"""LangGraph Agent (No Supabase)"""
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
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers and return the result."""
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Add two integers and return the result."""
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtract b from a and return the result."""
    return a - b

@tool
def divide(a: int, b: int) -> float:
    """Divide a by b and return the result. Raises an error if b is zero."""
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """Return the modulus (remainder) of a divided by b."""
    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()
    results = "\n\n---\n\n".join(
        f"<Document>\n{doc.page_content}\n</Document>" for doc in search_docs
    )
    return {"wiki_results": results}

@tool
def web_search(query: str) -> dict:
    """Search the web via Tavily and return up to 3 results."""
    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
    results = "\n\n---\n\n".join(
        f"<Document>\n{doc.page_content}\n</Document>" for doc in search_docs
    )
    return {"web_results": results}

@tool
def arvix_search(query: str) -> dict:
    """Search Arxiv and return up to 3 truncated results."""
    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
    results = "\n\n---\n\n".join(
        f"<Document>\n{doc.page_content[:500]}\n</Document>" for doc in search_docs
    )
    return {"arvix_results": results}

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

sys_msg = SystemMessage(content=system_prompt)

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

def build_graph(provider: str = "groq"):
    """Build the LangGraph agent with selected LLM provider."""
    if provider == "google":
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
    elif provider == "groq":
        groq_api_key = os.environ.get("GROQ_API_KEY")
        if not groq_api_key:
            raise ValueError("GROQ_API_KEY is not set in the environment.")
        llm = ChatGroq(model="qwen-qwq-32b", temperature=0, api_key=groq_api_key)
    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"])]}

    builder = StateGraph(MessagesState)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))
    builder.add_edge(START, "assistant")
    builder.add_conditional_edges("assistant", tools_condition)
    builder.add_edge("tools", "assistant")

    return builder.compile()

if __name__ == "__main__":
    from langchain_core.messages import HumanMessage
    question = "What is the capital of France and its population?"
    graph = build_graph()
    messages = [HumanMessage(content=question)]
    result = graph.invoke({"messages": messages})
    for msg in result["messages"]:
        print(msg.content)