Refactor: bring back original download_mpta

.gitignore

@@ -12,3 +12,4 @@ detector_worker.log
 no_frame_debug.log
 
 feeder/
+.venv/

app.py

@@ -13,7 +13,13 @@ import requests
 import asyncio
 import psutil
 import zipfile
+import ssl
+import urllib3
+import subprocess
+import tempfile
 from urllib.parse import urlparse
+from requests.adapters import HTTPAdapter
+from urllib3.util.ssl_ import create_urllib3_context
 from fastapi import FastAPI, WebSocket, HTTPException
 from fastapi.websockets import WebSocketDisconnect
 from fastapi.responses import Response
@@ -240,16 +246,14 @@ async def detect(websocket: WebSocket):
             logger.debug(f"Processing frame for camera {camera_id} with model {stream['modelId']}")
             start_time = time.time()
             
-            # Extract display identifier for session ID lookup
+            # Extract display identifier for pipeline context
             subscription_parts = stream["subscriptionIdentifier"].split(';')
             display_identifier = subscription_parts[0] if subscription_parts else None
-            session_id = session_ids.get(display_identifier) if display_identifier else None
             
-            # Create context for pipeline execution
+            # Create context for pipeline execution (session_id will be generated by pipeline)
             pipeline_context = {
                 "camera_id": camera_id,
-                "display_id": display_identifier,
-                "session_id": session_id
+                "display_id": display_identifier
             }
             
             detection_result = run_pipeline(cropped_frame, model_tree, context=pipeline_context)
@@ -259,57 +263,63 @@ async def detect(websocket: WebSocket):
             # Log the raw detection result for debugging
             logger.debug(f"Raw detection result for camera {camera_id}:\n{json.dumps(detection_result, indent=2, default=str)}")
             
-            # Direct class result (no detections/classifications structure)
-            if detection_result and isinstance(detection_result, dict) and "class" in detection_result and "confidence" in detection_result:
-                highest_confidence_detection = {
-                    "class": detection_result.get("class", "none"),
-                    "confidence": detection_result.get("confidence", 1.0),
-                    "box": [0, 0, 0, 0]  # Empty bounding box for classifications
-                }
-            # Handle case when no detections found or result is empty
-            elif not detection_result or not detection_result.get("detections"):
-                # Check if we have classification results
-                if detection_result and detection_result.get("classifications"):
-                    # Get the highest confidence classification
-                    classifications = detection_result.get("classifications", [])
-                    highest_confidence_class = max(classifications, key=lambda x: x.get("confidence", 0)) if classifications else None
+            # Extract session_id from pipeline result (generated during database record creation)
+            session_id = None
+            if detection_result and isinstance(detection_result, dict):
+                # Check if pipeline generated a session_id (happens when Car+Frontal detected together)
+                if "session_id" in detection_result:
+                    session_id = detection_result["session_id"]
+                    logger.debug(f"Extracted session_id from pipeline result: {session_id}")
             
-                    if highest_confidence_class:
-                        highest_confidence_detection = {
-                            "class": highest_confidence_class.get("class", "none"),
-                            "confidence": highest_confidence_class.get("confidence", 1.0),
-                            "box": [0, 0, 0, 0]  # Empty bounding box for classifications
-                        }
-                    else:
-                        highest_confidence_detection = {
-                            "class": "none",
-                            "confidence": 1.0,
-                            "box": [0, 0, 0, 0]
-                        }
-                else:
-                    highest_confidence_detection = {
-                        "class": "none",
-                        "confidence": 1.0,
-                        "box": [0, 0, 0, 0]
-                    }
+            # Process detection result - run_pipeline returns the primary detection directly
+            if detection_result and isinstance(detection_result, dict) and "class" in detection_result:
+                highest_confidence_detection = detection_result
             else:
-                # Find detection with highest confidence
-                detections = detection_result.get("detections", [])
-                highest_confidence_detection = max(detections, key=lambda x: x.get("confidence", 0)) if detections else {
+                # No detection found
+                highest_confidence_detection = {
                     "class": "none",
                     "confidence": 1.0,
-                    "box": [0, 0, 0, 0]
+                    "bbox": [0, 0, 0, 0],
+                    "branch_results": {}
                 }
             
-            # Convert detection format to match protocol - flatten detection attributes
-            detection_dict = {}
+            # Convert detection format to match backend expectations exactly as in worker.md section 4.2
+            detection_dict = {
+                "carModel": None,
+                "carBrand": None,
+                "carYear": None,
+                "bodyType": None,
+                "licensePlateText": None,
+                "licensePlateConfidence": None
+            }
             
