diff --git a/metro-ai-suite/smart-traffic-intersection-agent/.gitignore b/metro-ai-suite/smart-traffic-intersection-agent/.gitignore new file mode 100644 index 000000000..eb0d8b19e --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/.gitignore @@ -0,0 +1,89 @@ +# Copyright (C) 2026 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +.python-version + +# Ignore environment variables +.env + +# Ignore Python cache files +__pycache__/ +*.py[cod] +*.py[codz] +*$py.class + +# Ignore virtual environment directories +venv/ +env/ +.venv/ +.env/ + +# Ignore log files +*.log + +# Ignore IDE specific files +.vscode/ + +*.manifest +*.spec + +.DS_Store +.idea/ +logs/ + +# Ignore model files +ov-model/ +ov-models/ + + +# Ignore pytest cache +.pytest_cache/ + +# Ignore pytest result files +*.pytest_cache + +# pytest related dir +empty_model_dir +mock_model_dir +.coverage +htmlcov/ +.tox/ +.nox/ +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py.cover +.hypothesis/ +.pytest_cache/ +cover/ + +# secrets +**/secrets +**/secrets/ca +**/secrets/certs +**/secrets/django +**/secrets/*.auth +**/secrets/supass +**/dlstreamer-pipeline-server/videos/**.ts + +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +**/edge-ai-suites/ \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/readme.md b/metro-ai-suite/smart-traffic-intersection-agent/README.md similarity index 100% rename from metro-ai-suite/smart-traffic-intersection-agent/readme.md rename to metro-ai-suite/smart-traffic-intersection-agent/README.md diff --git a/metro-ai-suite/smart-traffic-intersection-agent/docker/compose.yaml b/metro-ai-suite/smart-traffic-intersection-agent/docker/compose.yaml new file mode 100644 index 000000000..f85919842 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/docker/compose.yaml @@ -0,0 +1,127 @@ +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +name: scene-intelligence + +secrets: + root-cert: + file: ../edge-ai-suites/metro-ai-suite/metro-vision-ai-app-recipe/smart-intersection/src/secrets/certs/scenescape-ca.pem + +services: + # VLM OpenVINO Serving Service + vlm-openvino-serving: + image: intel/vlm-openvino-serving:1.3.1 + ports: + - "${VLM_SERVICE_PORT:-9764}:8000" + ipc: host + networks: + - scenescape + environment: + # Proxy settings + no_proxy_env: ${no_proxy_env},${HOST_IP} + no_proxy: ${no_proxy_env},${HOST_IP} + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + # VLM Model configuration + VLM_MODEL_NAME: ${VLM_MODEL_NAME:-Qwen/Qwen2.5-VL-3B-Instruct} + VLM_COMPRESSION_WEIGHT_FORMAT: ${VLM_COMPRESSION_WEIGHT_FORMAT:-int4} + VLM_DEVICE: ${VLM_DEVICE:-CPU} + VLM_SEED: ${VLM_SEED:-42} + WORKERS: ${VLM_WORKERS:-1} + VLM_MAX_COMPLETION_TOKENS: ${VLM_MAX_COMPLETION_TOKENS:-1500} + HUGGINGFACE_TOKEN: ${HUGGINGFACE_TOKEN} + OV_CONFIG: ${OV_CONFIG} + VLM_LOG_LEVEL: ${VLM_LOG_LEVEL:-info} + OPENVINO_LOG_LEVEL: ${VLM_OPENVINO_LOG_LEVEL:-1} + VLM_ACCESS_LOG_FILE: ${VLM_ACCESS_LOG_FILE:-/dev/null} + restart: unless-stopped + devices: + - /dev/dri:/dev/dri + group_add: + - ${USER_GROUP_ID:-1000} + - ${VIDEO_GROUP_ID:-44} + - ${RENDER_GROUP_ID:-109} + healthcheck: + test: ["CMD-SHELL", "curl -f http://localhost:8000/health || exit 1"] + interval: 10s + timeout: 10s + retries: 60 + start_period: 10s + volumes: + - ov-models:/root/.cache/huggingface + - ov-models:/app/ov-model + + # Traffic Intelligence Service + traffic-intelligence: + build: + context: ../src + dockerfile: Dockerfile + image: ${REGISTRY:-}traffic-intelligence:${TAG:-latest} + ports: + - "${TRAFFIC_INTELLIGENCE_PORT:-8081}:8081" + - "${TRAFFIC_INTELLIGENCE_UI_PORT:-7860}:7860" + networks: + - scenescape + depends_on: + vlm-openvino-serving: + condition: service_healthy + environment: + # Service configuration + TRAFFIC_INTELLIGENCE_HOST: 0.0.0.0 + TRAFFIC_INTELLIGENCE_PORT: ${TRAFFIC_INTELLIGENCE_PORT:-8081} + LOG_LEVEL: ${LOG_LEVEL:-INFO} + + # UI configuration + TRAFFIC_INTELLIGENCE_UI_PORT: ${TRAFFIC_INTELLIGENCE_UI_PORT:-7860} + USE_API: "true" + API_URL: http://localhost:${TRAFFIC_INTELLIGENCE_PORT:-8081}/api/v1/traffic/current + REFRESH_INTERVAL: ${REFRESH_INTERVAL:-15} + + # Proxy settings + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + no_proxy: ${no_proxy_env},.scenescape.intel.com,${HOST_IP},broker.scenescape.intel.com,vlm-openvino-serving + NO_PROXY: ${no_proxy_env},.scenescape.intel.com,${HOST_IP},broker.scenescape.intel.com,vlm-openvino-serving + HTTP_PROXY: ${http_proxy} + HTTPS_PROXY: ${https_proxy} + + # MQTT configuration (connects to broker service) + MQTT_HOST: ${MQTT_HOST:-broker.scenescape.intel.com} + MQTT_PORT: ${MQTT_PORT:-1883} + + # Intersection configuration (overrides config/traffic_intelligence.json) + INTERSECTION_NAME: ${INTERSECTION_NAME:-Intersection-1} + INTERSECTION_LATITUDE: ${INTERSECTION_LATITUDE:-37.7049108} + INTERSECTION_LONGITUDE: ${INTERSECTION_LONGITUDE:--121.9096158} + + # Weather configuration (overrides config/traffic_intelligence.json) + WEATHER_MOCK: ${WEATHER_MOCK:-true} + + # VLM configuration (overrides config/traffic_intelligence.json) + VLM_MODEL: ${VLM_MODEL_NAME:-Qwen/Qwen2.5-VL-3B-Instruct} + + # Traffic analysis configuration (overrides config/traffic_intelligence.json) + HIGH_DENSITY_THRESHOLD: ${HIGH_DENSITY_THRESHOLD:-10} + + volumes: + - ../src/config:/app/config:ro + - traffic-intelligence-data:/app/data + secrets: + - source: root-cert + target: /app/secrets/certs/scenescape-ca.pem + healthcheck: + test: ["CMD-SHELL", "curl -f http://localhost:8081/health || exit 1"] + interval: ${HEALTH_CHECK_INTERVAL:-30s} + timeout: ${HEALTH_CHECK_TIMEOUT:-10s} + retries: ${HEALTH_CHECK_RETRIES:-3} + start_period: ${HEALTH_CHECK_START_PERIOD:-10s} + restart: unless-stopped + +volumes: + ov-models: + traffic-intelligence-data: + +networks: + scenescape: + external: true + name: metro-vision-ai-app-recipe_scenescape diff --git a/metro-ai-suite/smart-traffic-intersection-agent/docs/toc.rst b/metro-ai-suite/smart-traffic-intersection-agent/docs/toc.rst new file mode 100644 index 000000000..a8fb32670 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/docs/toc.rst @@ -0,0 +1,3 @@ +.. toctree:: + + user-guide/index diff --git a/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/Overview.md b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/Overview.md new file mode 100644 index 000000000..775766056 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/Overview.md @@ -0,0 +1,14 @@ +# Scene Intelligence microservice + +The Scene Intelligence microservice is a comprehensive traffic analysis