resolve merge conflicts by accepting main branch versions

.gitea/workflows/build.yml

@@ -1,13 +1,68 @@
-name: Build Backend Application and Docker Image
+name: Build Worker Base and Application Images
 
 on:
   push:
     branches:
       - main
+      - dev
   workflow_dispatch:
+    inputs:
+      force_base_build:
+        description: 'Force base image build regardless of changes'
+        required: false
+        default: 'false'
+        type: boolean
 
 jobs:
+  check-base-changes:
+    runs-on: ubuntu-latest
+    outputs:
+      base-changed: ${{ steps.changes.outputs.base-changed }}
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+      - name: Check for base changes
+        id: changes
+        run: |
+          if git diff HEAD^ HEAD --name-only | grep -E "(Dockerfile\.base|requirements\.base\.txt)" > /dev/null; then
+            echo "base-changed=true" >> $GITHUB_OUTPUT
+          else
+            echo "base-changed=false" >> $GITHUB_OUTPUT
+          fi
+
+  build-base:
+    needs: check-base-changes
+    if: needs.check-base-changes.outputs.base-changed == 'true' || (github.event_name == 'workflow_dispatch' && github.event.inputs.force_base_build == 'true')
+    runs-on: ubuntu-latest
+    permissions:
+      packages: write
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: git.siwatsystem.com
+          username: ${{ github.actor }}
+          password: ${{ secrets.RUNNER_TOKEN }}
+        
+      - name: Build and push base Docker image
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          file: ./Dockerfile.base
+          push: true
+          tags: git.siwatsystem.com/adsist-cms/worker-base:latest
+
   build-docker:
+    needs: [check-base-changes, build-base]
+    if: always() && (needs.build-base.result == 'success' || needs.build-base.result == 'skipped')
     runs-on: ubuntu-latest
     permissions:
       packages: write
@@ -31,4 +86,27 @@ jobs:
           context: .
           file: ./Dockerfile
           push: true
-          tags: git.siwatsystem.com/adsist-cms/worker:latest
+          tags: git.siwatsystem.com/adsist-cms/worker:${{ github.ref_name == 'main' && 'latest' || 'dev' }}
+
+  deploy-stack:
+    needs: build-docker
+    runs-on: adsist
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+      - name: Set up SSH connection
+        run: |
+          mkdir -p ~/.ssh
+          echo "${{ secrets.DEPLOY_KEY_CMS }}" > ~/.ssh/id_rsa
+          chmod 600 ~/.ssh/id_rsa
+          ssh-keyscan -H ${{ vars.DEPLOY_HOST_CMS }} >> ~/.ssh/known_hosts
+      - name: Deploy stack
+        run: |
+          echo "Pulling and starting containers on server..."
+          if [ "${{ github.ref_name }}" = "main" ]; then
+            echo "Deploying production stack..."
+            ssh -i ~/.ssh/id_rsa ${{ vars.DEPLOY_USER_CMS }}@${{ vars.DEPLOY_HOST_CMS }} "cd ~/cms-system-k8s && docker compose -f docker-compose.production.yml pull && docker compose -f docker-compose.production.yml up -d"
+          else
+            echo "Deploying staging stack..."
+            ssh -i ~/.ssh/id_rsa ${{ vars.DEPLOY_USER_CMS }}@${{ vars.DEPLOY_HOST_CMS }} "cd ~/cms-system-k8s && docker compose -f docker-compose.staging.yml pull && docker compose -f docker-compose.staging.yml up -d"
+          fi

CLAUDE.md

@@ -1,13 +1,23 @@
 # Python Detector Worker - CLAUDE.md
 
 ## Project Overview
-This is a FastAPI-based computer vision detection worker that processes video streams from RTSP/HTTP sources and runs YOLO-based machine learning pipelines for object detection and classification. The system is designed to work within a larger CMS (Content Management System) architecture.
+This is a FastAPI-based computer vision detection worker that processes video streams from RTSP/HTTP sources and runs advanced YOLO-based machine learning pipelines for multi-class object detection and parallel classification. The system features comprehensive database integration, Redis support, and hierarchical pipeline execution designed to work within a larger CMS (Content Management System) architecture.
+
+### Key Features
+- **Multi-Class Detection**: Simultaneous detection of multiple object classes (e.g., Car + Frontal)
+- **Parallel Processing**: Concurrent execution of classification branches using ThreadPoolExecutor
+- **Database Integration**: Automatic PostgreSQL schema management and record updates
+- **Redis Actions**: Image storage with region cropping and pub/sub messaging
+- **Pipeline Synchronization**: Branch coordination with `waitForBranches` functionality
+- **Dynamic Field Mapping**: Template-based field resolution for database operations
 
 ## Architecture & Technology Stack
 - **Framework**: FastAPI with WebSocket support
 - **ML/CV**: PyTorch, Ultralytics YOLO, OpenCV
 - **Containerization**: Docker (Python 3.13-bookworm base)
-- **Data Storage**: Redis integration for action handling
+- **Data Storage**: Redis integration for action handling + PostgreSQL for persistent storage
+- **Database**: Automatic schema management with gas_station_1 database
+- **Parallel Processing**: ThreadPoolExecutor for concurrent classification
 - **Communication**: WebSocket-based real-time protocol
 
 ## Core Components
@@ -24,9 +34,20 @@ This is a FastAPI-based computer vision detection worker that processes video st
 ### Pipeline System (`siwatsystem/pympta.py`)
 - **MPTA file handling** - ZIP archives containing model configurations
 - **Hierarchical pipeline execution** with detection → classification branching
-- **Redis action system** for image saving and message publishing
+- **Multi-class detection** - Simultaneous detection of multiple classes (Car + Frontal)
+- **Parallel processing** - Concurrent classification branches with ThreadPoolExecutor
+- **Redis action system** - Image saving with region cropping and message publishing
+- **PostgreSQL integration** - Automatic table creation and combined updates
 - **Dynamic model loading** with GPU optimization
 - **Configurable trigger classes and confidence thresholds**
+- **Branch synchronization** - waitForBranches coordination for database updates
+
+### Database System (`siwatsystem/database.py`)
+- **DatabaseManager class** for PostgreSQL operations
+- **Automatic table creation** with gas_station_1.car_frontal_info schema
+- **Combined update operations** with field mapping from branch results
+- **Session management** with UUID generation
+- **Error handling** and connection management
 
 ### Testing & Debugging
 - **Protocol test script** (`test_protocol.py`) for WebSocket communication validation
@@ -92,33 +113,61 @@ This is a FastAPI-based computer vision detection worker that processes video st
 
 ## Model Pipeline (MPTA) Format
 
-### Structure
+### Enhanced Structure
 - **ZIP archive** containing models and configuration
-- **pipeline.json** - Main configuration file
+- **pipeline.json** - Main configuration file with Redis + PostgreSQL settings
 - **Model files** - YOLO .pt files for detection/classification
-- **Redis configuration** - Optional for action execution
+- **Multi-model support** - Detection + multiple classification models
 
-### Pipeline Flow
+### Advanced Pipeline Flow
-1. **Detection stage** - YOLO object detection with bounding boxes
+1. **Multi-class detection stage** - YOLO detection of Car + Frontal simultaneously
-2. **Trigger evaluation** - Check if detected class matches trigger conditions
+2. **Validation stage** - Check for expected classes (flexible matching)
-3. **Classification stage** - Crop detected region and run classification model
+3. **Database initialization** - Create initial record with session_id
-4. **Action execution** - Redis operations (image saving, message publishing)
+4. **Redis actions** - Save cropped frontal images with expiration
+5. **Parallel classification** - Concurrent brand and body type classification
+6. **Branch synchronization** - Wait for all classification branches to complete
+7. **Database update** - Combined update with all classification results
 
-### Branch Configuration
+### Enhanced Branch Configuration
 ```json
 {
-  "modelId": "detector-v1",
+  "modelId": "car_frontal_detection_v1",
-  "modelFile": "detector.pt",
+  "modelFile": "car_frontal_detection_v1.pt",
-  "triggerClasses": ["car", "truck"],
+  "multiClass": true,
-  "minConfidence": 0.5,
+  "expectedClasses": ["Car", "Frontal"],
-  "branches": [{
+  "triggerClasses": ["Car", "Frontal"],
-    "modelId": "classifier-v1", 
+  "minConfidence": 0.8,
-    "modelFile": "classifier.pt",
+  "actions": [
+    {
+      "type": "redis_save_image",
+      "region": "Frontal",
+      "key": "inference:{display_id}:{timestamp}:{session_id}:{filename}",
+      "expire_seconds": 600
+    }
+  ],
+  "branches": [
+    {
+      "modelId": "car_brand_cls_v1",
+      "modelFile": "car_brand_cls_v1.pt",
+      "parallel": true,
       "crop": true,
-    "triggerClasses": ["car"],
+      "cropClass": "Frontal",
-    "minConfidence": 0.3,
+      "triggerClasses": ["Frontal"],
-    "actions": [...]
+      "minConfidence": 0.85
-  }]
+    }
+  ],
+  "parallelActions": [
+    {
+      "type": "postgresql_update_combined",
+      "table": "car_frontal_info",
+      "key_field": "session_id",
+      "waitForBranches": ["car_brand_cls_v1", "car_bodytype_cls_v1"],
+      "fields": {
+        "car_brand": "{car_brand_cls_v1.brand}",
+        "car_body_type": "{car_bodytype_cls_v1.body_type}"
+      }
+    }
+  ]
 }
 ```
 
