"""LangGraph Agent"""

import os
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition, 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, 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

# ----------- Supabase Client (Hugging Face Safe) -----------
def get_supabase_client():
    supabase_url = os.environ.get("SUPABASE_URL")
    supabase_key = (
        os.environ.get("SUPABASE_SERVICE_KEY")
        or os.environ.get("SUPABASE_SERVICE_ROLE_KEY")
        or os.environ.get("SUPABASE_ANON_KEY")
    )
    if not supabase_url or not supabase_key:
        raise RuntimeError(
            "Missing Supabase credentials. Set SUPABASE_URL and SUPABASE_SERVICE_KEY "
            "(or _ROLE_KEY or _ANON_KEY) as Hugging Face Space secrets."
        )
    return create_client(supabase_url, supabase_key)

# ----------- Tools -----------
@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 up to 2 results."""
    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
    formatted = "\n\n---\n\n".join(
        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
        f'{d.page_content}\n</Document>'
        for d in search_docs
    )
    return {"wiki_results": formatted}

@tool
def web_search(query: str) -> dict:
    """Search Tavily for a query and return up to 3 results."""
    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
    formatted = "\n\n---\n\n".join(
        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
        f'{d.page_content}\n</Document>'
        for d in search_docs
    )
    return {"web_results": formatted}

@tool
def arxiv_search(query: str) -> dict:
    """Search Arxiv for a query and return up to 3 results."""
    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
    formatted = "\n\n---\n\n".join(
        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
        f'{d.page_content[:1000]}\n</Document>'
        for d in search_docs
    )
    return {"arxiv_results": formatted}

# ----------- System Prompt -----------
with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()
sys_msg = SystemMessage(content=system_prompt)

# ----------- Embeddings & VectorStore -----------
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
supabase: Client = get_supabase_client()
vector_store = SupabaseVectorStore(
    client=supabase,
    embedding=embeddings,
    table_name="documents",
    query_name="match_documents_langchain",
)

# ----------- Retriever Tool -----------
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 List -----------
tools = [
    multiply, add, subtract, divide, modulus,
    wiki_search, web_search, arxiv_search
]

# ----------- Graph Builder -----------
def build_graph(provider: str = "google"):
    """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(
                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):
        return {"messages": [llm_with_tools.invoke(state["messages"])]}

    def retriever(state: MessagesState):
        query = state["messages"][-1].content
        similar_doc = vector_store.similarity_search(query, k=1)[0]
        content = similar_doc.page_content
        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)
    builder.set_entry_point("retriever")
    builder.set_finish_point("retriever")
    return builder.compile()