# from langchain_ollama import ChatOllama
from langchain_core.messages import SystemMessage, HumanMessage
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode, tools_condition
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_core.tools import tool
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from math import sqrt

### =============== MATHEMATICAL TOOLS =============== ###


@tool
def multiply(a: float, b: float) -> float:
    """
    Multiplies two numbers.
    Args:
        a (float): the first number
        b (float): the second number
    """
    return a * b


@tool
def add(a: float, b: float) -> float:
    """
    Adds two numbers.
    Args:
        a (float): the first number
        b (float): the second number
    """
    return a + b


@tool
def subtract(a: float, b: float) -> int:
    """
    Subtracts two numbers.
    Args:
        a (float): the first number
        b (float): the second number
    """
    return a - b


@tool
def divide(a: float, b: float) -> float:
    """
    Divides two numbers.
    Args:
        a (float): the first float number
        b (float): the second float number
    """
    if b == 0:
        raise ValueError("Cannot divided by zero.")
    return a / b


@tool
def modulus(a: int, b: int) -> int:
    """
    Get the modulus of two numbers.
    Args:
        a (int): the first number
        b (int): the second number
    """
    return a % b


@tool
def power(a: float, b: float) -> float:
    """
    Get the power of two numbers.
    Args:
        a (float): the first number
        b (float): the second number
    """
    return a**b


@tool
def square_root(a: float) -> float | complex:
    """
    Get the square root of a number.
    Args:
        a (float): the number to get the square root of
    """
    if a >= 0:
        return a**0.5
    return sqrt(a)


### =============== BROWSER TOOLS =============== ###

search_tool = DuckDuckGoSearchRun()


@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return maximum 2 results.
    Args:
        query: The search query."""
    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
            for doc in search_docs
        ]
    )
    return {"wiki_results": formatted_search_docs}


@tool
def web_search(query: str) -> str:
    """Search Tavily for a query and return maximum 3 results.
    Args:
        query: The search query."""
    print(f"I'm running web_search with {query = }")
    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
            for doc in search_docs
        ]
    )
    return {"web_results": formatted_search_docs}


@tool
def arxiv_search(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result.
    Args:
        query: The search query."""
    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
            for doc in search_docs
        ]
    )
    return {"arxiv_results": formatted_search_docs}


tools = [multiply, add, subtract, divide, modulus, power, square_root, web_search, arxiv_search, wiki_search]


GAIA_SYSTEM_PROMPT = """
You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
"""
sys_msg = SystemMessage(content=GAIA_SYSTEM_PROMPT)


# Build graph function
def build_graph(provider: str = "huggingface"):
    """Build the graph"""
    # Load environment variables from .env file
    # if provider == "ollama":
    #     chat = ChatOllama(model="llama3.1")
    if provider == "huggingface":
        llm = HuggingFaceEndpoint(
            repo_id="Qwen/Qwen2.5-Coder-32B-Instruct"
        )

        chat = ChatHuggingFace(llm=llm, verbose=True)
    else:
        raise ValueError("Invalid provider. Choose 'ollama' or 'huggingface'.")
    # Bind tools to LLM
    chat_with_tools = chat.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        print([sys_msg] + state["messages"])
        return {"messages": [chat_with_tools.invoke([sys_msg] + state["messages"])]}

    builder = StateGraph(MessagesState)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))
    builder.add_edge(START, "assistant")
    builder.add_conditional_edges(
        "assistant",
        tools_condition,
    )
    builder.add_edge("tools", "assistant")

    return builder.compile()


# test
if __name__ == "__main__":
    question = "Examine the video at https://www.youtube.com/watch?v=1htKBjuUWec.\n\nWhat does Teal'c say in response to the question \"Isn't that hot?\""
    # fixed_answer = "extremely"
    graph = build_graph(provider="huggingface")
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()