-            # Handle different detection result formats
-            if isinstance(highest_confidence_detection, dict):
-                # Copy all fields from the detection result
-                for key, value in highest_confidence_detection.items():
-                    if key not in ["box", "id"]:  # Skip internal fields
-                        detection_dict[key] = value
+            # Extract and process branch results from parallel classification
+            branch_results = highest_confidence_detection.get("branch_results", {})
+            if branch_results:
+                logger.debug(f"Processing branch results: {branch_results}")
+                
+                # Transform branch results into backend-expected detection attributes
+                for branch_id, branch_data in branch_results.items():
+                    if isinstance(branch_data, dict):
+                        logger.debug(f"Processing branch {branch_id}: {branch_data}")
+                        
+                        # Map common classification fields to backend-expected names
+                        if "brand" in branch_data:
+                            detection_dict["carBrand"] = branch_data["brand"]
+                        if "body_type" in branch_data:
+                            detection_dict["bodyType"] = branch_data["body_type"]
+                        if "class" in branch_data:
+                            class_name = branch_data["class"]
+                            
+                            # Map based on branch/model type
+                            if "brand" in branch_id.lower():
+                                detection_dict["carBrand"] = class_name
+                            elif "bodytype" in branch_id.lower() or "body" in branch_id.lower():
+                                detection_dict["bodyType"] = class_name
+                
+                logger.info(f"Detection payload after branch processing: {detection_dict}")
+            else:
+                logger.debug("No branch results found in detection result")
             
             detection_data = {
                 "type": "imageDetection",
@@ -322,12 +332,14 @@ async def detect(websocket: WebSocket):
                 }
             }
             
-            # Add session ID if available
+            # Add session ID if available (generated by pipeline when Car+Frontal detected)
             if session_id is not None:
                 detection_data["sessionId"] = session_id
+                logger.debug(f"Added session_id to WebSocket response: {session_id}")
             
-            if highest_confidence_detection["class"] != "none":
-                logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {highest_confidence_detection['confidence']:.2f} using model {stream['modelName']}")
+            if highest_confidence_detection.get("class") != "none":
+                confidence = highest_confidence_detection.get("confidence", 0.0)
+                logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {confidence:.2f} using model {stream['modelName']}")
                 
                 # Log session ID if available
                 if session_id:
@@ -335,6 +347,7 @@ async def detect(websocket: WebSocket):
             
             await websocket.send_json(detection_data)
             logger.debug(f"Sent detection data to client for camera {camera_id}")
+            logger.debug(f"Sent this detection data: {detection_data}")
             return persistent_data
         except Exception as e:
             logger.error(f"Error in handle_detection for camera {camera_id}: {str(e)}", exc_info=True)
@@ -500,6 +513,199 @@ async def detect(websocket: WebSocket):
         finally:
             logger.info(f"Snapshot reader thread for camera {camera_id} is exiting")
 
