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, ArxivLoader
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_core.messages import SystemMessage, AIMessage, HumanMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from langchain_community.retrievers import BM25Retriever
from smolagents import DuckDuckGoSearchTool
from smolagents import Tool
from langchain.vectorstores import FAISS
import faiss
from langchain_community.docstore.in_memory import InMemoryDocstore

# Load environment variables
load_dotenv()

class QuestionRetrieverTool(Tool):
    name="Question Search",
    description="Retrieve similar questions from the vector store."
    inputs = {
        "query": {
            "type": "string",
            "description": "The question you want relation about."
        }
    }
    output_type = "string"

    def __init__(self, docs):
        self.is_initialized = False
        self.retriever = BM25Retriever.from_documents(docs)

    def forward(self, query: str):
        results = self.retriever.get_relevant_documents(query)
        if results:
            return "\n\n".join([doc.page_content for doc in results[:3]])
        else:
            return "No matching Questions found."


@tool
def wiki_search(query: str) -> dict:
    """Search Wikipedia and return up to 2 documents."""
    docs = WikipediaLoader(query=query, load_max_docs=2).load()
    results = [f"<Document source=\"{d.metadata['source']}\" page=\"{d.metadata.get('page','')}\"/>\n{d.page_content}" for d in docs]
    return {"wiki_results": "\n---\n".join(results)}

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

# --- Retriever Tool ---
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
embedding_dim = 768  # for 'all-mpnet-base-v2'
empty_index = faiss.IndexFlatL2(embedding_dim)
docstore = InMemoryDocstore({})
vector_store = FAISS(embedding_function=embeddings, index=empty_index, docstore=docstore, index_to_docstore_id={})


retriever_tool = create_retriever_tool(
    retriever=vector_store.as_retriever(),
    name="Question Search",
    description="Retrieve similar questions from the vector store."
)

tools = [
    wiki_search,
    web_search,
    retriever_tool,
]

# --- Graph Builder ---
def build_graph():
    llm = ChatHuggingFace(
        llm=HuggingFaceEndpoint(
            repo_id="meta-llama/Llama-2-7b-chat-hf",
            temperature=0,
            huggingfacehub_api_token=os.getenv("HF_TOKEN") 
        )
    )

    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)

    # Define nodes
    def assistant(state: MessagesState):
        return {"messages": [llm_with_tools.invoke(state["messages"])]}



    # Retriever node returns AIMessage
    def retriever(state: MessagesState):
        query = state["messages"][-1].content
        similar_docs = vector_store.similarity_search(query, k=1)

        if similar_docs:
            reference = similar_docs[0].page_content
            context_msg = HumanMessage(content=f"Here is a similar question and answer for reference:\n\n{reference}")
        else:
            context_msg = HumanMessage(content="No relevant example found.")

        return {
            "messages": [sys_msg] + state["messages"] + [context_msg]
        }


    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("retriever", "assistant")
    builder.add_conditional_edges("assistant", tools_condition)
    builder.add_edge("tools", "assistant")

    # Compile graph
    return builder.compile()