"""LangGraph Agent"""
import os
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
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.tools import DuckDuckGoSearchResults
from langchain_community.vectorstores import Chroma
import json
import chromadb
chromadb.config.Settings.telemetry_enabled = False



@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) -> dict:
    """Search DuckDuckGo for a query and return maximum 3 results using LangChain."""
    # Crea il tool DuckDuckGo
    search = DuckDuckGoSearchResults(max_results=3)
    docs = search.run(query)  # restituisce una lista di dict con 'title', 'link', 'snippet'

    # Formattiamo i risultati per il LLM
    formatted = "\n\n---\n\n".join(
        f'<Document source="{doc["link"]}" page="">\n{doc["title"]}: {doc["snippet"]}\n</Document>'
        for doc in docs
    )
    return {"web_results": formatted}

@tool
def arxiv_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()
print(system_prompt)
# System message
sys_msg = SystemMessage(content=system_prompt)



with open('metadata.jsonl', 'r') as jsonl_file:
        json_list = list(jsonl_file)

json_QA = []
for json_str in json_list:
    json_data = json.loads(json_str)
    json_QA.append(json_data)

# Usa gli stessi embeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

# Inizializza Chroma
from langchain.schema import Document
from langchain_community.vectorstores import Chroma

# Prepara la lista di documenti
docs = []
for sample in json_QA:
    print(len(docs))
    content = f"Question : {sample['Question']}\n\nFinal answer : {sample['Final answer']}"
    metadata = {"source": sample['task_id']}
    doc = Document(page_content=content, metadata=metadata)
    docs.append(doc)
print('fatto')
# Inizializza il vector store Chroma
vector_store = Chroma.from_documents(
    documents=docs,
    embedding=embeddings,
    persist_directory="./chroma_db" 
)

# Crea il retriever tool
create_retriever_tool = create_retriever_tool(
    retriever=vector_store.as_retriever(),
    name="Question Search",
    description="A tool to retrieve similar questions from a local Chroma vector store.",
)


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

# Build graph function
def build_graph():
    """Build the graph"""
    llm = ChatHuggingFace(
        llm=HuggingFaceEndpoint(
            repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
            temperature=0,
        ),
    )
    llm_with_tools = llm.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm_with_tools.invoke(state["messages"])]}
    
    def retriever(state: MessagesState):
        """Retriever node"""
        similar_question = vector_store.similarity_search(state["messages"][0].content)
        example_msg = HumanMessage(
            content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
        )
        return {"messages": [sys_msg] + state["messages"] + [example_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()

# test
if __name__ == "__main__":
    graph = build_graph()
    # Carica il file JSON
    with open('questions.json', 'r', encoding='utf-8') as f:
        data = json.load(f)

    # Estrai le domande
    questions = [entry['question'] for entry in data if 'question' in entry]

    # Mostra o usa la lista di domande
    for q in questions:
        print('orig:', q)
        messages = [HumanMessage(content=q)]
        messages = graph.invoke({"messages": messages})

        m=messages["messages"][-1]

        #for m in messages["messages"]:
        content = m.content if hasattr(m, "content") else str(m)
        print("Full response:", content)

        if "FINAL ANSWER:" in content:
            answer = content.rsplit("FINAL ANSWER:", 1)[-1].strip()
            print("✅ Estratto finale:", answer)
        else:
            print("❌ Nessuna risposta finale trovata.")
        
        break