3

app.py

@@ -1,29 +1,37 @@
+import base64
+import json
 import logging
 import sys
-import os
 import threading
-import json
 import time
 import traceback
-import base64
+
 import cv2
 import numpy as np
 import paho.mqtt.client as mqtt
-from flask import Flask, render_template, jsonify, request, Response
+from flask import Flask, Response, jsonify, render_template, request
 
-# Import Config, Manager, and NEW Inference Worker
 from config import Config
-from manager import CameraManager
 from inference import InferenceWorker
+from manager import CameraManager
 
-# --- Logging Setup ---
+
+def _cfg(*names, default=None):
+    """Return first matching attribute from Config, else default."""
+    for n in names:
+        if hasattr(Config, n):
+            return getattr(Config, n)
+    return default
+
+
+LOG_LEVEL = _cfg("LOG_LEVEL", "LOGLEVEL", default=logging.INFO)
 logging.basicConfig(
-    level=Config.LOG_LEVEL,
+    level=LOG_LEVEL,
     format='%(asctime)s [%(levelname)s] %(message)s',
-    handlers=[logging.StreamHandler(sys.stdout)]
+    handlers=[logging.StreamHandler(sys.stdout)],
 )
-
 logger = logging.getLogger(__name__)
+
 app = Flask(__name__)
 
 # --- Initialize Components ---
@@ -34,15 +42,22 @@ inference_worker.start()
 # --- MQTT Setup ---
 mqtt_client = mqtt.Client()
 
-if Config.MQTT_USERNAME and Config.MQTT_PASSWORD:
-    mqtt_client.username_pw_set(Config.MQTT_USERNAME, Config.MQTT_PASSWORD)
+MQTT_USERNAME = _cfg("MQTT_USERNAME", "MQTTUSERNAME", default=None)
+MQTT_PASSWORD = _cfg("MQTT_PASSWORD", "MQTTPASSWORD", default=None)
+MQTT_BROKER = _cfg("MQTT_BROKER", "MQTTBROKER", default="127.0.0.1")
+MQTT_PORT = int(_cfg("MQTT_PORT", "MQTTPORT", default=1883))
+MQTT_TOPIC = _cfg("MQTT_TOPIC", "MQTTTOPIC", default="homeassistant/sensor/RTSPCamDigitDetection/state")
+
+if MQTT_USERNAME and MQTT_PASSWORD:
+    mqtt_client.username_pw_set(MQTT_USERNAME, MQTT_PASSWORD)
 
 try:
-    mqtt_client.connect(Config.MQTT_BROKER, Config.MQTT_PORT, 60)
+    mqtt_client.connect(MQTT_BROKER, MQTT_PORT, 60)
     mqtt_client.loop_start()
-    logger.info(f"Connected to MQTT Broker at {Config.MQTT_BROKER}:{Config.MQTT_PORT}")
+    logger.info("Connected to MQTT Broker at %s:%s", MQTT_BROKER, MQTT_PORT)
 except Exception as e:
-    logger.error(f"Failed to connect to MQTT Broker: {e}")
+    logger.error("Failed to connect to MQTT Broker: %s", e)
+
 
 # --- Helper Functions (UI Only) ---
 def crop_image_for_ui(image, roi_list, scaleX, scaleY):
@@ -61,14 +76,17 @@ def crop_image_for_ui(image, roi_list, scaleX, scaleY):
             pass
     return cropped_images
 
+
 def publish_detected_number(camera_id, detected_number, confidence=None):
     """Publish result to MQTT with optional confidence score."""
-    topic = f"{Config.MQTT_TOPIC}/{camera_id}"
+    topic = f"{MQTT_TOPIC}/{camera_id}"
+
     payload_dict = {"value": detected_number}
     if confidence is not None:
-        payload_dict["confidence"] = round(confidence, 2)
+        payload_dict["confidence"] = round(float(confidence), 2)
 
     payload = json.dumps(payload_dict)
+
     try:
         mqtt_client.publish(topic, payload)
         log_msg = f"Published to {topic}: {detected_number}"
@@ -76,40 +94,77 @@ def publish_detected_number(camera_id, detected_number, confidence=None):
             log_msg += f" (Conf: {confidence:.2f})"
         logger.info(log_msg)
     except Exception as e:
-        logger.error(f"MQTT Publish failed: {e}")
+        logger.error("MQTT Publish failed: %s", e)
+
+
+# --- Debug helpers ---
+_last_log = {}
+
+def log_rl(level, key, msg, every_s=10):
+    now = time.time()
+    last = _last_log.get(key, 0.0)
+    if now - last >= every_s:
+        _last_log[key] = now
+        logger.log(level, msg)
+
 