+    async def reconcile_subscriptions(desired_subscriptions, websocket):
+        """
+        Declarative reconciliation: Compare desired vs current subscriptions and make changes
+        """
+        logger.info(f"Reconciling subscriptions: {len(desired_subscriptions)} desired")
+        
+        with streams_lock:
+            # Get current subscriptions
+            current_subscription_ids = set(streams.keys())
+            desired_subscription_ids = set(sub["subscriptionIdentifier"] for sub in desired_subscriptions)
+            
+            # Find what to add and remove
+            to_add = desired_subscription_ids - current_subscription_ids
+            to_remove = current_subscription_ids - desired_subscription_ids
+            to_check_for_changes = current_subscription_ids & desired_subscription_ids
+            
+            logger.info(f"Reconciliation: {len(to_add)} to add, {len(to_remove)} to remove, {len(to_check_for_changes)} to check for changes")
+            
+            # Remove subscriptions that are no longer wanted
+            for subscription_id in to_remove:
+                await unsubscribe_internal(subscription_id)
+                
+            # Check existing subscriptions for parameter changes
+            for subscription_id in to_check_for_changes:
+                desired_sub = next(sub for sub in desired_subscriptions if sub["subscriptionIdentifier"] == subscription_id)
+                current_stream = streams[subscription_id]
+                
+                # Check if parameters changed
+                if has_subscription_changed(desired_sub, current_stream):
+                    logger.info(f"Parameters changed for {subscription_id}, resubscribing")
+                    await unsubscribe_internal(subscription_id)
+                    await subscribe_internal(desired_sub, websocket)
+                    
+            # Add new subscriptions
+            for subscription_id in to_add:
+                desired_sub = next(sub for sub in desired_subscriptions if sub["subscriptionIdentifier"] == subscription_id)
+                await subscribe_internal(desired_sub, websocket)
+
+    def has_subscription_changed(desired_sub, current_stream):
+        """Check if subscription parameters have changed"""
+        return (
+            desired_sub.get("rtspUrl") != current_stream.get("rtsp_url") or
+            desired_sub.get("snapshotUrl") != current_stream.get("snapshot_url") or  
+            desired_sub.get("snapshotInterval") != current_stream.get("snapshot_interval") or
+            desired_sub.get("cropX1") != current_stream.get("cropX1") or
+            desired_sub.get("cropY1") != current_stream.get("cropY1") or
+            desired_sub.get("cropX2") != current_stream.get("cropX2") or
+            desired_sub.get("cropY2") != current_stream.get("cropY2") or
+            desired_sub.get("modelId") != current_stream.get("modelId") or
+            desired_sub.get("modelName") != current_stream.get("modelName")
+        )
+
+    async def subscribe_internal(subscription, websocket):
+        """Internal subscription logic extracted from original subscribe handler"""
+        subscriptionIdentifier = subscription.get("subscriptionIdentifier")
+        rtsp_url = subscription.get("rtspUrl") 
+        snapshot_url = subscription.get("snapshotUrl")
+        snapshot_interval = subscription.get("snapshotInterval")
+        model_url = subscription.get("modelUrl")
+        modelId = subscription.get("modelId")
+        modelName = subscription.get("modelName")
+        cropX1 = subscription.get("cropX1")
+        cropY1 = subscription.get("cropY1")
+        cropX2 = subscription.get("cropX2")
+        cropY2 = subscription.get("cropY2")
+
+        # Extract camera_id from subscriptionIdentifier
+        parts = subscriptionIdentifier.split(';')
+        if len(parts) != 2:
+            logger.error(f"Invalid subscriptionIdentifier format: {subscriptionIdentifier}")
+            return
+        
+        display_identifier, camera_identifier = parts
+        camera_id = subscriptionIdentifier
+
+        # Load model if needed
+        if model_url:
+            with models_lock:
+                if (camera_id not in models) or (modelId not in models[camera_id]):
+                    logger.info(f"Loading model from {model_url} for camera {camera_id}, modelId {modelId}")
+                    extraction_dir = os.path.join("models", camera_identifier, str(modelId))
+                    os.makedirs(extraction_dir, exist_ok=True)
+                    
+                    # Handle model loading (same as original)
+                    parsed = urlparse(model_url)
+                    if parsed.scheme in ("http", "https"):
+                        filename = os.path.basename(parsed.path) or f"model_{modelId}.mpta"
+                        local_mpta = os.path.join(extraction_dir, filename)
+                        local_path = download_mpta(model_url, local_mpta)
+                        if not local_path:
+                            logger.error(f"Failed to download model from {model_url}")
+                            return
+                        model_tree = load_pipeline_from_zip(local_path, extraction_dir)
+                    else:
+                        if not os.path.exists(model_url):
+                            logger.error(f"Model file not found: {model_url}")
+                            return
+                        model_tree = load_pipeline_from_zip(model_url, extraction_dir)
+                    
+                    if model_tree is None:
+                        logger.error(f"Failed to load model {modelId}")
+                        return
+                        
+                    if camera_id not in models:
+                        models[camera_id] = {}
+                    models[camera_id][modelId] = model_tree
+
+        # Create stream (same logic as original)
+        if camera_id and (rtsp_url or snapshot_url) and len(streams) < max_streams:
+            camera_url = snapshot_url if snapshot_url else rtsp_url
+            
+            # Check if we already have a stream for this camera URL
+            shared_stream = camera_streams.get(camera_url)
+            
+            if shared_stream:
+                # Reuse existing stream
+                buffer = shared_stream["buffer"]
+                stop_event = shared_stream["stop_event"] 
+                thread = shared_stream["thread"]
+                mode = shared_stream["mode"]
+                shared_stream["ref_count"] = shared_stream.get("ref_count", 0) + 1
+            else:
+                # Create new stream
+                buffer = queue.Queue(maxsize=1)
+                stop_event = threading.Event()
+                
+                if snapshot_url and snapshot_interval:
+                    thread = threading.Thread(target=snapshot_reader, args=(camera_id, snapshot_url, snapshot_interval, buffer, stop_event))
+                    thread.daemon = True
+                    thread.start()
+                    mode = "snapshot"
+                    shared_stream = {
+                        "buffer": buffer, "thread": thread, "stop_event": stop_event,
+                        "mode": mode, "url": snapshot_url, "snapshot_interval": snapshot_interval, "ref_count": 1
+                    }
+                    camera_streams[camera_url] = shared_stream
+                elif rtsp_url:
+                    cap = cv2.VideoCapture(rtsp_url)
+                    if not cap.isOpened():
+                        logger.error(f"Failed to open RTSP stream for camera {camera_id}")
+                        return
+                    thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
+                    thread.daemon = True
+                    thread.start()
+                    mode = "rtsp"
+                    shared_stream = {
+                        "buffer": buffer, "thread": thread, "stop_event": stop_event,
+                        "mode": mode, "url": rtsp_url, "cap": cap, "ref_count": 1
+                    }
+                    camera_streams[camera_url] = shared_stream
+                else:
+                    logger.error(f"No valid URL provided for camera {camera_id}")
+                    return
+
+            # Create stream info
+            stream_info = {
+                "buffer": buffer, "thread": thread, "stop_event": stop_event,
+                "modelId": modelId, "modelName": modelName, "subscriptionIdentifier": subscriptionIdentifier,
+                "cropX1": cropX1, "cropY1": cropY1, "cropX2": cropX2, "cropY2": cropY2,
+                "mode": mode, "camera_url": camera_url, "modelUrl": model_url
+            }
+            
+            if mode == "snapshot":
+                stream_info["snapshot_url"] = snapshot_url
+                stream_info["snapshot_interval"] = snapshot_interval
+            elif mode == "rtsp":
+                stream_info["rtsp_url"] = rtsp_url
+                stream_info["cap"] = shared_stream["cap"]
+            
+            streams[camera_id] = stream_info
+            subscription_to_camera[camera_id] = camera_url
+            logger.info(f"Subscribed to camera {camera_id}")
+
+    async def unsubscribe_internal(subscription_id):
+        """Internal unsubscription logic"""
+        if subscription_id in streams:
+            stream = streams.pop(subscription_id)
+            camera_url = subscription_to_camera.pop(subscription_id, None)
+            
+            if camera_url and camera_url in camera_streams:
+                shared_stream = camera_streams[camera_url]
+                shared_stream["ref_count"] -= 1
+                
+                if shared_stream["ref_count"] <= 0:
+                    shared_stream["stop_event"].set()
+                    shared_stream["thread"].join()
+                    if "cap" in shared_stream:
+                        shared_stream["cap"].release()
+                    del camera_streams[camera_url]
+            
+            latest_frames.pop(subscription_id, None)
+            logger.info(f"Unsubscribed from camera {subscription_id}")
+
     async def process_streams():
         logger.info("Started processing streams")
         try:
@@ -567,6 +773,10 @@ async def detect(websocket: WebSocket):
                         "modelId": stream["modelId"],
                         "modelName": stream["modelName"],
                         "online": True,
+                        # Include all subscription parameters for proper change detection
+                        "rtspUrl": stream.get("rtsp_url"),
+                        "snapshotUrl": stream.get("snapshot_url"),
+                        "snapshotInterval": stream.get("snapshot_interval"),
                         **{k: v for k, v in get_crop_coords(stream).items() if v is not None}
                     }
                     for camera_id, stream in streams.items()
@@ -595,29 +805,44 @@ async def detect(websocket: WebSocket):
                 data = json.loads(msg)
                 msg_type = data.get("type")
 
