"""LangGraph: agent graph w/ tools"""
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_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_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool

load_dotenv()

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

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

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

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

@tool
def power(a: float, b: float) -> float:
    """
    Get the power of two numbers.
    Args:
        a (float): the first number
        b (float): the second number
    """
    return a**b

@tool
def square_root(a: float) -> float | complex:
    """
    Get the square root of a number.
    Args:
        a (float): the number to get the square root of
    """
    if a >= 0:
        return a**0.5
    return cmath.sqrt(a)


@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return maximum 2 results.
    
    Args:
        query: The search query."""
    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) -> str:
    """Search Tavily for a query and return maximum 3 results.
    
    Args:
        query: The search query."""

    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}


# 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)

""" 
tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    power,
    square_root,
    wiki_search,
    web_search,
]
"""
tools = [web_search]


# Build graph function
def build_graph(provider: str = "google"):
    """Build the graph"""
    # Load environment variables from .env file
    if provider == "huggingface":
        # Huggingface endpoint
        """
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                #endpoint_url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
                #endpoint_url="https://api-inference.huggingface.co/models/Qwen/Qwen3-30B-A3B",
                endpoint_url="https://api-inference.huggingface.co/models/Qwen/Qwen2.5-Coder-32B.Instruct",
                #endpoint_url="https://api-inference.huggingface.co/models/Qwen/Qwen3-4B",
                temperature=0,
            ),
        )
        """
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                repo_id="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
                #endpoint_url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
                #endpoint_url="https://api-inference.huggingface.co/models/microsoft/phi-4",
                #endpoint_url="https://api-inference.huggingface.co/models/TinyLlama/TinyLlama-1.1B-Chat-v1.0",
                task="text-generation",  # for chat‐style use “text-generation”
                #max_new_tokens=1024,
                #do_sample=False,
                #repetition_penalty=1.03,
                temperature=0,
            ),
            #verbose=True,
        )

    elif provider == "google":
        # Google Gemini
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
        #llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0)
        
    else:
        raise ValueError("Invalid provider. Choose 'huggingface'.")
        
    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)
    
    # Node
    def assistant(state: MessagesState):
        """Assistant node"""      
        return {"messages": [llm_with_tools.invoke([sys_msg] + state["messages"])]}
        
    
    #def retriever(state: MessagesState):
    #    """Retriever node"""
    #    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(START, "assistant")
    #builder.add_edge("retriever", "assistant")
    builder.add_conditional_edges(
        "assistant",
        tools_condition,
    )
    #builder.add_edge("tools", "retriever")
    builder.add_edge("tools", "assistant")

    # Compile graph
    return builder.compile()

# test
if __name__ == "__main__":
    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
    # Build the graph
    graph = build_graph(provider="huggingface")
    # Run the graph
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()