new logic

.gitea/workflows/build.yml

@@ -1,112 +0,0 @@
-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
-    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: ${{ secrets.RUNNER_TOKEN }}
-        
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          file: ./Dockerfile
-          push: true
-          tags: git.siwatsystem.com/adsist-cms/worker:${{ 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

.gitignore

@@ -7,9 +7,3 @@ __pycache__/
 .mptacache
 
 mptas
-detector_worker.log
-.gitignore
-no_frame_debug.log
-
-feeder/
-.venv/

CLAUDE.md

@@ -1,277 +0,0 @@
-# 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 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 + 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
-
-### Main Application (`app.py`)
-- **FastAPI WebSocket server** for real-time communication
-- **Multi-camera stream management** with shared stream optimization
-- **HTTP REST endpoint** for image retrieval (`/camera/{camera_id}/image`)
-- **Threading-based frame readers** for RTSP streams and HTTP snapshots
-- **Model loading and inference** using MPTA (Machine Learning Pipeline Archive) format
-- **Session management** with display identifier mapping
-- **Resource monitoring** (CPU, memory, GPU usage via psutil)
-
-### Pipeline System (`siwatsystem/pympta.py`)
-- **MPTA file handling** - ZIP archives containing model configurations
-- **Hierarchical pipeline execution** with detection → classification branching
-- **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
-- **Pipeline webcam utility** (`pipeline_webcam.py`) for local testing with visual output
-- **RTSP streaming debug tool** (`debug/rtsp_webcam.py`) using GStreamer
-
-## Code Conventions & Patterns
-
-### Logging
-- **Structured logging** using Python's logging module
-- **File + console output** to `detector_worker.log`
-- **Debug level separation** for detailed troubleshooting
-- **Context-aware messages** with camera IDs and model information
-
-### Error Handling
-- **Graceful failure handling** with retry mechanisms (configurable max_retries)
-- **Thread-safe operations** using locks for streams and models
-- **WebSocket disconnect handling** with proper cleanup
-- **Model loading validation** with detailed error reporting
-
-### Configuration
-- **JSON configuration** (`config.json`) for runtime parameters:
-  - `poll_interval_ms`: Frame processing interval
-  - `max_streams`: Concurrent stream limit
-  - `target_fps`: Target frame rate
-  - `reconnect_interval_sec`: Stream reconnection delay
-  - `max_retries`: Maximum retry attempts (-1 for unlimited)
-
-### Threading Model
-- **Frame reader threads** for each camera stream (RTSP/HTTP)
-- **Shared stream optimization** - multiple subscriptions can reuse the same camera stream
-- **Async WebSocket handling** with concurrent task management
-- **Thread-safe data structures** with proper locking mechanisms
-
-## WebSocket Protocol
-
-### Message Types
-- **subscribe**: Start camera stream with model pipeline
-- **unsubscribe**: Stop camera stream processing
-- **requestState**: Request current worker status
-- **setSessionId**: Associate display with session identifier
-- **patchSession**: Update session data
-- **stateReport**: Periodic heartbeat with system metrics
-- **imageDetection**: Detection results with timestamp and model info
-
-### Subscription Format
-```json
-{
-  "type": "subscribe",
-  "payload": {
-    "subscriptionIdentifier": "display-001;cam-001",
-    "rtspUrl": "rtsp://...",  // OR snapshotUrl
-    "snapshotUrl": "http://...",
-    "snapshotInterval": 5000,
-    "modelUrl": "http://...model.mpta",
-    "modelId": 101,
-    "modelName": "Vehicle Detection",
-    "cropX1": 100, "cropY1": 200,
-    "cropX2": 300, "cropY2": 400
-  }
-}
-```
-
-## Model Pipeline (MPTA) Format
-
-### Enhanced Structure
-- **ZIP archive** containing models and configuration
-- **pipeline.json** - Main configuration file with Redis + PostgreSQL settings
-- **Model files** - YOLO .pt files for detection/classification
-- **Multi-model support** - Detection + multiple classification models
-
-### Advanced Pipeline Flow
-1. **Multi-class detection stage** - YOLO detection of Car + Frontal simultaneously
-2. **Validation stage** - Check for expected classes (flexible matching)
-3. **Database initialization** - Create initial record with session_id
-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
-
-### Enhanced Branch Configuration
-```json
-{
-  "modelId": "car_frontal_detection_v1",
-  "modelFile": "car_frontal_detection_v1.pt",
-  "multiClass": true,
-  "expectedClasses": ["Car", "Frontal"],
-  "triggerClasses": ["Car", "Frontal"],
-  "minConfidence": 0.8,
-  "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,
-      "cropClass": "Frontal",
-      "triggerClasses": ["Frontal"],
-      "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}"
-      }
-    }
-  ]
-}
-```
-
-## Stream Management
-
-### Shared Streams
-- Multiple subscriptions can share the same camera URL
-- Reference counting prevents premature stream termination
-- Automatic cleanup when last subscription ends
-
-### Frame Processing
-- **Queue-based buffering** with single frame capacity (latest frame only)
-- **Configurable polling interval** based on target FPS
-- **Automatic reconnection** with exponential backoff
-
-## Development & Testing
-
-### Local Development
-```bash
-# Install dependencies
-pip install -r requirements.txt
-
-# Run the worker
-python app.py
-
-# Test protocol compliance
-python test_protocol.py
-
-# Test pipeline with webcam
-python pipeline_webcam.py --mpta-file path/to/model.mpta --video 0
-```
-
-### Docker Deployment
-```bash
-# Build container
-docker build -t detector-worker .
-
-# Run with volume mounts for models
-docker run -p 8000:8000 -v ./models:/app/models detector-worker
-```
-
-### Testing Commands
-- **Protocol testing**: `python test_protocol.py`
-- **Pipeline validation**: `python pipeline_webcam.py --mpta-file <path> --video 0`
-- **RTSP debugging**: `python debug/rtsp_webcam.py`
-
-## Dependencies
-- **fastapi[standard]**: Web framework with WebSocket support
-- **uvicorn**: ASGI server
-- **torch, torchvision**: PyTorch for ML inference
-- **ultralytics**: YOLO implementation
-- **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
-- Redis connections use authentication when configured
-- 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

@@ -1,11 +1,19 @@
-# Use our pre-built base image with ML dependencies
+# Use the official Python image from the Docker Hub
-FROM git.siwatsystem.com/adsist-cms/worker-base:latest
+FROM python:3.13-bookworm
 
-# Copy and install application requirements (frequently changing dependencies)
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container at /app
 COPY requirements.txt .
+
+# Update apt, install libgl1, and clear apt cache
+RUN apt update && apt install -y libgl1 && rm -rf /var/lib/apt/lists/*
+
+# Install any dependencies specified in requirements.txt
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy the application code
+# Copy the rest of the application code into the container at /app
 COPY . .
 
 # Run the application

Dockerfile.base

@@ -1,15 +0,0 @@
-# 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_single.py

