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 CompositeBackend, LocalShellBackend, FilesystemBackend

# ---------- 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 ----------
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages

# Helper to format the prompt for each node
DRAFT_PROMPT = """Write a concise answer (5–10 sentences) to the following question:

{question}
"""

REFLECT_PROMPT = """You are a critic. Given the draft answer below, evaluate its completeness, specificity, and absence of filler. Respond with:
1. verdict: either "ok" or "needs_revision"
2. critique: 2–3 bullet points explaining what to improve (if any)

Draft:
{draft}
"""

REWRITE_PROMPT = """You are revising the draft answer based on the critique. Produce a new draft that addresses the points. Keep the answer concise (5–10 sentences).

Critique:
{critique}

Previous draft:
{draft}
"""

# Node functions
async def draft_answer(state: ReflectState) -> ReflectState:
    response = await llm.ainvoke([HumanMessage(content=DRAFT_PROMPT.format(question=state["question"]))])
    state["draft"] = response.content.strip()
    return state

async def reflect(state: ReflectState) -> ReflectState:
    response = await llm.ainvoke([HumanMessage(content=REFLECT_PROMPT.format(draft=state["draft"]))])
    # Parse verdict and critique
    text = response.content.strip()
    verdict_line = next((l for l in text.splitlines() if l.lower().startswith("verdict:")), "")
    critique_lines = [l for l in text.splitlines() if l.startswith("-") or l.startswith("•")]
    verdict = verdict_line.split(":",1)[1].strip().lower() if verdict_line else "needs_revision"
    critique = "\n".join(critique_lines) if critique_lines else ""
    state["verdict"] = verdict
    state["critique"] = critique
    return state

async def rewrite(state: ReflectState) -> ReflectState:
    response = await llm.ainvoke([HumanMessage(content=REWRITE_PROMPT.format(critique=state["critique"], draft=state["draft"]))])
    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
# After draft -> 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")

graph.compile()

# ---------- DeepAgent wrapper ----------
@tool
def run_reflect_graph(question: str, max_rounds: int = 2) -> str:
    """Run the reflection graph and return the final draft."""
    initial_state: ReflectState = {
        "question": question,
        "draft": "",
        "critique": "",
        "verdict": "",
        "round": 0,
        "max_rounds": max_rounds,
    }
    result = graph.invoke(initial_state)
    return result["draft"]

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

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

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