import os
import tempfile
import zipfile
from typing import List, Optional, Any

import uuid
from datetime import datetime

from fastapi import FastAPI, File, UploadFile, HTTPException, Query, Depends
from fastapi.responses import FileResponse, StreamingResponse
# Removed static files mounting for avatars as avatars are now served via GridFS in auth
#from fastapi.staticfiles import StaticFiles

from llm_initialization import get_llm
from embedding import get_embeddings
from document_loaders import DocumentLoader
from text_splitter import TextSplitter
from vector_store import VectorStoreManager
from prompt_templates import PromptTemplates
from chat_management import ChatManagement
from retrieval_chain import RetrievalChain
from urllib.parse import quote_plus
from dotenv import load_dotenv
from pymongo import MongoClient

# Load environment variables
load_dotenv()
MONGO_PASSWORD = quote_plus(os.getenv("MONGO_PASSWORD"))
MONGO_DATABASE_NAME = os.getenv("DATABASE_NAME")
MONGO_COLLECTION_NAME = os.getenv("COLLECTION_NAME")
MONGO_CLUSTER_URL = os.getenv("CONNECTION_STRING")

app = FastAPI(title="VectorStore & Document Management API")

# Note: Since user avatars are now stored in MongoDB via GridFS and served via /auth/avatar,
# we no longer mount a local avatars directory.

# Import auth router and dependencies
from auth import router as auth_router, get_current_user, users_collection

# Mount auth endpoints under /auth
app.include_router(auth_router, prefix="/auth")

from transcribe import router as transcribe_router
app.include_router(transcribe_router, prefix="/audio")


# Global variables (initialized on startup)
llm = None
embeddings = None
chat_manager = None
document_loader = None
text_splitter = None
vector_store_manager = None
vector_store = None
k = 3  # Number of documents to retrieve per query

# ----------------------- Startup Event -----------------------
@app.on_event("startup")
async def startup_event():
    global llm, embeddings, chat_manager, document_loader, text_splitter, vector_store_manager, vector_store

    print("Starting up: Initializing components...")

    # Initialize LLM and embeddings
    llm = get_llm()
    print("LLM initialized.")
    embeddings = get_embeddings()
    print("Embeddings initialized.")

    # Setup chat management
    chat_manager = ChatManagement(
        cluster_url=MONGO_CLUSTER_URL,
        database_name=MONGO_DATABASE_NAME,
        collection_name=MONGO_COLLECTION_NAME,
    )
    print("Chat management initialized.")

    # Initialize document loader and text splitter
    document_loader = DocumentLoader()
    text_splitter = TextSplitter()
    print("Document loader and text splitter initialized.")

    # Initialize vector store manager and set vector store
    vector_store_manager = VectorStoreManager(embeddings)
    vector_store = vector_store_manager.vectorstore
    print("Vector store initialized.")

# ----------------------- New Chat Endpoint (Updated) -----------------------
@app.post("/new_chat")
def new_chat(current_user: dict = Depends(get_current_user)):
    """
    Create a new chat session under the current user's document.
    """
    new_chat_id = str(uuid.uuid4())
    # Append a new chat session to the user's chat_histories
    users_collection.update_one(
       {"email": current_user["email"]},
       {"$push": {"chat_histories": {"chat_id": new_chat_id, "created_at": datetime.utcnow(), "messages": []}}}
    )
    return {"chat_id": new_chat_id}

# ----------------------- Create Chain Endpoint (Updated) -----------------------
@app.post("/create_chain")
def create_chain(
    chat_id: str = Query(..., description="Existing chat session ID"),
    template: str = Query(
        "quiz_solving",
        description="Select prompt template. Options: quiz_solving, assignment_solving, paper_solving, quiz_creation, assignment_creation, paper_creation",
    ),
    current_user: dict = Depends(get_current_user)
):
    valid_templates = [
        "quiz_solving",
        "assignment_solving",
        "paper_solving",
        "quiz_creation",
        "assignment_creation",
        "paper_creation",
    ]
    if template not in valid_templates:
        raise HTTPException(status_code=400, detail="Invalid template selection.")

    # Update the specific chat session's configuration in the user's document
    users_collection.update_one(
        {"email": current_user["email"], "chat_histories.chat_id": chat_id},
        {"$set": {"chat_histories.$.template": template}}
    )

    return {"message": "Retrieval chain configuration stored successfully.", "chat_id": chat_id, "template": template}

# ----------------------- Chat Endpoint -----------------------
@app.get("/chat")
def chat(
    query: str,
    chat_id: str = Query(..., description="Chat session ID"),
    current_user: dict = Depends(get_current_user)
):
    """
    Process a chat query using the retrieval chain associated with the given chat_id.
    """
    # Retrieve chat configuration from the user's document
    user = current_user
    chat_config = None
    for chat in user.get("chat_histories", []):
        if chat.get("chat_id") == chat_id:
            chat_config = chat
            break
    if not chat_config:
        raise HTTPException(status_code=400, detail="Chat configuration not found. Please create a chain using /create_chain.")

