"""LangGraph Agent"""
import os
from dotenv import load_dotenv
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from supabase.client import Client, create_client

load_dotenv()

supabase_url = 'https://qzydfaroejcpolxfgfim.supabase.co'
supabase_key = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6InF6eWRmYXJvZWpjcG9seGZnZmltIiwicm9sZSI6InNlcnZpY2Vfcm9sZSIsImlhdCI6MTc0OTUwNTQyMywiZXhwIjoyMDY1MDgxNDIzfQ.IBjtn1tPcogCF6DSf8dgR29aTsC61Qh0XueXYcEWG_Q'


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


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


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


@tool
def divide(a: int, b: int) -> float:
    """Divide two numbers."""
    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."""
    return a % b


@tool
def wiki_search(query: str) -> dict:
    """Search Wikipedia for a query and return maximum 2 results."""
    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) -> dict:
    """Search Tavily for a query and return maximum 3 results."""
    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) -> dict:
    """Search Arxiv for a query and return maximum 3 results."""
    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 the system prompt from the file
with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()

# System message
sys_msg = SystemMessage(content=system_prompt)

# Build embeddings and vector store client
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")  # dim=768
supabase: Client = create_client(supabase_url, supabase_key)

vector_store = SupabaseVectorStore(
    client=supabase,
    embedding=embeddings,
    table_name="documents",
    query_name="match_documents_langchain",
)

create_retriever_tool = create_retriever_tool(
    retriever=vector_store.as_retriever(),
    name="Question Search",
    description="A tool to retrieve similar questions from a vector store.",
)

tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    wiki_search,
    web_search,
    arvix_search,
]

# Build graph function
def build_graph(provider: str = "huggingface"):
    """Build the graph"""

    if provider == "google":
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
    elif provider == "groq":
        llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
    elif provider == "huggingface":
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(endpoint_url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf"),
            temperature=0,
        )
    else:
        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")

    llm_with_tools = llm.bind_tools(tools)

    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm_with_tools.invoke(state["messages"])]}

    def retriever(state: MessagesState):
        query = state["messages"][-1].content
        query_embedding = embeddings.embed_query(query)  # list of floats

        response = supabase.rpc(
            'match_documents_langchain',
            {
                'match_count': 2,
                'query_embedding': query_embedding
            }
        ).execute()

        docs = response.data
        if not docs or len(docs) == 0:
            answer = "Sorry, I couldn't find an answer to your question."
        else:
            content = docs[0]['content']  # get content of the first matched doc
            if "Final answer :" in content:
                answer = content.split("Final answer :")[-1].strip()
            else:
                answer = content.strip()

        return {"messages": [AIMessage(content=answer)]}

    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever)
    # If you want to integrate assistant and tools, uncomment and add edges accordingly
    # builder.add_node("assistant", assistant)
    # builder.add_node("tools", ToolNode(tools))
    # builder.add_edge(START, "retriever")
    # builder.add_edge("retriever", "assistant")
    # builder.add_conditional_edges("assistant", tools_condition)
    # builder.add_edge("tools", "assistant")

    builder.set_entry_point("retriever")
    builder.set_finish_point("retriever")

    return builder.compile()