from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json


def create_fundamentals_analyst(llm, toolkit):
    def fundamentals_analyst_node(state):
        current_date = state["trade_date"]
        ticker = state["company_of_interest"]
        company_name = state["company_of_interest"]

        if toolkit.config["online_tools"]:
            tools = [toolkit.get_fundamentals_openai]
        else:
            tools = [
                toolkit.get_finnhub_company_insider_sentiment,
                toolkit.get_finnhub_company_insider_transactions,
                toolkit.get_simfin_balance_sheet,
                toolkit.get_simfin_cashflow,
                toolkit.get_simfin_income_stmt,
            ]

        system_message = (
            "你是一名研究员，负责分析一家公司过去一周的基本面信息。请撰写一份关于该公司基本面信息的综合报告，例如财务文件、公司简介、基本公司财务状况、公司财务历史、内幕情绪和内幕交易，以全面了解该公司的基本面信息，为交易员提供信息。请确保包含尽可能多的细节。不要简单地说趋势好坏参半，要提供详细、细致的分析和见解，以帮助交易员做出决策。"
            + "确保在报告末尾附加一个Markdown表格，以整理报告中的要点，使其井井有条、易于阅读。",
        )

        prompt = ChatPromptTemplate.from_messages(
            [
                (
                    "system",
                    "You are a helpful AI assistant, collaborating with other assistants."
                    " Use the provided tools to progress towards answering the question."
                    " If you are unable to fully answer, that's OK; another assistant with different tools"
                    " will help where you left off. Execute what you can to make progress."
                    " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                    " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                    " You have access to the following tools: {tool_names}.\n{system_message}"
                    "For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
                ),
                MessagesPlaceholder(variable_name="messages"),
            ]
        )

        prompt = prompt.partial(system_message=system_message)
        prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
        prompt = prompt.partial(current_date=current_date)
        prompt = prompt.partial(ticker=ticker)

        chain = prompt | llm.bind_tools(tools)

        result = chain.invoke(state["messages"])

        return {
            "messages": [result],
            "fundamentals_report": result.content,
        }

    return fundamentals_analyst_node