@@ -173,6 +222,9 @@ docker run -p 8000:8000 -v ./models:/app/models detector-worker
 - **opencv-python**: Computer vision operations
 - **websockets**: WebSocket client/server
 - **redis**: Redis client for action execution
+- **psycopg2-binary**: PostgreSQL database adapter
+- **scipy**: Scientific computing for advanced algorithms
+- **filterpy**: Kalman filtering and state estimation
 
 ## Security Considerations
 - Model files are loaded from trusted sources only
@@ -180,9 +232,46 @@ docker run -p 8000:8000 -v ./models:/app/models detector-worker
 - WebSocket connections handle disconnects gracefully
 - Resource usage is monitored to prevent DoS
 
+## Database Integration
+
+### Schema Management
+The system automatically creates and manages PostgreSQL tables:
+
+```sql
+CREATE TABLE IF NOT EXISTS gas_station_1.car_frontal_info (
+    display_id VARCHAR(255),
+    captured_timestamp VARCHAR(255),
+    session_id VARCHAR(255) PRIMARY KEY,
+    license_character VARCHAR(255) DEFAULT NULL,
+    license_type VARCHAR(255) DEFAULT 'No model available',
+    car_brand VARCHAR(255) DEFAULT NULL,
+    car_model VARCHAR(255) DEFAULT NULL,
+    car_body_type VARCHAR(255) DEFAULT NULL,
+    created_at TIMESTAMP DEFAULT NOW(),
+    updated_at TIMESTAMP DEFAULT NOW()
+);
+```
+
+### Workflow
+1. **Detection**: When both "Car" and "Frontal" are detected, create initial database record with UUID session_id
+2. **Redis Storage**: Save cropped frontal image to Redis with session_id in key
+3. **Parallel Processing**: Run brand and body type classification concurrently
+4. **Synchronization**: Wait for all branches to complete using `waitForBranches`
+5. **Database Update**: Update record with combined classification results using field mapping
+
+### Field Mapping
+Templates like `{car_brand_cls_v1.brand}` are resolved to actual classification results:
+- `car_brand_cls_v1.brand` → "Honda"
+- `car_bodytype_cls_v1.body_type` → "Sedan"
+
 ## Performance Optimizations
 - GPU acceleration when CUDA is available
 - Shared camera streams reduce resource usage
 - Frame queue optimization (single latest frame)
 - Model caching across subscriptions
 - Trigger class filtering for faster inference
+- Parallel processing with ThreadPoolExecutor for classification branches
+- Multi-class detection reduces inference passes
+- Region-based cropping minimizes processing overhead
+- Database connection pooling and prepared statements
+- Redis image storage with automatic expiration

Dockerfile

@@ -1,20 +1,12 @@
-# Use newer, more secure base image
+# Use our pre-built base image with ML dependencies
-FROM python:3.13-alpine
+FROM git.siwatsystem.com/adsist-cms/worker-base:latest
 
-# Update system packages first
+# Copy and install application requirements (frequently changing dependencies)
-RUN apk update && apk upgrade
-
-# Install minimal dependencies
-RUN apk add --no-cache mesa-gl
-
-# Use specific package versions
 COPY requirements.txt .
-RUN pip install --no-cache-dir --upgrade pip && \
+RUN pip install --no-cache-dir -r requirements.txt
-    pip install --no-cache-dir -r requirements.txt
-
-# Run as non-root user
-RUN adduser -D -s /bin/sh appuser
-USER appuser
 
+# Copy the application code
 COPY . .
+
+# Run the application
 CMD ["python3", "-m", "fastapi", "run", "--host", "0.0.0.0", "--port", "8000"]

Dockerfile.base

@@ -0,0 +1,15 @@
+# Base image with all ML dependencies
+FROM python:3.13-bookworm
+
+# Install system dependencies
+RUN apt update && apt install -y libgl1 && rm -rf /var/lib/apt/lists/*
+
+# Copy and install base requirements (ML dependencies that rarely change)
+COPY requirements.base.txt .
+RUN pip install --no-cache-dir -r requirements.base.txt
+
+# Set working directory
+WORKDIR /app
+
+# This base image will be reused for all worker builds
+CMD ["python3", "-m", "fastapi", "run", "--host", "0.0.0.0", "--port", "8000"]

app.py

@@ -35,6 +35,8 @@ session_ids: Dict[str, int] = {}
 camera_streams: Dict[str, Dict[str, Any]] = {}
 # Map subscriptions to their camera URL
 subscription_to_camera: Dict[str, str] = {}
+# Store latest frames for REST API access (separate from processing buffer)
+latest_frames: Dict[str, Any] = {}
 
 with open("config.json", "r") as f:
     config = json.load(f)
@@ -109,20 +111,60 @@ def download_mpta(url: str, dest_path: str) -> str:
 # Add helper to fetch snapshot image from HTTP/HTTPS URL
 def fetch_snapshot(url: str):
     try:
-        response = requests.get(url, timeout=10)
+        from requests.auth import HTTPBasicAuth, HTTPDigestAuth
+        
+        # Parse URL to extract credentials
+        parsed = urlparse(url)
+        
+        # Prepare headers - some cameras require User-Agent
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (compatible; DetectorWorker/1.0)'
+        }
+        
+        # Reconstruct URL without credentials
+        clean_url = f"{parsed.scheme}://{parsed.hostname}"
+        if parsed.port:
+            clean_url += f":{parsed.port}"
+        clean_url += parsed.path
+        if parsed.query:
+            clean_url += f"?{parsed.query}"
+        
+        auth = None
+        if parsed.username and parsed.password:
+            # Try HTTP Digest authentication first (common for IP cameras)
+            try:
+                auth = HTTPDigestAuth(parsed.username, parsed.password)
+                response = requests.get(clean_url, auth=auth, headers=headers, timeout=10)
+                if response.status_code == 200:
+                    logger.debug(f"Successfully authenticated using HTTP Digest for {clean_url}")
+                elif response.status_code == 401:
+                    # If Digest fails, try Basic auth
+                    logger.debug(f"HTTP Digest failed, trying Basic auth for {clean_url}")
+                    auth = HTTPBasicAuth(parsed.username, parsed.password)
+                    response = requests.get(clean_url, auth=auth, headers=headers, timeout=10)
+                    if response.status_code == 200:
+                        logger.debug(f"Successfully authenticated using HTTP Basic for {clean_url}")
+            except Exception as auth_error:
+                logger.debug(f"Authentication setup error: {auth_error}")
+                # Fallback to original URL with embedded credentials
+                response = requests.get(url, headers=headers, timeout=10)
+        else:
+            # No credentials in URL, make request as-is
+            response = requests.get(url, headers=headers, timeout=10)
+        
         if response.status_code == 200:
             # Convert response content to numpy array
             nparr = np.frombuffer(response.content, np.uint8)
             # Decode image
             frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
             if frame is not None:
-                logger.debug(f"Successfully fetched snapshot from {url}, shape: {frame.shape}")
+                logger.debug(f"Successfully fetched snapshot from {clean_url}, shape: {frame.shape}")
                 return frame
             else:
-                logger.error(f"Failed to decode image from snapshot URL: {url}")
+                logger.error(f"Failed to decode image from snapshot URL: {clean_url}")
                 return None
         else:
-            logger.error(f"Failed to fetch snapshot (status code {response.status_code}): {url}")
+            logger.error(f"Failed to fetch snapshot (status code {response.status_code}): {clean_url}")
             return None
     except Exception as e:
         logger.error(f"Exception fetching snapshot from {url}: {str(e)}")
@@ -146,26 +188,24 @@ async def get_camera_image(camera_id: str):
     Get the current frame from a camera as JPEG image
     """
     try:
+        # URL decode the camera_id to handle encoded characters like %3B for semicolon
+        from urllib.parse import unquote
+        original_camera_id = camera_id
+        camera_id = unquote(camera_id)
+        logger.debug(f"REST API request: original='{original_camera_id}', decoded='{camera_id}'")
+        
         with streams_lock:
             if camera_id not in streams:
                 logger.warning(f"Camera ID '{camera_id}' not found in streams. Current streams: {list(streams.keys())}")
                 raise HTTPException(status_code=404, detail=f"Camera {camera_id} not found or not active")
             
-            stream = streams[camera_id]
+            # Check if we have a cached frame for this camera
-            buffer = stream["buffer"]
+            if camera_id not in latest_frames:
-            logger.debug(f"Camera '{camera_id}' buffer size: {buffer.qsize()}, buffer empty: {buffer.empty()}")
+                logger.warning(f"No cached frame available for camera '{camera_id}'.")
-            logger.debug(f"Buffer queue contents: {getattr(buffer, 'queue', None)}")
-            
-            if buffer.empty():
-                logger.warning(f"No frame available for camera '{camera_id}'. Buffer is empty.")
                 raise HTTPException(status_code=404, detail=f"No frame available for camera {camera_id}")
             
