from langgraph.prebuilt import create_react_agent
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from dotenv import load_dotenv, find_dotenv
from langchain_core.tools import tool
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_core.messages import HumanMessage
from supabase import create_client, Client
import os

load_dotenv(find_dotenv())

DEFAULT_PROMPT = """
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. 
"""


@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}


@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers.
    Args:
        a: first int
        b: second int
    """
    return a * b


@tool
def add(a: int, b: int) -> int:
    """Add two numbers.
    Args:
        a: first int
        b: second int
    """
    return a + b


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


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


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


class CustomAgent:
    def __init__(self):
        print("CustomAgent initialized.")

        # Initialize embeddings and vector store
        self.embeddings = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-mpnet-base-v2"
        )

        self.supabase: Client = create_client(
            os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_ROLE_KEY")
        )

        self.vector_store = SupabaseVectorStore(
            client=self.supabase,
            embedding=self.embeddings,
            table_name="documents_1",
            query_name="match_documents_1",
        )

        # Create the agent
        self.agent = create_react_agent(
            model="openai:gpt-4.1",
            tools=[
                web_search,
                add,
                subtract,
                multiply,
                divide,
                modulus,
                wiki_search,
                arvix_search,
            ],
            prompt=DEFAULT_PROMPT,
        )

    def retriever(self, query: str):
        """Retriever"""
        similar_question = self.vector_store.similarity_search(query)
        return HumanMessage(
            content=f"Here I provide a similar question and answer for reference, you can use it to answer the question: \n\n{similar_question[0].page_content}",
        )

    def __call__(self, question: str) -> str:
        """Run the agent on a question and return the answer."""
        print(f"CustomAgent received question (first 50 chars): {question[:50]}...")

        try:
            answer = self.agent.invoke(
                {
                    "messages": [
                        self.retriever(question),
                        HumanMessage(content=question),
                    ]
                }
            )
            result = answer["messages"][-1].content

            if "FINAL ANSWER: " in result:
                final_answer_start = result.find("FINAL ANSWER: ") + len(
                    "FINAL ANSWER: "
                )
                extracted_answer = result[final_answer_start:].strip()
                print(f"CustomAgent extracted answer: {extracted_answer}")
                return extracted_answer
            else:
                print(
                    f"CustomAgent returning full answer (no FINAL ANSWER found): {result}"
                )
                return result

        except Exception as e:
            print(f"Error in CustomAgent: {e}")
            return f"Error: {e}"


if __name__ == "__main__":
    agent = CustomAgent()
    agent(
        "How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia."
    )
    agent(
        "How many at bats did the Yankee with the most walks in the 1977 regular season have that same season?"
    )
    agent(
        "In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?"
    )