-                if msg_type == "subscribe":
+                if msg_type == "setSubscriptionList":
+                    # Declarative approach: Backend sends list of subscriptions this worker should have
+                    desired_subscriptions = data.get("subscriptions", [])
+                    logger.info(f"Received subscription list with {len(desired_subscriptions)} subscriptions")
+                    
+                    await reconcile_subscriptions(desired_subscriptions, websocket)
+                    
+                elif msg_type == "subscribe":
+                    # Legacy support - convert single subscription to list
+                    payload = data.get("payload", {})
+                    await reconcile_subscriptions([payload], websocket)
+                    
+                elif msg_type == "unsubscribe":
+                    # Legacy support - remove subscription
                     payload = data.get("payload", {})
                     subscriptionIdentifier = payload.get("subscriptionIdentifier")
-                    rtsp_url = payload.get("rtspUrl")
-                    snapshot_url = payload.get("snapshotUrl")
-                    snapshot_interval = payload.get("snapshotInterval")
-                    model_url = payload.get("modelUrl")
-                    modelId = payload.get("modelId")
-                    modelName = payload.get("modelName")
-                    cropX1 = payload.get("cropX1")
-                    cropY1 = payload.get("cropY1")
-                    cropX2 = payload.get("cropX2")
-                    cropY2 = payload.get("cropY2")
-
-                    # Extract camera_id from subscriptionIdentifier (format: displayIdentifier;cameraIdentifier)
-                    parts = subscriptionIdentifier.split(';')
-                    if len(parts) != 2:
-                        logger.error(f"Invalid subscriptionIdentifier format: {subscriptionIdentifier}")
-                        continue
-                    
-                    display_identifier, camera_identifier = parts
-                    camera_id = subscriptionIdentifier  # Use full subscriptionIdentifier as camera_id for mapping
+                    # Remove from current subscriptions and reconcile
+                    current_subs = []
+                    with streams_lock:
+                        for camera_id, stream in streams.items():
+                            if stream["subscriptionIdentifier"] != subscriptionIdentifier:
+                                # Convert stream back to subscription format
+                                current_subs.append({
+                                    "subscriptionIdentifier": stream["subscriptionIdentifier"],
+                                    "rtspUrl": stream.get("rtsp_url"),
+                                    "snapshotUrl": stream.get("snapshot_url"), 
+                                    "snapshotInterval": stream.get("snapshot_interval"),
+                                    "modelId": stream["modelId"],
+                                    "modelName": stream["modelName"],
+                                    "modelUrl": stream.get("modelUrl", ""),
+                                    "cropX1": stream.get("cropX1"),
+                                    "cropY1": stream.get("cropY1"),
+                                    "cropX2": stream.get("cropX2"),
+                                    "cropY2": stream.get("cropY2")
+                                })
+                    await reconcile_subscriptions(current_subs, websocket)
                     
