import os
import warnings
from typing import Annotated, TypedDict

from dotenv import load_dotenv
from langchain_chroma import Chroma
from langchain_community.cache import SQLiteCache
from langchain_core.globals import set_llm_cache
from langchain_core.messages.human import HumanMessage
from langchain_core.messages.system import SystemMessage
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph.message import add_messages
from langgraph.graph.state import END, START, CompiledStateGraph, StateGraph
from langgraph.prebuilt import ToolNode, tools_condition
from pydantic import SecretStr

warnings.filterwarnings("ignore", category=UserWarning, module="langchain_tavily")

load_dotenv()


# from langchain_core.caches import InMemoryCache
# set_llm_cache(InMemoryCache())
set_llm_cache(SQLiteCache(database_path=".langchain_cache.db"))

# Initialize RAG vector store
CHROMA_PATH = "./chroma_gaia_db"
EMBEDDINGS = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
VECTOR_STORE = Chroma(persist_directory=CHROMA_PATH, embedding_function=EMBEDDINGS)


class AgentState(TypedDict):
    """State passed between nods in the graph"""

    messages: Annotated[list, add_messages]


def load_system_prompt() -> SystemMessage:
    with open("system_prompt.txt", "r") as f:
        system_prompt = f.read()
        return SystemMessage(content=system_prompt)


SYSTEM_PROMPT: SystemMessage = load_system_prompt()


class GaiaAgent:
    """
    A LangGraph agent for Gaia questions
    """

    def __init__(self, model: str, temperature: float):
        """Initialize the agent with a specific model"""
        import asyncio

        from tools import get_tools

        self.tools = asyncio.run(get_tools())

        if model.startswith("glm"):
            api_key = SecretStr(secret_value=os.getenv("ZAI_API_KEY", ""))
            api_base = "https://api.z.ai/api/coding/paas/v4/"
        else:
            api_key = SecretStr(secret_value=os.getenv("OPENAI_API_KEY") or "")
            api_base = None

        self.llm = ChatOpenAI(
            model=model, temperature=temperature, base_url=api_base, api_key=api_key
        ).bind_tools(self.tools)

        self.graph = self._build_graph()

        print(f"Initialized GaiaAgent with model: {model}, temperature: {temperature}")
        print(f"Available tools: {[tool.name for tool in self.tools]}")

    def _build_graph(self) -> CompiledStateGraph:
        """Build the state graph for the agent"""

        graph = StateGraph(AgentState)

        graph.add_node("agent", self._agent_node)
        graph.add_node("tools", ToolNode(self.tools))

        graph.add_edge(START, "agent")
        graph.add_conditional_edges("agent", tools_condition)
        graph.add_edge("tools", "agent")

        memory = MemorySaver()
        return graph.compile(checkpointer=memory)

    def _retriever_node(self, state: AgentState) -> AgentState:
        """Retrieve similar questions and inject solving strategy into the question."""
        original_question = state["messages"][0].content

        similar_docs = VECTOR_STORE.similarity_search(original_question, k=1)

        if similar_docs:
            doc = similar_docs[0]
            steps = (
                doc.page_content.split("Steps to solve:")[-1]
                .split("Tools needed:")[0]
                .strip()
            )
            tools = doc.metadata.get("tools", "")

            # Build enhanced question with strategy
            enhanced_question = f"""{original_question}

---
Strategy (from similar solved question):
{steps}

Tools needed: {tools}

Follow a similar approach to solve the question above."""

            enhanced_msg = HumanMessage(content=enhanced_question)
            return {"messages": [SYSTEM_PROMPT, enhanced_msg]}

        return {"messages": [SYSTEM_PROMPT] + state["messages"]}

    def _tools_node(self, state: AgentState) -> AgentState:
        """Execute tools and log results."""
        tool_node = ToolNode(self.tools)
        result = tool_node.invoke(state)

        # Log tool results and check for answers
        for msg in result.get("messages", []):
            content = getattr(msg, "content", str(msg))
            name = getattr(msg, "name", "unknown")
            print(f"  Tool result [{name}]: {content[:300]}...")

