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


def create_fundamentals_analyst(llm):
    async def fundamentals_analyst_node(state):
        current_date = state["trade_date"]

        tools = [
            get_fundamentals,
        ]

        system_message = (
            "....",
        )

        prompt = ChatPromptTemplate.from_messages(
            [
                (
                    "system",
                    "You are a helpful AI assistant, collaborating with other assistants."
                    " 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 = await chain.ainvoke(state["messages"])

        report = ""

        if len(result.tool_calls) == 0:
            report = result.content

        return {
            "messages": [result],
            "fundamentals_report": report,
        }

    return fundamentals_analyst_node

            async for chunk in self.graph.astream(init_agent_state, **args):
                if len(chunk["messages"]) == 0:
                    pass
                else:
                    if chunk['messages'][-1].type == 'tool':
                        continue
                    #chunk["messages"][-1].pretty_print()
                    trace.append(chunk)
                    yield chunk["messages"][-1].content