-            # Get the latest frame (non-blocking)
+            frame = latest_frames[camera_id]
-            try:
+            logger.debug(f"Retrieved cached frame for camera '{camera_id}', frame shape: {frame.shape}")
-                frame = buffer.queue[-1]  # Get the most recent frame without removing it
-            except IndexError:
-                logger.warning(f"Buffer queue is empty for camera '{camera_id}' when trying to access last frame.")
-                raise HTTPException(status_code=404, detail=f"No frame available for camera {camera_id}")
         # Encode frame as JPEG
         success, buffer_img = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
         if not success:
@@ -199,7 +239,20 @@ async def detect(websocket: WebSocket):
             
             logger.debug(f"Processing frame for camera {camera_id} with model {stream['modelId']}")
             start_time = time.time()
-            detection_result = run_pipeline(cropped_frame, model_tree)
+            
+            # Extract display identifier for session ID lookup
+            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
+            pipeline_context = {
+                "camera_id": camera_id,
+                "display_id": display_identifier,
+                "session_id": session_id
+            }
+            
+            detection_result = run_pipeline(cropped_frame, model_tree, context=pipeline_context)
             process_time = (time.time() - start_time) * 1000
             logger.debug(f"Detection for camera {camera_id} completed in {process_time:.2f}ms")
             
@@ -258,11 +311,6 @@ async def detect(websocket: WebSocket):
                     if key not in ["box", "id"]:  # Skip internal fields
                         detection_dict[key] = value
             
-            # Extract display identifier for session ID lookup
-            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
-            
             detection_data = {
                 "type": "imageDetection",
                 "subscriptionIdentifier": stream["subscriptionIdentifier"],
@@ -282,9 +330,6 @@ async def detect(websocket: WebSocket):
                 logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {highest_confidence_detection['confidence']:.2f} using model {stream['modelName']}")
                 
                 # Log session ID if available
-                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
                 if session_id:
                     logger.debug(f"Detection associated with session ID: {session_id}")
             
@@ -476,6 +521,10 @@ async def detect(websocket: WebSocket):
                     logger.debug(f"Got frame from buffer for camera {camera_id}")
                     frame = buffer.get()
                     
+                    # Cache the frame for REST API access
+                    latest_frames[camera_id] = frame.copy()
+                    logger.debug(f"Cached frame for REST API access for camera {camera_id}")
+                    
                     with models_lock:
                         model_tree = models.get(camera_id, {}).get(stream["modelId"])
                         if not model_tree:
@@ -647,7 +696,7 @@ async def detect(websocket: WebSocket):
                                     
                                     if snapshot_url and snapshot_interval:
                                         logger.info(f"Creating new snapshot stream for camera {camera_id}: {snapshot_url}")
-                                        thread = threading.Thread(target=snapshot_reader, args=(camera_identifier, snapshot_url, snapshot_interval, buffer, stop_event))
+                                        thread = threading.Thread(target=snapshot_reader, args=(camera_id, snapshot_url, snapshot_interval, buffer, stop_event))
                                         thread.daemon = True
                                         thread.start()
                                         mode = "snapshot"
@@ -670,7 +719,7 @@ async def detect(websocket: WebSocket):
                                         if not cap.isOpened():
                                             logger.error(f"Failed to open RTSP stream for camera {camera_id}")
                                             continue
-                                        thread = threading.Thread(target=frame_reader, args=(camera_identifier, cap, buffer, stop_event))
+                                        thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
                                         thread.daemon = True
                                         thread.start()
                                         mode = "rtsp"
@@ -744,6 +793,8 @@ async def detect(websocket: WebSocket):
                                 else:
                                     logger.info(f"Shared stream for {camera_url} still has {shared_stream['ref_count']} references")
                             
+                            # Clean up cached frame
+                            latest_frames.pop(camera_id, None)
                             logger.info(f"Unsubscribed from camera {camera_id}")
                             # Note: Keep models in memory for potential reuse
                 elif msg_type == "requestState":
@@ -847,5 +898,6 @@ async def detect(websocket: WebSocket):
             subscription_to_camera.clear()
         with models_lock:
             models.clear()
+        latest_frames.clear()
         session_ids.clear()
         logger.info("WebSocket connection closed")

pympta.md

@@ -32,14 +32,15 @@ This modular structure allows for creating complex and efficient inference logic
 
 ## `pipeline.json` Specification
 
-This file defines the entire pipeline logic. The root object contains a `pipeline` key for the pipeline definition and an optional `redis` key for Redis configuration.
+This file defines the entire pipeline logic. The root object contains a `pipeline` key for the pipeline definition, optional `redis` key for Redis configuration, and optional `postgresql` key for database integration.
 
 ### Top-Level Object Structure
 
 | Key          | Type   | Required | Description                                             |
-| ---------- | ------ | -------- | ------------------------------------------------------- |
+| ------------ | ------ | -------- | ------------------------------------------------------- |
 | `pipeline`   | Object | Yes      | The root node object of the pipeline.                   |
 | `redis`      | Object | No       | Configuration for connecting to a Redis server.         |
+| `postgresql` | Object | No       | Configuration for connecting to a PostgreSQL database.  |
 
 ### Redis Configuration (`redis`)
 
@@ -50,6 +51,16 @@ This file defines the entire pipeline logic. The root object contains a `pipelin
 | `password` | String | No       | The password for Redis authentication.                  |
 | `db`       | Number | No       | The Redis database number to use. Defaults to `0`.      |
 
+### PostgreSQL Configuration (`postgresql`)
+
+| Key        | Type   | Required | Description                                             |
+| ---------- | ------ | -------- | ------------------------------------------------------- |
+| `host`     | String | Yes      | The hostname or IP address of the PostgreSQL server.    |
+| `port`     | Number | Yes      | The port number of the PostgreSQL server.              |
+| `database` | String | Yes      | The database name to connect to.                       |
+| `username` | String | Yes      | The username for database authentication.              |
+| `password` | String | Yes      | The password for database authentication.              |
+
 ### Node Object Structure
 
 | Key                 | Type          | Required | Description                                                                                                                            |
@@ -59,12 +70,17 @@ This file defines the entire pipeline logic. The root object contains a `pipelin
 | `minConfidence`     | Float         | Yes      | The minimum confidence score (0.0 to 1.0) required for a detection to be considered valid and potentially trigger a branch.              |
 | `triggerClasses`    | Array<String> | Yes      | A list of class names that, when detected by the parent, can trigger this node. For the root node, this lists all classes of interest. |
 | `crop`              | Boolean       | No       | If `true`, the image is cropped to the parent's detection bounding box before being passed to this node's model. Defaults to `false`.    |
+| `cropClass`         | String        | No       | The specific class to use for cropping (e.g., "Frontal" for frontal view cropping).                                                   |
+| `multiClass`        | Boolean       | No       | If `true`, enables multi-class detection mode where multiple classes can be detected simultaneously.                                   |
+| `expectedClasses`   | Array<String> | No       | When `multiClass` is true, defines which classes are expected. At least one must be detected for processing to continue.              |
+| `parallel`          | Boolean       | No       | If `true`, this branch will be processed in parallel with other parallel branches.                                                    |
 | `branches`          | Array<Node>   | No       | A list of child node objects that can be triggered by this node's detections.                                                          |
 | `actions`           | Array<Action> | No       | A list of actions to execute upon a successful detection in this node.                                                                 |
+| `parallelActions`   | Array<Action> | No       | A list of actions to execute after all specified branches have completed.                                                             |
 
 ### Action Object Structure
 
-Actions allow the pipeline to interact with Redis. They are executed sequentially for a given detection.
+Actions allow the pipeline to interact with Redis and PostgreSQL databases. They are executed sequentially for a given detection.
 
 #### Action Context & Dynamic Keys
 
@@ -72,7 +88,12 @@ All actions have access to a dynamic context for formatting keys and messages. T
 
 - All key-value pairs from the detection result (e.g., `class`, `confidence`, `id`).
 - `{timestamp_ms}`: The current Unix timestamp in milliseconds.
+- `{timestamp}`: Formatted timestamp string (YYYY-MM-DDTHH-MM-SS).
 - `{uuid}`: A unique identifier (UUID4) for the detection event.
+- `{filename}`: Generated filename with UUID.
+- `{camera_id}`: Full camera subscription identifier.
+- `{display_id}`: Display identifier extracted from subscription.
+- `{session_id}`: Session ID for database operations.
 - `{image_key}`: If a `redis_save_image` action has already been executed for this event, this placeholder will be replaced with the key where the image was stored.
 
 #### `redis_save_image`
@@ -83,6 +104,9 @@ Saves the current image frame (or cropped sub-image) to a Redis key.
 | ---------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- |
 | `type`           | String | Yes      | Must be `"redis_save_image"`.                                                                           |
 | `key`            | String | Yes      | The Redis key to save the image to. Can contain any of the dynamic placeholders.                        |
+| `region`         | String | No       | Specific detected region to crop and save (e.g., "Frontal").                                           |
+| `format`         | String | No       | Image format: "jpeg" or "png". Defaults to "jpeg".                                                     |
+| `quality`        | Number | No       | JPEG quality (1-100). Defaults to 90.                                                                  |
 | `expire_seconds` | Number | No       | If provided, sets an expiration time (in seconds) for the Redis key.                                    |
 
 #### `redis_publish`
@@ -95,35 +119,98 @@ Publishes a message to a Redis channel.
 | `channel` | String | Yes      | The Redis channel to publish the message to.                                                            |
 | `message` | String | Yes      | The message to publish. Can contain any of the dynamic placeholders, including `{image_key}`.           |
 
