"""LangGraph Agent for Level 1, 2, 3 Reasoning Tasks"""
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
from langchain_core.tools import tool
from langchain_core.tools import create_retriever_tool
from supabase.client import Client, create_client

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 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}

@tool
def arvix_search(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result.
    
    Args:
        query: The search query."""
    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
            for doc in search_docs
        ])
    return {"arvix_results": formatted_search_docs}


# Load the system prompt
prompt_path = os.path.join(os.path.dirname(__file__), "system_prompt.txt")
try:
    with open(prompt_path, "r", encoding="utf-8") as f:
        system_prompt = f.read()
except FileNotFoundError:
    system_prompt = (
        "You are a helpful assistant tasked with answering questions using a set of tools.\n\n"
        "Your final answer must strictly follow this format:\n"
        "FINAL ANSWER: [ANSWER]\n\n"
        "Only write the answer in that exact format. Do not explain anything. Do not include any other text.\n\n"
        "If you are provided with a similar question and its final answer, and the current question is **exactly the same**, then simply return the same final answer without using any tools.\n\n"
        "Only use tools if the current question is different from the similar one.\n\n"
        "Examples:\n"
        "- FINAL ANSWER: FunkMonk\n"
        "- FINAL ANSWER: Paris\n"
        "- FINAL ANSWER: 128\n\n"
        "If you do not follow this format exactly, your response will be considered incorrect."
    )

sys_msg = SystemMessage(content=system_prompt)

# Build a retriever
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
supabase: Client = create_client(
    os.environ.get("SUPABASE_URL", ""), 
    os.environ.get("SUPABASE_SERVICE_KEY", "")
)
vector_store = SupabaseVectorStore(
    client=supabase,
    embedding=embeddings,
    table_name="documents",
    query_name="match_documents_langchain",
)

question_search_tool = create_retriever_tool(
    retriever=vector_store.as_retriever(),
    name="question_search",
    description="A tool to retrieve similar questions from a vector store.",
)

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

def build_graph(provider: str = "google"):
    """Build the ReAct graph for Level 1, 2, 3 tasks"""
    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):
        """Assistant node that dynamically thinks and uses tools"""
        # We prepend the system message so the LLM respects the strict output constraints
        return {"messages": [llm_with_tools.invoke([sys_msg] + state["messages"])]}

    builder = StateGraph(MessagesState)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))
    
    # Setup the ReAct loop
    builder.add_edge(START, "assistant")
    builder.add_conditional_edges(
        "assistant",
        tools_condition, # Routes to "tools" if there are tool calls, otherwise "END"
    )
    builder.add_edge("tools", "assistant")

    return builder.compile()

if __name__ == "__main__":
    # Little test to show it compiles and works
    graph = build_graph()
    print("Level 1-3 Agent successfully built!")