@@ -0,0 +1,366 @@
+from typing import List
+from fastapi import FastAPI, WebSocket
+from fastapi.websockets import WebSocketDisconnect
+from websockets.exceptions import ConnectionClosedError
+from ultralytics import YOLO
+import torch
+import cv2
+import base64
+import numpy as np
+import json
+import logging
+import threading
+import queue
+import os
+import requests
+from urllib.parse import urlparse
+import asyncio
+import psutil
+
+app = FastAPI()
+
+models = {}
+
+with open("config.json", "r") as f:
+    config = json.load(f)
+
+poll_interval = config.get("poll_interval_ms", 100)
+reconnect_interval = config.get("reconnect_interval_sec", 5) 
+TARGET_FPS = config.get("target_fps", 10)
+poll_interval = 1000 / TARGET_FPS
+logging.info(f"Poll interval: {poll_interval}ms")
+max_streams = config.get("max_streams", 5)
+max_retries = config.get("max_retries", 3)
+
+# Configure logging
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[
+        logging.FileHandler("app.log"),
+        logging.StreamHandler()
+    ]
+)
+
+# Ensure the models directory exists
+os.makedirs("models", exist_ok=True)
+
+# Add constants for heartbeat
+HEARTBEAT_INTERVAL = 2  # seconds
+WORKER_TIMEOUT_MS = 10000
+
+# Add a lock for thread-safe operations on shared resources
+streams_lock = threading.Lock()
+models_lock = threading.Lock()
+
+@app.websocket("/")
+async def detect(websocket: WebSocket):
+    import asyncio
+    import time
+
+    logging.info("WebSocket connection accepted")
+
+    streams = {}
+
+    # This function is user-modifiable
+    # Save data you want to persist across frames in the persistent_data dictionary
+    async def handle_detection(camera_id, stream, frame, websocket, model: YOLO, persistent_data):
+        try:
+            highest_conf_box = None
+            max_conf = -1
+            
+            for r in model.track(frame, stream=False, persist=True):
+                for box in r.boxes:
+                    box_cpu = box.cpu()
+                    conf = float(box_cpu.conf[0])
+                    if conf > max_conf and hasattr(box, "id") and box.id is not None:
+                        max_conf = conf
+                        highest_conf_box = {
+                            "class": model.names[int(box_cpu.cls[0])],
+                            "confidence": conf,
+                            "id": box.id.item(),
+                        }
+            
+            # Broadcast to all subscribers of this URL
+            detection_data = {
+                "type": "imageDetection",
+                "cameraIdentifier": camera_id,
+                "timestamp": time.time(),
+                "data": {
+                    "detections": highest_conf_box if highest_conf_box else None,
+                    "modelId": stream['modelId'],
+                    "modelName": stream['modelName']
+                }
+            }
+            logging.debug(f"Sending detection data for camera {camera_id}: {detection_data}")
+            await websocket.send_json(detection_data)
+            return persistent_data
+        except Exception as e:
+            logging.error(f"Error in handle_detection for camera {camera_id}: {e}")
+            return persistent_data
+
+    def frame_reader(camera_id, cap, buffer, stop_event):
+        import time
+        retries = 0
+        try:
+            while not stop_event.is_set():
+                try:
+                    ret, frame = cap.read()
+                    if not ret:
+                        logging.warning(f"Connection lost for camera: {camera_id}, retry {retries+1}/{max_retries}")
+                        cap.release()
+                        time.sleep(reconnect_interval)
+                        retries += 1
+                        if retries > max_retries and max_retries != -1:
+                            logging.error(f"Max retries reached for camera: {camera_id}")
+                            break
+                        # Re-open the VideoCapture
+                        cap = cv2.VideoCapture(streams[camera_id]['rtsp_url'])
+                        if not cap.isOpened():
+                            logging.error(f"Failed to reopen RTSP stream for camera: {camera_id}")
+                            continue
+                        continue
+                    retries = 0  # Reset on success
+                    if not buffer.empty():
+                        try:
+                            buffer.get_nowait()  # Discard the old frame
+                        except queue.Empty:
+                            pass
+                    buffer.put(frame)
+                except cv2.error as e:
+                    logging.error(f"OpenCV error for camera {camera_id}: {e}")
+                    cap.release()
+                    time.sleep(reconnect_interval)
+                    retries += 1
+                    if retries > max_retries and max_retries != -1:
+                        logging.error(f"Max retries reached after OpenCV error for camera: {camera_id}")
+                        break
+                    # Re-open the VideoCapture
+                    cap = cv2.VideoCapture(streams[camera_id]['rtsp_url'])
+                    if not cap.isOpened():
+                        logging.error(f"Failed to reopen RTSP stream for camera {camera_id} after OpenCV error")
+                        continue
+                except Exception as e:
+                    logging.error(f"Unexpected error for camera {camera_id}: {e}")
+                    cap.release()
+                    break
+        except Exception as e:
+            logging.error(f"Error in frame_reader thread for camera {camera_id}: {e}")
+
+    async def process_streams():
+        global models
+        logging.info("Started processing streams")
+        persistent_data_dict = {} 
+        try:
+            while True:
+                start_time = time.time()
+                # Round-robin processing
+                with streams_lock:
+                    current_streams = list(streams.items())
+                for camera_id, stream in current_streams:
+                    buffer = stream['buffer']
+                    if not buffer.empty():
+                        frame = buffer.get()
+                        with models_lock:
+                            model = models.get(camera_id, {}).get(stream['modelId'])
+                        key = (camera_id, stream['modelId'])
+                        persistent_data = persistent_data_dict.get(key, {})
+                        updated_persistent_data = await handle_detection(camera_id, stream, frame, websocket, model, persistent_data)
+                        persistent_data_dict[key] = updated_persistent_data
+                elapsed_time = (time.time() - start_time) * 1000  # in ms
+                sleep_time = max(poll_interval - elapsed_time, 0)
+                logging.debug(f"Elapsed time: {elapsed_time}ms, sleeping for: {sleep_time}ms")
+                await asyncio.sleep(sleep_time / 1000.0)
+        except asyncio.CancelledError:
+            logging.info("Stream processing task cancelled")
+        except Exception as e:
+            logging.error(f"Error in process_streams: {e}")
+
+    async def send_heartbeat():
+        while True:
+            try:
+                cpu_usage = psutil.cpu_percent()
+                memory_usage = psutil.virtual_memory().percent
+                if torch.cuda.is_available():
+                    gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)  # Convert to MB
+                    gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)  # Convert to MB
+                else:
+                    gpu_usage = None
+                    gpu_memory_usage = None
+            
+                camera_connections = [
+                    {
+                        "cameraIdentifier": camera_id,
+                        "modelId": stream['modelId'],
+                        "modelName": stream['modelName'],
+                        "online": True
+                    }
+                    for camera_id, stream in streams.items()
+                ]
+            
+                state_report = {
+                    "type": "stateReport",
+                    "cpuUsage": cpu_usage,
+                    "memoryUsage": memory_usage,
+                    "gpuUsage": gpu_usage,
+                    "gpuMemoryUsage": gpu_memory_usage,
+                    "cameraConnections": camera_connections
+                }
+                await websocket.send_text(json.dumps(state_report))
+                logging.debug("Sent stateReport as heartbeat")
+                await asyncio.sleep(HEARTBEAT_INTERVAL)
+            except Exception as e:
+                logging.error(f"Error sending stateReport heartbeat: {e}")
+                break
+
+    async def on_message():
+        global models
+        while True:
+            try:
+                msg = await websocket.receive_text()
+                logging.debug(f"Received message: {msg}")
+                print(f"Received message: {msg}")
+                data = json.loads(msg)
+                msg_type = data.get("type")
+
+                if msg_type == "subscribe":
+                    payload = data.get("payload", {})
+                    camera_id = payload.get("cameraIdentifier")
+                    rtsp_url = payload.get("rtspUrl")
+                    model_url = payload.get("modelUrl")
+                    modelId = payload.get("modelId")
+                    modelName = payload.get("modelName")
+    
+                    if model_url:
+                        with models_lock:
+                            if camera_id not in models:
+                                models[camera_id] = {}
+                            if modelId not in models[camera_id]:
+                                print(f"Downloading model from {model_url}")
+                                parsed_url = urlparse(model_url)
+                                filename = os.path.basename(parsed_url.path)    
+                                model_filename = os.path.join("models", filename)
+                                # Download the model
+                                response = requests.get(model_url, stream=True)
+                                if response.status_code == 200:
+                                    with open(model_filename, 'wb') as f:
+                                        for chunk in response.iter_content(chunk_size=8192):
+                                            f.write(chunk)
+                                    logging.info(f"Downloaded model from {model_url} to {model_filename}")
+                                    model = YOLO(model_filename)
+                                    if torch.cuda.is_available():
+                                        model.to('cuda')
+                                    models[camera_id][modelId] = model
+                                    logging.info(f"Loaded model {modelId} for camera {camera_id}")
+                                else:
+                                    logging.error(f"Failed to download model from {model_url}")
+                                    continue
+                    if camera_id and rtsp_url:
+                        with streams_lock:
+                            if camera_id not in streams and len(streams) < max_streams:
+                                cap = cv2.VideoCapture(rtsp_url)
+                                if not cap.isOpened():
+                                    logging.error(f"Failed to open RTSP stream for camera {camera_id}")
+                                    continue
+                                buffer = queue.Queue(maxsize=1)
+                                stop_event = threading.Event()
+                                thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
+                                thread.daemon = True
+                                thread.start()
+                                streams[camera_id] = {
+                                    'cap': cap,
+                                    'buffer': buffer,
+                                    'thread': thread,
+                                    'rtsp_url': rtsp_url,
+                                    'stop_event': stop_event,
+                                    'modelId': modelId,
+                                    'modelName': modelName
+                                }
+                                logging.info(f"Subscribed to camera {camera_id} with modelId {modelId}, modelName {modelName} and URL {rtsp_url}")
+                            elif camera_id and camera_id in streams:
+                                stream = streams.pop(camera_id)
+                                stream['cap'].release()
+                                logging.info(f"Unsubscribed from camera {camera_id}")
+                                if camera_id in models and modelId in models[camera_id]:
+                                    del models[camera_id][modelId]
+                                    if not models[camera_id]:
+                                        del models[camera_id]
+                elif msg_type == "unsubscribe":
+                    payload = data.get("payload", {})
+                    camera_id = payload.get("cameraIdentifier")
+                    logging.debug(f"Unsubscribing from camera {camera_id}")
+                    with streams_lock:
+                        if camera_id and camera_id in streams:
+                            stream = streams.pop(camera_id)
+                            stream['stop_event'].set()
+                            stream['thread'].join()
+                            stream['cap'].release()
+                            logging.info(f"Unsubscribed from camera {camera_id}")
+                            if camera_id in models and modelId in models[camera_id]:
+                                del models[camera_id][modelId]
+                                if not models[camera_id]:
+                                    del models[camera_id]
+                elif msg_type == "requestState":
+                    # Handle state request
+                    cpu_usage = psutil.cpu_percent()
+                    memory_usage = psutil.virtual_memory().percent
+                    if torch.cuda.is_available():
+                        gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)  # Convert to MB
+                        gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)  # Convert to MB
+                    else:
+                        gpu_usage = None
+                        gpu_memory_usage = None
+                    
+                    camera_connections = [
+                        {
+                            "cameraIdentifier": camera_id,
+                            "modelId": stream['modelId'],
+                            "modelName": stream['modelName'],
+                            "online": True
+                        }
+                        for camera_id, stream in streams.items()
+                    ]
+                    
+                    state_report = {
+                        "type": "stateReport",
+                        "cpuUsage": cpu_usage,
+                        "memoryUsage": memory_usage,
+                        "gpuUsage": gpu_usage,
+                        "gpuMemoryUsage": gpu_memory_usage,
+                        "cameraConnections": camera_connections
+                    }
+                    await websocket.send_text(json.dumps(state_report))
+                else:
+                    logging.error(f"Unknown message type: {msg_type}")
+            except json.JSONDecodeError:
+                logging.error("Received invalid JSON message")
+            except (WebSocketDisconnect, ConnectionClosedError) as e:
+                logging.warning(f"WebSocket disconnected: {e}")
+                break 
+            except Exception as e:
+                logging.error(f"Error handling message: {e}")
+                break
+
+    try:
+        await websocket.accept()
+        task = asyncio.create_task(process_streams())
+        heartbeat_task = asyncio.create_task(send_heartbeat())
+        message_task = asyncio.create_task(on_message())
+
+        await asyncio.gather(heartbeat_task, message_task)
+    except Exception as e:
+        logging.error(f"Error in detect websocket: {e}")
+    finally:
+        task.cancel()
+        await task
+        with streams_lock:
+            for camera_id, stream in streams.items():
+                stream['stop_event'].set()
+                stream['thread'].join()
+                stream['cap'].release()
+                stream['buffer'].queue.clear()
+                logging.info(f"Released camera {camera_id} and cleaned up resources")
+            streams.clear()
+        with models_lock:
+            models.clear()
+        logging.info("WebSocket connection closed")