service that provides real-time intersection monitoring, directional traffic density analysis, and Vision Language Model (VLM) powered traffic insights. It processes MQTT traffic data, manages camera images, and delivers intelligent traffic analysis through RESTful APIs. + +## Overview + +The microservice processes real-time traffic data from MQTT streams and provides advanced analytics including directional traffic density monitoring, VLM-powered traffic scene analysis, and comprehensive traffic summaries. It supports sliding window analysis, sustained traffic detection, and intelligent camera image management for enhanced traffic insights. + + +## Supporting Resources + +- [Get Started Guide](get-started.md) +- [API Reference](api-reference.md) +- [System Requirements](system-requirements.md) diff --git a/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/api-docs/openapi.yaml b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/api-docs/openapi.yaml new file mode 100644 index 000000000..b7bf7dd2e --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/api-docs/openapi.yaml @@ -0,0 +1,92 @@ +--- +openapi: 3.1.0 +info: + title: Traffic Intelligence API + description: Lightweight traffic analysis service for single intersection monitoring + version: 1.0.0 +paths: + "/api/v1/traffic/current": + get: + summary: Get Current Traffic Intelligence + description: |- + Get current traffic intelligence data for the intersection. + + Returns complete traffic intelligence response matching data.json schema + with weather data and VLM analysis. + operationId: get_current_traffic_intelligence_api_v1_traffic_current_get + parameters: + - name: images + in: query + required: false + schema: + type: boolean + default: true + description: Include camera images in response + title: Images + description: Include camera images in response + responses: + '200': + description: Successful Response + content: + application/json: + schema: + type: object + title: Response Get Current Traffic Intelligence Api V1 Traffic Current + Get + '404': + description: No traffic data available + '422': + description: Validation Error + content: + application/json: + schema: + "$ref": "#/components/schemas/HTTPValidationError" + "/health": + get: + summary: Health Check + description: Health check endpoint. + operationId: health_check_health_get + responses: + '200': + description: Successful Response + content: + application/json: + schema: {} +components: + schemas: + HTTPValidationError: + properties: + detail: + items: + "$ref": "#/components/schemas/ValidationError" + type: array + title: Detail + type: object + title: HTTPValidationError + ValidationError: + properties: + loc: + items: + anyOf: + - type: string + - type: integer + type: array + title: Location + msg: + type: string + title: Message + type: + type: string + title: Error Type + type: object + required: + - loc + - msg + - type + title: ValidationError + WeatherType: + type: string + enum: + - clear + title: WeatherType + description: Weather condition diff --git a/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/api-reference.md b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/api-reference.md new file mode 100644 index 000000000..307b9dc6d --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/api-reference.md @@ -0,0 +1,6 @@ +# API Reference +**Version: 1.0.0** + +```{eval-rst} +.. swagger-plugin:: api-docs/openapi.yaml +``` diff --git a/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/environment-variables.md b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/environment-variables.md new file mode 100644 index 000000000..4ee142ab0 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/environment-variables.md @@ -0,0 +1,602 @@ +# Environment Variables + +This document provides comprehensive information about all environment variables used by the Scene Intelligence microservice stack. + +## Recommended Setup Method + +**The preferred way to configure and deploy Scene Intelligence is using the provided setup script:** + +```bash +# Complete setup with sensible defaults +source setup.sh --setup + +# Or just set environment variables +source setup.sh --setenv +``` + +**The setup script automatically configures all required environment variables with production-ready defaults.** Manual environment variable configuration is only needed for advanced customization or when integrating with existing infrastructure. + +## When to Use Manual Configuration + +Manual environment variable configuration is recommended for: + +- Integration with existing CI/CD pipelines +- Custom deployment environments +- Advanced performance tuning +- Development with specific configurations + +For most users, **the setup script approach documented in [Get Started](./get-started.md) is sufficient**. + +## Environment Variable Reference + +## Core Stack Configuration + +### Service Ports + +#### SCENE_INTELLIGENCE_PORT + +**Description**: External port for Scene Intelligence API service +**Default**: `8081` +**Example**: `export SCENE_INTELLIGENCE_PORT=8081` + +#### VLM_SERVICE_PORT + +**Description**: External port for VLM OpenVINO serving service +**Default**: `9764` +**Example**: `export VLM_SERVICE_PORT=9764` + +#### SCENESCAPE_PORT + +**Description**: External port for SceneScape web interface +**Default**: `443` +**Example**: `export SCENESCAPE_PORT=443` + +#### MQTT_PORT + +**Description**: External port for MQTT broker +**Default**: `1883` +**Example**: `export MQTT_PORT=1883` + +#### DLSTREAMER_PORT + +**Description**: External port for DL Streamer pipeline server +**Default**: `8555` +**Example**: `export DLSTREAMER_PORT=8555` + +### VLM Configuration + +#### VLM_BASE_URL + +**Description**: Internal URL for VLM service communication +**Default**: `http://vlm-openvino-serving:8000` +**Example**: `export VLM_BASE_URL=http://vlm-openvino-serving:8000` + +#### VLM_MODEL + +**Description**: VLM model name for Scene Intelligence +**Default**: `Qwen/Qwen2.5-VL-3B-Instruct` +**Example**: `export VLM_MODEL=Qwen/Qwen2.5-VL-3B-Instruct` + +#### VLM_MODEL_NAME + +**Description**: VLM model name for OpenVINO serving +**Default**: `Qwen/Qwen2.5-VL-3B-Instruct` +**Example**: `export VLM_MODEL_NAME=Qwen/Qwen2.5-VL-3B-Instruct` + +#### VLM_WORKERS + +**Description**: Number of VLM worker processes +**Default**: `4` +**Example**: `export VLM_WORKERS=4` + +#### HIGH_DENSITY_THRESHOLD + +**Description**: Traffic density threshold for VLM analysis +**Default**: `5.0` +**Example**: `export HIGH_DENSITY_THRESHOLD=5.0` + +#### VLM_TIMEOUT_SECONDS + +**Description**: Timeout for VLM requests +**Default**: `10` +**Example**: `export VLM_TIMEOUT_SECONDS=10` + +#### VLM_COOLDOWN_MINUTES + +**Description**: Cooldown between VLM analyses +**Default**: `1` +**Example**: `export VLM_COOLDOWN_MINUTES=1` + +### MQTT Configuration + +#### MQTT_BROKER_HOST + +**Description**: MQTT broker hostname +**Default**: `broker.scenescape.intel.com` +**Example**: `export MQTT_BROKER_HOST=broker.scenescape.intel.com` + +#### MQTT_BROKER_PORT + +**Description**: MQTT broker port +**Default**: `1883` +**Example**: `export MQTT_BROKER_PORT=1883` + +### Container Registry + +#### REGISTRY + +**Description**: Container registry URL prefix +**Default**: Empty (uses default registry) +**Example**: `export REGISTRY=your-registry.com/` + +#### TAG + +**Description**: Container image tag +**Default**: `latest` +**Example**: `export TAG=v1.0.0` + +## Additional VLM Parameters + +#### VLM_MAX_COMPLETION_TOKENS + +**Description**: Maximum tokens for VLM responses +**Default**: `500` +**Example**: `export VLM_MAX_COMPLETION_TOKENS=500` + +#### VLM_TEMPERATURE + +**Description**: VLM sampling temperature +**Default**: `0.3` +**Example**: `export VLM_TEMPERATURE=0.3` + +#### VLM_TOP_P + +**Description**: VLM nucleus sampling parameter +**Default**: `0.9` +**Example**: `export VLM_TOP_P=0.9` + +#### VLM_DEVICE + +**Description**: Device for VLM inference +**Default**: `CPU` +**Options**: `CPU`, `GPU` +**Example**: `export VLM_DEVICE=GPU` + +#### VLM_COMPRESSION_WEIGHT_FORMAT + +**Description**: Model compression format +**Default**: `int8` +**Options**: `int8`, `int4`, `fp16` +**Example**: `export VLM_COMPRESSION_WEIGHT_FORMAT=int8` + +## Quick Setup Examples + +### Using Setup Script (Recommended) + +```bash +# Development setup with debug logging +export LOG_LEVEL=DEBUG +export VLM_WORKERS=2 +source setup.sh --setup + +# Production setup with performance tuning +export VLM_WORKERS=4 +export HIGH_DENSITY_THRESHOLD=5.0 +source setup.sh --setup + +# Custom registry setup +export REGISTRY_URL=your-registry.com +export PROJECT_NAME=scene-intelligence +source setup.sh --setup +``` + +### Manual Configuration (Advanced) + +### Manual Development Setup + +```bash +export SCENE_INTELLIGENCE_PORT=8081 +export VLM_SERVICE_PORT=9764 +export VLM_WORKERS=2 +export LOG_LEVEL=DEBUG +export HIGH_DENSITY_THRESHOLD=3.0 +``` + +### Manual Production Setup + +```bash +export SCENE_INTELLIGENCE_PORT=8081 +export VLM_SERVICE_PORT=9764 +export VLM_WORKERS=4 +export LOG_LEVEL=INFO +export HIGH_DENSITY_THRESHOLD=5.0 +export DATA_RETENTION_HOURS=168 +``` + +### Manual High Performance Setup + +```bash +export VLM_DEVICE=GPU +export VLM_WORKERS=8 +export VLM_TIMEOUT_SECONDS=30 +export TRAFFIC_BUFFER_DURATION=120 +``` + +**Note**: After manually setting environment variables, you still need to run the Docker Compose stack manually. The setup script approach handles this automatically. + +#### MQTT_BROKER_PORT + +**Description**: MQTT broker port number. + +**Default**: `1883` + +**Examples**: + +```bash +# Standard MQTT port +export MQTT_BROKER_PORT=1883 + +# Secure MQTT port +export MQTT_BROKER_PORT=8883 + +# Custom port +export MQTT_BROKER_PORT=1884 +``` + +#### MQTT_USERNAME + +**Description**: MQTT broker username for authentication. + +**Default**: None (anonymous connection) + +**Examples**: + +```bash +export MQTT_USERNAME="scene_intelligence_user" +export MQTT_USERNAME="mqtt_client" +``` + +#### MQTT_PASSWORD + +**Description**: MQTT broker password for authentication. + +**Default**: None (anonymous connection) + +**Examples**: + +```bash +export MQTT_PASSWORD="secure_password123" +export MQTT_PASSWORD="${MQTT_SECRET}" +``` + +#### MQTT_KEEPALIVE + +**Description**: MQTT keepalive interval in seconds. + +**Default**: `60` + +**Examples**: + +```bash +export MQTT_KEEPALIVE=60 +export MQTT_KEEPALIVE=30 +``` + +### VLM Service Configuration + +#### VLM_BASE_URL + +**Description**: Base URL for the VLM service API. + +**Default**: `http://localhost:8000` + +**Examples**: + +```bash +export VLM_BASE_URL="http://vlm-service:8000" +export VLM_BASE_URL="http://192.168.1.101:8000" +export VLM_BASE_URL="https://vlm.example.com" +``` + +#### VLM_MODEL + +**Description**: VLM model name to use for analysis. + +**Default**: From configuration file + +**Examples**: + +```bash +export VLM_MODEL="Qwen/Qwen2.5-VL-3B-Instruct" +export VLM_MODEL="microsoft/Phi-3.5-vision-instruct" +``` + +#### VLM_WORKERS + +**Description**: Number of concurrent VLM workers. + +**Default**: `3` + +**Range**: 1-10 + +**Examples**: + +```bash +# Low resource usage +export VLM_WORKERS=1 + +# Balanced performance +export VLM_WORKERS=3 + +# High throughput +export VLM_WORKERS=6 +``` + +#### VLM_TIMEOUT_SECONDS + +**Description**: VLM API request timeout in seconds. + +**Default**: `30` + +**Examples**: + +```bash +export VLM_TIMEOUT_SECONDS=30 +export VLM_TIMEOUT_SECONDS=60 +export VLM_TIMEOUT_SECONDS=10 +``` + +### Traffic Analysis Configuration + +#### HIGH_DENSITY_THRESHOLD + +**Description**: Traffic density threshold for triggering VLM analysis. + +**Default**: `5.0` + +**Examples**: + +```bash +# Conservative threshold +export HIGH_DENSITY_THRESHOLD=3.0 + +# Default threshold +export HIGH_DENSITY_THRESHOLD=5.0 + +# High threshold for busy areas +export HIGH_DENSITY_THRESHOLD=8.0 +``` + +#### VLM_COOLDOWN_MINUTES + +**Description**: Cooldown period between VLM analyses for the same intersection. + +**Default**: `1` + +**Examples**: + +```bash +export VLM_COOLDOWN_MINUTES=1 +export VLM_COOLDOWN_MINUTES=5 +export VLM_COOLDOWN_MINUTES=15 +``` + +#### MINIMUM_DURATION_FOR_CONSISTENTLY_HIGH_TRAFFIC_SECONDS + +**Description**: Minimum duration of sustained high traffic before triggering VLM analysis. + +**Default**: `30` + +**Examples**: + +```bash +export MINIMUM_DURATION_FOR_CONSISTENTLY_HIGH_TRAFFIC_SECONDS=30 +export MINIMUM_DURATION_FOR_CONSISTENTLY_HIGH_TRAFFIC_SECONDS=60 +export MINIMUM_DURATION_FOR_CONSISTENTLY_HIGH_TRAFFIC_SECONDS=15 +``` + +### VLM Model Parameters + +#### VLM_MAX_COMPLETION_TOKENS + +**Description**: Maximum number of tokens in VLM response. + +**Default**: `500` + +**Examples**: + +```bash +export VLM_MAX_COMPLETION_TOKENS=500 +export VLM_MAX_COMPLETION_TOKENS=1000 +export VLM_MAX_COMPLETION_TOKENS=200 +``` + +#### VLM_TEMPERATURE + +**Description**: Temperature parameter for VLM model (creativity). + +**Default**: `0.3` + +**Range**: 0.0-2.0 + +**Examples**: + +```bash +# More deterministic +export VLM_TEMPERATURE=0.1 + +# Balanced +export VLM_TEMPERATURE=0.3 + +# More creative +export VLM_TEMPERATURE=0.7 +``` + +#### VLM_TOP_P + +**Description**: Top-p parameter for VLM model (nucleus sampling). + +**Default**: `0.9` + +**Range**: 0.0-1.0 + +**Examples**: + +```bash +export VLM_TOP_P=0.9 +export VLM_TOP_P=0.95 +export VLM_TOP_P=0.8 +``` + +### Custom Prompts + +#### VLM_TRAFFIC_ANALYSIS_PROMPT + +**Description**: Custom prompt for VLM traffic analysis. + +**Default**: From configuration file + +**Examples**: + +```bash +export VLM_TRAFFIC_ANALYSIS_PROMPT="Analyze the traffic situation in these intersection images and provide insights." +``` + +#### VLM_SYSTEM_PROMPT + +**Description**: System prompt for VLM model. + +**Default**: From configuration file + +**Examples**: + +```bash +export VLM_SYSTEM_PROMPT="You are an expert traffic analyst with computer vision capabilities." +``` + +### Service Configuration + +#### SERVICE_PORT + +**Description**: Port number for the HTTP API server. + +**Default**: `8001` + +**Examples**: + +```bash +export SERVICE_PORT=8001 +export SERVICE_PORT=8080 +export SERVICE_PORT=3000 +``` + +#### LOG_LEVEL + +**Description**: Logging level for the service. + +**Default**: `INFO` + +**Supported Values**: `DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL` + +**Examples**: + +```bash +# Development +export LOG_LEVEL=DEBUG + +# Production +export LOG_LEVEL=INFO + +# Quiet production +export LOG_LEVEL=WARNING +``` + +#### DATABASE_URL + +**Description**: Database connection URL (optional). + +**Default**: SQLite in-memory database + +**Examples**: + +```bash +# SQLite file +export DATABASE_URL="sqlite:///data/scene_intelligence.db" + +# PostgreSQL +export DATABASE_URL="postgresql://user:password@localhost:5432/scene_intelligence" +``` + +## Configuration File Variables + +The following variables can be overridden by environment variables but are typically set in configuration files: + +### Traffic Analysis Window Configuration + +- **Traffic Window Duration**: 15 seconds (hardcoded) +- **Sustained Threshold**: 3 seconds (hardcoded) +- **Analysis Display Duration**: 20 minutes (hardcoded) + +### Image Service Configuration + +- **Max Images Per Camera**: Configurable in service initialization +- **Image Freshness Window**: 3-5 minutes for API responses + +## Environment Variable Precedence + +Environment variables take precedence over configuration file values in the following order: + +1. **Environment Variables** (highest precedence) +2. **Configuration Files** (VLM config, Scene Intelligence config) +3. **Default Values** (lowest precedence) + +## Examples by Use Case + +### Development Environment + +```bash +export SCENE_INTELLIGENCE_CONFIG_PATH="./config/scene_intelligence_config.json" +export VLM_CONFIG_PATH="./config/vlm_config.json" +export MQTT_BROKER_HOST="localhost" +export MQTT_BROKER_PORT=1883 +export VLM_BASE_URL="http://localhost:8000" +export LOG_LEVEL=DEBUG +export VLM_WORKERS=1 +``` + +### Production Environment + +```bash +export SCENE_INTELLIGENCE_CONFIG_PATH="/app/config/scene_intelligence_config.json" +export VLM_CONFIG_PATH="/app/config/vlm_config.json" +export MQTT_BROKER_HOST="mqtt-broker.production.com" +export MQTT_BROKER_PORT=1883 +export MQTT_USERNAME="scene_intelligence_prod" +export MQTT_PASSWORD="${MQTT_PROD_PASSWORD}" +export VLM_BASE_URL="http://vlm-service:8000" +export LOG_LEVEL=INFO +export VLM_WORKERS=3 +export HIGH_DENSITY_THRESHOLD=5.0 +export VLM_TIMEOUT_SECONDS=30 +``` + +### High-Performance Environment + +```bash +export VLM_WORKERS=6 +export VLM_TIMEOUT_SECONDS=60 +export HIGH_DENSITY_THRESHOLD=3.0 +export VLM_COOLDOWN_MINUTES=1 +export MINIMUM_DURATION_FOR_CONSISTENTLY_HIGH_TRAFFIC_SECONDS=15 +export VLM_MAX_COMPLETION_TOKENS=1000 +``` + +### Secure Environment + +```bash +export MQTT_BROKER_PORT=8883 +export MQTT_USERNAME="${MQTT_SECURE_USER}" +export MQTT_PASSWORD="${MQTT_SECURE_PASSWORD}" +export VLM_BASE_URL="https://vlm-service.secure.com" +export DATABASE_URL="postgresql://${DB_USER}:${DB_PASSWORD}@db.secure.com:5432/scene_intelligence" +``` diff --git a/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/get-started.md b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/get-started.md new file mode 100644 index 000000000..8733216da --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/get-started.md @@ -0,0 +1,357 @@ +# Get Started + +The **Scene Intelligence microservice** provides comprehensive traffic analysis capabilities including real-time intersection monitoring, directional traffic density analysis, and VLM-powered traffic insights. This guide provides step-by-step instructions to: + +- Set up the microservice using the automated setup script for quick deployment. +- Run predefined tasks to explore its functionality. +- Learn how to modify configurations to suit specific requirements. + +## Prerequisites + +Before you begin, ensure the following: + +- **System Requirements**: Verify that your system meets the [minimum requirements](./system-requirements.md). +- **Docker Installed**: Install Docker. For installation instructions, see [Get Docker](https://docs.docker.com/get-docker/). +- **MQTT Broker**: Ensure access to an MQTT broker for traffic data streaming (or use the included broker). + +This guide assumes basic familiarity with Docker commands and terminal usage. If you are new to Docker, see [Docker Documentation](https://docs.docker.com/) for an introduction. + +## Quick Start with Setup Script + +The Scene Intelligence microservice includes an automated setup script that handles environment configuration, secrets generation, building, and deployment. This is the **recommended approach** for getting started. + +### 1. Clone the Repository + +```bash +git clone https://github.com/open-edge-platform/edge-ai-libraries.git +cd edge-ai-libraries/microservices/scene-intelligence +``` + +### 2. Run the Complete Setup + +The setup script provides several options. For a complete setup (recommended for first-time users): + +```bash +# Complete setup: generates secrets, builds images, and starts all services +source setup.sh --setup +``` + +This single command will: + +- Set all required environment variables with sensible defaults +- Generate required TLS certificates and authentication files +- Download demo video files for testing +- Build Docker images +- Start all services in the Scene Intelligence stack + +The setup command starts