+                elif msg_type == "old_subscribe_logic_removed":
                     if model_url:
                         with models_lock:
                             if (camera_id not in models) or (modelId not in models[camera_id]):
@@ -813,6 +1038,10 @@ async def detect(websocket: WebSocket):
                             "modelId": stream["modelId"],
                             "modelName": stream["modelName"],
                             "online": True,
+                            # Include all subscription parameters for proper change detection
+                            "rtspUrl": stream.get("rtsp_url"),
+                            "snapshotUrl": stream.get("snapshot_url"),
+                            "snapshotInterval": stream.get("snapshot_interval"),
                             **{k: v for k, v in get_crop_coords(stream).items() if v is not None}
                         }
                         for camera_id, stream in streams.items()

siwatsystem/pympta.py

@@ -514,6 +514,65 @@ def resolve_field_mapping(value_template, branch_results, action_context):
         logger.error(f"Error resolving field mapping '{value_template}': {e}")
         return None
 
+def validate_pipeline_execution(node, regions_dict):
+    """
+    Pre-validate that all required branches will execute successfully before 
+    committing to Redis actions and database records.
+    
+    Returns:
+        - (True, []) if pipeline can execute completely
+        - (False, missing_branches) if some required branches won't execute
+    """
+    # Get all branches that parallel actions are waiting for
+    required_branches = set()
+    
+    for action in node.get("parallelActions", []):
+        if action.get("type") == "postgresql_update_combined":
+            wait_for_branches = action.get("waitForBranches", [])
+            required_branches.update(wait_for_branches)
+    
+    if not required_branches:
+        # No parallel actions requiring specific branches
+        logger.debug("No parallel actions with waitForBranches - validation passes")
+        return True, []
+    
+    logger.debug(f"Pre-validation: checking if required branches {list(required_branches)} will execute")
+    
+    # Check each required branch
+    missing_branches = []
+    
+    for branch in node.get("branches", []):
+        branch_id = branch["modelId"]
+        
+        if branch_id not in required_branches:
+            continue  # This branch is not required by parallel actions
+            
+        # Check if this branch would be triggered
+        trigger_classes = branch.get("triggerClasses", [])
+        min_conf = branch.get("minConfidence", 0)
+        
+        branch_triggered = False
+        for det_class in regions_dict:
+            det_confidence = regions_dict[det_class]["confidence"]
+            
+            if (det_class in trigger_classes and det_confidence >= min_conf):
+                branch_triggered = True
+                logger.debug(f"Pre-validation: branch {branch_id} WILL be triggered by {det_class} (conf={det_confidence:.3f} >= {min_conf})")
+                break
+        
+        if not branch_triggered:
+            missing_branches.append(branch_id)
+            logger.warning(f"Pre-validation: branch {branch_id} will NOT be triggered - no matching classes or insufficient confidence")
+            logger.debug(f"  Required: {trigger_classes} with min_conf={min_conf}")
+            logger.debug(f"  Available: {[(cls, regions_dict[cls]['confidence']) for cls in regions_dict]}")
+    
+    if missing_branches:
+        logger.error(f"Pipeline pre-validation FAILED: required branches {missing_branches} will not execute")
+        return False, missing_branches
+    else:
+        logger.info(f"Pipeline pre-validation PASSED: all required branches {list(required_branches)} will execute")
+        return True, []
+
 def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
     """
     Enhanced pipeline that supports:
@@ -646,6 +705,14 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
         else:
             logger.debug("No multi-class validation - proceeding with all detections")
 