debug.py

@@ -0,0 +1,143 @@
+import argparse
+import os
+import cv2
+import time
+import logging
+import shutil
+import threading  # added threading
+import yaml  # for silencing YOLO
+
+from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+
+# Silence YOLO logging
+os.environ["YOLO_VERBOSE"] = "False"
+for logger_name in ["ultralytics", "ultralytics.hub", "ultralytics.yolo.utils"]:
+    logging.getLogger(logger_name).setLevel(logging.WARNING)
+
+# Global variables for frame sharing
+global_frame = None
+global_ret = False
+capture_running = False
+
+def video_capture_loop(cap):
+    global global_frame, global_ret, capture_running
+    while capture_running:
+        global_ret, global_frame = cap.read()
+        time.sleep(0.01)  # slight delay to reduce CPU usage
+
+def clear_cache(cache_dir: str):
+    if os.path.exists(cache_dir):
+        shutil.rmtree(cache_dir)
+
+def log_pipeline_flow(frame, model_tree, level=0):
+    """
+    Wrapper around run_pipeline that logs the model flow and detection results.
+    Returns the same output as the original run_pipeline function.
+    """
+    indent = "  " * level
+    model_id = model_tree.get("modelId", "unknown")
+    logging.info(f"{indent}→ Running model: {model_id}")
+    
+    detection, bbox = run_pipeline(frame, model_tree, return_bbox=True)
+    
+    if detection:
+        confidence = detection.get("confidence", 0) * 100
+        class_name = detection.get("class", "unknown")
+        object_id = detection.get("id", "N/A")
+        
+        logging.info(f"{indent}✓ Detected: {class_name} (ID: {object_id}, confidence: {confidence:.1f}%)")
+        
+        # Check if any branches were triggered
+        triggered = False
+        for branch in model_tree.get("branches", []):
+            trigger_classes = branch.get("triggerClasses", [])
+            min_conf = branch.get("minConfidence", 0)
+            
+            if class_name in trigger_classes and detection.get("confidence", 0) >= min_conf:
+                triggered = True
+                if branch.get("crop", False) and bbox:
+                    x1, y1, x2, y2 = bbox
+                    cropped_frame = frame[y1:y2, x1:x2]
+                    logging.info(f"{indent}  ⌊ Triggering branch with cropped region {x1},{y1} to {x2},{y2}")
+                    branch_result = log_pipeline_flow(cropped_frame, branch, level + 1)
+                else:
+                    logging.info(f"{indent}  ⌊ Triggering branch with full frame")
+                    branch_result = log_pipeline_flow(frame, branch, level + 1)
+                
+                if branch_result[0]:  # If branch detection successful, return it
+                    return branch_result
+        
+        if not triggered and model_tree.get("branches"):
+            logging.info(f"{indent}  ⌊ No branches triggered")
+    else:
+        logging.info(f"{indent}✗ No detection for {model_id}")
+    
+    return detection, bbox
+
+def main(mpta_file: str, video_source: str):
+    global capture_running
+    CACHE_DIR = os.path.join(".", ".mptacache")
+    clear_cache(CACHE_DIR)
+    logging.info(f"Loading pipeline from local file: {mpta_file}")
+    model_tree = load_pipeline_from_zip(mpta_file, CACHE_DIR)
+    if model_tree is None:
+        logging.error("Failed to load pipeline.")
+        return
+
+    cap = cv2.VideoCapture(video_source)
+    if not cap.isOpened():
+        logging.error(f"Cannot open video source {video_source}")
+        return
+
+    # Start video capture in a separate thread
+    capture_running = True
+    capture_thread = threading.Thread(target=video_capture_loop, args=(cap,))
+    capture_thread.start()
+
+    logging.info("Press 'q' to exit.")
+    try:
+        while True:
+            # Use the global frame and ret updated by the thread
+            if not global_ret or global_frame is None:
+                continue  # wait until a frame is available
+            
+            frame = global_frame.copy()  # local copy to work with
+
+            # Replace run_pipeline with our logging version
+            detection, bbox = log_pipeline_flow(frame, model_tree)
+
+            # Stop if "honda" is detected
+            if detection and detection.get("class", "").lower() == "toyota":
+                logging.info("Detected 'toyota'. Stopping pipeline.")
+                break
+
+            if bbox:
+                x1, y1, x2, y2 = bbox
+                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                label = detection["class"] if detection else "Detection"
+                cv2.putText(frame, label, (x1, y1 - 10),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
+
+            cv2.imshow("Pipeline Webcam", frame)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+    finally:
+        # Stop capture thread and cleanup
+        capture_running = False
+        capture_thread.join()
+        cap.release()
+        cv2.destroyAllWindows()
+        clear_cache(CACHE_DIR)
+        logging.info("Cleaned up .mptacache directory on shutdown.")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run pipeline webcam utility.")
+    parser.add_argument("--mpta-file", type=str, required=True, help="Path to the local pipeline mpta (ZIP) file.")
+    parser.add_argument("--video", type=str, default="0", help="Video source (default webcam index 0).")
+    args = parser.parse_args()
+    video_source = int(args.video) if args.video.isdigit() else args.video
+    main(args.mpta_file, video_source)

