from langchain.schema import HumanMessage, AIMessage, SystemMessage
from langchain_openai import ChatOpenAI
from langchain_core.messages import AnyMessage, SystemMessage
from langchain_core.tools import tool
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader

from langchain_community.tools.tavily_search import TavilySearchResults
from langchain.tools.retriever import create_retriever_tool

from langgraph.graph.message import add_messages
from langgraph.graph import START, StateGraph, MessagesState, END
from langgraph.prebuilt import tools_condition, ToolNode

import os
from dotenv import load_dotenv
from typing import TypedDict, Annotated, Optional
from langchain_community.tools import DuckDuckGoSearchResults

from langchain_huggingface import (
    ChatHuggingFace,
    HuggingFaceEndpoint,
    HuggingFaceEmbeddings,
)


load_dotenv()

embddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-mpnet-base-v2",
)


# Initialize the DuckDuckGo search tool
search_tool = DuckDuckGoSearchResults()


@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."""
    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 arvix_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 {"arvix_results": formatted_search_docs}


# Load LLM model
# llm = ChatOpenAI(
#     model="gpt-4o",
#     base_url="https://models.inference.ai.azure.com",
#     api_key=os.environ["GITHUB_TOKEN"],
#     temperature=0.2,
#     max_tokens=4096,
# )
llm = ChatHuggingFace(
    llm=HuggingFaceEndpoint(
        repo_id="microsoft/Phi-3-mini-4k-instruct",
        temperature=0,
        # huggingfacehub_api_token=os.environ["HUGGINGFACEHUB_API_TOKEN"],
    ),
    verbose=True,
)

tools = [
    arvix_search,
    wiki_search,
    # web_search,
    search_tool,
]
# Bind the tools to the LLM
model_with_tools = llm.bind_tools(tools)
tool_node = ToolNode(tools)


def build_agent_workflow():

    def should_continue(state: MessagesState):
        messages = state["messages"]
        last_message = messages[-1]
        if last_message.tool_calls:
            return "tools"
        return END

    def call_model(state: MessagesState):
        system_message = SystemMessage(
            content=f"""
        You are a helpful assistant tasked with answering questions using a set of tools.
        Now, 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.
        Your answer should only start with "FINAL ANSWER: ", then follows with the answer. """
        )

        messages = [system_message] + state["messages"]
        print("Messages to LLM:", messages)

        response = model_with_tools.invoke(messages)
        return {"messages": [response]}

    # Define the state graph
    workflow = StateGraph(MessagesState)
    workflow.add_node("agent", call_model)
    workflow.add_node("tools", tool_node)

    workflow.add_edge(START, "agent")
    workflow.add_conditional_edges("agent", should_continue, ["tools", END])
    workflow.add_edge("tools", "agent")

    app = workflow.compile()

    return app


if __name__ == "__main__":
    question = "Who nominated the only Featured Article on English Wikipedia about a dinosaur that was promoted in November 2016?"
    # Build the graph
    graph = build_agent_workflow()
    # Run the graph
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()