+        # ─── Pre-validate pipeline execution ────────────────────────
+        pipeline_valid, missing_branches = validate_pipeline_execution(node, regions_dict)
+        
+        if not pipeline_valid:
+            logger.error(f"Pipeline execution validation FAILED - required branches {missing_branches} cannot execute")
+            logger.error("Aborting pipeline: no Redis actions or database records will be created")
+            return (None, None) if return_bbox else None
+
         # ─── Execute actions with region information ────────────────
         detection_result = {
             "detections": all_detections,
@@ -786,9 +853,11 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
         primary_detection = max(all_detections, key=lambda x: x["confidence"])
         primary_bbox = primary_detection["bbox"]
         
-        # Add branch results to primary detection for compatibility
+        # Add branch results and session_id to primary detection for compatibility
         if "branch_results" in detection_result:
             primary_detection["branch_results"] = detection_result["branch_results"]
+        if "session_id" in detection_result:
+            primary_detection["session_id"] = detection_result["session_id"]
         
         return (primary_detection, primary_bbox) if return_bbox else primary_detection
 

worker.md

@@ -2,12 +2,6 @@
 
 This document outlines the WebSocket-based communication protocol between the CMS backend and a detector worker. As a worker developer, your primary responsibility is to implement a WebSocket server that adheres to this protocol.
 
-The current Python Detector Worker implementation supports advanced computer vision pipelines with:
-- Multi-class YOLO detection with parallel processing
-- PostgreSQL database integration with automatic schema management
-- Redis integration for image storage and pub/sub messaging
-- Hierarchical pipeline execution with detection → classification branching
-
 ## 1. Connection
 
 The worker must run a WebSocket server, preferably on port `8000`. The backend system, which is managed by a container orchestration service, will automatically discover and establish a WebSocket connection to your worker.
@@ -31,34 +25,14 @@ To enable modularity and dynamic configuration, the backend will send you a URL
 2. Extracting its contents.
 3. Interpreting the contents to configure its internal pipeline.
 
-**The current implementation supports comprehensive pipeline configurations including:**
+**The contents of the `.mpta` file are entirely up to the user who configures the model in the CMS.** This allows for maximum flexibility. For example, the archive could contain:
 
-- **AI/ML Models**: YOLO models (.pt files) for detection and classification
-- **Pipeline Configuration**: `pipeline.json` defining hierarchical detection→classification workflows
-- **Multi-class Detection**: Simultaneous detection of multiple object classes (e.g., Car + Frontal)
-- **Parallel Processing**: Concurrent execution of classification branches with ThreadPoolExecutor
-- **Database Integration**: PostgreSQL configuration for automatic table creation and updates
-- **Redis Actions**: Image storage with region cropping and pub/sub messaging
-- **Dynamic Field Mapping**: Template-based field resolution for database operations
+- AI/ML Models: Pre-trained models for libraries like TensorFlow, PyTorch, or ONNX.
+- Configuration Files: A `config.json` or `pipeline.yaml` that defines a sequence of operations, specifies model paths, or sets detection thresholds.
+- Scripts: Custom Python scripts for pre-processing or post-processing.
+- API Integration Details: A JSON file with endpoint information and credentials for interacting with third-party detection services.
 