pipeline.log

@@ -0,0 +1,23 @@
+2025-05-12 18:10:04,590 [INFO] Loading pipeline from local file: demoa.mpta
+2025-05-12 18:10:04,610 [INFO] Copied local .mpta file from demoa.mpta to .\.mptacache\pipeline.mpta
+2025-05-12 18:10:04,901 [INFO] Extracted .mpta file to .\.mptacache
+2025-05-12 18:10:04,905 [INFO] Loading model for node DetectionDraft from .\.mptacache\demoa\DetectionDraft.pt
+2025-05-12 18:10:05,083 [INFO] Loading model for node ClassificationDraft from .\.mptacache\demoa\ClassificationDraft.pt
+2025-05-12 18:10:08,035 [INFO] Press 'q' to exit.
+2025-05-12 18:10:12,217 [INFO] Cleaned up .mptacache directory on shutdown.
+2025-05-12 18:13:08,465 [INFO] Loading pipeline from local file: demoa.mpta
+2025-05-12 18:13:08,512 [INFO] Copied local .mpta file from demoa.mpta to .\.mptacache\pipeline.mpta
+2025-05-12 18:13:08,769 [INFO] Extracted .mpta file to .\.mptacache
+2025-05-12 18:13:08,773 [INFO] Loading model for node DetectionDraft from .\.mptacache\demoa\DetectionDraft.pt
+2025-05-12 18:13:09,083 [INFO] Loading model for node ClassificationDraft from .\.mptacache\demoa\ClassificationDraft.pt
+2025-05-12 18:13:12,187 [INFO] Press 'q' to exit.
+2025-05-12 18:13:14,146 [INFO] → Running model: DetectionDraft
+2025-05-12 18:13:17,119 [INFO] Cleaned up .mptacache directory on shutdown.
+2025-05-12 18:14:25,665 [INFO] Loading pipeline from local file: demoa.mpta
+2025-05-12 18:14:25,687 [INFO] Copied local .mpta file from demoa.mpta to .\.mptacache\pipeline.mpta
+2025-05-12 18:14:25,953 [INFO] Extracted .mpta file to .\.mptacache
+2025-05-12 18:14:25,957 [INFO] Loading model for node DetectionDraft from .\.mptacache\demoa\DetectionDraft.pt
+2025-05-12 18:14:26,138 [INFO] Loading model for node ClassificationDraft from .\.mptacache\demoa\ClassificationDraft.pt
+2025-05-12 18:14:29,171 [INFO] Press 'q' to exit.
+2025-05-12 18:14:30,146 [INFO] → Running model: DetectionDraft
+2025-05-12 18:14:32,080 [INFO] Cleaned up .mptacache directory on shutdown.

pympta.md

@@ -1,327 +0,0 @@
-# pympta: Modular Pipeline Task Executor
-
-`pympta` is a Python module designed to load and execute modular, multi-stage AI pipelines defined in a special package format (`.mpta`). It is primarily used within the detector worker to run complex computer vision tasks where the output of one model can trigger a subsequent model on a specific region of interest.
-
-## Core Concepts
-
-### 1. MPTA Package (`.mpta`)
-
-An `.mpta` file is a standard `.zip` archive with a different extension. It bundles all the necessary components for a pipeline to run.
-
-A typical `.mpta` file has the following structure:
-
-```
-my_pipeline.mpta/
-├── pipeline.json
-├── model1.pt
-├── model2.pt
-└── ...
-```
-
-- **`pipeline.json`**: (Required) The manifest file that defines the structure of the pipeline, the models to use, and the logic connecting them.
-- **Model Files (`.pt`, etc.)**: The actual pre-trained model files (e.g., PyTorch, ONNX). The pipeline currently uses `ultralytics.YOLO` models.
-
-### 2. Pipeline Structure
-
-A pipeline is a tree-like structure of "nodes," defined in `pipeline.json`.
-
-- **Root Node**: The entry point of the pipeline. It processes the initial, full-frame image.
-- **Branch Nodes**: Child nodes that are triggered by specific detection results from their parent. For example, a root node might detect a "vehicle," which then triggers a branch node to detect a "license plate" within the vehicle's bounding box.
-
-This modular structure allows for creating complex and efficient inference logic, avoiding the need to run every model on every frame.
-
-## `pipeline.json` Specification
-
-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`)
-
-| Key        | Type   | Required | Description                                             |
-| ---------- | ------ | -------- | ------------------------------------------------------- |
-| `host`     | String | Yes      | The hostname or IP address of the Redis server.         |
-| `port`     | Number | Yes      | The port number of the Redis server.                    |
-| `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                                                                                                                            |
-| ------------------- | ------------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------- |
-| `modelId`           | String        | Yes      | A unique identifier for this model node (e.g., "vehicle-detector").                                                                    |
-| `modelFile`         | String        | Yes      | The path to the model file within the `.mpta` archive (e.g., "yolov8n.pt").                                                            |
-| `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 and PostgreSQL databases. They are executed sequentially for a given detection.
-
-#### Action Context & Dynamic Keys
-
-All actions have access to a dynamic context for formatting keys and messages. The context is created for each detection event and includes:
-
-- 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`
-
-Saves the current image frame (or cropped sub-image) to a Redis key.
-
-| Key              | Type   | Required | Description                                                                                             |
-| ---------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- |
-| `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`
-
-Publishes a message to a Redis channel.
-
-| Key       | Type   | Required | Description                                                                                             |
-| --------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- |
-| `type`    | String | Yes      | Must be `"redis_publish"`.                                                                              |
-| `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}`.           |
-
-#### `postgresql_update_combined`
-
-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": "10.100.1.3",
-    "port": 6379,
-    "password": "your-redis-password",
-    "db": 0
-  },
-  "postgresql": {
-    "host": "10.100.1.3",
-    "port": 5432,
-    "database": "inference",
-    "username": "root",
-    "password": "your-db-password"
-  },
-  "pipeline": {
-    "modelId": "car_frontal_detection_v1",
-    "modelFile": "car_frontal_detection_v1.pt",
-    "crop": false,
-    "triggerClasses": ["Car", "Frontal"],
-    "minConfidence": 0.8,
-    "multiClass": true,
-    "expectedClasses": ["Car", "Frontal"],
-    "actions": [
-      {
-        "type": "redis_save_image",
-        "region": "Frontal",
-        "key": "inference:{display_id}:{timestamp}:{session_id}:{filename}",
-        "expire_seconds": 600,
-        "format": "jpeg",
-        "quality": 90
-      },
-      {
-        "type": "redis_publish",
-        "channel": "car_detections",
-        "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}"
-        }
-      }
-    ]
-  }
-}
-```
-
-## API Reference
-
-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 Redis and PostgreSQL connections if configured in `pipeline.json`.
-
-- **Parameters:**
-  - `zip_source` (str): The file path to the local `.mpta` zip archive.
-  - `target_dir` (str): A directory path where the archive's contents will be extracted.
-- **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, context: dict = None)`
-
-Executes the inference pipeline on a single image frame.
-
-- **Parameters:**
-  - `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 shows how to use `pympta` with the enhanced features:
-
-```python
-import cv2
-from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
-
-# 1. Define paths
-MPTA_FILE = "path/to/your/pipeline.mpta"
-CACHE_DIR = ".mptacache"
-
-# 2. Load the pipeline from the .mpta file
-# This reads pipeline.json and loads the YOLO models into memory.
-model_tree = load_pipeline_from_zip(MPTA_FILE, CACHE_DIR)
-
-if not model_tree:
-    print("Failed to load pipeline.")
-    exit()
-
-# 3. Open a video source
-cap = cv2.VideoCapture(0)
-
-while True:
-    ret, frame = cap.read()
-    if not ret:
-        break
-
-    # 4. Run the pipeline on the current frame with context
-    context = {
-        "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:
-        print(f"Detected: {detection_result['class']} with confidence {detection_result['confidence']:.2f}")
-        if bounding_box:
-            x1, y1, x2, y2 = bounding_box
-            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
-            cv2.putText(frame, detection_result['class'], (x1, y1 - 10),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
-
-    cv2.imshow("Pipeline Output", frame)
-
-    if cv2.waitKey(1) & 0xFF == ord('q'):
-        break
-
-cap.release()
-cv2.destroyAllWindows()
-```

requirements.base.txt

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

requirements.txt

@@ -1,6 +1,8 @@
 fastapi
 uvicorn
+torch
+torchvision
+ultralytics
+opencv-python
 websockets
 fastapi[standard]
-redis
-urllib3<2.0.0

siwatsystem/database.py

@@ -1,211 +0,0 @@
-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

@@ -5,624 +5,131 @@ import torch
 import cv2
 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
+def load_pipeline_node(node_config: dict, mpta_dir: str) -> dict:
-logger = logging.getLogger("detector_worker.pympta")
-
-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):
-        logger.error(f"Model file {model_path} not found. Current directory: {os.getcwd()}")
+        logging.error(f"Model file {model_path} not found.")
-        logger.error(f"Directory content: {os.listdir(os.path.dirname(model_path))}")
         raise FileNotFoundError(f"Model file {model_path} not found.")
-    logger.info(f"Loading model for node {node_config['modelId']} from {model_path}")
+    logging.info(f"Loading model {node_config['modelId']} from {model_path}")
     model = YOLO(model_path)
     if torch.cuda.is_available():
-        logger.info(f"CUDA available. Moving model {node_config['modelId']} to GPU")
         model.to("cuda")
-    else:
-        logger.info(f"CUDA not available. Using CPU for model {node_config['modelId']}")
 
