import asyncio
from datetime import date

from llama_index.core.agent.workflow import AgentWorkflow, ReActAgent
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec

from src.agent_hackathon.consts import PROJECT_ROOT_DIR

# from dotenv import find_dotenv, load_dotenv
from src.agent_hackathon.generate_arxiv_responses import ArxivResponseGenerator
from src.agent_hackathon.logger import get_logger

# _ = load_dotenv(dotenv_path=find_dotenv(raise_error_if_not_found=False), override=True)

logger = get_logger(log_name="multiagent", log_dir=PROJECT_ROOT_DIR / "logs")


class MultiAgentWorkflow:
    """Multi-agent workflow for retrieving research papers and related events."""

    def __init__(self) -> None:
        """Initialize the workflow with LLM, tools, and generator."""
        logger.info("Initializing MultiAgentWorkflow.")
        self.llm = HuggingFaceInferenceAPI(
            model="meta-llama/Llama-3.3-70B-Instruct",
            provider="auto",
            # provider="nebius",
            temperature=0.1,
            top_p=0.95,
            max_tokens=8192,
            # api_key=os.getenv(key="NEBIUS_API_KEY"),
            # base_url="https://api.studio.nebius.com/v1/",
        )
        self._generator = ArxivResponseGenerator(
            vector_store_path=PROJECT_ROOT_DIR / "db/arxiv_docs.db"
        )
        # self._arxiv_rag_tool = FunctionTool.from_defaults(
        #     fn=self._arxiv_rag,
        #     name="arxiv_rag",
        #     description="Retrieves arxiv research papers.",
        #     return_direct=True,
        # )
        self._duckduckgo_search_tool = [
            tool
            for tool in DuckDuckGoSearchToolSpec().to_tool_list()
            if tool.metadata.name == "duckduckgo_full_search"
        ]
        # self._arxiv_agent = ReActAgent(
        #     name="arxiv_agent",
        #     description="Retrieves information about arxiv research papers",
        #     system_prompt="You are arxiv research paper agent, who retrieves information "
        #     "about arxiv research papers.",
        #     tools=[self._arxiv_rag_tool],
        #     llm=self.llm,
        # )
        self._websearch_agent = ReActAgent(
            name="web_search",
            description="Searches the web",
            system_prompt="You are search engine who searches the web using duckduckgo tool",
            tools=self._duckduckgo_search_tool,
            llm=self.llm,
        )

        self._workflow = AgentWorkflow(
            agents=[self._websearch_agent],
            root_agent="web_search",
            timeout=180,
        )
        # AgentWorkflow.from_tools_or_functions(
        #     tools_or_functions=self._duckduckgo_search_tool,
        #     llm=self.llm,
        #     system_prompt="You are an expert that  "
        #     "searches for any corresponding events related to the "
        #     "user query "
        #     "using the duckduckgo_search_tool and returns the final results." \
        #     "Don't return the steps but execute the necessary tools that you have " \
        #     "access to and return the results.",
        #     timeout=180,
        # )

        logger.info("MultiAgentWorkflow initialized.")

    def _arxiv_rag(self, query: str) -> str:
        """Retrieve research papers from arXiv based on the query.

        Args:
            query (str): The search query.

        Returns:
            str: Retrieved research papers as a string.
        """
        return self._generator.retrieve_arxiv_papers(query=query)

    def _clean_response(self, result: str) -> str:
        """Removes the think tags.

        Args:
            result (str): The result with the <think></think> content.

        Returns:
            str: The result without the <think></think> content.
        """
        if result.find("</think>"):
            result = result[result.find("</think>") + len("</think>") :]
        return result

    async def run(self, user_query: str) -> str:
        """Run the multi-agent workflow for a given user query.

        Args:
            user_query (str): The user's search query.

        Returns:
            str: The output string.
        """
        logger.info("Running multi-agent workflow.")
        try:
            research_papers = self._arxiv_rag(query=user_query)
            user_msg = (
                f"search with the web search agent to find any relevant events related to: {user_query}.\n"
                f" The web search results relevant to the current year: {date.today().year}. \n"
            )
            web_search_results = await self._workflow.run(user_msg=user_msg)
            final_res = (
                research_papers + "\n\n" + web_search_results.response.blocks[0].text
            )
            logger.info("Workflow run completed successfully.")
            return final_res
        except Exception as err:
            logger.error(f"Workflow run failed: {err}")
            raise


if __name__ == "__main__":
    USER_QUERY = "i want to learn more about nlp"
    workflow = MultiAgentWorkflow()
    logger.info("Starting workflow for user query.")
    try:
        result = asyncio.run(workflow.run(user_query=USER_QUERY))
        logger.info("Workflow finished. Output below:")
        print(result)
    except Exception as err:
        logger.error(f"Error during workflow execution: {err}")