"""LangGraph Agent"""
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_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
import shutil
import pandas as pd # Ny import för pandas
import json # För att parsa metadata-kolumnen

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 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)

# --- Start ChromaDB Setup ---
# Define the directory for ChromaDB persistence
CHROMA_DB_DIR = "./chroma_db"
CSV_FILE_PATH = "./supabase_docs.csv" # Path to your CSV file

# Build embeddings (this remains the same)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") # dim=768

# Initialize ChromaDB
# If the directory exists and contains data, load the existing vector store.
# Otherwise, create a new one and add documents from the CSV file.
if os.path.exists(CHROMA_DB_DIR) and os.listdir(CHROMA_DB_DIR):
    print(f"Loading existing ChromaDB from {CHROMA_DB_DIR}")
    vector_store = Chroma(
        persist_directory=CHROMA_DB_DIR,
        embedding_function=embeddings
    )
else:
    print(f"Creating new ChromaDB at {CHROMA_DB_DIR} and loading documents from {CSV_FILE_PATH}.")
    # Ensure the directory is clean before creating new
    if os.path.exists(CHROMA_DB_DIR):
        shutil.rmtree(CHROMA_DB_DIR)
    os.makedirs(CHROMA_DB_DIR)

    # Load data from the CSV file
    if not os.path.exists(CSV_FILE_PATH):
        raise FileNotFoundError(f"CSV file not found at {CSV_FILE_PATH}. Please ensure it's in the root directory.")

    df = pd.read_csv(CSV_FILE_PATH)
    documents = []
    for index, row in df.iterrows():
        content = row["content"]
        
        # Extract the question part from the content
        # Assuming the question is everything before "Final answer :"
        question_part = content.split("Final answer :")[0].strip()
        
        # Extract the final answer part from the content
        final_answer_part = content.split("Final answer :")[-1].strip() if "Final answer :" in content else ""

        # Parse the metadata string into a dictionary
        # The metadata column might be stored as a string representation of a dictionary
        try:
            metadata = json.loads(row["metadata"].replace("'", "\"")) # Replace single quotes for valid JSON
        except json.JSONDecodeError:
            metadata = {} # Fallback if parsing fails
        
        # Add the extracted final answer to the metadata for easy retrieval
        metadata["final_answer"] = final_answer_part
        
        # Create a Document object. The page_content should be the question for similarity search.
        # The answer will be in metadata.
        documents.append(Document(page_content=question_part, metadata=metadata))

    if not documents:
        print("No documents loaded from CSV. ChromaDB will be empty.")
        # Create an empty ChromaDB if no documents are found
        vector_store = Chroma(
            persist_directory=CHROMA_DB_DIR,
            embedding_function=embeddings
        )
    else:
        vector_store = Chroma.from_documents(
            documents=documents,
            embedding=embeddings,
            persist_directory=CHROMA_DB_DIR
        )
        vector_store.persist() # Save the new vector store to disk
        print(f"ChromaDB initialized and persisted with {len(documents)} documents from CSV.")

# Create retriever tool using the Chroma vector store
retriever_tool = create_retriever_tool(
    retriever=vector_store.as_retriever(),
    name="Question_Search",
    description="A tool to retrieve similar questions from a vector store. The retrieved document's metadata contains the 'final_answer' to the question.",
)

# Add the new retriever tool to your list of tools
tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    wiki_search,
    web_search,
    arvix_search,
    retriever_tool,
]

# Build graph function
def build_graph(provider: str = "google"):
    """Build the 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. Choose 'google', 'groq' or 'huggingface'.")
    
    llm_with_tools = llm.bind_tools(tools)

    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm_with_tools.invoke(state["messages"])]}
    
    from langchain_core.messages import AIMessage

    def retriever(state: MessagesState):
        query = state["messages"][-1].content
        # Use the vector_store directly for similarity search to get the full Document object
        similar_docs = vector_store.similarity_search(query, k=1)
        
        if similar_docs:
            similar_doc = similar_docs[0]
            # Prioritize 'final_answer' from metadata, then check page_content
            if "final_answer" in similar_doc.metadata and similar_doc.metadata["final_answer"]:
                answer = similar_doc.metadata["final_answer"]
            elif "Final answer :" in similar_doc.page_content:
                answer = similar_doc.page_content.split("Final answer :")[-1].strip()
            else:
                answer = similar_doc.page_content.strip() # Fallback to page_content if no explicit answer

            # The system prompt expects "FINAL ANSWER: [ANSWER]".
            # We should return the extracted answer directly, as the prompt handles the formatting.
            return {"messages": [AIMessage(content=answer)]}
        else:
            return {"messages": [AIMessage(content="No similar questions found in the knowledge base.")]}

    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever)
    builder.set_entry_point("retriever")
    builder.set_finish_point("retriever")

    return builder.compile()