-### Example `pipeline.json` with Redis
+#### `postgresql_update_combined`
 
-This example demonstrates a pipeline that detects vehicles, saves a uniquely named image of each detection that expires in one hour, and then publishes a notification with the image key.
+Updates PostgreSQL database with results from multiple branches after they complete.
+
+| Key                | Type          | Required | Description                                                                                             |
+| ------------------ | ------------- | -------- | ------------------------------------------------------------------------------------------------------- |
+| `type`             | String        | Yes      | Must be `"postgresql_update_combined"`.                                                                 |
+| `table`            | String        | Yes      | The database table name (will be prefixed with `gas_station_1.` schema).                              |
+| `key_field`        | String        | Yes      | The field to use as the update key (typically "session_id").                                           |
+| `key_value`        | String        | Yes      | Template for the key value (e.g., "{session_id}").                                                     |
+| `waitForBranches`  | Array<String> | Yes      | List of branch model IDs to wait for completion before executing update.                               |
+| `fields`           | Object        | Yes      | Field mapping object where keys are database columns and values are templates (e.g., "{branch.field}").|
+
+### Complete Example `pipeline.json`
+
+This example demonstrates a comprehensive pipeline for vehicle detection with parallel classification and database integration:
 
 ```json
 {
   "redis": {
-    "host": "redis.local",
+    "host": "10.100.1.3",
     "port": 6379,
-    "password": "your-super-secret-password"
+    "password": "your-redis-password",
+    "db": 0
+  },
+  "postgresql": {
+    "host": "10.100.1.3",
+    "port": 5432,
+    "database": "inference",
+    "username": "root",
+    "password": "your-db-password"
   },
   "pipeline": {
-    "modelId": "vehicle-detector",
+    "modelId": "car_frontal_detection_v1",
-    "modelFile": "vehicle_model.pt",
+    "modelFile": "car_frontal_detection_v1.pt",
-    "minConfidence": 0.6,
+    "crop": false,
-    "triggerClasses": ["car", "truck"],
+    "triggerClasses": ["Car", "Frontal"],
+    "minConfidence": 0.8,
+    "multiClass": true,
+    "expectedClasses": ["Car", "Frontal"],
     "actions": [
       {
         "type": "redis_save_image",
-        "key": "detections:{class}:{timestamp_ms}:{uuid}",
+        "region": "Frontal",
-        "expire_seconds": 3600
+        "key": "inference:{display_id}:{timestamp}:{session_id}:{filename}",
+        "expire_seconds": 600,
+        "format": "jpeg",
+        "quality": 90
       },
       {
         "type": "redis_publish",
-        "channel": "vehicle_events",
+        "channel": "car_detections",
-        "message": "{\"event\":\"new_detection\",\"class\":\"{class}\",\"confidence\":{confidence},\"image_key\":\"{image_key}\"}"
+        "message": "{\"event\":\"frontal_detected\"}"
       }
     ],
+    "branches": [
+      {
+        "modelId": "car_brand_cls_v1",
+        "modelFile": "car_brand_cls_v1.pt",
+        "crop": true,
+        "cropClass": "Frontal",
+        "resizeTarget": [224, 224],
+        "triggerClasses": ["Frontal"],
+        "minConfidence": 0.85,
+        "parallel": true,
         "branches": []
+      },
+      {
+        "modelId": "car_bodytype_cls_v1",
+        "modelFile": "car_bodytype_cls_v1.pt",
+        "crop": true,
+        "cropClass": "Car",
+        "resizeTarget": [224, 224],
+        "triggerClasses": ["Car"],
+        "minConfidence": 0.85,
+        "parallel": true,
+        "branches": []
+      }
+    ],
+    "parallelActions": [
+      {
+        "type": "postgresql_update_combined",
+        "table": "car_frontal_info",
+        "key_field": "session_id",
+        "key_value": "{session_id}",
+        "waitForBranches": ["car_brand_cls_v1", "car_bodytype_cls_v1"],
+        "fields": {
+          "car_brand": "{car_brand_cls_v1.brand}",
+          "car_body_type": "{car_bodytype_cls_v1.body_type}"
+        }
+      }
+    ]
   }
 }
 ```
@@ -134,7 +221,7 @@ The `pympta` module exposes two main functions.
 
 ### `load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict`
 
-Loads, extracts, and parses an `.mpta` file to build a pipeline tree in memory. It also establishes a Redis connection if configured in `pipeline.json`.
+Loads, extracts, and parses an `.mpta` file to build a pipeline tree in memory. It also establishes Redis and PostgreSQL connections if configured in `pipeline.json`.
 
 - **Parameters:**
   - `zip_source` (str): The file path to the local `.mpta` zip archive.
@@ -142,7 +229,7 @@ Loads, extracts, and parses an `.mpta` file to build a pipeline tree in memory.
 - **Returns:**
   - A dictionary representing the root node of the pipeline, ready to be used with `run_pipeline`. Returns `None` if loading fails.
 
-### `run_pipeline(frame, node: dict, return_bbox: bool = False)`
+### `run_pipeline(frame, node: dict, return_bbox: bool = False, context: dict = None)`
 
 Executes the inference pipeline on a single image frame.
 
@@ -150,12 +237,43 @@ Executes the inference pipeline on a single image frame.
   - `frame`: The input image frame (e.g., a NumPy array from OpenCV).
   - `node` (dict): The pipeline node to execute (typically the root node returned by `load_pipeline_from_zip`).
   - `return_bbox` (bool): If `True`, the function returns a tuple `(detection, bounding_box)`. Otherwise, it returns only the `detection`.
+  - `context` (dict): Optional context dictionary containing camera_id, display_id, session_id for action formatting.
 - **Returns:**
   - The final detection result from the last executed node in the chain. A detection is a dictionary like `{'class': 'car', 'confidence': 0.95, 'id': 1}`. If no detection meets the criteria, it returns `None` (or `(None, None)` if `return_bbox` is `True`).
 
+## Database Integration
+
+The pipeline system includes automatic PostgreSQL database management:
+
+### Table Schema (`gas_station_1.car_frontal_info`)
+
+The system automatically creates and manages the following table structure:
+
+```sql
+CREATE TABLE IF NOT EXISTS gas_station_1.car_frontal_info (
+    display_id VARCHAR(255),
+    captured_timestamp VARCHAR(255),
+    session_id VARCHAR(255) PRIMARY KEY,
+    license_character VARCHAR(255) DEFAULT NULL,
+    license_type VARCHAR(255) DEFAULT 'No model available',
+    car_brand VARCHAR(255) DEFAULT NULL,
+    car_model VARCHAR(255) DEFAULT NULL,
+    car_body_type VARCHAR(255) DEFAULT NULL,
+    created_at TIMESTAMP DEFAULT NOW(),
+    updated_at TIMESTAMP DEFAULT NOW()
+);
+```
+
+### Workflow
+
+1. **Initial Record Creation**: When both "Car" and "Frontal" are detected, an initial database record is created with a UUID session_id.
+2. **Redis Storage**: Vehicle images are stored in Redis with keys containing the session_id.
+3. **Parallel Classification**: Brand and body type classification run concurrently.
+4. **Database Update**: After all branches complete, the database record is updated with classification results.
+
 ## Usage Example
 