all services including the containerized Traffic Intelligence service. + +### 3. Verify Services + +Check that all services are running: + +```bash +# Check container status +docker ps + +# Verify Traffic Intelligence API +curl -s http://localhost:8081/health + +# Verify Traffic Intelligence UI +curl -s http://localhost:7860/ + +# Check Scene Intelligence (if deployed separately) +curl -s http://localhost:8082/health +``` + +### 4. Access Services + +The stack provides multiple interfaces: + +- **Traffic Intelligence API**: `http://localhost:8081` +- **Traffic Intelligence UI**: `http://localhost:7860` +- **SceneScape Web**: `https://localhost:443` +- **API Documentation**: `http://localhost:8081/docs` (Swagger UI) + +## Manual Setup (Advanced Users) + +For advanced users who need more control over the configuration, you can manually set up the stack using Docker Compose. + +### Manual Environment Configuration + +If you prefer to manually configure environment variables instead of using the setup script, see the [Environment Variables Guide](./environment-variables.md) for complete details. Key variables include: + +```bash +# Core Scene Intelligence Configuration +export SCENE_INTELLIGENCE_PORT=8082 +export LOG_LEVEL=INFO + +# MQTT Broker Configuration +export MQTT_BROKER_HOST=broker.scenescape.intel.com +export MQTT_BROKER_PORT=1883 +export MQTT_PORT=1883 + +# VLM Service Configuration +export VLM_BASE_URL=http://vlm-openvino-serving:8000 +export VLM_MODEL_NAME=Qwen/Qwen2.5-VL-3B-Instruct +export VLM_SERVICE_PORT=9764 + +# SceneScape Configuration +export SCENESCAPE_PORT=443 +export DLSTREAMER_PORT=8555 + +# Traffic Analysis Parameters +export HIGH_DENSITY_THRESHOLD=5.0 +export VLM_WORKERS=4 +export VLM_COOLDOWN_MINUTES=1 +export VLM_TIMEOUT_SECONDS=10 +``` + +## Services Included + +The Scene Intelligence stack includes these containerized services: + +- **MQTT Broker** (Eclipse Mosquitto) - Message broker for traffic data +- **DL Streamer Pipeline Server** - Video analytics and AI inference +- **SceneScape Database** - Configuration and metadata storage +- **SceneScape Web Server** - Management interface +- **SceneScape Controller** - Orchestration service +- **VLM OpenVINO Serving** - Vision Language Model inference +- **Traffic Intelligence** - Real-time traffic analysis with dual interface (API + UI) + +## Testing the API + +### 1. Traffic Intelligence Service + +The Traffic Intelligence service provides real-time intersection monitoring: + +```bash +# Check service health +curl -s http://localhost:8081/health + +# Get current traffic data +curl -s http://localhost:8081/api/v1/traffic/current + +# Get weather data +curl -s http://localhost:8081/api/v1/weather/current + +``` + +### 2. Access UI Dashboard + +Open the Traffic Intelligence UI in your browser: + +Visit http://localhost:7860 in your browser + + +The UI provides: +- Real-time traffic visualization +- Camera image display +- Weather information +- VLM analysis results +- Traffic alerts and recommendations + + +## Service Ports + +The complete stack exposes several services on different ports: + +| Service | Port | Description | +|---------|------|-------------| +| Traffic Intelligence API | 8081 | Real-time traffic analysis REST API | +| Traffic Intelligence UI | 7860 | Interactive Gradio dashboard | +| Scene Intelligence API | 8082 | Scene analytics service (optional) | +| VLM OpenVINO Serving | 9764 | Vision Language Model service | +| SceneScape Web | 443 | Management web interface (HTTPS) | +| MQTT Broker | 1883 | Message broker | +| DL Streamer | 8555 | Video analytics pipeline | + +## Service Ports + +The complete stack exposes several services on different ports: + +| Service | Port | Description | +|---------|------|-------------| +| Scene Intelligence API | 8082 | Main traffic analysis API | +| VLM OpenVINO Serving | 9764 | Vision Language Model service | +| SceneScape Web | 443 | Management web interface (HTTPS) | +| MQTT Broker | 1883 | Message broker | +| DL Streamer | 8555 | Video analytics pipeline | + +## Configuration Files + +The Scene Intelligence stack uses several configuration files located in the `config/` and `src/traffic-intelligence/config/` directories: + +### Traffic Intelligence Configuration + +The Traffic Intelligence service configuration is at `src/traffic-intelligence/config/traffic_intelligence.json`: + +```json +{ + "intersection": { + "id": "97781c36-b53a-4749-87e6-8815da99bac7", + "name": "Intersection-Demo", + "latitude": 33.3091336, + "longitude": -111.9353095 + }, + "mqtt": { + "host": "broker.scenescape.intel.com", + "port": 1883, + "use_tls": true, + "ca_cert_path": "secrets/certs/scenescape-ca.pem", + "camera_topics": [ + "scenescape/data/camera/camera1", + "scenescape/data/camera/camera2", + "scenescape/data/camera/camera3", + "scenescape/data/camera/camera4" + ] + }, + "vlm": { + "base_url": "http://vlm-openvino-serving:8000", + "model": "Qwen/Qwen2.5-VL-3B-Instruct", + "timeout_seconds": 300 + }, + "traffic": { + "high_density_threshold": 10, + "analysis_window_seconds": 30, + "vlm_trigger_duration_seconds": 15 + } +} +``` + +Note: Configuration values can be overridden by environment variables set in `setup.sh`. + +## Next Steps + +- **Traffic Intelligence**: Access the UI dashboard at `http://localhost:7860` for interactive traffic monitoring +- **API Documentation**: Explore the Traffic Intelligence API at `http://localhost:8081/docs` (Swagger UI) +- **Advanced Configuration**: For detailed environment variable options, see [Environment Variables](./environment-variables.md) +- **Traffic Analysis Deep Dive**: See [Traffic Data Analysis Workflow](./traffic-data-analysis-workflow.md) for VLM integration details +- **SceneScape Management**: Access the web interface at `https://localhost:443` for visual management +- **Video Analytics**: Configure video streams through DL Streamer integration +- **Build from Source**: See [How to Build from Source](./how-to-build-from-source.md) for development and custom builds + + +### Key Integration Points + +- **MQTT Communication**: All services communicate via the shared MQTT broker +- **Docker Network**: Services discover each other via Docker service names +- **Shared Secrets**: TLS certificates and auth files mounted from `src/secrets/` +- **Persistent Storage**: Traffic data stored in Docker volume `traffic-intelligence-data` +- **Health Monitoring**: All services include health check endpoints + +## Troubleshooting + +### Stack Not Starting + +Check status and logs: + +```bash +# View container status +docker ps -a + +# Check specific service logs +docker compose -f docker/compose.yaml logs traffic-intelligence +docker compose -f docker/compose.yaml logs vlm-openvino-serving +docker compose -f docker/compose.yaml logs broker + +# Restart services +source setup.sh --stop +source setup.sh --run +``` + +Common issues: +- Missing secrets/certificates in `src/secrets/` directory +- Port conflicts (check ports 8081, 7860, 9764, 443, 1883, 8555) +- Insufficient system resources for VLM service +- Proxy configuration issues + +### Traffic Intelligence Service Issues + +Check service health: + +```bash +# Verify API is responding +curl http://localhost:8081/health + +# Check UI accessibility +curl http://localhost:7860/ + +# View detailed logs +docker logs -f scene-intelligence-traffic-intelligence + +# Check container is running +docker ps | grep traffic-intelligence +``` + +### Service Health Issues + +Verify individual service health: + +```bash +# Traffic Intelligence +curl http://localhost:8081/health + +# VLM Service +curl http://localhost:9764/health + +``` + +### MQTT Connection Issues + +Verify MQTT broker connectivity: + +```bash +# Check broker is running +docker ps | grep broker + +# Verify certificate is mounted +docker exec scene-intelligence-traffic-intelligence ls -la /app/secrets/certs/ + +# Check network connectivity +docker compose -f docker/compose.yaml exec traffic-intelligence ping broker.scenescape.intel.com +``` + +### VLM Analysis Not Working + +Debug VLM integration: + +```bash +# Check VLM service health +curl http://localhost:9764/health + +# Verify traffic threshold configuration +curl http://localhost:8081/api/v1/config + +# Check camera data availability +docker logs scene-intelligence-traffic-intelligence | grep "camera" + +# Monitor VLM requests +docker logs scene-intelligence-traffic-intelligence | grep -i vlm +``` + +### Performance Issues + +Monitor resource usage: + +```bash +# Check container resource usage +docker stats + +# Adjust VLM workers if needed +export VLM_WORKERS=2 +docker compose -f docker/compose.yaml up -d vlm-openvino-serving +``` + +### Configuration Issues + +Validate configuration files: + +```bash +# Check JSON syntax +cat src/traffic-intelligence/config/traffic_intelligence.json | jq . +cat config/scene_intelligence_config.json | jq . + +# Verify mounted configuration +docker compose -f docker/compose.yaml exec traffic-intelligence ls -la /app/config/ +``` diff --git a/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/how-to-build-from-source.md b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/how-to-build-from-source.md new file mode 100644 index 000000000..1d1d2bf95 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/how-to-build-from-source.md @@ -0,0 +1,82 @@ +# How to Build from Source + +Build the Scene Intelligence microservice from source to customize or extend its functionality. This guide covers building the containerized Traffic Intelligence service. + +## Prerequisites + +- **System Requirements**: Verify your system meets the [minimum requirements](./system-requirements.md). +- **Docker**: Install Docker from [Get Docker](https://docs.docker.com/get-docker/). +- Basic familiarity with Git and Docker commands. + +## Build Traffic Intelligence Service + +### 1. Clone the Repository + +```bash +git clone https://github.com/open-edge-platform/edge-ai-libraries.git +cd edge-ai-libraries/microservices/scene-intelligence +``` + +### 2. Build the Docker Image + +Build the Traffic Intelligence service: + +```bash +docker compose -f docker/compose.yaml build traffic-intelligence +``` + +### 3. Run the Service + +```bash +# Using setup script (recommended) +source setup.sh --run + +# Or manually with Docker Compose +docker compose -f docker/compose.yaml up traffic-intelligence +``` + +### 4. Verify the Build + +```bash +# Check service health +curl http://localhost:8081/health + +# Access UI +curl http://localhost:7860/ + +# View logs +docker logs scene-intelligence-traffic-intelligence +``` + +### Verify API Endpoints + +```bash +# Health check +curl http://localhost:8081/health + +# Get current traffic data +curl http://localhost:8081/api/v1/traffic/current +``` + +## Rebuild After Changes + +When you modify code: + +```bash +# Rebuild the image +docker compose -f docker/compose.yaml build traffic-intelligence + +# Restart the service +docker compose -f docker/compose.yaml up -d traffic-intelligence + +# View startup logs +docker logs -f scene-intelligence-traffic-intelligence +``` + + +## Supporting Resources + +- [Get Started Guide](get-started.md) +- [Environment Variables](environment-variables.md) +- [System Requirements](system-requirements.md) + diff --git a/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/index.rst b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/index.rst new file mode 100644 index 000000000..f4c88454b --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/index.rst @@ -0,0 +1,24 @@ +.. toctree:: + :hidden: + + Overview + +.. toctree:: + + system-requirements + get-started + +.. toctree:: + :caption: How to + + how-to-build-from-source + +.. toctree:: + :caption: References + + environment-variables + api-reference + +.. toctree:: + + release-notes diff --git a/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/system-requirements.md b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/system-requirements.md new file mode 100644 index 000000000..8d209ac02 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/system-requirements.md @@ -0,0 +1,36 @@ +# System Requirements + +This page provides detailed hardware, software, and platform requirements to help you set up and run the microservice efficiently. + +## Supported Platforms + +This microservice currently supports CPU and GPU based runs. This microservice is intended to run in the context of video summary pipeline. Hence, supported platform, OS configuration etc. is as per the documentation in the sample application. The documentation here, hence, does not provide separate requirements. + +**Operating Systems** + +- As per sample application documentation. + +**Hardware Platforms** + +- As per sample application documentation. + +## Minimum Requirements + +- As per sample application documentation. + +## Software Requirements + +**Required Software**: + +- Docker 24.0 +- Python 3.10 +- Docker Compose: [Installation Guide](https://docs.docker.com/compose/install/). + +## Validation + +- Ensure all required software are installed and configured before proceeding to [Get Started](./get-started.md). + +## Supporting Resources + +- [Overview](Overview.md) +- [API Reference](api-reference.md) diff --git a/metro-ai-suite/smart-traffic-intersection-agent/setup.sh b/metro-ai-suite/smart-traffic-intersection-agent/setup.sh new file mode 100755 index 000000000..13d177ca4 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/setup.sh @@ -0,0 +1,516 @@ +#!