import asyncio
import aiohttp
import logging
import os
import io

import torch
from torchvision import transforms
from PIL import Image

from model import (CropLowerRightTriangle, GarageDoorCNN, TRIANGLE_CROP_WIDTH,
                   TRIANGLE_CROP_HEIGHT, RESIZE_DIM)

# --- Configuration ---
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

BLUEIRIS_KEY = os.getenv('BLUEIRIS_KEY')
if not BLUEIRIS_KEY:
    raise ValueError("BLUEIRIS_KEY environment variable not set.")

CAMERA_URL = "http://cameras.dns.t0.vc/image/Dump?&w=9999&decode=1"
MODEL_PATH = 'garage_door_cnn.pth'
CLASS_NAMES = ['closed', 'open'] # From training, sorted alphabetically
POLL_INTERVAL_SECONDS = 30
REQUEST_TIMEOUT_SECONDS = 5

# --- Model Inference ---
def get_prediction(model, image_bytes, device):
    """Run model inference on the provided image bytes."""
    try:
        image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
    except Exception as e:
        logging.error(f"Failed to open image from bytes: {e}")
        return None

    # Define the same transforms as used in validation
    transform = transforms.Compose([
        CropLowerRightTriangle(triangle_width=TRIANGLE_CROP_WIDTH, triangle_height=TRIANGLE_CROP_HEIGHT),
        transforms.Resize((RESIZE_DIM, RESIZE_DIM)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    input_tensor = transform(image).unsqueeze(0).to(device)

    with torch.no_grad():
        output = model(input_tensor)
        _, pred_idx = torch.max(output, 1)
        return CLASS_NAMES[pred_idx.item()]

# --- Background Task ---
async def monitor_garage_door(app):
    """Periodically fetches an image and logs the garage door status."""
    logging.info("Starting garage door monitoring task.")
    session = app['client_session']
    model = app['model']
    device = app['device']
    headers = {'Authorization': 'Basic ' + BLUEIRIS_KEY}

    while True:
        try:
            await asyncio.sleep(POLL_INTERVAL_SECONDS)
            logging.debug("Fetching new image from camera...")
            async with session.get(CAMERA_URL, headers=headers, timeout=REQUEST_TIMEOUT_SECONDS) as response:
                if response.status == 200:
                    image_bytes = await response.read()
                    prediction = get_prediction(model, image_bytes, device)
                    if prediction:
                        logging.debug(f"Garage door status: {prediction}")
                else:
                    logging.error(f"Failed to fetch image. Status: {response.status}, Reason: {response.reason}")

        except asyncio.TimeoutError:
            logging.warning("Request to camera timed out.")
        except aiohttp.ClientError as e:
            logging.error(f"Client error during image fetch: {e}")
        except asyncio.CancelledError:
            logging.info("Monitoring task cancelled.")
            break
        except Exception as e:
            logging.error(f"An unexpected error occurred in the monitoring task: {e}", exc_info=True)
            # Add a small delay before retrying on unexpected errors
            await asyncio.sleep(5)


# --- Web Server ---
async def handle_root(request):
    """Handler for the root GET request."""
    return aiohttp.web.Response(text="hello world")

async def on_startup(app):
    """Actions to perform on application startup."""
    # Set up device
    app['device'] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    logging.info(f"Using device: {app['device']}")

    # Load model
    logging.info(f"Loading model from {MODEL_PATH}...")
    model = GarageDoorCNN(resize_dim=RESIZE_DIM).to(app['device'])
    model.load_state_dict(torch.load(MODEL_PATH, map_location=app['device']))
    model.eval()
    app['model'] = model
    logging.info("Model loaded successfully.")

    # Create client session
    app['client_session'] = aiohttp.ClientSession()

    # Start background task
    app['monitor_task'] = asyncio.create_task(monitor_garage_door(app))

async def on_cleanup(app):
    """Actions to perform on application cleanup."""
    logging.info("Cleaning up...")
    app['monitor_task'].cancel()
    try:
        await app['monitor_task']
    except asyncio.CancelledError:
        pass
    await app['client_session'].close()
    logging.info("Cleanup complete.")

def main():
    app = aiohttp.web.Application()
    app.router.add_get('/', handle_root)
    app.on_startup.append(on_startup)
    app.on_cleanup.append(on_cleanup)
    aiohttp.web.run_app(app)

if __name__ == '__main__':
    if not os.path.exists(MODEL_PATH):
        logging.error(f"Model file '{MODEL_PATH}' not found. Please run train.py first.")
    else:
        main()