-This snippet, inspired by `pipeline_webcam.py`, shows how to use `pympta` to load a pipeline and process an image from a webcam.
+This snippet shows how to use `pympta` with the enhanced features:
 
 ```python
 import cv2
@@ -181,9 +299,14 @@ while True:
     if not ret:
         break
 
-    # 4. Run the pipeline on the current frame
+    # 4. Run the pipeline on the current frame with context
-    # The function will handle the entire logic tree (e.g., find a car, then find its license plate).
+    context = {
-    detection_result, bounding_box = run_pipeline(frame, model_tree, return_bbox=True)
+        "camera_id": "display-001;cam-001",
+        "display_id": "display-001", 
+        "session_id": None  # Will be generated automatically
+    }
+    
+    detection_result, bounding_box = run_pipeline(frame, model_tree, return_bbox=True, context=context)
 
     # 5. Display the results
     if detection_result:

requirements.base.txt

@@ -0,0 +1,7 @@
+torch
+torchvision
+ultralytics
+opencv-python
+scipy
+filterpy
+psycopg2-binary

requirements.txt

@@ -1,66 +1,6 @@
 fastapi
 uvicorn
-# torch
-# torchvision
-# ultralytics
-# opencv-python
 websockets
 fastapi[standard]
 redis
-
+urllib3<2.0.0
-# Trackers Environment
-# pip install -r requirements.txt
-ultralytics==8.0.20
-
-# Base ----------------------------------------
-gitpython
-ipython  # interactive notebook
-matplotlib>=3.2.2
-numpy==1.23.1
-opencv-python>=4.1.1
-Pillow>=7.1.2
-psutil  # system resources
-PyYAML>=5.3.1
-requests>=2.23.0
-scipy>=1.4.1
-thop>=0.1.1  # FLOPs computation
-torch>=1.7.0,<=2.5.1  # see https://pytorch.org/get-started/locally (recommended)
-torchvision>=0.8.1,<=0.20.1
-tqdm>=4.64.0
-# protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012
-
-# Logging ---------------------------------------------------------------------
-tensorboard>=2.4.1
-# clearml>=1.2.0
-# comet
-
-# Plotting --------------------------------------------------------------------
-pandas>=1.1.4
-seaborn>=0.11.0
-
-# StrongSORT ------------------------------------------------------------------
-easydict
-
-# torchreid -------------------------------------------------------------------
-gdown
-
-# ByteTrack -------------------------------------------------------------------
-lap
-
-# OCSORT ----------------------------------------------------------------------
-filterpy
-
-# Export ----------------------------------------------------------------------
-# onnx>=1.9.0               # ONNX export
-# onnx-simplifier>=0.4.1    # ONNX simplifier
-# nvidia-pyindex            # TensorRT export
-# nvidia-tensorrt           # TensorRT export
-# openvino-dev              # OpenVINO export
-
-# Hyperparam search -----------------------------------------------------------
-# optuna
-# plotly                    # for hp importance and pareto front plots
-# kaleido
-# joblib
-pyzmq
-loguru

siwatsystem/database.py

@@ -0,0 +1,211 @@
+import psycopg2
+import psycopg2.extras
+from typing import Optional, Dict, Any
+import logging
+import uuid
+
+logger = logging.getLogger(__name__)
+
+class DatabaseManager:
+    def __init__(self, config: Dict[str, Any]):
+        self.config = config
+        self.connection: Optional[psycopg2.extensions.connection] = None
+        
+    def connect(self) -> bool:
+        try:
+            self.connection = psycopg2.connect(
+                host=self.config['host'],
+                port=self.config['port'],
+                database=self.config['database'],
+                user=self.config['username'],
+                password=self.config['password']
+            )
+            logger.info("PostgreSQL connection established successfully")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to connect to PostgreSQL: {e}")
+            return False
+    
+    def disconnect(self):
+        if self.connection:
+            self.connection.close()
+            self.connection = None
+            logger.info("PostgreSQL connection closed")
+    
+    def is_connected(self) -> bool:
+        try:
+            if self.connection and not self.connection.closed:
+                cur = self.connection.cursor()
+                cur.execute("SELECT 1")
+                cur.fetchone()
+                cur.close()
+                return True
+        except:
+            pass
+        return False
+    
+    def update_car_info(self, session_id: str, brand: str, model: str, body_type: str) -> bool:
+        if not self.is_connected():
+            if not self.connect():
+                return False
+        
+        try:
+            cur = self.connection.cursor()
+            query = """
+            INSERT INTO car_frontal_info (session_id, car_brand, car_model, car_body_type, updated_at)
+            VALUES (%s, %s, %s, %s, NOW())
+            ON CONFLICT (session_id) 
+            DO UPDATE SET 
+                car_brand = EXCLUDED.car_brand,
+                car_model = EXCLUDED.car_model,
+                car_body_type = EXCLUDED.car_body_type,
+                updated_at = NOW()
+            """
+            cur.execute(query, (session_id, brand, model, body_type))
+            self.connection.commit()
+            cur.close()
+            logger.info(f"Updated car info for session {session_id}: {brand} {model} ({body_type})")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to update car info: {e}")
+            if self.connection:
+                self.connection.rollback()
+            return False
+    
+    def execute_update(self, table: str, key_field: str, key_value: str, fields: Dict[str, str]) -> bool:
+        if not self.is_connected():
+            if not self.connect():
+                return False
+        
+        try:
+            cur = self.connection.cursor()
+            
+            # Build the UPDATE query dynamically
+            set_clauses = []
+            values = []
+            
+            for field, value in fields.items():
+                if value == "NOW()":
+                    set_clauses.append(f"{field} = NOW()")
+                else:
+                    set_clauses.append(f"{field} = %s")
+                    values.append(value)
+            
+            # Add schema prefix if table doesn't already have it
+            full_table_name = table if '.' in table else f"gas_station_1.{table}"
+            
+            query = f"""
+            INSERT INTO {full_table_name} ({key_field}, {', '.join(fields.keys())})
+            VALUES (%s, {', '.join(['%s'] * len(fields))})
+            ON CONFLICT ({key_field})
+            DO UPDATE SET {', '.join(set_clauses)}
+            """
+            
+            # Add key_value to the beginning of values list
+            all_values = [key_value] + list(fields.values()) + values
+            
+            cur.execute(query, all_values)
+            self.connection.commit()
+            cur.close()
+            logger.info(f"Updated {table} for {key_field}={key_value}")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to execute update on {table}: {e}")
+            if self.connection:
+                self.connection.rollback()
+            return False
+    
+    def create_car_frontal_info_table(self) -> bool:
+        """Create the car_frontal_info table in gas_station_1 schema if it doesn't exist."""
+        if not self.is_connected():
+            if not self.connect():
+                return False
+        
+        try:
+            cur = self.connection.cursor()
+            
+            # Create schema if it doesn't exist
+            cur.execute("CREATE SCHEMA IF NOT EXISTS gas_station_1")
+            
+            # Create table if it doesn't exist
+            create_table_query = """
+            CREATE TABLE IF NOT EXISTS gas_station_1.car_frontal_info (
+                display_id VARCHAR(255),
+                captured_timestamp VARCHAR(255),
+                session_id VARCHAR(255) PRIMARY KEY,
+                license_character VARCHAR(255) DEFAULT NULL,
+                license_type VARCHAR(255) DEFAULT 'No model available',
+                car_brand VARCHAR(255) DEFAULT NULL,
+                car_model VARCHAR(255) DEFAULT NULL,
+                car_body_type VARCHAR(255) DEFAULT NULL,
+                updated_at TIMESTAMP DEFAULT NOW()
+            )
+            """
+            
+            cur.execute(create_table_query)
+            
+            # Add columns if they don't exist (for existing tables)
+            alter_queries = [
+                "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS car_brand VARCHAR(255) DEFAULT NULL",
+                "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS car_model VARCHAR(255) DEFAULT NULL", 
+                "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS car_body_type VARCHAR(255) DEFAULT NULL",
+                "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS updated_at TIMESTAMP DEFAULT NOW()"
+            ]
+            
+            for alter_query in alter_queries:
+                try:
+                    cur.execute(alter_query)
+                    logger.debug(f"Executed: {alter_query}")
+                except Exception as e:
+                    # Ignore errors if column already exists (for older PostgreSQL versions)
+                    if "already exists" in str(e).lower():
+                        logger.debug(f"Column already exists, skipping: {alter_query}")
+                    else:
+                        logger.warning(f"Error in ALTER TABLE: {e}")
+            
+            self.connection.commit()
+            cur.close()
+            logger.info("Successfully created/verified car_frontal_info table with all required columns")
+            return True
+            
+        except Exception as e:
+            logger.error(f"Failed to create car_frontal_info table: {e}")
+            if self.connection:
+                self.connection.rollback()
+            return False
+    
+    def insert_initial_detection(self, display_id: str, captured_timestamp: str, session_id: str = None) -> str:
+        """Insert initial detection record and return the session_id."""
+        if not self.is_connected():
+            if not self.connect():
+                return None
+        
+        # Generate session_id if not provided
+        if not session_id:
+            session_id = str(uuid.uuid4())
+        
+        try:
+            # Ensure table exists
+            if not self.create_car_frontal_info_table():
+                logger.error("Failed to create/verify table before insertion")
+                return None
+            
+            cur = self.connection.cursor()
+            insert_query = """
+            INSERT INTO gas_station_1.car_frontal_info 
+            (display_id, captured_timestamp, session_id, license_character, license_type, car_brand, car_model, car_body_type)
+            VALUES (%s, %s, %s, NULL, 'No model available', NULL, NULL, NULL)
+            ON CONFLICT (session_id) DO NOTHING
+            """
+            
+            cur.execute(insert_query, (display_id, captured_timestamp, session_id))
+            self.connection.commit()
+            cur.close()
+            logger.info(f"Inserted initial detection record with session_id: {session_id}")
+            return session_id
+            
+        except Exception as e:
+            logger.error(f"Failed to insert initial detection record: {e}")
+            if self.connection:
+                self.connection.rollback()
+            return None

siwatsystem/pympta.py

@@ -3,20 +3,72 @@ import json
 import logging
 import torch
 import cv2
-import requests
 import zipfile
 import shutil
 import traceback
 import redis
 import time
 import uuid
+import concurrent.futures
 from ultralytics import YOLO
 from urllib.parse import urlparse
+from .database import DatabaseManager
 
 # Create a logger specifically for this module
 logger = logging.getLogger("detector_worker.pympta")
 
-def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client) -> dict:
+def validate_redis_config(redis_config: dict) -> bool:
+    """Validate Redis configuration parameters."""
+    required_fields = ["host", "port"]
+    for field in required_fields:
+        if field not in redis_config:
+            logger.error(f"Missing required Redis config field: {field}")