-    # Prepare trigger class indices for optimization
+    # map triggerClasses names → indices for YOLO
-    trigger_classes = node_config.get("triggerClasses", [])
+    names = model.names  # idx -> class name
-    trigger_class_indices = None
+    trigger_names = node_config.get("triggerClasses", [])
-    if trigger_classes and hasattr(model, "names"):
+    trigger_inds = [i for i, nm in names.items() if nm in trigger_names]
-        # Convert class names to indices for the model
-        trigger_class_indices = [i for i, name in model.names.items() 
-                                if name in trigger_classes]
-        logger.debug(f"Converted trigger classes to indices: {trigger_class_indices}")
 
-    node = {
+    return {
         "modelId": node_config["modelId"],
         "modelFile": node_config["modelFile"],
-        "triggerClasses": trigger_classes,
+        "triggerClasses": trigger_names,
-        "triggerClassIndices": trigger_class_indices,
+        "triggerClassIndices": trigger_inds,
         "crop": node_config.get("crop", False),
-        "cropClass": node_config.get("cropClass"),
+        "minConfidence": node_config.get("minConfidence", 0.0),
-        "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": [],
+        "branches": [
-        "redis_client": redis_client,
+            load_pipeline_node(child, mpta_dir)
-        "db_manager": db_manager
+            for child in node_config.get("branches", [])
+        ]
     }
-    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, db_manager))
-    return node
 
 def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
-    logger.info(f"Attempting to load pipeline from {zip_source} to {target_dir}")
     os.makedirs(target_dir, exist_ok=True)
     zip_path = os.path.join(target_dir, "pipeline.mpta")
-    
-    # Parse the source; only local files are supported here.
     parsed = urlparse(zip_source)
     if parsed.scheme in ("", "file"):
-        local_path = parsed.path if parsed.scheme == "file" else zip_source
+        local = parsed.path if parsed.scheme == "file" else zip_source
-        logger.debug(f"Checking if local file exists: {local_path}")
+        if not os.path.exists(local):
-        if os.path.exists(local_path):
+            logging.error(f"Local file {local} does not exist.")
-            try:
-                shutil.copy(local_path, zip_path)
-                logger.info(f"Copied local .mpta file from {local_path} to {zip_path}")
-            except Exception as e:
-                logger.error(f"Failed to copy local .mpta file from {local_path}: {str(e)}", exc_info=True)
-                return None
-        else:
-            logger.error(f"Local file {local_path} does not exist. Current directory: {os.getcwd()}")
-            # List all subdirectories of models directory to help debugging
-            if os.path.exists("models"):
-                logger.error(f"Content of models directory: {os.listdir('models')}")
-                for root, dirs, files in os.walk("models"):
-                    logger.error(f"Directory {root} contains subdirs: {dirs} and files: {files}")
-            else:
-                logger.error("The models directory doesn't exist")
             return None
+        shutil.copy(local, zip_path)
     else:
-        logger.error(f"HTTP download functionality has been moved. Use a local file path here. Received: {zip_source}")
+        logging.error("HTTP download not supported; use local file.")
         return None
 
-    try:
+    with zipfile.ZipFile(zip_path, "r") as z:
-        if not os.path.exists(zip_path):
+        z.extractall(target_dir)
-            logger.error(f"Zip file not found at expected location: {zip_path}")
+    os.remove(zip_path)
-            return None
 
-        logger.debug(f"Extracting .mpta file from {zip_path} to {target_dir}")
+    base = os.path.splitext(os.path.basename(zip_source))[0]
-        # Extract contents and track the directories created
+    mpta_dir = os.path.join(target_dir, base)
-        extracted_dirs = []
+    cfg = os.path.join(mpta_dir, "pipeline.json")
-        with zipfile.ZipFile(zip_path, "r") as zip_ref:
+    if not os.path.exists(cfg):
-            file_list = zip_ref.namelist()
+        logging.error("pipeline.json not found in archive.")
-            logger.debug(f"Files in .mpta archive: {file_list}")
-            
-            # Extract and track the top-level directories
-            for file_path in file_list:
-                parts = file_path.split('/')
-                if len(parts) > 1:
-                    top_dir = parts[0]
-                    if top_dir and top_dir not in extracted_dirs:
-                        extracted_dirs.append(top_dir)
-            
-            # Now extract the files
-            zip_ref.extractall(target_dir)
-            
-        logger.info(f"Successfully extracted .mpta file to {target_dir}")
-        logger.debug(f"Extracted directories: {extracted_dirs}")
-        
-        # Check what was actually created after extraction
-        actual_dirs = [d for d in os.listdir(target_dir) if os.path.isdir(os.path.join(target_dir, d))]
-        logger.debug(f"Actual directories created: {actual_dirs}")
-    except zipfile.BadZipFile as e:
-        logger.error(f"Bad zip file {zip_path}: {str(e)}", exc_info=True)
-        return None
-    except Exception as e:
-        logger.error(f"Failed to extract .mpta file {zip_path}: {str(e)}", exc_info=True)
-        return None
-    finally:
-        if os.path.exists(zip_path):
-            os.remove(zip_path)
-            logger.debug(f"Removed temporary zip file: {zip_path}")
-
-    # Use the first extracted directory if it exists, otherwise use the expected name
-    pipeline_name = os.path.basename(zip_source)
-    pipeline_name = os.path.splitext(pipeline_name)[0]
-    
-    # Find the directory with pipeline.json
-    mpta_dir = None
-    # First try the expected directory name
-    expected_dir = os.path.join(target_dir, pipeline_name)
-    if os.path.exists(expected_dir) and os.path.exists(os.path.join(expected_dir, "pipeline.json")):
-        mpta_dir = expected_dir
-        logger.debug(f"Found pipeline.json in the expected directory: {mpta_dir}")
-    else:
-        # Look through all subdirectories for pipeline.json
-        for subdir in actual_dirs:
-            potential_dir = os.path.join(target_dir, subdir)
-            if os.path.exists(os.path.join(potential_dir, "pipeline.json")):
-                mpta_dir = potential_dir
-                logger.info(f"Found pipeline.json in directory: {mpta_dir} (different from expected: {expected_dir})")
-                break
-    
-    if not mpta_dir:
-        logger.error(f"Could not find pipeline.json in any extracted directory. Directory content: {os.listdir(target_dir)}")
         return None
 
-    pipeline_json_path = os.path.join(mpta_dir, "pipeline.json")
+    with open(cfg) as f:
-    if not os.path.exists(pipeline_json_path):
+        pipeline_config = json.load(f)
-        logger.error(f"pipeline.json not found in the .mpta file. Files in directory: {os.listdir(mpta_dir)}")
+    return load_pipeline_node(pipeline_config["pipeline"], mpta_dir)
-        return None
 
-    try:
-        with open(pipeline_json_path, "r") as f:
-            pipeline_config = json.load(f)
-        logger.info(f"Successfully loaded pipeline configuration from {pipeline_json_path}")
-        logger.debug(f"Pipeline config: {json.dumps(pipeline_config, indent=2)}")
 
