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
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

# Load env variables
load_dotenv()

# === Define 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:
    """Modulus of two numbers."""
    return a % b

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query (max 2 results)."""
    docs = WikipediaLoader(query=query, load_max_docs=2).load()
    return "\n\n".join(doc.page_content for doc in docs)

@tool
def web_search(query: str) -> str:
    """Search Tavily for a query (max 3 results)."""
    docs = TavilySearchResults(max_results=3).invoke(query=query)
    return "\n\n".join(doc.page_content for doc in docs)

@tool
def arvix_search(query: str) -> str:
    """Search Arxiv for a query (max 3 results)."""
    docs = ArxivLoader(query=query, load_max_docs=3).load()
    return "\n\n".join(doc.page_content[:1000] for doc in docs)

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

# === System Prompt ===

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

sys_msg = SystemMessage(content=system_prompt)

# === Build Graph ===

def build_graph(provider: str = "groq"):
    """Build LangGraph agent 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")

    llm_with_tools = llm.bind_tools(tools)

    def assistant(state: MessagesState):
        return {"messages": [sys_msg] + [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()

# === Run Agent ===

if __name__ == "__main__":
    question = "What’s the latest research on transformers from arxiv?"
    graph = build_graph(provider="groq")
    messages = [HumanMessage(content=question)]
    result = graph.invoke({"messages": messages})
    for msg in result["messages"]:
        msg.pretty_print()