import io
import os
import asyncio
import functools
import logging
import polars as pl
import numpy as np
from functools import lru_cache
from pathlib import Path
from contextlib import asynccontextmanager
from fastapi import FastAPI
from pathlib import Path
from cryptography.fernet import Fernet

from src.config import config
from src.utils.logging import context_logger

logger = logging.getLogger(__name__)

DEV_MODE = bool(os.getenv("DEV_MODE", "false").lower())
DATA_CACHE_DIR = Path("cache/processed_data")

def _load_from_cache():

    try:
        DATA_CACHE_DIR.mkdir(parents=True, exist_ok=True)
        
        meta_path = DATA_CACHE_DIR / "meta.parquet"
        item_path = DATA_CACHE_DIR / "item_centroids.npy"
        scale_path = DATA_CACHE_DIR / "scale_centroids.npy"
        
        if not all(p.exists() for p in [meta_path, item_path, scale_path]):
            logger.info("Cache not found")
            return None
        
        return {
            'meta': pl.read_parquet(meta_path),
            'item_centroids': np.load(item_path),
            'scale_centroids': np.load(scale_path),
        }
    except Exception as e:
        logger.warning(f"Failed to load from cache: {e}")
        return None

def _save_to_cache(data):

    try:
        DATA_CACHE_DIR.mkdir(parents=True, exist_ok=True)
        
        data['meta'].write_parquet(DATA_CACHE_DIR / "meta.parquet")
        np.save(DATA_CACHE_DIR / "item_centroids.npy", data['item_centroids'])
        np.save(DATA_CACHE_DIR / "scale_centroids.npy", data['scale_centroids'])
    except Exception as e:
        logger.warning(f"Failed to save to cache: {e}")


async def load_search_data(app: FastAPI):

    with context_logger(f"💾 Loading search database"):

        encryption_key = config.data.encryption_key
        if not encryption_key:
            logger.error("DATA_ENCRYPTION_KEY not found")
            app.state.search_data = None
            return

        try:
            loop = asyncio.get_event_loop()

            if DEV_MODE:
                cached_data = await loop.run_in_executor(None, _load_from_cache)
                if cached_data is not None:
                    app.state.data = cached_data
                    logger.info("✅ Loaded from cache (dev mode)")
                    logger.info(f"`data['meta']` shape: {app.state.data['meta'].shape}")
                    logger.info(f"`data['item_centroids']` shape: {app.state.data['item_centroids'].shape}")
                    logger.info(f"`data['scale_centroids']` shape: {app.state.data['scale_centroids'].shape}")
                    return

            df = await loop.run_in_executor(None, _load_dataset_sync, config.data.dataset_path, config.data.encryption_key)

            app.state.data = {
                'meta': df.drop('item_centroid', 'scale_centroid'),
                'item_centroids': np.vstack(df['item_centroid'].to_list()),
                'scale_centroids': np.vstack(df['scale_centroid'].to_list()),
            }

            if DEV_MODE:
                await loop.run_in_executor(None, _save_to_cache, app.state.data)
                logger.info("✅ Saved to cache (dev mode)")

            logger.info(f"`data['meta']` shape:{ app.state.data['meta'].shape }")
            logger.info(f"`data['item_centroids']` shape:{ app.state.data['item_centroids'].shape }")
            logger.info(f"`data['scale_centroids']` shape:{ app.state.data['scale_centroids'].shape }")

        except Exception as e:
            logger.error(f"Error loading search data: {e}")
            app.state.search_data = None

def _load_dataset_sync(dataset_path: str, encryption_key: str) -> pl.DataFrame:

    import pickle
    import pandas as pd
    from datasets import load_dataset

    dataset = load_dataset(dataset_path, split="train")

    cipher = Fernet(encryption_key)
    decrypted_rows = []
    for row in dataset:
        decrypted_row = {
            col: pickle.loads(cipher.decrypt(row[col]))
            for col in row.keys()
        }
        decrypted_rows.append(decrypted_row)

    df = pd.DataFrame(decrypted_rows)
    return pl.from_pandas(df)

# @lru_cache()
async def setup_marimo(app: FastAPI):
    """Setup Marimo ASGI app during startup"""
    try:
        logger.info("🔄 Loading Marimo ASGI app...")
        
        import marimo as mo
        
        notebook_path = Path("notebooks/marimo-demo.py")
        
        if not notebook_path.exists():
            logger.warning(f"Notebook not found at {notebook_path}")
            app.state.marimo_app = None
            return
            
        # Run marimo setup in executor to avoid blocking
        loop = asyncio.get_event_loop()
        marimo_app = await loop.run_in_executor(
            None,
            lambda: setup_marimo_sync(notebook_path)
        )
        
        if marimo_app:
            app.mount("/marimo", marimo_app)
            app.state.marimo_app = marimo_app
            logger.info("✅ Marimo ASGI app mounted at /marimo")
        else:
            app.state.marimo_app = None
            logger.error("❌ Failed to mount Marimo ASGI app")
            
    except Exception as e:
        logger.error(f"❌ Error setting up marimo: {e}")
        app.state.marimo_app = None

# @lru_cache()
def setup_marimo_sync(notebook_path):
    """Synchronous marimo setup function"""
    import marimo as mo
    
    try:
        marimo_server = mo.create_asgi_app()
        marimo_server = marimo_server.with_app(
            path="/demo",
            root=str(notebook_path.absolute()),
        )
        return marimo_server.build()
    except Exception as e:
        logger.error(f"Error creating marimo ASGI app: {e}")
        return None

async def setup_hot_reload(app: FastAPI):
    """Setup hot reload for development"""
    if not DEV_MODE:
        app.state.hot_reload = None
        return None
        
    import arel
    
    hot_reload = arel.HotReload(
        paths=[
            arel.Path("./public"),
            arel.Path("./src/templates"),
        ]
    )
    app.add_websocket_route("/hot-reload", hot_reload, name="hot-reload")
    await hot_reload.startup()
    
    # Store in app state so templates can access it
    app.state.hot_reload = hot_reload
    
    logger.info("🔥 Hot reload enabled for development")
    return hot_reload

async def cleanup_hot_reload(app: FastAPI):
    """Cleanup hot reload on shutdown"""
    hot_reload = getattr(app.state, 'hot_reload', None)
    if DEV_MODE and hot_reload:
        try:
            await hot_reload.shutdown()
            logger.info("🔥 Hot reload stopped")
        except Exception as e:
            logger.error(f"Error stopping hot reload: {e}")
        finally:
            app.state.hot_reload = None

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan management"""
    
    try:

        await load_search_data(app)
        
        # # Setup marimo (in background to avoid blocking startup)
        # asyncio.create_task(setup_marimo(app))
        
        # Setup hot reload for development
        await setup_hot_reload(app)
        
        yield
        
    finally:
        # Cleanup
        await cleanup_hot_reload(app)

def get_hot_reload(app: FastAPI):
    """Get the hot reload instance from app state"""
    return getattr(app.state, 'hot_reload', None)