-        # Establish Redis connection if configured
+def run_pipeline(frame, node: dict, return_bbox: bool=False):
-        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"],
-                        port=redis_config["port"],
-                        password=redis_config.get("password"),
-                        db=redis_config.get("db", 0),
-                        decode_responses=True
-                    )
-                    redis_client.ping()
-                    logger.info(f"Successfully connected to Redis at {redis_config['host']}:{redis_config['port']}")
-                except redis.exceptions.ConnectionError as e:
-                    logger.error(f"Failed to connect to Redis: {e}")
-                    redis_client = None
-        
-        # 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
-    except KeyError as e:
-        logger.error(f"Missing key in pipeline.json: {str(e)}", exc_info=True)
-        return None
-    except Exception as e:
-        logger.error(f"Error loading pipeline.json: {str(e)}", exc_info=True)
-        return None
-
-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)
-                
-                # 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())
-                    logger.info(f"Saved image to Redis with key: {key} (expires in {expire_seconds}s)")
-                else:
-                    node["redis_client"].set(key, buffer.tobytes())
-                    logger.info(f"Saved image to Redis with key: {key}")
-                action_context["image_key"] = key
-            elif action["type"] == "redis_publish":
-                channel = action["channel"]
-                try:
-                    # 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 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 validate_pipeline_execution(node, regions_dict):
     """
-    Pre-validate that all required branches will execute successfully before 
+    - For detection nodes (task != 'classify'):
-    committing to Redis actions and database records.
+        • runs `track(..., classes=triggerClassIndices)`
-    
+        • picks top box ≥ minConfidence
-    Returns:
+        • optionally crops & resizes → recurse into child
-        - (True, []) if pipeline can execute completely
+        • else returns (det_dict, bbox)
-        - (False, missing_branches) if some required branches won't execute
+    - For classify nodes:
-    """
+        • runs `predict()`
-    # Get all branches that parallel actions are waiting for
+        • returns top (class,confidence) and no bbox
-    required_branches = set()
-    
-    for action in node.get("parallelActions", []):
-        if action.get("type") == "postgresql_update_combined":
-            wait_for_branches = action.get("waitForBranches", [])
-            required_branches.update(wait_for_branches)
-    
-    if not required_branches:
-        # No parallel actions requiring specific branches
-        logger.debug("No parallel actions with waitForBranches - validation passes")
-        return True, []
-    
-    logger.debug(f"Pre-validation: checking if required branches {list(required_branches)} will execute")
-    
-    # Check each required branch
-    missing_branches = []
-    
-    for branch in node.get("branches", []):
-        branch_id = branch["modelId"]
-        
-        if branch_id not in required_branches:
-            continue  # This branch is not required by parallel actions
-            
-        # Check if this branch would be triggered
-        trigger_classes = branch.get("triggerClasses", [])
-        min_conf = branch.get("minConfidence", 0)
-        
-        branch_triggered = False
-        for det_class in regions_dict:
-            det_confidence = regions_dict[det_class]["confidence"]
-            
-            if (det_class in trigger_classes and det_confidence >= min_conf):
-                branch_triggered = True
-                logger.debug(f"Pre-validation: branch {branch_id} WILL be triggered by {det_class} (conf={det_confidence:.3f} >= {min_conf})")
-                break
-        
-        if not branch_triggered:
-            missing_branches.append(branch_id)
-            logger.warning(f"Pre-validation: branch {branch_id} will NOT be triggered - no matching classes or insufficient confidence")
-            logger.debug(f"  Required: {trigger_classes} with min_conf={min_conf}")
-            logger.debug(f"  Available: {[(cls, regions_dict[cls]['confidence']) for cls in regions_dict]}")
-    
-    if missing_branches:
-        logger.error(f"Pipeline pre-validation FAILED: required branches {missing_branches} will not execute")
-        return False, missing_branches
-    else:
-        logger.info(f"Pipeline pre-validation PASSED: all required branches {list(required_branches)} will execute")
-        return True, []
-
-def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
-    """
-    Enhanced pipeline that supports:
-    - Multi-class detection (detecting multiple classes simultaneously)
-    - Parallel branch processing
-    - Region-based actions and cropping
-    - Context passing for session/camera information
     """
     try:
         task = getattr(node["model"], "task", None)
 
         # ─── Classification stage ───────────────────────────────────
+        # if task == "classify":
+        #     results = node["model"].predict(frame, stream=False)
+        #     dets = []
+        #     for r in results:
+        #         probs = r.probs
+        #         if probs is not None:
+        #             # sort descending
+        #             idxs = probs.argsort(descending=True)
+        #             for cid in idxs:
+        #                 dets.append({
+        #                     "class": node["model"].names[int(cid)],
+        #                     "confidence": float(probs[int(cid)]),
+        #                     "id": None
+        #                 })
+        #     if not dets:
+        #         return (None, None) if return_bbox else None
+
+        #     best = dets[0]
+        #     return (best, None) if return_bbox else best
+        
         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
 
-            r = results[0]
+            # 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
 
-            top1_idx = int(probs.top1)
+            # 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": class_name,
+                "class": node["model"].names[top1_idx],
                 "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 ──────────────────
-        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)}")
 
+        # ─── Detection stage ────────────────────────────────────────
+        # only look for your triggerClasses
+        tk = node["triggerClassIndices"]
         res = node["model"].track(
             frame,
             stream=False,
@@ -630,238 +137,46 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
             **({"classes": tk} if tk else {})
         )[0]
 
-        # Collect all detections above confidence threshold
+        dets, boxes = [], []
-        all_detections = []
+        for box in res.boxes:
-        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])
+            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)
+            x1,y1,x2,y2 = map(int, xy)
-            bbox = (x1, y1, x2, y2)
+            dets.append({"class": name, "confidence": conf,
+                         "id": box.id.item() if hasattr(box, "id") else None})
+            boxes.append((x1, y1, x2, y2))
 
-            detection = {
+        if not dets:
-                "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
 
-        # ─── Multi-class validation ─────────────────────────────────
+        # take highest‐confidence
-        if node.get("multiClass", False) and node.get("expectedClasses"):
+        best_idx = max(range(len(dets)), key=lambda i: dets[i]["confidence"])
-            expected_classes = node["expectedClasses"]
+        best_det = dets[best_idx]
-            detected_classes = list(regions_dict.keys())
+        best_box = boxes[best_idx]
 
-            logger.info(f"Multi-class validation: expected={expected_classes}, detected={detected_classes}")
+        # ─── Branch (classification) ───────────────────────────────
+        for br in node["branches"]:
+            if (best_det["class"] in br["triggerClasses"]
+                    and best_det["confidence"] >= br["minConfidence"]):
+                # 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))
 
-            # Check if at least one expected class is detected (flexible mode)
+                det2, _ = run_pipeline(sub, br, return_bbox=True)
-            matching_classes = [cls for cls in expected_classes if cls in detected_classes]
+                if det2:
-            missing_classes = [cls for cls in expected_classes if cls not in detected_classes]
+                    # return classification result + original bbox
+                    return (det2, best_box) if return_bbox else det2
 
-            logger.debug(f"Matching classes: {matching_classes}, Missing classes: {missing_classes}")
+        # ─── No branch matched → return this detection ─────────────
-            
+        return (best_det, best_box) if return_bbox else best_det
-            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")
-
-        # ─── Pre-validate pipeline execution ────────────────────────
-        pipeline_valid, missing_branches = validate_pipeline_execution(node, regions_dict)
-        
-        if not pipeline_valid:
-            logger.error(f"Pipeline execution validation FAILED - required branches {missing_branches} cannot execute")
-            logger.error("Aborting pipeline: no Redis actions or database records will be created")
-            return (None, None) if return_bbox else None
-
-        # ─── Execute actions with region information ────────────────
-        detection_result = {
-            "detections": all_detections,
-            "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"]:
-                trigger_classes = br.get("triggerClasses", [])
-                min_conf = br.get("minConfidence", 0)
-                
-                logger.debug(f"Evaluating branch {br['modelId']}: trigger_classes={trigger_classes}, min_conf={min_conf}")
-                
-                # Check if any detected class matches branch trigger
-                branch_triggered = False
-                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 and session_id to primary detection for compatibility
-        if "branch_results" in detection_result:
-            primary_detection["branch_results"] = detection_result["branch_results"]
-        if "session_id" in detection_result:
-            primary_detection["session_id"] = detection_result["session_id"]
-        
-        return (primary_detection, primary_bbox) if return_bbox else primary_detection
 
     except Exception as e:
-        logger.error(f"Error in node {node.get('modelId')}: {e}")
+        logging.error(f"Error in node {node.get('modelId')}: {e}")
-        traceback.print_exc()
         return (None, None) if return_bbox else None