-**Enhanced MPTA Structure:**
-```
-pipeline.mpta/
-├── pipeline.json          # Main configuration with redis/postgresql settings
-├── car_detection.pt       # Primary YOLO detection model
-├── brand_classifier.pt    # Classification model for car brands
-├── bodytype_classifier.pt # Classification model for body types
-└── ...
-```
-
-The `pipeline.json` now supports advanced features like:
-- Multi-class detection with `expectedClasses` validation
-- Parallel branch processing with `parallel: true`
-- Database actions with `postgresql_update_combined`
-- Redis actions with region-specific image cropping
-- Branch synchronization with `waitForBranches`
-
-Essentially, the `.mpta` file is a self-contained package that tells your worker *how* to process the video stream for a given subscription, including complex multi-stage AI pipelines with database persistence.
+Essentially, the `.mpta` file is a self-contained package that tells your worker _how_ to process the video stream for a given subscription.
 
 ## 4. Messages from Worker to Backend
 
@@ -105,15 +79,6 @@ Sent when the worker detects a relevant object. The `detection` object should be
 
 - **Type:** `imageDetection`
 
-**Enhanced Detection Capabilities:**
-
-The current implementation supports multi-class detection with parallel classification processing. When a vehicle is detected, the system:
-
-1. **Multi-Class Detection**: Simultaneously detects "Car" and "Frontal" classes
-2. **Parallel Processing**: Runs brand and body type classification concurrently
-3. **Database Integration**: Automatically creates and updates PostgreSQL records
-4. **Redis Storage**: Saves cropped frontal images with expiration
-
 **Payload Example:**
 
 ```json
@@ -123,38 +88,19 @@ The current implementation supports multi-class detection with parallel classifi
   "timestamp": "2025-07-14T12:34:56.789Z",
   "data": {
     "detection": {
-      "class": "Car",
-      "confidence": 0.92,
-      "carBrand": "Honda",
       "carModel": "Civic",
+      "carBrand": "Honda",
+      "carYear": 2023,
       "bodyType": "Sedan",
-      "branch_results": {
-        "car_brand_cls_v1": {
-          "class": "Honda",
-          "confidence": 0.89,
-          "brand": "Honda"
-        },
-        "car_bodytype_cls_v1": {
-          "class": "Sedan", 
-          "confidence": 0.85,
-          "body_type": "Sedan"
-        }
-      }
+      "licensePlateText": "ABCD1234",
+      "licensePlateConfidence": 0.95
     },
     "modelId": 101,
-    "modelName": "Car Frontal Detection V1"
+    "modelName": "US-LPR-and-Vehicle-ID"
   }
 }
 ```
 
-**Database Integration:**
-
-Each detection automatically:
-- Creates a record in `gas_station_1.car_frontal_info` table
-- Generates a unique `session_id` for tracking
-- Updates the record with classification results after parallel processing completes
-- Stores cropped frontal images in Redis with the session_id as key
-
 ### 4.3. Patch Session
 
 > **Note:** Patch messages are only used when the worker can't keep up and needs to retroactively send detections. Normally, detections should be sent in real-time using `imageDetection` messages. Use `patchSession` only to update session data after the fact.
@@ -171,9 +117,9 @@ Allows the worker to request a modification to an active session's data. The `da
   "sessionId": 12345,
   "data": {
     "currentCar": {
-        "carModel": "Civic",
-        "carBrand": "Honda",
-        "licensePlateText": "ABCD1234"
+      "carModel": "Civic",
+      "carBrand": "Honda",
+      "licensePlateText": "ABCD1234"
     }
   }
 }
@@ -187,24 +133,33 @@ The `data` object in the `patchSession` message is merged with the existing `Dis
 
 ```typescript
 interface DisplayPersistentData {
-    progressionStage: "welcome" | "car_fueling" | "car_waitpayment" | "car_postpayment" | null;
-    qrCode: string | null;
-    adsPlayback: {
-        playlistSlotOrder: number; // The 'order' of the current slot
-        adsId: number | null;
-        adsUrl: string | null;
-    } | null;
-    currentCar: {
-        carModel?: string;
-        carBrand?: string;
-        carYear?: number;
-        bodyType?: string;
-        licensePlateText?: string;
-        licensePlateType?: string;
-    } | null;
-    fuelPump: { /* FuelPumpData structure */ } | null;
-    weatherData: { /* WeatherResponse structure */ } | null;
-    sessionId: number | null;
+  progressionStage:
+    | 'welcome'
+    | 'car_fueling'
+    | 'car_waitpayment'
+    | 'car_postpayment'
+    | null;
+  qrCode: string | null;
+  adsPlayback: {
+    playlistSlotOrder: number; // The 'order' of the current slot
+    adsId: number | null;
+    adsUrl: string | null;
+  } | null;
+  currentCar: {
+    carModel?: string;
+    carBrand?: string;
+    carYear?: number;
+    bodyType?: string;
+    licensePlateText?: string;
+    licensePlateType?: string;
+  } | null;
+  fuelPump: {
+    /* FuelPumpData structure */
+  } | null;
+  weatherData: {
+    /* WeatherResponse structure */
+  } | null;
+  sessionId: number | null;
 }
 ```
 