 # --- Main Processing Loop (Refactored) ---
 last_processed_time = {}
 
 def process_all_cameras():
-    """
-    Revised Loop with Rate Limiting
-    """
-    DETECTION_INTERVAL = 10  # Configurable interval (seconds)
+    """Revised loop with rate limiting + debug instrumentation."""
+    DETECTION_INTERVAL = int(_cfg("DETECTION_INTERVAL", default=10))
+    hb_last = 0.0
 
     while True:
         try:
+            # Heartbeat (proves loop is alive even when no publishes happen)
+            now = time.time()
+            if now - hb_last >= 5.0:
+                hb_last = now
+                in_q = getattr(inference_worker, "input_queue", None)
+                out_q = getattr(inference_worker, "result_queue", None)
+                logger.info(
+                    "HB mainloop alive; in_q=%s out_q=%s dropped=%s processed=%s last_invoke_s=%s",
+                    (in_q.qsize() if in_q else "n/a"),
+                    (out_q.qsize() if out_q else "n/a"),
+                    getattr(inference_worker, "dropped_tasks", "n/a"),
+                    getattr(inference_worker, "processed_tasks", "n/a"),
+                    getattr(inference_worker, "last_invoke_secs", "n/a"),
+                )
+
             # --- Part 1: Process Results ---
             while True:
                 result = inference_worker.get_result()
                 if not result:
                     break
 
-                cam_id = result['camera_id']
+                cam_id = result.get('camera_id')
+
+                # End-to-end latency tracing
+                task_ts = result.get("task_ts")
+                if task_ts is not None:
+                    try:
+                        age = time.time() - float(task_ts)
+                        logger.info(
+                            "Result cam=%s type=%s task_id=%s age_s=%.3f timing=%s",
+                            cam_id,
+                            result.get("type"),
+                            result.get("task_id"),
+                            age,
+                            result.get("timing_s"),
+                        )
+                    except Exception:
+                        pass
 
-                # Check Result Type
                 if result.get('type') == 'success':
                     val = result['value']
                     conf = result.get('confidence')
-                    # Update State & Publish
                     camera_manager.results[cam_id] = val
                     publish_detected_number(cam_id, val, conf)
-
                 elif result.get('type') == 'error':
-                    # Log the error (Range or Confidence or Parse)
-                    # This ensures the log appears exactly when the result is processed
                     msg = result.get('message', 'Unknown error')
-                    logger.warning(f"[{cam_id}] Detection skipped: {msg}")
+                    logger.warning("[%s] Detection skipped: %s", cam_id, msg)
 
             # --- Part 2: Feed Frames ---
             camera_manager.load_roi_config()
@@ -118,54 +173,73 @@ def process_all_cameras():
                 if not camera_data.get("active", True):
                     continue
 
-                # RATE LIMIT CHECK
                 current_time = time.time()
-                last_time = last_processed_time.get(camera_id, 0)
+                last_time = last_processed_time.get(camera_id, 0.0)
 
                 if current_time - last_time < DETECTION_INTERVAL:
-                    continue  # Skip this camera, it's too soon
+                    log_rl(
+                        logging.DEBUG,
+                        f"{camera_id}:rate",
+                        f"[{camera_id}] skip: rate limit ({current_time - last_time:.2f}s<{DETECTION_INTERVAL}s)",
+                        every_s=30,
+                    )
+                    continue
 
                 stream = camera_data.get("stream")
-                if not stream: continue
+                if not stream:
+                    log_rl(logging.WARNING, f"{camera_id}:nostream", f"[{camera_id}] skip: no stream", every_s=10)
+                    continue
 