test_protocol.py

@@ -1,125 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script to verify the worker implementation follows the protocol
-"""
-import json
-import asyncio
-import websockets
-import time
-
-async def test_protocol():
-    """Test the worker protocol implementation"""
-    uri = "ws://localhost:8000"
-    
-    try:
-        async with websockets.connect(uri) as websocket:
-            print("✓ Connected to worker")
-            
-            # Test 1: Check if we receive heartbeat (stateReport)
-            print("\n1. Testing heartbeat...")
-            try:
-                message = await asyncio.wait_for(websocket.recv(), timeout=5)
-                data = json.loads(message)
-                if data.get("type") == "stateReport":
-                    print("✓ Received stateReport heartbeat")
-                    print(f"  - CPU Usage: {data.get('cpuUsage', 'N/A')}%")
-                    print(f"  - Memory Usage: {data.get('memoryUsage', 'N/A')}%")
-                    print(f"  - Camera Connections: {len(data.get('cameraConnections', []))}")
-                else:
-                    print(f"✗ Expected stateReport, got {data.get('type')}")
-            except asyncio.TimeoutError:
-                print("✗ No heartbeat received within 5 seconds")
-            
-            # Test 2: Request state
-            print("\n2. Testing requestState...")
-            await websocket.send(json.dumps({"type": "requestState"}))
-            try:
-                message = await asyncio.wait_for(websocket.recv(), timeout=5)
-                data = json.loads(message)
-                if data.get("type") == "stateReport":
-                    print("✓ Received stateReport response")
-                else:
-                    print(f"✗ Expected stateReport, got {data.get('type')}")
-            except asyncio.TimeoutError:
-                print("✗ No response to requestState within 5 seconds")
-            
-            # Test 3: Set session ID
-            print("\n3. Testing setSessionId...")
-            session_message = {
-                "type": "setSessionId",
-                "payload": {
-                    "displayIdentifier": "display-001",
-                    "sessionId": 12345
-                }
-            }
-            await websocket.send(json.dumps(session_message))
-            print("✓ Sent setSessionId message")
-            
-            # Test 4: Test patchSession
-            print("\n4. Testing patchSession...")
-            patch_message = {
-                "type": "patchSession",
-                "sessionId": 12345,
-                "data": {
-                    "currentCar": {
-                        "carModel": "Civic",
-                        "carBrand": "Honda"
-                    }
-                }
-            }
-            await websocket.send(json.dumps(patch_message))
-            
-            # Wait for patchSessionResult
-            try:
-                message = await asyncio.wait_for(websocket.recv(), timeout=5)
-                data = json.loads(message)
-                if data.get("type") == "patchSessionResult":
-                    print("✓ Received patchSessionResult")
-                    print(f"  - Success: {data.get('payload', {}).get('success')}")
-                    print(f"  - Message: {data.get('payload', {}).get('message')}")
-                else:
-                    print(f"✗ Expected patchSessionResult, got {data.get('type')}")
-            except asyncio.TimeoutError:
-                print("✗ No patchSessionResult received within 5 seconds")
-            
-            # Test 5: Test subscribe message format (without actual camera)
-            print("\n5. Testing subscribe message format...")
-            subscribe_message = {
-                "type": "subscribe",
-                "payload": {
-                    "subscriptionIdentifier": "display-001;cam-001",
-                    "snapshotUrl": "http://example.com/snapshot.jpg",
-                    "snapshotInterval": 5000,
-                    "modelUrl": "http://example.com/model.mpta",
-                    "modelName": "Test Model",
-                    "modelId": 101,
-                    "cropX1": 100,
-                    "cropY1": 200,
-                    "cropX2": 300,
-                    "cropY2": 400
-                }
-            }
-            await websocket.send(json.dumps(subscribe_message))
-            print("✓ Sent subscribe message (will fail without actual camera/model)")
-            
-            # Listen for a few more messages to catch any errors
-            print("\n6. Listening for additional messages...")
-            for i in range(3):
-                try:
-                    message = await asyncio.wait_for(websocket.recv(), timeout=2)
-                    data = json.loads(message)
-                    msg_type = data.get("type")
-                    print(f"  - Received {msg_type}")
-                    if msg_type == "error":
-                        print(f"    Error: {data.get('error')}")
-                except asyncio.TimeoutError:
-                    break
-            
-            print("\n✓ Protocol test completed successfully!")
-            
-    except Exception as e:
-        print(f"✗ Connection failed: {e}")
-        print("Make sure the worker is running on localhost:8000")
-
-if __name__ == "__main__":
-    asyncio.run(test_protocol())

