from datetime import datetime, timezone
from dotenv import load_dotenv
from colorama import Fore, Style  # type: ignore[import]
from langchain.agents import create_agent
from langchain_core.messages import HumanMessage
from agent.api.api import get_llm
from agent.tools.math_solver import math_solver
from agent.tools.file_downloader import file_downloader
from agent.tools.ocr_reader import ocr_reader
from agent.tools.list_files import list_files
from agent.tools.http_get import http_get

from agent.agents.websearchagents import web_search_agents

# from agent.agents.websearchagent import websearch_agent
from agent.agents.answer_extractor import extract_answer

load_dotenv()


def supervisor_agent():
    """Return a supervisor agent instance with math_solver and websearch_agent."""
    return create_agent(
        model=get_llm(),
        # tools=[math_solver, websearch_agent, web_search_agents],
        tools=[
            math_solver,
            web_search_agents,
            file_downloader,
            ocr_reader,
            list_files,
            http_get,
        ],
        system_prompt=(
            f"You are a supervisor agent. "
            f"Current time is: {datetime.now(timezone.utc).isoformat()}. "
            f"Your memory are out of date. "
            f"For any math or calculation questions, use the math_solver tool for check, "
            f"the accurate is the most important. "
            f"All questions that need real-time, must use the web_search_agents tool "
            f"to get a concise and accurate final answer. "
            f"If an image or photo file is attached, download and use the ocr_reader tool "
            f"to extract and describe the content before answering. "
            f"Once you have found the answer, respond immediately. "
            f"Do NOT continue searching or verifying unnecessarily — "
            f"you have a limited number of action steps and must avoid exceeding them. "
            f"If you do not have enough information to answer the question "
            f"and no tool can help, respond with: "
            f"'Insufficient information to provide an answer.'"
        ),
    )


def run(query: str, file_url: str | None = None, max_retries: int = 3) -> str:
    """Entry point: let the supervisor agent finish the work."""
    last_error: str | None = None

    # Append file URL info to the query if available
    full_query = query
    if file_url:
        full_query += f"\n\nAttached file URL: {file_url}"

    for attempt in range(1, max_retries + 1):
        print(
            f"{Fore.CYAN}[Supervisor] Processing query (attempt {attempt}/{max_retries})...\n"
            f"[Supervisor] Query: {full_query}{Style.RESET_ALL}"
        )
        agent = supervisor_agent()

        messages = [HumanMessage(content=full_query)]
        if last_error:
            messages.append(
                HumanMessage(
                    content=(
                        f"Previous attempt failed with error: {last_error}\n"
                        f"Try thinking about the problem more simply. "
                        f"Use fewer steps and a more straightforward approach."
                    )
                )
            )

        try:
            result = agent.invoke(
                {"messages": messages},
                # Max agent action is 30 turn.
                config={"recursion_limit": 30},
            )
            content = result["messages"][-1].content
            if isinstance(content, list):
                content = content[0].get("text", "")
            else:
                content = str(content)
            return extract_answer(content, query)
        except Exception as e:
            last_error = str(e)
            print(
                f"{Fore.RED}[Supervisor] Attempt {attempt} failed: {last_error}{Style.RESET_ALL}"
            )

    print(f"{Fore.RED}[Supervisor] All {max_retries} attempts failed.{Style.RESET_ALL}")
    return extract_answer(
        f"Agent failed after {max_retries} attempts. Last error: {last_error}", query
    )


if __name__ == "__main__":
    # run(input("Query:"))
    ######################
    agent = supervisor_agent()
    chat_history: list = []
    while True:
        query = input("\nYou: ")
        if query.lower() in ("exit", "quit"):
            break
        chat_history.append(HumanMessage(content=query))
        result = agent.invoke({"messages": chat_history})
        chat_history = result["messages"]
        content = chat_history[-1].content
        if isinstance(content, list):
            content = content[0].get("text", "")
        print(f"Agent: {content}")