/bin/bash + +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +# Color codes for terminal output +RED='\033[0;31m' +GREEN='\033[0;32m' +YELLOW='\033[0;33m' +BLUE='\033[0;34m' +NC='\033[0m' # No Color + +# Setting command usage and invalid arguments handling before the actual setup starts +if [ "$#" -eq 0 ] || ([ "$#" -eq 1 ] && [ "$1" = "--help" ]); then + # If no valid argument is passed, print usage information + echo -e "-----------------------------------------------------------------" + echo -e "${YELLOW}USAGE: ${GREEN}source setup.sh ${BLUE}[--setenv | --run | --setup | --restart [agent|prerequisite] | --stop | --clean | --help]" + echo -e "${YELLOW}" + echo -e " --setenv: Set environment variables without starting any containers" + echo -e " --run: Start the services" + echo -e " --setup: Build and run the services (first time setup)" + echo -e " --restart [service]: Restart services with updated environment variables" + echo -e " • agent - Restart only Scene Intelligence services" + echo -e " • prerequisite - Restart only prerequisite services (edge-ai-suites)" + echo -e " • (no argument) - Restart all services" + echo -e " --stop: Stop the services" + echo -e " --clean: Clean up containers, volumes, and logs" + echo -e " --help: Show this help message${NC}" + echo -e "-----------------------------------------------------------------" + return 0 + +elif [ "$#" -gt 2 ]; then + echo -e "${RED}ERROR: Too many arguments provided.${NC}" + echo -e "${YELLOW}Use --help for usage information${NC}" + return 1 + +elif [ "$1" != "--help" ] && [ "$1" != "--setenv" ] && [ "$1" != "--run" ] && [ "$1" != "--setup" ] && [ "$1" != "--restart" ] && [ "$1" != "--stop" ] && [ "$1" != "--clean" ]; then + # Default case for unrecognized option + echo -e "${RED}Unknown option: $1 ${NC}" + echo -e "${YELLOW}Use --help for usage information${NC}" + return 1 + +elif [ "$1" = "--restart" ] && [ "$#" -eq 2 ] && [ "$2" != "agent" ] && [ "$2" != "prerequisite" ]; then + echo -e "${RED}ERROR: Invalid restart argument: $2${NC}" + echo -e "${YELLOW}Valid options: agent, prerequisite${NC}" + echo -e "${YELLOW}Use --help for usage information${NC}" + return 1 + +elif [ "$1" = "--stop" ]; then + # If --stop is passed, bring down the Docker containers and stop + echo -e "${YELLOW}Stopping Scene Intelligence services... ${NC}" + + # Stop Docker services + docker compose -f docker/compose.yaml down + if [ $? -ne 0 ]; then + return 1 + fi + echo -e "${GREEN}Scene Intelligence services stopped successfully. ${NC}" + + # Stop edge-ai-suites services if they exist + EDGE_AI_SUITES_DIR="edge-ai-suites" + if [ -d "$EDGE_AI_SUITES_DIR/metro-ai-suite/metro-vision-ai-app-recipe" ]; then + cd "$EDGE_AI_SUITES_DIR/metro-ai-suite/metro-vision-ai-app-recipe" + if [ -f "docker-compose.yml" ] || [ -f "compose.yaml" ]; then + echo -e "${YELLOW}Stopping edge-ai-suites services... ${NC}" + docker compose down 2>/dev/null + if [ $? -eq 0 ]; then + echo -e "${GREEN}edge-ai-suites services stopped successfully. ${NC}" + else + echo -e "${YELLOW}Warning: Could not stop edge-ai-suites services${NC}" + fi + fi + cd - > /dev/null + else + echo -e "${YELLOW}edge-ai-suites services not found, skipping... ${NC}" + fi + + return 0 + +elif [ "$1" = "--clean" ]; then + # If --clean is passed, clean up containers and volumes + echo -e "${YELLOW}Cleaning up containers and volumes... ${NC}" + + docker compose -f docker/compose.yaml down 2>/dev/null || true + + echo -e "${YELLOW}Removing scene intelligence volumes... ${NC}" + docker volume ls | grep scene-intelligence | awk '{ print $2 }' | xargs docker volume rm 2>/dev/null || true + + if [ $? -ne 0 ]; then + return 1 + fi + echo -e "${GREEN}Docker cleanup completed successfully. ${NC}" + + # Clean up the cloned edge-ai-suites repository + EDGE_AI_SUITES_DIR="edge-ai-suites" + if [ -d "$EDGE_AI_SUITES_DIR" ]; then + echo -e "${YELLOW}Cleaning up edge-ai-suites repository... ${NC}" + + # Stop docker services in edge-ai-suites if they exist + if [ -d "$EDGE_AI_SUITES_DIR/metro-ai-suite/metro-vision-ai-app-recipe" ]; then + cd "$EDGE_AI_SUITES_DIR/metro-ai-suite/metro-vision-ai-app-recipe" + if [ -f "docker-compose.yml" ] || [ -f "compose.yaml" ]; then + echo -e "${YELLOW}Stopping edge-ai-suites docker services... ${NC}" + docker compose down 2>/dev/null || true + + echo -e "${YELLOW}Removing metro-vision-ai-app-recipe volumes... ${NC}" + docker volume ls | grep metro-vision-ai-app-recipe | awk '{ print $2 }' | xargs docker volume rm 2>/dev/null || true + fi + cd - > /dev/null + fi + + else + echo -e "${YELLOW}edge-ai-suites repository not found, skipping... ${NC}" + fi + + echo -e "${GREEN}Full cleanup completed successfully. ${NC}" + return 0 + +fi + +# ============================================================================ +# PREREQUISITES: Setup edge-ai-suites before running the application +# ============================================================================ + +# Export HOST_IP early so it can be used in prerequisite checks +export HOST_IP=$(ip route get 1 2>/dev/null | awk '{print $7}') +# If HOST_IP is empty, use localhost +if [ -z "$HOST_IP" ]; then + export HOST_IP="127.0.0.1" +fi +# Function to check if prerequisites are met +check_and_setup_prerequisites() { + local EDGE_AI_SUITES_DIR="edge-ai-suites" + local REQUIRED_BRANCH="release-1.2.0" + + echo -e "${BLUE}==> Checking prerequisites...${NC}" + + # Check if edge-ai-suites directory exists + if [ ! -d "$EDGE_AI_SUITES_DIR" ]; then + echo -e "${YELLOW}edge-ai-suites not found. Cloning repository...