worker.md

@@ -1,495 +0,0 @@
-# Worker Communication Protocol
-
-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.
-
-## 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.
-
-Upon a successful connection from the backend, you should begin sending `stateReport` messages as heartbeats.
-
-## 2. Communication Overview
-
-Communication is bidirectional and asynchronous. All messages are JSON objects with a `type` field that indicates the message's purpose, and an optional `payload` field containing the data.
-
-- **Worker -> Backend:** You will send messages to the backend to report status, forward detection events, or request changes to session data.
-- **Backend -> Worker:** The backend will send commands to you to manage camera subscriptions.
-
-## 3. Dynamic Configuration via MPTA File
-
-To enable modularity and dynamic configuration, the backend will send you a URL to a `.mpta` file when it issues a `subscribe` command. This file is a renamed `.zip` archive that contains everything your worker needs to perform its task.
-
-**Your worker is responsible for:**
-
-1. Fetching this file from the provided 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:
-
-- AI/ML Models: Pre-trained models for libraries like TensorFlow, PyTorch, or ONNX.
-- Configuration Files: A `config.json` or `pipeline.yaml` that defines a sequence of operations, specifies model paths, or sets detection thresholds.
-- Scripts: Custom Python scripts for pre-processing or post-processing.
-- API Integration Details: A JSON file with endpoint information and credentials for interacting with third-party detection services.
-
-Essentially, the `.mpta` file is a self-contained package that tells your worker _how_ to process the video stream for a given subscription.
-
-## 4. Messages from Worker to Backend
-
-These are the messages your worker is expected to send to the backend.
-
-### 4.1. State Report (Heartbeat)
-
-This message is crucial for the backend to monitor your worker's health and status, including GPU usage.
-
-- **Type:** `stateReport`
-- **When to Send:** Periodically (e.g., every 2 seconds) after a connection is established.
-
-**Payload:**
-
-```json
-{
-  "type": "stateReport",
-  "cpuUsage": 75.5,
-  "memoryUsage": 40.2,
-  "gpuUsage": 60.0,
-  "gpuMemoryUsage": 25.1,
-  "cameraConnections": [
-    {
-      "subscriptionIdentifier": "display-001;cam-001",
-      "modelId": 101,
-      "modelName": "General Object Detection",
-      "online": true,
-      "cropX1": 100,
-      "cropY1": 200,
-      "cropX2": 300,
-      "cropY2": 400
-    }
-  ]
-}
-```
-
-> **Note:**
->
-> - `cropX1`, `cropY1`, `cropX2`, `cropY2` (optional, integer) should be included in each camera connection to indicate the crop coordinates for that subscription.
-
-### 4.2. Image Detection
-
-Sent when the worker detects a relevant object. The `detection` object should be flat and contain key-value pairs corresponding to the detected attributes.
-
-- **Type:** `imageDetection`
-
-**Payload Example:**
-
-```json
-{
-  "type": "imageDetection",
-  "subscriptionIdentifier": "display-001;cam-001",
-  "timestamp": "2025-07-14T12:34:56.789Z",
-  "data": {
-    "detection": {
-      "carModel": "Civic",
-      "carBrand": "Honda",
-      "carYear": 2023,
-      "bodyType": "Sedan",
-      "licensePlateText": "ABCD1234",
-      "licensePlateConfidence": 0.95
-    },
-    "modelId": 101,
-    "modelName": "US-LPR-and-Vehicle-ID"
-  }
-}
-```
-
-### 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.
-
-Allows the worker to request a modification to an active session's data. The `data` payload must be a partial object of the `DisplayPersistentData` structure.
-
-- **Type:** `patchSession`
-
-**Payload Example:**
-
-```json
-{
-  "type": "patchSession",
-  "sessionId": 12345,
-  "data": {
-    "currentCar": {
-      "carModel": "Civic",
-      "carBrand": "Honda",
-      "licensePlateText": "ABCD1234"
-    }
-  }
-}
-```
-
-The backend will respond with a `patchSessionResult` command.
-
-#### `DisplayPersistentData` Structure
-
-The `data` object in the `patchSession` message is merged with the existing `DisplayPersistentData` on the backend. Here is its structure:
-
-```typescript
-interface DisplayPersistentData {
-  progressionStage:
-    | 'welcome'
-    | 'car_fueling'
-    | 'car_waitpayment'
-    | 'car_postpayment'
-    | null;
-  qrCode: string | null;
-  adsPlayback: {
-    playlistSlotOrder: number; // The 'order' of the current slot
-    adsId: number | null;
-    adsUrl: string | null;
-  } | null;
-  currentCar: {
-    carModel?: string;
-    carBrand?: string;
-    carYear?: number;
-    bodyType?: string;
-    licensePlateText?: string;
-    licensePlateType?: string;
-  } | null;
-  fuelPump: {
-    /* FuelPumpData structure */
-  } | null;
-  weatherData: {
-    /* WeatherResponse structure */
-  } | null;
-  sessionId: number | null;
-}
-```
-
-#### Patching Behavior
-
-- The patch is a **deep merge**.
-- **`undefined`** values are ignored.
-- **`null`** values will set the corresponding field to `null`.
-- Nested objects are merged recursively.
-
-## 5. Commands from Backend to Worker
-
-These are the commands your worker will receive from the backend.
-
-### 5.1. Subscribe to Camera
-
-Instructs the worker to process a camera's RTSP stream using the configuration from the specified `.mpta` file.
-
-- **Type:** `subscribe`
-
-**Payload:**
-
-```json
-{
-  "type": "subscribe",
-  "payload": {
-    "subscriptionIdentifier": "display-001;cam-002",
-    "rtspUrl": "rtsp://user:pass@host:port/stream",
-    "snapshotUrl": "http://go2rtc/snapshot/1",
-    "snapshotInterval": 5000,
-    "modelUrl": "http://storage/models/us-lpr.mpta",
-    "modelName": "US-LPR-and-Vehicle-ID",
-    "modelId": 102,
-    "cropX1": 100,
-    "cropY1": 200,
-    "cropX2": 300,
-    "cropY2": 400
-  }
-}
-```
-
-> **Note:**
->
-> - `cropX1`, `cropY1`, `cropX2`, `cropY2` (optional, integer) specify the crop coordinates for the camera stream. These values are configured per display and passed in the subscription payload. If not provided, the worker should process the full frame.
->
-> **Important:**
-> If multiple displays are bound to the same camera, your worker must ensure that only **one stream** is opened per camera. When you receive multiple subscriptions for the same camera (with different `subscriptionIdentifier` values), you should:
->
-> - Open the RTSP stream **once** for that camera if using RTSP.
-> - Capture each snapshot only once per cycle, and reuse it for all display subscriptions sharing that camera.
-> - Capture each frame/image only once per cycle.
-> - Reuse the same captured image and snapshot for all display subscriptions that share the camera, processing and routing detection results separately for each display as needed.
->   This avoids unnecessary load and bandwidth usage, and ensures consistent detection results and snapshots across all displays sharing the same camera.
-
-### 5.2. Unsubscribe from Camera
-
-Instructs the worker to stop processing a camera's stream.
-
-- **Type:** `unsubscribe`
-
-**Payload:**
-
-```json
-{
-  "type": "unsubscribe",
-  "payload": {
-    "subscriptionIdentifier": "display-001;cam-002"
-  }
-}
-```
-
-### 5.3. Request State
-
-Direct request for the worker's current state. Respond with a `stateReport` message.
-
-- **Type:** `requestState`
-
-**Payload:**
-
-```json
-{
-  "type": "requestState"
-}
-```
-
-### 5.4. Patch Session Result
-
-Backend's response to a `patchSession` message.
-
-- **Type:** `patchSessionResult`
-
-**Payload:**
-
-```json
-{
-  "type": "patchSessionResult",
-  "payload": {
-    "sessionId": 12345,
-    "success": true,
-    "message": "Session updated successfully."
-  }
-}
-```
-
-### 5.5. Set Session ID
-
-Allows the backend to instruct the worker to associate a session ID with a subscription. This is useful for linking detection events to a specific session. The session ID can be `null` to indicate no active session.
-
-- **Type:** `setSessionId`
-
-**Payload:**
-
-```json
-{
-  "type": "setSessionId",
-  "payload": {
-    "displayIdentifier": "display-001",
-    "sessionId": 12345
-  }
-}
-```
-
-Or to clear the session:
-
-```json
-{
-  "type": "setSessionId",
-  "payload": {
-    "displayIdentifier": "display-001",
-    "sessionId": null
-  }
-}
-```
-
-> **Note:**
->
-> - The worker should store the session ID for the given subscription and use it in subsequent detection or patch messages as appropriate. If `sessionId` is `null`, the worker should treat the subscription as having no active session.
-
-## Subscription Identifier Format
-
-The `subscriptionIdentifier` used in all messages is constructed as:
-
-```
-displayIdentifier;cameraIdentifier
-```
-
-This uniquely identifies a camera subscription for a specific display.
-
-### Session ID Association
-
-When the backend sends a `setSessionId` command, it will only provide the `displayIdentifier` (not the full `subscriptionIdentifier`).
-
-**Worker Responsibility:**
-
-- The worker must match the `displayIdentifier` to all active subscriptions for that display (i.e., all `subscriptionIdentifier` values that start with `displayIdentifier;`).
-- The worker should set or clear the session ID for all matching subscriptions.
-
-## 6. Example Communication Log
-
-This section shows a typical sequence of messages between the backend and the worker. Patch messages are not included, as they are only used when the worker cannot keep up.
-
-> **Note:** Unsubscribe is triggered when a user removes a camera or when the node is too heavily loaded and needs rebalancing.
-
-1.  **Connection Established** & **Heartbeat**
-    - **Worker -> Backend**
-    ```json
-    {
-      "type": "stateReport",
-      "cpuUsage": 70.2,
-      "memoryUsage": 38.1,
-      "gpuUsage": 55.0,
-      "gpuMemoryUsage": 20.0,
-      "cameraConnections": []
-    }
-    ```
-2.  **Backend Subscribes Camera**
-    - **Backend -> Worker**
-    ```json
-    {
-      "type": "subscribe",
-      "payload": {
-        "subscriptionIdentifier": "display-001;entry-cam-01",
-        "rtspUrl": "rtsp://192.168.1.100/stream1",
-        "modelUrl": "http://storage/models/vehicle-id.mpta",
-        "modelName": "Vehicle Identification",
-        "modelId": 201
-      }
-    }
-    ```
-3.  **Worker Acknowledges in Heartbeat**
-    - **Worker -> Backend**
-    ```json
-    {
-      "type": "stateReport",
-      "cpuUsage": 72.5,
-      "memoryUsage": 39.0,
-      "gpuUsage": 57.0,
-      "gpuMemoryUsage": 21.0,
-      "cameraConnections": [
-        {
-          "subscriptionIdentifier": "display-001;entry-cam-01",
-          "modelId": 201,
-          "modelName": "Vehicle Identification",
-          "online": true
-        }
-      ]
-    }
-    ```
-4.  **Worker Detects a Car**
-    - **Worker -> Backend**
-    ```json
-    {
-      "type": "imageDetection",
-      "subscriptionIdentifier": "display-001;entry-cam-01",
-      "timestamp": "2025-07-15T10:00:00.000Z",
-      "data": {
-        "detection": {
-          "carBrand": "Honda",
-          "carModel": "CR-V",
-          "bodyType": "SUV",
-          "licensePlateText": "GEMINI-AI",
-          "licensePlateConfidence": 0.98
-        },
-        "modelId": 201,
-        "modelName": "Vehicle Identification"
-      }
-    }
-    ```
-    - **Worker -> Backend**
-    ```json
-    {
-      "type": "imageDetection",
-      "subscriptionIdentifier": "display-001;entry-cam-01",
-      "timestamp": "2025-07-15T10:00:01.000Z",
-      "data": {
-        "detection": {
-          "carBrand": "Toyota",
-          "carModel": "Corolla",
-          "bodyType": "Sedan",
-          "licensePlateText": "CMS-1234",
-          "licensePlateConfidence": 0.97
-        },
-        "modelId": 201,
-        "modelName": "Vehicle Identification"
-      }
-    }
-    ```
-    - **Worker -> Backend**
-    ```json
-    {
-      "type": "imageDetection",
-      "subscriptionIdentifier": "display-001;entry-cam-01",
-      "timestamp": "2025-07-15T10:00:02.000Z",
-      "data": {
-        "detection": {
-          "carBrand": "Ford",
-          "carModel": "Focus",
-          "bodyType": "Hatchback",
-          "licensePlateText": "CMS-5678",
-          "licensePlateConfidence": 0.96
-        },
-        "modelId": 201,
-        "modelName": "Vehicle Identification"
-      }
-    }
-    ```
-5.  **Backend Unsubscribes Camera**
-    - **Backend -> Worker**
-    ```json
-    {
-      "type": "unsubscribe",
-      "payload": {
-        "subscriptionIdentifier": "display-001;entry-cam-01"
-      }
-    }
-    ```
-6.  **Worker Acknowledges Unsubscription**
-    - **Worker -> Backend**
-    ```json
-    {
-      "type": "stateReport",
-      "cpuUsage": 68.0,
-      "memoryUsage": 37.0,
-      "gpuUsage": 50.0,
-      "gpuMemoryUsage": 18.0,
-      "cameraConnections": []
-    }
-    ```
-
-## 7. HTTP API: Image Retrieval
-
-In addition to the WebSocket protocol, the worker exposes an HTTP endpoint for retrieving the latest image frame from a camera.
-
-### Endpoint
-
-```
-GET /camera/{camera_id}/image
-```
-
-- **`camera_id`**: The full `subscriptionIdentifier` (e.g., `display-001;cam-001`).
-
-### Response
-
-- **Success (200):** Returns the latest JPEG image from the camera stream.
-
-  - `Content-Type: image/jpeg`
-  - Binary JPEG data.
-
-- **Error (404):** If the camera is not found or no frame is available.
-
-  - JSON error response.
-
-- **Error (500):** Internal server error.
-
-### Example Request
-
-```
-GET /camera/display-001;cam-001/image
-```
-
-### Example Response
-
-- **Headers:**
-  ```
-  Content-Type: image/jpeg
-  ```
-- **Body:** Binary JPEG image.
-
-### Notes
-
-- The endpoint returns the most recent frame available for the specified camera subscription.
-- If multiple displays share the same camera, each subscription has its own buffer; the endpoint uses the buffer for the given `camera_id`.
-- This API is useful for debugging, monitoring, or integrating with external systems that require direct image access.