-                # Warmup Check
-                if (current_time - stream.start_time) < 5:
+                # Warmup check
+                start_time = getattr(stream, "start_time", getattr(stream, "starttime", None))
+                if start_time is not None and (current_time - start_time) < 5:
+                    log_rl(logging.DEBUG, f"{camera_id}:warmup", f"[{camera_id}] skip: warmup", every_s=10)
                     continue
 
                 frame = stream.read()
                 if frame is None:
+                    log_rl(logging.WARNING, f"{camera_id}:noframe", f"[{camera_id}] skip: frame is None", every_s=5)
                     continue
 
-                if np.std(frame) < 10:
+                frame_std = float(np.std(frame))
+                if frame_std < 5:
+                    log_rl(
+                        logging.INFO,
+                        f"{camera_id}:lowstd",
+                        f"[{camera_id}] skip: low frame std={frame_std:.2f} (<10) (disturbed/blank/frozen?)",
+                        every_s=5,
+                    )
+                    mqtt_client.publish(f"{Config.MQTT_TOPIC}/{camera_id}/status", "disturbed")
+
                     continue
 
                 roi_list = camera_manager.rois.get(camera_id, [])
                 if not roi_list:
+                    log_rl(logging.WARNING, f"{camera_id}:norois", f"[{camera_id}] skip: no ROIs", every_s=30)
                     continue
 
-                # SEND TO WORKER
-                inference_worker.add_task(camera_id, roi_list, frame)
-
-                # Update last processed time
+                inference_worker.add_task(camera_id, roi_list, frame, frame_std=frame_std)
                 last_processed_time[camera_id] = current_time
 
-            # Sleep briefly to prevent CPU spinning, but keep it responsive for results
             time.sleep(0.1)
 
         except Exception as e:
-            logger.error(f"Global process loop error: {e}")
+            logger.error("Global process loop error: %s", e)
             traceback.print_exc()
             time.sleep(5)
 
+
 # --- Flask Routes ---
 @app.route('/')
 def index():
     return render_template('index.html')
 
+
 @app.route('/cameras', methods=['GET'])
 def get_cameras():
     return jsonify(camera_manager.get_camera_list())
 
+
 @app.route('/video/<camera_id>')
 def video_feed(camera_id):
     def generate():
@@ -174,11 +248,16 @@ def video_feed(camera_id):
             if frame is not None:
                 ret, jpeg = cv2.imencode('.jpg', frame)
                 if ret:
-                    yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n\r\n')
+                    yield (
+                        b'--frame\r\n'
+                        b'Content-Type: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n\r\n'
+                    )
             else:
                 time.sleep(0.1)
+
     return Response(generate(), mimetype='multipart/x-mixed-replace; boundary=frame')
 
+
 @app.route('/snapshot/<camera_id>')
 def snapshot(camera_id):
     frame = camera_manager.get_frame(camera_id)
@@ -188,6 +267,7 @@ def snapshot(camera_id):
             return Response(jpeg.tobytes(), mimetype='image/jpeg')
     return 'No frame available', 404
 
+
 @app.route('/rois/<camera_id>', methods=['GET'])
 def get_rois(camera_id):
     try:
@@ -218,28 +298,34 @@ def get_rois(camera_id):
                 "y": int(round(roi["y"] * scaleY)),
                 "width": int(round(roi["width"] * scaleX)),
                 "height": int(round(roi["height"] * scaleY)),
-                "angle": roi["angle"]
+                "angle": roi.get("angle", 0),
             })
+
         return jsonify(scaled_rois)
+
     except Exception as e:
         return jsonify({"error": str(e)}), 500
 
+
 @app.route("/save_rois", methods=["POST"])
 def save_rois_api():
     data = request.json
+
     camera_id = data.get("camera_id")
     new_rois = data.get("rois")
     img_width = data.get("img_width")
     img_height = data.get("img_height")
 
