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

# ---------- State ----------
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()
    state["round"] = 0
    state["max_rounds"] = state.get("max_rounds", 2)
    return state

async def reflect(state: ReflectState) -> ReflectState:
    prompt = (
        "You are a critic. Evaluate the following draft answer for completeness, specificity, and lack of filler.\n"
        f"Draft: {state['draft']}\n"
        "Return a JSON object with fields: verdict (ok or needs_revision) and critique (2–3 bullet points)."
    )
    response = await llm.ainvoke([HumanMessage(content=prompt)])
    # Simple extraction of JSON
    import json, re
    try:
        data = json.loads(re.search(r"\{.*\}", response.content, re.S).group(0))
    except Exception:
        data = {"verdict": "needs_revision", "critique": "Could not parse critique."}
    state["critique"] = data.get("critique", "")
    state["verdict"] = data.get("verdict", "needs_revision")
    return state

async def rewrite(state: ReflectState) -> ReflectState:
    prompt = (
        "Rewrite the draft answer incorporating the following critique. Keep the answer concise (5–10 sentences).\n"
        f"Critique: {state['critique']}\n"
        f"Original draft: {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")

# Transitions
graph.add_edge("draft_answer", "reflect")
# If ok -> END
graph.add_conditional_edges(
    "reflect",
    lambda state: "rewrite" if state["verdict"] == "needs_revision" and state["round"] < state["max_rounds"] else "END",
)
# rewrite -> reflect
graph.add_edge("rewrite", "reflect")

app = graph.compile()

# ---------- DeepAgent wrapper ----------
@tool
def run_graph(question: str) -> str:
    """Run the reflection graph for a given question."""
    state: ReflectState = {
        "question": question,
        "draft": "",
        "critique": "",
        "verdict": "",
        "round": 0,
        "max_rounds": 2,
    }
    result = app.invoke(state)
    return result["draft"]

agent = create_deep_agent(
    model=llm,
    tools=[run_graph],
    backend=backend,
    system_prompt="You are a helpful agent that can answer questions and self‑critique using the provided tool.",
)

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("Final answer:\n", result["messages"][-1].content)

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