From f734703dc3652bd3b821de5ca8c688c16116a5ba Mon Sep 17 00:00:00 2001
From: Tanner Collin <git@tannercollin.com>
Date: Thu, 31 Jul 2025 18:23:12 -0600
Subject: [PATCH] feat: add aiohttp server to monitor garage door status

Co-authored-by: aider (gemini/gemini-2.5-pro-preview-05-06) <aider@aider.chat>
---
 server.py | 133 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 133 insertions(+)

diff --git a/server.py b/server.py
index e69de29..6c4a633 100644
--- a/server.py
+++ b/server.py
@@ -0,0 +1,133 @@
+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()