-    if not camera_id or new_rois is None: return jsonify({"success": False})
+    if not camera_id or new_rois is None:
+        return jsonify({"success": False})
 
     cam = camera_manager.cameras.get(camera_id)
-    if not cam: return jsonify({"success": False})
+    if not cam:
+        return jsonify({"success": False})
 
     stream = cam.get("stream")
-    real_w = stream.width if stream and stream.width else cam["width"]
-    real_h = stream.height if stream and stream.height else cam["height"]
+    real_w = stream.width if stream and getattr(stream, "width", None) else cam["width"]
+    real_h = stream.height if stream and getattr(stream, "height", None) else cam["height"]
 
     scaleX = real_w / img_width if img_width else 1
     scaleY = real_h / img_height if img_height else 1
@@ -252,21 +338,24 @@ def save_rois_api():
             "y": int(round(roi["y"] * scaleY)),
             "width": int(round(roi["width"] * scaleX)),
             "height": int(round(roi["height"] * scaleY)),
-            "angle": roi["angle"]
+            "angle": roi.get("angle", 0),
         })
 
     camera_manager.rois[camera_id] = scaled_rois
     return jsonify(camera_manager.save_roi_config())
 
+
 @app.route('/crop', methods=['POST'])
 def crop():
     data = request.json
+
     camera_id = data.get('camera_id')
     scaleX = data.get('scaleX', 1)
     scaleY = data.get('scaleY', 1)
 
     frame = camera_manager.get_frame(camera_id)
-    if frame is None: return jsonify({'error': 'No frame'}), 500
+    if frame is None:
+        return jsonify({'error': 'No frame'}), 500
 
     roi_list = camera_manager.rois.get(camera_id, [])
     cropped_images = crop_image_for_ui(frame, roi_list, scaleX, scaleY)
@@ -279,31 +368,29 @@ def crop():
 
     return jsonify({'cropped_images': cropped_base64_list})
 
+
 @app.route('/detect_digits', methods=['POST'])
 def detect_digits():
     """Manual trigger: Runs inference immediately and returns result with validation."""
     data = request.json
+
     camera_id = data.get('camera_id')
     if not camera_id:
         return jsonify({'error': 'Invalid camera ID'}), 400
 
-    # 1. Get Frame
     frame = camera_manager.get_frame(camera_id)
     if frame is None:
         return jsonify({'error': 'Failed to capture image'}), 500
 
-    # 2. Get ROIs
     roi_list = camera_manager.rois.get(camera_id, [])
     if not roi_list:
         return jsonify({'error': 'No ROIs defined'}), 400
 
-    # 3. Crop
     cropped_images = crop_image_for_ui(frame, roi_list, scaleX=1, scaleY=1)
     if not cropped_images:
         return jsonify({'error': 'Failed to crop ROIs'}), 500
 
     try:
-        # 4. Run Inference Synchronously
         predictions = inference_worker.predict_batch(cropped_images)
 
         valid_digits_str = []
@@ -318,30 +405,24 @@ def detect_digits():
             if p['confidence'] < CONFIDENCE_THRESHOLD:
                 msg = f"Digit {i} ('{p['digit']}') rejected: conf {p['confidence']:.2f} < {CONFIDENCE_THRESHOLD}"
                 rejected_reasons.append(msg)
-                logger.warning(f"[Manual] {msg}")
+                logger.warning("[Manual] %s", msg)
             else:
                 valid_digits_str.append(p['digit'])
                 confidences.append(p['confidence'])
 
         if len(valid_digits_str) != len(predictions):
-            return jsonify({
-                'error': 'Low confidence detection', 
-                'details': rejected_reasons,
-                'raw': predictions
-            }), 400
+            return jsonify({'error': 'Low confidence detection', 'details': rejected_reasons, 'raw': predictions}), 400
 
         final_number_str = "".join(valid_digits_str)
         try:
             final_number = int(final_number_str)
 
-            # Range Check
             if not (MIN_VALUE <= final_number <= MAX_VALUE):
                 msg = f"Value {final_number} out of range ({MIN_VALUE}-{MAX_VALUE})"
-                logger.warning(f"[Manual] {msg}")
+                logger.warning("[Manual] %s", msg)
                 return jsonify({'error': 'Value out of range', 'value': final_number}), 400
 
-            # Valid result
-            avg_conf = float(np.mean(confidences))
+            avg_conf = float(np.mean(confidences)) if confidences else None
             publish_detected_number(camera_id, final_number, avg_conf)
             camera_manager.results[camera_id] = final_number
 