+            return False
+    
+    if not isinstance(redis_config["port"], int) or redis_config["port"] <= 0:
+        logger.error(f"Invalid Redis port: {redis_config['port']}")
+        return False
+    
+    return True
+
+def validate_postgresql_config(pg_config: dict) -> bool:
+    """Validate PostgreSQL configuration parameters."""
+    required_fields = ["host", "port", "database", "username", "password"]
+    for field in required_fields:
+        if field not in pg_config:
+            logger.error(f"Missing required PostgreSQL config field: {field}")
+            return False
+    
+    if not isinstance(pg_config["port"], int) or pg_config["port"] <= 0:
+        logger.error(f"Invalid PostgreSQL port: {pg_config['port']}")
+        return False
+    
+    return True
+
+def crop_region_by_class(frame, regions_dict, class_name):
+    """Crop a specific region from frame based on detected class."""
+    if class_name not in regions_dict:
+        logger.warning(f"Class '{class_name}' not found in detected regions")
+        return None
+    
+    bbox = regions_dict[class_name]['bbox']
+    x1, y1, x2, y2 = bbox
+    cropped = frame[y1:y2, x1:x2]
+    
+    if cropped.size == 0:
+        logger.warning(f"Empty crop for class '{class_name}' with bbox {bbox}")
+        return None
+    
+    return cropped
+
+def format_action_context(base_context, additional_context=None):
+    """Format action context with dynamic values."""
+    context = {**base_context}
+    if additional_context:
+        context.update(additional_context)
+    return context
+
+def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client, db_manager=None) -> dict:
     # Recursively load a model node from configuration.
     model_path = os.path.join(mpta_dir, node_config["modelFile"])
     if not os.path.exists(model_path):
@@ -46,16 +98,22 @@ def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client) -> dict:
         "triggerClasses": trigger_classes,
         "triggerClassIndices": trigger_class_indices,
         "crop": node_config.get("crop", False),
+        "cropClass": node_config.get("cropClass"),
         "minConfidence": node_config.get("minConfidence", None),
+        "multiClass": node_config.get("multiClass", False),
+        "expectedClasses": node_config.get("expectedClasses", []),
+        "parallel": node_config.get("parallel", False),
         "actions": node_config.get("actions", []),
+        "parallelActions": node_config.get("parallelActions", []),
         "model": model,
         "branches": [],
-        "redis_client": redis_client
+        "redis_client": redis_client,
+        "db_manager": db_manager
     }
     logger.debug(f"Configured node {node_config['modelId']} with trigger classes: {node['triggerClasses']}")
     for child in node_config.get("branches", []):
         logger.debug(f"Loading branch for parent node {node_config['modelId']}")
-        node["branches"].append(load_pipeline_node(child, mpta_dir, redis_client))
+        node["branches"].append(load_pipeline_node(child, mpta_dir, redis_client, db_manager))
     return node
 
 def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
@@ -183,6 +241,9 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
         redis_client = None
         if "redis" in pipeline_config:
             redis_config = pipeline_config["redis"]
+            if not validate_redis_config(redis_config):
+                logger.error("Invalid Redis configuration, skipping Redis connection")
+            else:
                 try:
                     redis_client = redis.Redis(
                         host=redis_config["host"],
@@ -197,7 +258,25 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
                     logger.error(f"Failed to connect to Redis: {e}")
                     redis_client = None
         
-        return load_pipeline_node(pipeline_config["pipeline"], mpta_dir, redis_client)
+        # Establish PostgreSQL connection if configured
+        db_manager = None
+        if "postgresql" in pipeline_config:
+            pg_config = pipeline_config["postgresql"]
+            if not validate_postgresql_config(pg_config):
+                logger.error("Invalid PostgreSQL configuration, skipping database connection")
+            else:
+                try:
+                    db_manager = DatabaseManager(pg_config)
+                    if db_manager.connect():
+                        logger.info(f"Successfully connected to PostgreSQL at {pg_config['host']}:{pg_config['port']}")
+                    else:
+                        logger.error("Failed to connect to PostgreSQL")
+                        db_manager = None
+                except Exception as e:
+                    logger.error(f"Error initializing PostgreSQL connection: {e}")
+                    db_manager = None
+        
+        return load_pipeline_node(pipeline_config["pipeline"], mpta_dir, redis_client, db_manager)
     except json.JSONDecodeError as e:
         logger.error(f"Error parsing pipeline.json: {str(e)}", exc_info=True)
         return None
@@ -208,22 +287,53 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
         logger.error(f"Error loading pipeline.json: {str(e)}", exc_info=True)
         return None
 
-def execute_actions(node, frame, detection_result):
+def execute_actions(node, frame, detection_result, regions_dict=None):
     if not node["redis_client"] or not node["actions"]:
         return
 
     # Create a dynamic context for this detection event
+    from datetime import datetime
     action_context = {
         **detection_result,
         "timestamp_ms": int(time.time() * 1000),
         "uuid": str(uuid.uuid4()),
+        "timestamp": datetime.now().strftime("%Y-%m-%dT%H-%M-%S"),
+        "filename": f"{uuid.uuid4()}.jpg"
     }
 
     for action in node["actions"]:
         try:
             if action["type"] == "redis_save_image":
                 key = action["key"].format(**action_context)
-                _, buffer = cv2.imencode('.jpg', frame)
+                
+                # Check if we need to crop a specific region
+                region_name = action.get("region")
+                image_to_save = frame
+                
+                if region_name and regions_dict:
+                    cropped_image = crop_region_by_class(frame, regions_dict, region_name)
+                    if cropped_image is not None:
+                        image_to_save = cropped_image
+                        logger.debug(f"Cropped region '{region_name}' for redis_save_image")
+                    else:
+                        logger.warning(f"Could not crop region '{region_name}', saving full frame instead")
+                
+                # Encode image with specified format and quality (default to JPEG)
+                img_format = action.get("format", "jpeg").lower()
+                quality = action.get("quality", 90)
+                
+                if img_format == "jpeg":
+                    encode_params = [cv2.IMWRITE_JPEG_QUALITY, quality]
+                    success, buffer = cv2.imencode('.jpg', image_to_save, encode_params)
+                elif img_format == "png":
+                    success, buffer = cv2.imencode('.png', image_to_save)
+                else:
+                    success, buffer = cv2.imencode('.jpg', image_to_save, [cv2.IMWRITE_JPEG_QUALITY, quality])
+                
+                if not success:
+                    logger.error(f"Failed to encode image for redis_save_image")
+                    continue
+                
                 expire_seconds = action.get("expire_seconds")
                 if expire_seconds:
                     node["redis_client"].setex(key, expire_seconds, buffer.tobytes())
@@ -231,60 +341,244 @@ def execute_actions(node, frame, detection_result):
                 else:
                     node["redis_client"].set(key, buffer.tobytes())
                     logger.info(f"Saved image to Redis with key: {key}")
-                # Add the generated key to the context for subsequent actions
                 action_context["image_key"] = key
             elif action["type"] == "redis_publish":
                 channel = action["channel"]
-                message = action["message"].format(**action_context)
+                try:
-                node["redis_client"].publish(channel, message)
+                    # Handle JSON message format by creating it programmatically
+                    message_template = action["message"]
+                    
+                    # Check if the message is JSON-like (starts and ends with braces)
+                    if message_template.strip().startswith('{') and message_template.strip().endswith('}'):
+                        # Create JSON data programmatically to avoid formatting issues
+                        json_data = {}
+                        
+                        # Add common fields
+                        json_data["event"] = "frontal_detected"
+                        json_data["display_id"] = action_context.get("display_id", "unknown")
+                        json_data["session_id"] = action_context.get("session_id")
+                        json_data["timestamp"] = action_context.get("timestamp", "")
+                        json_data["image_key"] = action_context.get("image_key", "")
+                        
+                        # Convert to JSON string
+                        message = json.dumps(json_data)
+                    else:
+                        # Use regular string formatting for non-JSON messages
+                        message = message_template.format(**action_context)
+                    
+                    # Publish to Redis
+                    if not node["redis_client"]:
+                        logger.error("Redis client is None, cannot publish message")
+                        continue
+                        
+                    # Test Redis connection
+                    try:
+                        node["redis_client"].ping()
+                        logger.debug("Redis connection is active")
+                    except Exception as ping_error:
+                        logger.error(f"Redis connection test failed: {ping_error}")
+                        continue
+                    
+                    result = node["redis_client"].publish(channel, message)
                     logger.info(f"Published message to Redis channel '{channel}': {message}")
+                    logger.info(f"Redis publish result (subscribers count): {result}")
+                    
+                    # Additional debug info
+                    if result == 0:
+                        logger.warning(f"No subscribers listening to channel '{channel}'")
+                    else:
+                        logger.info(f"Message delivered to {result} subscriber(s)")
+                    
+                except KeyError as e:
+                    logger.error(f"Missing key in redis_publish message template: {e}")
+                    logger.debug(f"Available context keys: {list(action_context.keys())}")
+                except Exception as e:
+                    logger.error(f"Error in redis_publish action: {e}")
+                    logger.debug(f"Message template: {action['message']}")
+                    logger.debug(f"Available context keys: {list(action_context.keys())}")
+                    import traceback
+                    logger.debug(f"Full traceback: {traceback.format_exc()}")
         except Exception as e:
             logger.error(f"Error executing action {action['type']}: {e}")
 
-def run_pipeline(frame, node: dict, return_bbox: bool=False):
+def execute_parallel_actions(node, frame, detection_result, regions_dict):
+    """Execute parallel actions after all required branches have completed."""
+    if not node.get("parallelActions"):
+        return
+    
+    logger.debug("Executing parallel actions...")
+    branch_results = detection_result.get("branch_results", {})
+    
+    for action in node["parallelActions"]:
+        try:
+            action_type = action.get("type")
+            logger.debug(f"Processing parallel action: {action_type}")
+            
+            if action_type == "postgresql_update_combined":
+                # Check if all required branches have completed
