import os
import zipfile
import tempfile

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_groq import ChatGroq
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate

app = FastAPI()

# === Globals ===
llm = None
embeddings = None
vectorstore = None
retriever = None
chain = None


class QueryRequest(BaseModel):
    question: str


def _unpack_faiss(src_path: str, extract_to: str) -> str:
    """
    If src_path is a valid .zip archive, unzip it into extract_to and
    return the subdirectory that contains the .faiss index.
    If src_path is already a directory, return it directly.
    """
    # 1) True ZIP file?
    if zipfile.is_zipfile(src_path):
        with zipfile.ZipFile(src_path, "r") as zf:
            zf.extractall(extract_to)
        # scan until we find any .faiss file
        for root, _, files in os.walk(extract_to):
            if any(f.endswith(".faiss") for f in files):
                return root
        raise RuntimeError(f"No .faiss index found inside ZIP: {src_path}")

    # 2) Already a folder?
    if os.path.isdir(src_path):
        return src_path

    raise RuntimeError(f"Path is neither a valid ZIP nor a directory: {src_path}")


@app.on_event("startup")
def load_components():
    global llm, embeddings, vectorstore, retriever, chain

    # --- 1) Init LLM & Embeddings ---
    llm = ChatGroq(
        model="meta-llama/llama-4-scout-17b-16e-instruct",
        temperature=0,
        max_tokens=1024,
        api_key=os.getenv("api_key"),
    )
    embeddings = HuggingFaceEmbeddings(
        model_name="intfloat/multilingual-e5-large",
        model_kwargs={"device": "cpu"},
        encode_kwargs={"normalize_embeddings": True},
    )

    # --- 2) Load & merge two FAISS indexes ---
    # (these can be either real .zip files or existing folders)
    src1 = "faiss_index.zip"          # or "faiss_index" if it's already a folder
    src2 = "faiss_index_extra.zip"    # or "faiss_index_extra"

    tmp1 = tempfile.TemporaryDirectory()
    tmp2 = tempfile.TemporaryDirectory()

    dir1 = _unpack_faiss(src1, tmp1.name)
    dir2 = _unpack_faiss(src2, tmp2.name)

    vs1 = FAISS.load_local(dir1, embeddings, allow_dangerous_deserialization=True)
    vs2 = FAISS.load_local(dir2, embeddings, allow_dangerous_deserialization=True)

    vs1.merge_from(vs2)
    vectorstore = vs1

    # --- 3) Build retriever & QA chain ---
    retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
    prompt = PromptTemplate(
        template="""
You are an expert assistant on Islamic knowledge.  
Use **only** the information in the “Retrieved context” to answer the user’s question.  
Do **not** add any outside information, personal opinions, or conjecture—if the answer is not contained in the context, reply with “لا أعلم”.  
Be concise, accurate, and directly address the user’s question.

Retrieved context:
{context}

User’s question:
{question}

Your response:
""",
        input_variables=["context", "question"],
    )
    chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=False,
        chain_type_kwargs={"prompt": prompt},
    )

    print("✅ Loaded & merged both FAISS indexes, QA chain ready.")


@app.get("/")
def root():
    return {"message": "Arabic Hadith Finder API is up..."}


@app.post("/query")
def query(request: QueryRequest):
    try:
        result = chain.invoke({"query": request.question})
        return {"answer": result["result"]}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))