([HumanMessage(content=prompt)])
    state["draft"] = response.content.strip()
    state["round"] += 1
    return state

# ---------- Graph ----------
graph = StateGraph(ReflectState)
graph.add_node("draft_answer", draft_answer)
graph.add_node("reflect", reflect)
graph.add_node("rewrite", rewrite)

# Entry point
graph.set_entry_point("draft_answer")

# Transition logic
# After draft_answer -> reflect
graph.add_edge("draft_answer", "reflect")
# After reflect
# if ok -> END
# if needs_revision and round < max_rounds -> rewrite
# else -> END

def reflect_conditional(state: ReflectState):
    if state["verdict"] == "ok":
        return "END"
    if state["round"] < state["max_rounds"]:
        return "rewrite"
    return "END"

graph.add_conditional_edges("reflect", reflect_conditional, {
    "rewrite": "rewrite",
    "END": "END",
})

# After rewrite -> reflect
graph.add_edge("rewrite", "reflect")

app = graph.compile()

# ---------- DeepAgent wrapper ----------
# The deepagent will simply forward the human message to the graph and return the final draft.
@tool
def run_graph(question: str) -> str:
    """Run the reflection graph for a given question."""
    initial_state: ReflectState = {
        "question": question,
        "draft": "",
        "critique": "",
        "verdict": "",
        "round": 0,
        "max_rounds": 2,
    }
    result = app.invoke(initial_state)
    return result["draft"]

agent = create_deep_agent(
    model=llm,
    tools=[run_graph],
    backend=backend,
    system_prompt="You are an assistant that can answer questions using a self‑checking process.",
)

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

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