+                wait_for_branches = action.get("waitForBranches", [])
+                missing_branches = [branch for branch in wait_for_branches if branch not in branch_results]
+                
+                if missing_branches:
+                    logger.warning(f"Cannot execute postgresql_update_combined: missing branch results for {missing_branches}")
+                    continue
+                
+                logger.info(f"All required branches completed: {wait_for_branches}")
+                
+                # Execute the database update
+                execute_postgresql_update_combined(node, action, detection_result, branch_results)
+            else:
+                logger.warning(f"Unknown parallel action type: {action_type}")
+                
+        except Exception as e:
+            logger.error(f"Error executing parallel action {action.get('type', 'unknown')}: {e}")
+            import traceback
+            logger.debug(f"Full traceback: {traceback.format_exc()}")
+
+def execute_postgresql_update_combined(node, action, detection_result, branch_results):
+    """Execute a PostgreSQL update with combined branch results."""
+    if not node.get("db_manager"):
+        logger.error("No database manager available for postgresql_update_combined action")
+        return
+        
+    try:
+        table = action["table"]
+        key_field = action["key_field"]
+        key_value_template = action["key_value"]
+        fields = action["fields"]
+        
+        # Create context for key value formatting
+        action_context = {**detection_result}
+        key_value = key_value_template.format(**action_context)
+        
+        logger.info(f"Executing database update: table={table}, {key_field}={key_value}")
+        
+        # Process field mappings
+        mapped_fields = {}
+        for db_field, value_template in fields.items():
+            try:
+                mapped_value = resolve_field_mapping(value_template, branch_results, action_context)
+                if mapped_value is not None:
+                    mapped_fields[db_field] = mapped_value
+                    logger.debug(f"Mapped field: {db_field} = {mapped_value}")
+                else:
+                    logger.warning(f"Could not resolve field mapping for {db_field}: {value_template}")
+            except Exception as e:
+                logger.error(f"Error mapping field {db_field} with template '{value_template}': {e}")
+        
+        if not mapped_fields:
+            logger.warning("No fields mapped successfully, skipping database update")
+            return
+            
+        # Execute the database update
+        success = node["db_manager"].execute_update(table, key_field, key_value, mapped_fields)
+        
+        if success:
+            logger.info(f"Successfully updated database: {table} with {len(mapped_fields)} fields")
+        else:
+            logger.error(f"Failed to update database: {table}")
+            
+    except KeyError as e:
+        logger.error(f"Missing required field in postgresql_update_combined action: {e}")
+    except Exception as e:
+        logger.error(f"Error in postgresql_update_combined action: {e}")
+        import traceback
+        logger.debug(f"Full traceback: {traceback.format_exc()}")
+
+def resolve_field_mapping(value_template, branch_results, action_context):
+    """Resolve field mapping templates like {car_brand_cls_v1.brand}."""
+    try:
+        # Handle simple context variables first (non-branch references)
+        if not '.' in value_template:
+            return value_template.format(**action_context)
+        
+        # Handle branch result references like {model_id.field}
+        import re
+        branch_refs = re.findall(r'\{([^}]+\.[^}]+)\}', value_template)
+        
+        resolved_template = value_template
+        for ref in branch_refs:
+            try:
+                model_id, field_name = ref.split('.', 1)
+                
+                if model_id in branch_results:
+                    branch_data = branch_results[model_id]
+                    if field_name in branch_data:
+                        field_value = branch_data[field_name]
+                        resolved_template = resolved_template.replace(f'{{{ref}}}', str(field_value))
+                        logger.debug(f"Resolved {ref} to {field_value}")
+                    else:
+                        logger.warning(f"Field '{field_name}' not found in branch '{model_id}' results. Available fields: {list(branch_data.keys())}")
+                        return None
+                else:
+                    logger.warning(f"Branch '{model_id}' not found in results. Available branches: {list(branch_results.keys())}")
+                    return None
+            except ValueError as e:
+                logger.error(f"Invalid branch reference format: {ref}")
+                return None
+        
+        # Format any remaining simple variables
+        try:
+            final_value = resolved_template.format(**action_context)
+            return final_value
+        except KeyError as e:
+            logger.warning(f"Could not resolve context variable in template: {e}")
+            return resolved_template
+            
+    except Exception as e:
+        logger.error(f"Error resolving field mapping '{value_template}': {e}")
+        return None
+
+def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
     """
-    - For detection nodes (task != 'classify'):
+    Enhanced pipeline that supports:
-        • runs `track(..., classes=triggerClassIndices)`
+    - Multi-class detection (detecting multiple classes simultaneously)
-        • picks top box ≥ minConfidence
+    - Parallel branch processing
-        • optionally crops & resizes → recurse into child
+    - Region-based actions and cropping
-        • else returns (det_dict, bbox)
+    - Context passing for session/camera information
-    - For classify nodes:
-        • runs `predict()`
-        • returns top (class,confidence) and no bbox
     """
     try:
         task = getattr(node["model"], "task", None)
 
         # ─── Classification stage ───────────────────────────────────
         if task == "classify":
-            # run the classifier and grab its top-1 directly via the Probs API
             results = node["model"].predict(frame, stream=False)
-            # nothing returned?
             if not results:
                 return (None, None) if return_bbox else None
 
-            # take the first result's probs object
             r = results[0]
             probs = r.probs
             if probs is None:
                 return (None, None) if return_bbox else None
 
-            # get the top-1 class index and its confidence
             top1_idx = int(probs.top1)
             top1_conf = float(probs.top1conf)
+            class_name = node["model"].names[top1_idx]
 
             det = {
-                "class": node["model"].names[top1_idx],
+                "class": class_name,
                 "confidence": top1_conf,
-                "id": None
+                "id": None,
+                class_name: class_name  # Add class name as key for backward compatibility
             }
+            
+            # Add specific field mappings for database operations based on model type
+            model_id = node.get("modelId", "").lower()
+            if "brand" in model_id or "brand_cls" in model_id:
+                det["brand"] = class_name
+            elif "bodytype" in model_id or "body" in model_id:
+                det["body_type"] = class_name
+            elif "color" in model_id:
+                det["color"] = class_name
+            
             execute_actions(node, frame, det)
             return (det, None) if return_bbox else det
 
-
+        # ─── Detection stage - Multi-class support ──────────────────
-        # ─── Detection stage ────────────────────────────────────────
-        # only look for your triggerClasses
         tk = node["triggerClassIndices"]
+        logger.debug(f"Running detection for node {node['modelId']} with trigger classes: {node.get('triggerClasses', [])} (indices: {tk})")
+        logger.debug(f"Node configuration: minConfidence={node['minConfidence']}, multiClass={node.get('multiClass', False)}")
+        
         res = node["model"].track(
             frame,
             stream=False,
@@ -292,48 +586,228 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False):
             **({"classes": tk} if tk else {})
         )[0]
 
-        dets, boxes = [], []
+        # Collect all detections above confidence threshold
-        for box in res.boxes:
+        all_detections = []
+        all_boxes = []
+        regions_dict = {}
+        
+        logger.debug(f"Raw detection results from model: {len(res.boxes) if res.boxes is not None else 0} detections")
+        
+        for i, box in enumerate(res.boxes):
             conf = float(box.cpu().conf[0])
             cid = int(box.cpu().cls[0])
             name = node["model"].names[cid]
+            
+            logger.debug(f"Detection {i}: class='{name}' (id={cid}), confidence={conf:.3f}, threshold={node['minConfidence']}")
+            
             if conf < node["minConfidence"]:
+                logger.debug(f"  -> REJECTED: confidence {conf:.3f} < threshold {node['minConfidence']}")
                 continue
+                
             xy = box.cpu().xyxy[0]
             x1, y1, x2, y2 = map(int, xy)
-            dets.append({"class": name, "confidence": conf,
+            bbox = (x1, y1, x2, y2)
-                         "id": box.id.item() if hasattr(box, "id") else None})
-            boxes.append((x1, y1, x2, y2))
             
-        if not dets:
+            detection = {
+                "class": name,
+                "confidence": conf,
+                "id": box.id.item() if hasattr(box, "id") else None,
+                "bbox": bbox
+            }
+            
+            all_detections.append(detection)
+            all_boxes.append(bbox)
+            
+            logger.debug(f"  -> ACCEPTED: {name} with confidence {conf:.3f}, bbox={bbox}")
+            
+            # Store highest confidence detection for each class
+            if name not in regions_dict or conf > regions_dict[name]["confidence"]:
+                regions_dict[name] = {
+                    "bbox": bbox,
+                    "confidence": conf,
+                    "detection": detection
+                }
+                logger.debug(f"  -> Updated regions_dict['{name}'] with confidence {conf:.3f}")
+
+        logger.info(f"Detection summary: {len(all_detections)} accepted detections from {len(res.boxes) if res.boxes is not None else 0} total")
+        logger.info(f"Detected classes: {list(regions_dict.keys())}")
+
+        if not all_detections:
+            logger.warning("No detections above confidence threshold - returning null")
             return (None, None) if return_bbox else None
 
-        # take highest‐confidence
+        # ─── Multi-class validation ─────────────────────────────────
-        best_idx = max(range(len(dets)), key=lambda i: dets[i]["confidence"])
+        if node.get("multiClass", False) and node.get("expectedClasses"):
-        best_det = dets[best_idx]
+            expected_classes = node["expectedClasses"]
-        best_box = boxes[best_idx]
+            detected_classes = list(regions_dict.keys())
             
-        # ─── Branch (classification) ───────────────────────────────
+            logger.info(f"Multi-class validation: expected={expected_classes}, detected={detected_classes}")
+            
+            # Check if at least one expected class is detected (flexible mode)
+            matching_classes = [cls for cls in expected_classes if cls in detected_classes]
+            missing_classes = [cls for cls in expected_classes if cls not in detected_classes]
+            
+            logger.debug(f"Matching classes: {matching_classes}, Missing classes: {missing_classes}")
+            
+            if not matching_classes:
+                # No expected classes found at all
+                logger.warning(f"PIPELINE REJECTED: No expected classes detected. Expected: {expected_classes}, Detected: {detected_classes}")