@@ -349,25 +430,28 @@ def detect_digits():
                 'detected_digits': valid_digits_str,
                 'final_number': final_number,
                 'confidences': confidences,
-                'avg_confidence': avg_conf
+                'avg_confidence': avg_conf,
             })
 
         except ValueError:
             return jsonify({'error': 'Could not parse digits', 'raw': valid_digits_str}), 500
 
     except Exception as e:
-        logger.error(f"Error during manual detection: {e}")
+        logger.error("Error during manual detection: %s", e)
         return jsonify({'error': str(e)}), 500
 
+
 @app.route('/update_camera_config', methods=['POST'])
 def update_camera_config():
     data = request.json
     success = camera_manager.update_camera_flip(data.get("camera_id"), data.get("flip_type"))
     return jsonify({"success": success})
 
+
 # --- Main ---
 if __name__ == '__main__':
     t = threading.Thread(target=process_all_cameras, daemon=True)
     t.start()
+
     logger.info("Starting Flask Server...")
     app.run(host='0.0.0.0', port=5000, threaded=True)

inference.py

@@ -1,66 +1,98 @@
-import threading
-import queue
-import time
 import logging
+import queue
+import threading
+import time
+
 import cv2
 import numpy as np
 import tflite_runtime.interpreter as tflite
+
 from config import Config
 
 logger = logging.getLogger(__name__)
 
+
+def _cfg(*names, default=None):
+    for n in names:
+        if hasattr(Config, n):
+            return getattr(Config, n)
+    return default
+
+
 class InferenceWorker:
     def __init__(self):
         self.input_queue = queue.Queue(maxsize=10)
         self.result_queue = queue.Queue()
         self.running = False
+
         self.interpreter = None
         self.input_details = None
         self.output_details = None
         self.lock = threading.Lock()
 
-        # Validation thresholds
-        self.CONFIDENCE_THRESHOLD = 0.80  # Minimum confidence (0-1) to accept a digit
-        self.MIN_VALUE = 5               # Minimum allowed temperature value
-        self.MAX_VALUE = 100             # Maximum allowed temperature value
+        # Debug counters / telemetry
+        self.task_seq = 0
+        self.dropped_tasks = 0
+        self.processed_tasks = 0
+        self.last_invoke_secs = None
+
+        # Validation thresholds
+        self.CONFIDENCE_THRESHOLD = 0.10
+        self.MIN_VALUE = 5
+        self.MAX_VALUE = 100
 
-        # Load Model
         self.load_model()
 
     def load_model(self):
         try:
-            logger.info(f"Loading TFLite model from: {Config.MODEL_PATH}")
-            self.interpreter = tflite.Interpreter(model_path=Config.MODEL_PATH)
+            model_path = _cfg("MODEL_PATH", "MODELPATH", default=None)
+            logger.info("Loading TFLite model from: %s", model_path)
+
+            self.interpreter = tflite.Interpreter(model_path=model_path)
             self.interpreter.allocate_tensors()
+
             self.input_details = self.interpreter.get_input_details()
             self.output_details = self.interpreter.get_output_details()
 
-            # Store original input shape for resizing logic
             self.original_input_shape = self.input_details[0]['shape']
-            logger.info(f"Model loaded. Default input shape: {self.original_input_shape}")
+            logger.info("Model loaded. Default input shape: %s", self.original_input_shape)
+
         except Exception as e:
-            logger.critical(f"Failed to load TFLite model: {e}")
+            logger.critical("Failed to load TFLite model: %s", e)
             self.interpreter = None
 
     def start(self):
-        if self.running: return
+        if self.running:
+            return
         self.running = True
         threading.Thread(target=self._worker_loop, daemon=True).start()
         logger.info("Inference worker started.")
 
-    def add_task(self, camera_id, rois, frame):
+    def add_task(self, camera_id, rois, frame, frame_std=None):
         """Add task (non-blocking)."""
-        if not self.interpreter: return
-        try:
+        if not self.interpreter:
+            return
+
+        self.task_seq += 1
         task = {
             'camera_id': camera_id,
             'rois': rois,
             'frame': frame,
-                'timestamp': time.time()
+            'timestamp': time.time(),
+            'task_id': self.task_seq,
+            'frame_std': frame_std,
         }
+
+        try:
             self.input_queue.put(task, block=False)
         except queue.Full:
-            pass
+            self.dropped_tasks += 1
+            logger.warning(
+                "add_task drop cam=%s qsize=%d dropped=%d",
+                camera_id,
+                self.input_queue.qsize(),
+                self.dropped_tasks,
+            )
 
     def get_result(self):
         try:
@@ -68,6 +100,14 @@ class InferenceWorker:
         except queue.Empty:
             return None
 