    template = chat_config.get("template", "quiz_solving")
    if template == "quiz_solving":
        prompt = PromptTemplates.get_quiz_solving_prompt()
    elif template == "assignment_solving":
        prompt = PromptTemplates.get_assignment_solving_prompt()
    elif template == "paper_solving":
        prompt = PromptTemplates.get_paper_solving_prompt()
    elif template == "quiz_creation":
        prompt = PromptTemplates.get_quiz_creation_prompt()
    elif template == "assignment_creation":
        prompt = PromptTemplates.get_assignment_creation_prompt()
    elif template == "paper_creation":
        prompt = PromptTemplates.get_paper_creation_prompt()
    else:
        raise HTTPException(status_code=400, detail="Invalid chat configuration.")

    retrieval_chain = RetrievalChain(
        llm,
        vector_store.as_retriever(search_kwargs={"k": k}),
        prompt,
        verbose=True,
    )

    try:
        stream_generator = retrieval_chain.stream_chat_response(
            query=query,
            chat_id=chat_id,
            get_chat_history=chat_manager.get_chat_history,
            initialize_chat_history=chat_manager.initialize_chat_history,
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error processing chat query: {str(e)}")

    return StreamingResponse(stream_generator, media_type="text/event-stream")

# ----------------------- Remaining Endpoints -----------------------
@app.post("/add_document")
async def add_document(
    file: Optional[UploadFile] = File(None),  # File parameter now is an UploadFile
    wiki_query: Optional[str] = Query(None),
    wiki_url: Optional[str] = Query(None)
):
    if file is None and wiki_query is None and wiki_url is None:
        raise HTTPException(status_code=400, detail="No document input provided (file, wiki_query, or wiki_url).")

    if file is not None:
        with tempfile.NamedTemporaryFile(delete=False) as tmp:
            contents = await file.read()
            tmp.write(contents)
            tmp_filename = tmp.name

        ext = file.filename.split(".")[-1].lower()
        try:
            if ext == "pdf":
                documents = document_loader.load_pdf(tmp_filename)
            elif ext == "csv":
                documents = document_loader.load_csv(tmp_filename)
            elif ext in ["doc", "docx"]:
                documents = document_loader.load_doc(tmp_filename)
            elif ext in ["html", "htm"]:
                documents = document_loader.load_text_from_html(tmp_filename)
            elif ext in ["md", "markdown"]:
                documents = document_loader.load_markdown(tmp_filename)
            else:
                documents = document_loader.load_unstructured(tmp_filename)
        except Exception as e:
            os.remove(tmp_filename)
            raise HTTPException(status_code=400, detail=f"Error loading document from file: {str(e)}")
        os.remove(tmp_filename)
    elif wiki_query is not None:
        try:
            documents = document_loader.wikipedia_query(wiki_query)
        except Exception as e:
            raise HTTPException(status_code=400, detail=f"Error loading Wikipedia query: {str(e)}")
    elif wiki_url is not None:
        try:
            documents = document_loader.load_urls([wiki_url])
        except Exception as e:
            raise HTTPException(status_code=400, detail=f"Error loading URL: {str(e)}")

    try:
        chunks = text_splitter.split_documents(documents)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error splitting document: {str(e)}")

    try:
        ids = vector_store_manager.add_documents(chunks)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error indexing document chunks: {str(e)}")

    return {"message": f"Added {len(chunks)} document chunks.", "ids": ids}

@app.post("/delete_document")
def delete_document(ids: List[str]):
    try:
        success = vector_store_manager.delete_documents(ids)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error deleting documents: {str(e)}")
    if not success:
        raise HTTPException(status_code=400, detail="Failed to delete documents.")
    return {"message": f"Deleted documents with IDs: {ids}"}

@app.get("/save_vectorstore")
def save_vectorstore():
    try:
        save_result = vector_store_manager.save("faiss_index")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error saving vectorstore: {str(e)}")
    return FileResponse(
        path=save_result["file_path"],
        media_type=save_result["media_type"],
        filename=save_result["serve_filename"],
    )

@app.post("/load_vectorstore")
async def load_vectorstore(file: UploadFile = File(...)):
    tmp_filename = None
    try:
        with tempfile.NamedTemporaryFile(delete=False) as tmp:
            file_bytes = await file.read()
            tmp.write(file_bytes)
            tmp_filename = tmp.name

        instance, message = VectorStoreManager.load(tmp_filename, embeddings)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error loading vectorstore: {str(e)}")
    finally:
        if tmp_filename and os.path.exists(tmp_filename):
            os.remove(tmp_filename)
    global vector_store_manager
    vector_store_manager = instance
    return {"message": message}

@app.post("/merge_vectorstore")
async def merge_vectorstore(file: UploadFile = File(...)):
    tmp_filename = None
    try:
        with tempfile.NamedTemporaryFile(delete=False) as tmp:
            file_bytes = await file.read()
            tmp.write(file_bytes)
            tmp_filename = tmp.name

        result = vector_store_manager.merge(tmp_filename, embeddings)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error merging vectorstore: {str(e)}")
    finally:
        if tmp_filename and os.path.exists(tmp_filename):
            os.remove(tmp_filename)
    return result

@app.get("/")
async def root():
    """
    Root endpoint that provides a welcome message.
    """
    return {
        "message": "Welcome to the EduLearn AI."
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)