feat: Calculate and log prediction confidence

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

server.py

@@ -6,6 +6,7 @@ import os
 import io
 
 import torch
+import torch.nn.functional as F
 from torchvision import transforms
 from PIL import Image
 
@@ -46,8 +47,9 @@ def get_prediction(model, image_bytes, device):
 
     with torch.no_grad():
         output = model(input_tensor)
-        _, pred_idx = torch.max(output, 1)
-        return CLASS_NAMES[pred_idx.item()]
+        probabilities = F.softmax(output, dim=1)
+        confidence, pred_idx = torch.max(probabilities, 1)
+        return CLASS_NAMES[pred_idx.item()], confidence.item()
 
 # --- Background Task ---
 async def monitor_garage_door(app):
@@ -65,9 +67,10 @@ async def monitor_garage_door(app):
             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}")
+                    result = get_prediction(model, image_bytes, device)
+                    if result:
+                        prediction, confidence = result
+                        logging.debug(f"Garage door status: {prediction} (confidence: {confidence:.4f})")
                 else:
                     logging.error(f"Failed to fetch image. Status: {response.status}, Reason: {response.reason}")