+    def _put_result(self, d):
+        """Best-effort put so failures never go silent."""
+        try:
+            self.result_queue.put(d, block=False)
+        except Exception:
+            # Should be extremely rare; log + drop
+            logger.exception("Failed to enqueue result")
+
     def _worker_loop(self):
         while self.running:
             try:
@@ -78,86 +118,122 @@ class InferenceWorker:
             cam_id = task['camera_id']
             rois = task['rois']
             frame = task['frame']
+            task_id = task.get('task_id')
+            task_ts = task.get('timestamp')
 
             try:
-                # 1. Crop all ROIs
+                age_s = (time.time() - task_ts) if task_ts else None
+                logger.info(
+                    "Worker got task cam=%s task_id=%s age_s=%s frame_std=%s rois=%d in_q=%d",
+                    cam_id,
+                    task_id,
+                    (f"{age_s:.3f}" if age_s is not None else "n/a"),
+                    task.get('frame_std'),
+                    len(rois) if rois else 0,
+                    self.input_queue.qsize(),
+                )
+
+                t0 = time.time()
                 crops = self._crop_rois(frame, rois)
+                t_crop = time.time()
+
                 if not crops:
-                    # Report failure to queue so main loop knows we tried
-                    self.result_queue.put({
+                    self._put_result({
                         'type': 'error',
                         'camera_id': cam_id,
-                        'message': 'No ROIs cropped'
+                        'message': 'No ROIs cropped',
+                        'task_id': task_id,
+                        'task_ts': task_ts,
+                        'timing_s': {'crop': t_crop - t0, 'total': t_crop - t0},
                     })
                     continue
 
-                # 2. Batch Predict
                 predictions = self.predict_batch(crops)
+                t_pred = time.time()
 
-                # 3. Validation Logic
                 valid_digits_str = []
                 confidences = []
-
-                all_confident = True
                 low_conf_details = []
 
                 for i, p in enumerate(predictions):
                     if p['confidence'] < self.CONFIDENCE_THRESHOLD:
-                        low_conf_details.append(f"Digit {i} conf {p['confidence']:.2f} < {self.CONFIDENCE_THRESHOLD}")
-                        all_confident = False
+                        low_conf_details.append(
+                            f"Digit {i} conf {p['confidence']:.2f} < {self.CONFIDENCE_THRESHOLD}"
+                        )
                     valid_digits_str.append(p['digit'])
                     confidences.append(p['confidence'])
 
-                if not all_confident:
-                    # Send failure result
-                    self.result_queue.put({
+                if low_conf_details:
+                    self._put_result({
                         'type': 'error',
                         'camera_id': cam_id,
                         'message': f"Low confidence: {', '.join(low_conf_details)}",
-                        'digits': valid_digits_str
+                        'digits': valid_digits_str,
+                        'task_id': task_id,
+                        'task_ts': task_ts,
+                        'timing_s': {'crop': t_crop - t0, 'predict': t_pred - t_crop, 'total': t_pred - t0},
                     })
                     continue
 
                 if not valid_digits_str:
+                    self._put_result({
+                        'type': 'error',
+                        'camera_id': cam_id,
+                        'message': 'No digits produced',
+                        'task_id': task_id,
+                        'task_ts': task_ts,
+                        'timing_s': {'crop': t_crop - t0, 'predict': t_pred - t_crop, 'total': t_pred - t0},
+                    })
                     continue
 
-                # Parse number
-                try:
                 final_number_str = "".join(valid_digits_str)
-                    final_number = int(final_number_str)
 