@@ -257,7 +212,7 @@ Instructs the worker to process a camera's RTSP stream using the configuration f
 > - Capture each snapshot only once per cycle, and reuse it for all display subscriptions sharing that camera.
 > - Capture each frame/image only once per cycle.
 > - Reuse the same captured image and snapshot for all display subscriptions that share the camera, processing and routing detection results separately for each display as needed.
-> This avoids unnecessary load and bandwidth usage, and ensures consistent detection results and snapshots across all displays sharing the same camera.
+>   This avoids unnecessary load and bandwidth usage, and ensures consistent detection results and snapshots across all displays sharing the same camera.
 
 ### 5.2. Unsubscribe from Camera
 
@@ -369,7 +324,7 @@ This section shows a typical sequence of messages between the backend and the wo
 > **Note:** Unsubscribe is triggered when a user removes a camera or when the node is too heavily loaded and needs rebalancing.
 
 1.  **Connection Established** & **Heartbeat**
-    *   **Worker -> Backend**
+    - **Worker -> Backend**
     ```json
     {
       "type": "stateReport",
@@ -381,7 +336,7 @@ This section shows a typical sequence of messages between the backend and the wo
     }
     ```
 2.  **Backend Subscribes Camera**
-    *   **Backend -> Worker**
+    - **Backend -> Worker**
     ```json
     {
       "type": "subscribe",
@@ -395,7 +350,7 @@ This section shows a typical sequence of messages between the backend and the wo
     }
     ```
 3.  **Worker Acknowledges in Heartbeat**
-    *   **Worker -> Backend**
+    - **Worker -> Backend**
     ```json
     {
       "type": "stateReport",
@@ -414,7 +369,7 @@ This section shows a typical sequence of messages between the backend and the wo
     }
     ```
 4.  **Worker Detects a Car**
-    *   **Worker -> Backend**
+    - **Worker -> Backend**
     ```json
     {
       "type": "imageDetection",
@@ -433,7 +388,7 @@ This section shows a typical sequence of messages between the backend and the wo
       }
     }
     ```
-    *   **Worker -> Backend**
+    - **Worker -> Backend**
     ```json
     {
       "type": "imageDetection",
@@ -452,7 +407,7 @@ This section shows a typical sequence of messages between the backend and the wo
       }
     }
     ```
-    *   **Worker -> Backend**
+    - **Worker -> Backend**
     ```json
     {
       "type": "imageDetection",
@@ -472,7 +427,7 @@ This section shows a typical sequence of messages between the backend and the wo
     }
     ```
 5.  **Backend Unsubscribes Camera**
-    *   **Backend -> Worker**
+    - **Backend -> Worker**
     ```json
     {
       "type": "unsubscribe",
@@ -482,7 +437,7 @@ This section shows a typical sequence of messages between the backend and the wo
     }
     ```
 6.  **Worker Acknowledges Unsubscription**
-    *   **Worker -> Backend**
+    - **Worker -> Backend**
     ```json
     {
       "type": "stateReport",
@@ -493,6 +448,7 @@ This section shows a typical sequence of messages between the backend and the wo
       "cameraConnections": []
     }
     ```
+
 ## 7. HTTP API: Image Retrieval
 
 In addition to the WebSocket protocol, the worker exposes an HTTP endpoint for retrieving the latest image frame from a camera.
@@ -508,11 +464,13 @@ GET /camera/{camera_id}/image
 ### Response
 
 - **Success (200):** Returns the latest JPEG image from the camera stream.
-    - `Content-Type: image/jpeg`
-    - Binary JPEG data.
+
+  - `Content-Type: image/jpeg`
+  - Binary JPEG data.
 
 - **Error (404):** If the camera is not found or no frame is available.
-    - JSON error response.
+
+  - JSON error response.
 
 - **Error (500):** Internal server error.
 
@@ -525,9 +483,9 @@ GET /camera/display-001;cam-001/image
 ### Example Response
 
 - **Headers:**
-    ```
-    Content-Type: image/jpeg
-    ```
+  ```
+  Content-Type: image/jpeg
+  ```
 - **Body:** Binary JPEG image.
 
 ### Notes