        return result

    async def __call__(self, question: str) -> str:
        """
        Run the agent on a given question and return the answer

        Args:
            question (str): The input question to the agent

        Returns:
            str: The agent's answer to the question
        """

        print(f"\n{'='*60}")
        print(f"Agent received question: {question[:100]}...")
        print(f"{'='*60}\n")

        initial_state = {
            "messages": [HumanMessage(content=question)],
        }

        try:
            import uuid

            thread_id = str(uuid.uuid4())
            config = {"configurable": {"thread_id": thread_id}, "recursion_limit": 50}
            final_state = await self.graph.ainvoke(initial_state, config)

            last_message = final_state["messages"][-1]

            answer = (
                str(last_message.content)
                if hasattr(last_message, "content")
                else str(last_message)
            )

            # Clean up answer - extract from tags if present
            answer = self._clean_answer(answer)

            print(f"Agent final response: {answer[:200]}...\n")

            return answer
        except Exception as e:
            print(f"Error during agent execution: {e}")
            return f"AGENT ERROR: {e}"

    def _clean_answer(self, answer: str) -> str:
        """Extract clean answer from various formats."""
        import re

        # Extract from <solution>...</solution>
        match = re.search(r"<solution>(.*?)</solution>", answer, re.DOTALL)
        if match:
            return match.group(1).strip()

        # Extract from FINAL ANSWER: ... (to end of line or string)
        match = re.search(r"FINAL ANSWER:\s*(.+?)(?:\n|$)", answer, re.IGNORECASE)
        if match:
            return match.group(1).strip()

        # Extract from **FINAL ANSWER:** or similar markdown
        match = re.search(
            r"\*\*FINAL ANSWER:?\*\*:?\s*(.+?)(?:\n|$)", answer, re.IGNORECASE
        )
        if match:
            return match.group(1).strip()

        # If answer contains a colon followed by a list, extract just the list part
        # e.g., "...ingredients: cornstarch, sugar, ..."
        match = re.search(
            r":\s*\n?\s*([a-z][a-z\s,]+(?:,\s*[a-z][a-z\s]+)+)\s*$",
            answer,
            re.IGNORECASE,
        )
        if match:
            return match.group(1).strip()

        # Last resort: if there's a clear comma-separated list at the end, extract it
        lines = answer.strip().split("\n")
        last_line = lines[-1].strip()
        if "," in last_line and len(last_line) < 500:
            # Check if it looks like a list (multiple comma-separated items)
            items = [i.strip() for i in last_line.split(",")]
            if len(items) >= 2 and all(len(i) < 100 for i in items):
                return last_line

        return answer.strip()

    def _agent_node(self, state: AgentState) -> AgentState:
        """The main agent node that processes messages and generates responses"""
        messages = state["messages"]

        # Debug: show message count
        print(f"\n[AGENT] Message count: {len(messages)}")

        # Prepend system prompt if not already there
        if not messages or not isinstance(messages[0], SystemMessage):
            messages = [SYSTEM_PROMPT] + messages

        # Print the full prompt/messages
        print("[AGENT] === MESSAGES ===")
        for i, msg in enumerate(messages):
            msg_type = type(msg).__name__
            content = (
                str(msg.content)[:500] if hasattr(msg, "content") else str(msg)[:500]
            )
            print(f"  [{i}] {msg_type}: {content}...")
        print("[AGENT] === END MESSAGES ===\n")

        response = self.llm.invoke(messages)

        # Log what the agent is doing
        if hasattr(response, "tool_calls") and response.tool_calls:
            print(
                f"[AGENT] Calling tools: {[tc['name'] for tc in response.tool_calls]}"
            )
        else:
            content = (
                str(response.content)[:200]
                if hasattr(response, "content")
                else str(response)[:200]
            )
            print(f"[AGENT] Final response: {content}...")

        return {"messages": [response]}


# model="o3-mini"
MODEL = "glm-4.7"

BasicAgent = GaiaAgent(model=MODEL, temperature=1.0)