-                    # Check Range
+                try:
+                    final_number = int(final_number_str)
+                except ValueError:
+                    self._put_result({
+                        'type': 'error',
+                        'camera_id': cam_id,
+                        'message': f"Parse error: {valid_digits_str}",
+                        'task_id': task_id,
+                        'task_ts': task_ts,
+                        'timing_s': {'crop': t_crop - t0, 'predict': t_pred - t_crop, 'total': t_pred - t0},
+                    })
+                    continue
+
                 if self.MIN_VALUE <= final_number <= self.MAX_VALUE:
-                        avg_conf = float(np.mean(confidences))
-                        self.result_queue.put({
+                    avg_conf = float(np.mean(confidences)) if confidences else None
+                    self._put_result({
                         'type': 'success',
                         'camera_id': cam_id,
                         'value': final_number,
                         'digits': valid_digits_str,
-                            'confidence': avg_conf
+                        'confidence': avg_conf,
+                        'task_id': task_id,
+                        'task_ts': task_ts,
+                        'timing_s': {'crop': t_crop - t0, 'predict': t_pred - t_crop, 'total': t_pred - t0},
                     })
                 else:
-                        # Send range error result
-                        self.result_queue.put({
+                    self._put_result({
                         'type': 'error',
                         'camera_id': cam_id,
                         'message': f"Value {final_number} out of range ({self.MIN_VALUE}-{self.MAX_VALUE})",
-                            'value': final_number
+                        'value': final_number,
+                        'task_id': task_id,
+                        'task_ts': task_ts,
+                        'timing_s': {'crop': t_crop - t0, 'predict': t_pred - t_crop, 'total': t_pred - t0},
                     })
 
-                except ValueError:
-                    self.result_queue.put({
-                        'type': 'error',
-                        'camera_id': cam_id,
-                        'message': f"Parse error: {valid_digits_str}"
-                    })
+                self.processed_tasks += 1
 
-            except Exception as e:
-                logger.error(f"Inference error for {cam_id}: {e}")
-                self.result_queue.put({
+            except Exception:
+                logger.exception("Inference error cam=%s task_id=%s", cam_id, task_id)
+                self._put_result({
                     'type': 'error',
                     'camera_id': cam_id,
-                    'message': str(e)
+                    'message': 'Exception during inference; see logs',
+                    'task_id': task_id,
+                    'task_ts': task_ts,
                 })
 
     def _crop_rois(self, image, roi_list):
@@ -173,12 +249,17 @@ class InferenceWorker:
         return cropped_images
 
     def predict_batch(self, images):
-        """Run inference on a batch of images at once. Returns list of dicts: {'digit': str, 'confidence': float}"""
+        """Run inference on a batch of images.
+
+        Returns list of dicts: {'digit': str, 'confidence': float}
+        """
         with self.lock:
-            if not self.interpreter: return []
+            if not self.interpreter:
+                return []
 
             num_images = len(images)
-            if num_images == 0: return []
+            if num_images == 0:
+                return []
 
             input_index = self.input_details[0]['index']
             output_index = self.output_details[0]['index']
@@ -194,23 +275,33 @@ class InferenceWorker:
 
             input_tensor = np.array(batch_input)
 
+            # NOTE: Keeping original behavior (resize+allocate) but timing it.
             self.interpreter.resize_tensor_input(input_index, [num_images, target_h, target_w, 3])
             self.interpreter.allocate_tensors()
+
             self.interpreter.set_tensor(input_index, input_tensor)
+
+            t0 = time.time()
             self.interpreter.invoke()
+            self.last_invoke_secs = time.time() - t0
+            if self.last_invoke_secs > 1.0:
+                logger.warning("Slow invoke: %.3fs (batch=%d)", self.last_invoke_secs, num_images)
 
             output_data = self.interpreter.get_tensor(output_index)
 
             results = []
             for i in range(num_images):
                 logits = output_data[i]
-                probs = np.exp(logits) / np.sum(np.exp(logits)) 
-                digit_class = np.argmax(probs)
-                confidence = probs[digit_class]
 
-                results.append({
-                    'digit': str(digit_class),
-                    'confidence': float(confidence)
-                })
+                # More stable softmax
+                logits = logits - np.max(logits)
+                ex = np.exp(logits)
+                denom = np.sum(ex)
+                probs = (ex / denom) if denom != 0 else np.zeros_like(ex)
+
+                digit_class = int(np.argmax(probs))
+                confidence = float(probs[digit_class]) if probs.size else 0.0
+
+                results.append({'digit': str(digit_class), 'confidence': confidence})
 
             return results