"""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
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from supabase.client import Client, create_client

load_dotenv()

# === TOOLS === #

@tool
def multiply(a: int, b: int) -> int: return a * b

@tool
def add(a: int, b: int) -> int: return a + b

@tool
def subtract(a: int, b: int) -> int: return a - b

@tool
def divide(a: int, b: int) -> float:
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int: return a % b

@tool
def wiki_search(query: str) -> str:
    docs = WikipediaLoader(query=query, load_max_docs=2).load()
    return {"wiki_results": "\n\n---\n\n".join(doc.page_content for doc in docs)}

@tool
def web_search(query: str) -> str:
    docs = TavilySearchResults(max_results=3).invoke(query)
    return {"web_results": "\n\n---\n\n".join(doc.page_content for doc in docs)}

@tool
def arvix_search(query: str) -> str:
    docs = ArxivLoader(query=query, load_max_docs=3).load()
    return {"arvix_results": "\n\n---\n\n".join(doc.page_content[:1000] for doc in docs)}

# === SYSTEM PROMPT === #

with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()

sys_msg = SystemMessage(content=system_prompt)

# === EMBEDDING + RETRIEVER === #

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
supabase: Client = create_client(os.getenv("SUPABASE_URL"), os.getenv("SUPABASE_SERVICE_KEY"))
vector_store = SupabaseVectorStore(
    client=supabase,
    embedding=embeddings,
    table_name="Vector_Test",
    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."
)

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

# === BUILD GRAPH === #
def build_graph(provider: str = "groq"):
    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(
            url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
            temperature=0))
    else:
        raise ValueError("Invalid provider.")

    llm_with_tools = llm.bind_tools(tools)

    def assistant(state: MessagesState):
        response = llm_with_tools.invoke(state["messages"])
        answer = response.content.strip()
        if "FINAL ANSWER:" not in answer:
            answer = f"FINAL ANSWER: {answer.strip().splitlines()[0]}"
        return {"messages": [AIMessage(content=answer)]}

    def retriever(state: MessagesState):
        similar = vector_store.similarity_search(state["messages"][0].content)
        if similar:
            ref = HumanMessage(content=f"Here is a similar example: \n{similar[0].page_content}")
            return {"messages": [sys_msg] + state["messages"] + [ref]}
        return {"messages": [sys_msg] + state["messages"]}

    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever)
    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")
    return builder.compile()

if __name__ == "__main__":
    graph = build_graph()
    question = "What is 12 + 4?"
    result = graph.invoke({"messages": [HumanMessage(content=question)]})
    for m in result["messages"]:
        print(m.content)