feat: log and save low-confidence predictions

Co-authored-by: aider (gemini/gemini-2.5-pro-preview-05-06) <aider@aider.chat>

server.py

@@ -4,6 +4,7 @@ from aiohttp import web
 import logging
 import os
 import io
+from datetime import datetime
 
 import torch
 import torch.nn.functional as F
@@ -25,6 +26,7 @@ MODEL_PATH = 'garage_door_cnn.pth'
 CLASS_NAMES = ['closed', 'open'] # From training, sorted alphabetically
 POLL_INTERVAL_SECONDS = 10
 REQUEST_TIMEOUT_SECONDS = 5
+UNSURE_CONFIDENCE_THRESHOLD = 0.97
 
 # --- Model Inference ---
 def get_prediction(model, image_bytes, device):
@@ -71,6 +73,21 @@ async def monitor_garage_door(app):
                     if result:
                         prediction, confidence = result
                         logging.debug(f"Garage door status: {prediction} (confidence: {confidence:.4f})")
+
+                        if confidence < UNSURE_CONFIDENCE_THRESHOLD:
+                            # Sanitize timestamp for use in filename
+                            timestamp = datetime.now().isoformat().replace(':', '-')
+                            filename = f"{timestamp}.jpg"
+                            
+                            # Construct path and save file
+                            unsure_dir = os.path.join('data', 'unsure', prediction)
+                            os.makedirs(unsure_dir, exist_ok=True)
+                            filepath = os.path.join(unsure_dir, filename)
+                            
+                            with open(filepath, 'wb') as f:
+                                f.write(image_bytes)
+                            
+                            logging.info(f"Low confidence prediction: {prediction} ({confidence:.4f}). Saved for review: {filepath}")
                 else:
                     logging.error(f"Failed to fetch image. Status: {response.status}, Reason: {response.reason}")