import mimetypes
import pathlib
import time

from langchain_core.messages import SystemMessage, HumanMessage, AIMessage, RemoveMessage
from langchain_openai import ChatOpenAI

from config.settings import config
from core.messages import attachmentHandler
from core.state import State
from nodes.chunking_handler import OversizedContentHandler
from tools.audio_tool import query_audio
from tools.chess_tool import chess_analysis_tool
from tools.excel_tool import query_excel_file
from tools.math_agent import math_tool
from tools.python_executor import execute_python_code
from tools.tavily_tools import web_search_tools
from utils.prompt_manager import prompt_mgmt

agent_tools = []
agent_tools.extend(web_search_tools)
agent_tools.append(query_audio)
agent_tools.append(query_excel_file)
agent_tools.append(execute_python_code)
agent_tools.append(math_tool)
agent_tools.append(chess_analysis_tool)

model = ChatOpenAI(model=config.model_name)
model = model.bind_tools(agent_tools, parallel_tool_calls=False)

response_processing_model = ChatOpenAI(model=config.response_processing_model_name)


# Node
def pre_processor(state: State):
    # Get original question if it exists
    question = state.get("question", "")
    if not question:
        question = state["messages"][0].content

    file_reference = state.get("file_reference", "")
    extension = pathlib.Path(file_reference).suffix
    if extension == ".png":
        content_bytes = attachmentHandler.fetch_file_from_reference(file_reference)
        mime_type = mimetypes.guess_type(file_reference)[0]
        state["messages"][0].content = [{"type": "text", "text": question},
                                        attachmentHandler.get_representation("image", content_bytes, "png", mime_type)]

    return {"question": question}


# Node
def assistant(state: State):
    # set up the question
    # Get summary if it exists
    summary = state.get("summary", "")

    # Get original question if it exists
    question = state.get("question", "")
    if not question:
        question = state["messages"][0].content[0]

    attachment = ""
    file_reference = state.get("file_reference", "")
    if file_reference:
        attachment = f" you have access to the file with the following reference {file_reference}"
    prompt_params = {"summary": summary, "chunked_last_tool_call": state.get("chunked_last_tool_call", False),
                     "attachment": attachment, "question": question}
    sys_msg = SystemMessage(content=prompt_mgmt.render_template("base_system", prompt_params))
    try:
        response = model.invoke([sys_msg] + state["messages"])
    except Exception as e:
        if "429" in str(e):
            time.sleep(20)
            print("Retrying after receiving 429 error")
            response = model.invoke([sys_msg] + state["messages"])
            return {"messages": [response]}
        raise
    return {"question": question, "messages": [response]}


def response_processing(state: State):
    question = state.get("question", "")
    answer = state["messages"][-1]
    gaia_messages = [HumanMessage(content=question), AIMessage(content=answer.content)]
    gaia_sys_msg = SystemMessage(content=prompt_mgmt.render_template("final_answer_processor", {}))
    response = response_processing_model.invoke([gaia_sys_msg] + gaia_messages)

    return {"messages": [response]}


def optimize_memory(state: State):
    # First, we get any existing summary
    summary = state.get("summary", "")

    # Create our summarization prompt
    if summary:

        # A summary already exists
        summary_message = prompt_mgmt.render_template("summarization", {"summary": summary})

    else:
        summary_message = "Create a summary of the conversation above:"

    # Add prompt to our history
    messages = state["messages"][:-2] + [HumanMessage(content=summary_message)]
    try:
        response = model.invoke(messages)
    except Exception as e:
        if "429" in str(e):
            time.sleep(20)
            print("Retrying after receiving 429 error")
            response = model.invoke(messages)
        else:
            raise

    # Delete all but the 2 most recent messages and the first one
    remaining_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]

    # If the last message returned from a tool is oversized, chunk it and replace it with the relevant chunks
    content_handler = OversizedContentHandler()
    chunked = content_handler.process_oversized_message(state["messages"][-1], state.get("question"))

    return {"summary": response.content, "messages": remaining_messages, "chunked_last_tool_call": chunked}