+                return (None, None) if return_bbox else None
+            
+            if missing_classes:
+                logger.info(f"Partial multi-class detection: {matching_classes} found, {missing_classes} missing")
+            else:
+                logger.info(f"Complete multi-class detection success: {detected_classes}")
+        else:
+            logger.debug("No multi-class validation - proceeding with all detections")
+
+        # ─── Execute actions with region information ────────────────
+        detection_result = {
+            "detections": all_detections,
+            "regions": regions_dict,
+            **(context or {})
+        }
+        
+        # ─── Create initial database record when Car+Frontal detected ────
+        if node.get("db_manager") and node.get("multiClass", False):
+            # Only create database record if we have both Car and Frontal
+            has_car = "Car" in regions_dict
+            has_frontal = "Frontal" in regions_dict
+            
+            if has_car and has_frontal:
+                # Generate UUID session_id since client session is None for now
+                import uuid as uuid_lib
+                from datetime import datetime
+                generated_session_id = str(uuid_lib.uuid4())
+                
+                # Insert initial detection record
+                display_id = detection_result.get("display_id", "unknown")
+                timestamp = datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+                
+                inserted_session_id = node["db_manager"].insert_initial_detection(
+                    display_id=display_id,
+                    captured_timestamp=timestamp,
+                    session_id=generated_session_id
+                )
+                
+                if inserted_session_id:
+                    # Update detection_result with the generated session_id for actions and branches
+                    detection_result["session_id"] = inserted_session_id
+                    detection_result["timestamp"] = timestamp  # Update with proper timestamp
+                    logger.info(f"Created initial database record with session_id: {inserted_session_id}")
+            else:
+                logger.debug(f"Database record not created - missing required classes. Has Car: {has_car}, Has Frontal: {has_frontal}")
+        
+        execute_actions(node, frame, detection_result, regions_dict)
+
+        # ─── Parallel branch processing ─────────────────────────────
+        if node["branches"]:
+            branch_results = {}
+            
+            # Filter branches that should be triggered
+            active_branches = []
             for br in node["branches"]:
-            if (best_det["class"] in br["triggerClasses"]
+                trigger_classes = br.get("triggerClasses", [])
-                    and best_det["confidence"] >= br["minConfidence"]):
+                min_conf = br.get("minConfidence", 0)
-                # crop if requested
-                sub = frame
-                if br["crop"]:
-                    x1,y1,x2,y2 = best_box
-                    sub = frame[y1:y2, x1:x2]
-                    sub = cv2.resize(sub, (224, 224))
                 
-                det2, _ = run_pipeline(sub, br, return_bbox=True)
+                logger.debug(f"Evaluating branch {br['modelId']}: trigger_classes={trigger_classes}, min_conf={min_conf}")
-                if det2:
-                    # return classification result + original bbox
-                    execute_actions(br, sub, det2)
-                    return (det2, best_box) if return_bbox else det2
                 
-        # ─── No branch matched → return this detection ─────────────
+                # Check if any detected class matches branch trigger
-        execute_actions(node, frame, best_det)
+                branch_triggered = False
-        return (best_det, best_box) if return_bbox else best_det
+                for det_class in regions_dict:
+                    det_confidence = regions_dict[det_class]["confidence"]
+                    logger.debug(f"  Checking detected class '{det_class}' (confidence={det_confidence:.3f}) against triggers {trigger_classes}")
+                    
+                    if (det_class in trigger_classes and det_confidence >= min_conf):
+                        active_branches.append(br)
+                        branch_triggered = True
+                        logger.info(f"Branch {br['modelId']} activated by class '{det_class}' (conf={det_confidence:.3f} >= {min_conf})")
+                        break
+                
+                if not branch_triggered:
+                    logger.debug(f"Branch {br['modelId']} not triggered - no matching classes or insufficient confidence")
+            
+            if active_branches:
+                if node.get("parallel", False) or any(br.get("parallel", False) for br in active_branches):
+                    # Run branches in parallel
+                    with concurrent.futures.ThreadPoolExecutor(max_workers=len(active_branches)) as executor:
+                        futures = {}
+                        
+                        for br in active_branches:
+                            crop_class = br.get("cropClass", br.get("triggerClasses", [])[0] if br.get("triggerClasses") else None)
+                            sub_frame = frame
+                            
+                            logger.info(f"Starting parallel branch: {br['modelId']}, crop_class: {crop_class}")
+                            
+                            if br.get("crop", False) and crop_class:
+                                cropped = crop_region_by_class(frame, regions_dict, crop_class)
+                                if cropped is not None:
+                                    sub_frame = cv2.resize(cropped, (224, 224))
+                                    logger.debug(f"Successfully cropped {crop_class} region for {br['modelId']}")
+                                else:
+                                    logger.warning(f"Failed to crop {crop_class} region for {br['modelId']}, skipping branch")
+                                    continue
+                            
+                            future = executor.submit(run_pipeline, sub_frame, br, True, context)
+                            futures[future] = br
+                        
+                        # Collect results
+                        for future in concurrent.futures.as_completed(futures):
+                            br = futures[future]
+                            try:
+                                result, _ = future.result()
+                                if result:
+                                    branch_results[br["modelId"]] = result
+                                    logger.info(f"Branch {br['modelId']} completed: {result}")
+                            except Exception as e:
+                                logger.error(f"Branch {br['modelId']} failed: {e}")
+                else:
+                    # Run branches sequentially  
+                    for br in active_branches:
+                        crop_class = br.get("cropClass", br.get("triggerClasses", [])[0] if br.get("triggerClasses") else None)
+                        sub_frame = frame
+                        
+                        logger.info(f"Starting sequential branch: {br['modelId']}, crop_class: {crop_class}")
+                        
+                        if br.get("crop", False) and crop_class:
+                            cropped = crop_region_by_class(frame, regions_dict, crop_class)
+                            if cropped is not None:
+                                sub_frame = cv2.resize(cropped, (224, 224))
+                                logger.debug(f"Successfully cropped {crop_class} region for {br['modelId']}")
+                            else:
+                                logger.warning(f"Failed to crop {crop_class} region for {br['modelId']}, skipping branch")
+                                continue
+                        
+                        try:
+                            result, _ = run_pipeline(sub_frame, br, True, context)
+                            if result:
+                                branch_results[br["modelId"]] = result
+                                logger.info(f"Branch {br['modelId']} completed: {result}")
+                            else:
+                                logger.warning(f"Branch {br['modelId']} returned no result")
+                        except Exception as e:
+                            logger.error(f"Error in sequential branch {br['modelId']}: {e}")
+                            import traceback
+                            logger.debug(f"Branch error traceback: {traceback.format_exc()}")
+
+            # Store branch results in detection_result for parallel actions
+            detection_result["branch_results"] = branch_results
+
+        # ─── Execute Parallel Actions ───────────────────────────────
+        if node.get("parallelActions") and "branch_results" in detection_result:
+            execute_parallel_actions(node, frame, detection_result, regions_dict)
+
+        # ─── Return detection result ────────────────────────────────
+        primary_detection = max(all_detections, key=lambda x: x["confidence"])
+        primary_bbox = primary_detection["bbox"]
+        
+        # Add branch results to primary detection for compatibility
+        if "branch_results" in detection_result:
+            primary_detection["branch_results"] = detection_result["branch_results"]
+        
+        return (primary_detection, primary_bbox) if return_bbox else primary_detection
 
     except Exception as e:
-        logging.error(f"Error in node {node.get('modelId')}: {e}")
+        logger.error(f"Error in node {node.get('modelId')}: {e}")
+        traceback.print_exc()
         return (None, None) if return_bbox else None

worker.md

@@ -2,6 +2,12 @@
 
 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.
@@ -25,14 +31,34 @@ 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 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:
+**The current implementation supports comprehensive pipeline configurations including:**
 
-- AI/ML Models: Pre-trained models for libraries like TensorFlow, PyTorch, or ONNX.
+- **AI/ML Models**: YOLO models (.pt files) for detection and classification
-- Configuration Files: A `config.json` or `pipeline.yaml` that defines a sequence of operations, specifies model paths, or sets detection thresholds.
+- **Pipeline Configuration**: `pipeline.json` defining hierarchical detection→classification workflows
-- Scripts: Custom Python scripts for pre-processing or post-processing.
+- **Multi-class Detection**: Simultaneous detection of multiple object classes (e.g., Car + Frontal)
-- API Integration Details: A JSON file with endpoint information and credentials for interacting with third-party detection services.
+- **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
 
-Essentially, the `.mpta` file is a self-contained package that tells your worker *how* to process the video stream for a given subscription.
+**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.
 
 ## 4. Messages from Worker to Backend
 
@@ -79,6 +105,15 @@ 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
@@ -88,19 +123,38 @@ Sent when the worker detects a relevant object. The `detection` object should be
   "timestamp": "2025-07-14T12:34:56.789Z",
   "data": {
     "detection": {
-      "carModel": "Civic",
+      "class": "Car",
+      "confidence": 0.92,
       "carBrand": "Honda",
-      "carYear": 2023,
+      "carModel": "Civic",
       "bodyType": "Sedan",
-      "licensePlateText": "ABCD1234",
+      "branch_results": {
-      "licensePlateConfidence": 0.95
+        "car_brand_cls_v1": {
+          "class": "Honda",
+          "confidence": 0.89,
+          "brand": "Honda"
+        },
+        "car_bodytype_cls_v1": {
+          "class": "Sedan", 
+          "confidence": 0.85,
+          "body_type": "Sedan"
+        }
+      }
     },
     "modelId": 101,
-    "modelName": "US-LPR-and-Vehicle-ID"
+    "modelName": "Car Frontal Detection V1"
   }
 }
 ```
 
+**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.