${NC}" + + # Clone the repository with the specific branch (shallow clone, only latest layer) + git clone --depth 1 --single-branch --branch $REQUIRED_BRANCH https://github.com/open-edge-platform/edge-ai-suites.git + + if [ $? -ne 0 ]; then + echo -e "${RED}Failed to clone edge-ai-suites repository${NC}" + return 1 + fi + + echo -e "${GREEN}Successfully cloned edge-ai-suites${NC}" + + else + echo -e "${GREEN}edge-ai-suites directory already exists${NC}" + + fi + + # Navigate to the metro-vision-ai-app-recipe directory + local METRO_DIR="$EDGE_AI_SUITES_DIR/metro-ai-suite/metro-vision-ai-app-recipe" + + if [ ! -d "$METRO_DIR" ]; then + echo -e "${RED}Directory $METRO_DIR not found${NC}" + return 1 + fi + + cd "$METRO_DIR" + + # Check if install.sh exists + if [ ! -f "install.sh" ]; then + echo -e "${RED}install.sh not found in $METRO_DIR${NC}" + cd - > /dev/null + return 1 + fi + + # Comment out the problematic chown lines in the smart-intersection install.sh + echo -e "${BLUE}==> Updating install.sh to comment out chown commands...${NC}" + if [ -f "smart-intersection/install.sh" ]; then + sed -i 's/^sudo chown -R \$USER:\$USER chart\/files\/secrets$/# &/' smart-intersection/install.sh + sed -i 's/^sudo chown -R \$USER:\$USER src\/secrets$/# &/' smart-intersection/install.sh + echo -e "${GREEN}Successfully commented out chown commands in smart-intersection/install.sh${NC}" + else + echo -e "${YELLOW}Warning: smart-intersection/install.sh not found, skipping sed modifications${NC}" + fi + + # Run the installation script + echo -e "${BLUE}==> Running installation script for smart-intersection...${NC}" + ./install.sh smart-intersection + + if [ $? -ne 0 ]; then + echo -e "${RED}Failed to run install.sh for smart-intersection${NC}" + cd - > /dev/null + return 1 + fi + + echo -e "${GREEN}Installation script completed successfully${NC}" + + # Download container images and run with Docker Compose + echo -e "${BLUE}==> Downloading container images and starting services...${NC}" + docker compose up -d + + if [ $? -ne 0 ]; then + echo -e "${RED}Failed to start services with docker compose${NC}" + cd - > /dev/null + return 1 + fi + + echo -e "${GREEN}Container images downloaded and services started${NC}" + + # Verify running status + echo -e "${BLUE}==> Verifying running status...${NC}" + sleep 5 # Give services a moment to start + + docker compose ps + + if [ $? -eq 0 ]; then + echo -e "${GREEN}Services are running. Verification completed.${NC}" + else + echo -e "${YELLOW}Warning: Could not verify service status${NC}" + fi + + # Return to the original directory + cd - > /dev/null + + echo -e "${GREEN}Prerequisites setup completed successfully!${NC}" + echo "" + + # Display edge-ai-suites service URLs + echo -e "${BLUE}Edge AI Suites Services:${NC}" + echo -e " • SceneScape Web UI: ${YELLOW}https://${HOST_IP}:443${NC}" + echo -e " • DLStreamer Pipeline Server API: ${YELLOW}http://${HOST_IP}:8080${NC}" + echo -e " • InfluxDB UI: ${YELLOW}http://${HOST_IP}:8086${NC}" + echo -e " • Grafana Dashboard: ${YELLOW}http://${HOST_IP}:3000${NC}" + echo -e " • Node-RED UI: ${YELLOW}http://${HOST_IP}:1880${NC}" + echo "" + + return 0 +} + +# Run prerequisites check and setup (skip if only stopping or cleaning) +if [ "$1" != "--stop" ] && [ "$1" != "--clean" ] && [ "$1" != "--help" ] && [ "$1" != "--restart" ]; then + check_and_setup_prerequisites + + if [ $? -ne 0 ]; then + echo -e "${RED}Failed to setup prerequisites. Please check the errors above.${NC}" + return 1 + fi +fi + +# ============================================================================ +# END PREREQUISITES +# ============================================================================ + +# Export required environment variables (HOST_IP already set above) +export TAG=${TAG:-latest} +export REGISTRY=${REGISTRY:-} + +# Traffic Intelligence Service Configuration +export TRAFFIC_INTELLIGENCE_PORT=${TRAFFIC_INTELLIGENCE_PORT:-8081} +export TRAFFIC_INTELLIGENCE_UI_PORT=${TRAFFIC_INTELLIGENCE_UI_PORT:-7860} +export REFRESH_INTERVAL=${REFRESH_INTERVAL:-15} + +# User and group IDs for containers +export USER_GROUP_ID=$(id -g) +export VIDEO_GROUP_ID=$(getent group video | awk -F: '{printf "%s\n", $3}' 2>/dev/null || echo "44") +export RENDER_GROUP_ID=$(getent group render | awk -F: '{printf "%s\n", $3}' 2>/dev/null || echo "109") + +# Traffic Analysis Configuration +export TRAFFIC_BUFFER_DURATION=${TRAFFIC_BUFFER_DURATION:-60} +export LOG_LEVEL=${LOG_LEVEL:-INFO} +export DATA_RETENTION_HOURS=${DATA_RETENTION_HOURS:-24} + +# VLM Service Configuration +export VLM_SERVICE_PORT=${VLM_SERVICE_PORT:-9764} +export VLM_MODEL_NAME=${VLM_MODEL_NAME:-microsoft/Phi-3.5-vision-instruct} +export VLM_TIMEOUT_SECONDS=${VLM_TIMEOUT_SECONDS:-300} +export VLM_MAX_COMPLETION_TOKENS=${VLM_MAX_COMPLETION_TOKENS:-1500} +export VLM_TEMPERATURE=${VLM_TEMPERATURE:-0.1} +export VLM_TOP_P=${VLM_TOP_P:-0.1} + +# VLM OpenVINO Configuration +export VLM_DEVICE=${VLM_DEVICE:-CPU} +export VLM_COMPRESSION_WEIGHT_FORMAT=${VLM_COMPRESSION_WEIGHT_FORMAT:-int8} +export VLM_SEED=${VLM_SEED:-42} +export VLM_WORKERS=${VLM_WORKERS:-1} +export VLM_LOG_LEVEL=${VLM_LOG_LEVEL:-info} +export VLM_ACCESS_LOG_FILE=${VLM_ACCESS_LOG_FILE:-/dev/null} + +# Automatically adjust VLM settings for GPU +if [[ "$VLM_DEVICE" == "GPU" ]]; then + export VLM_COMPRESSION_WEIGHT_FORMAT=int4 + export VLM_WORKERS=1 # GPU works best with single worker +fi + +# Health Check Configuration +export HEALTH_CHECK_INTERVAL=${HEALTH_CHECK_INTERVAL:-30s} +export HEALTH_CHECK_TIMEOUT=${HEALTH_CHECK_TIMEOUT:-10s} +export HEALTH_CHECK_RETRIES=${HEALTH_CHECK_RETRIES:-3} +export HEALTH_CHECK_START_PERIOD=${HEALTH_CHECK_START_PERIOD:-10s} + +# Proxy settings +export no_proxy_env=${no_proxy} + +# Function to build and start the services +build_and_start_service() { + echo -e "${BLUE}==> Building and Starting Scene Intelligence Services...${NC}" + + # Build and start the services + docker compose -f docker/compose.yaml up -d --build 2>&1 1>/dev/null + + if [ $? -eq 0 ]; then + echo -e "${GREEN}Scene Intelligence Services built and started successfully!${NC}" + + echo "" + echo -e "${BLUE}Services:${NC}" + echo -e " • Traffic Intelligence API Docs: ${YELLOW}http://${HOST_IP}:${TRAFFIC_INTELLIGENCE_PORT}/docs${NC}" + echo -e " • Traffic Intelligence UI: ${YELLOW}http://${HOST_IP}:${TRAFFIC_INTELLIGENCE_UI_PORT}${NC}" + echo -e " • VLM Service API Docs: ${YELLOW}http://${HOST_IP}:${VLM_SERVICE_PORT}/docs${NC}" + echo "" + echo -e "${BLUE}To view logs:${NC}" + echo -e " ${YELLOW}docker compose -f docker/compose.yaml logs -f${NC}" + echo -e "${BLUE}To stop the services:${NC}" + echo -e " ${YELLOW}source setup.sh --stop${NC}" + else + echo -e "${RED}Failed to build and start Scene Intelligence Services${NC}" + return 1 + fi +} + +# Function to start the services +start_service() { + echo -e "${BLUE}==> Starting Scene Intelligence Services...${NC}" + + # Start the services + docker compose -f docker/compose.yaml up -d + + if [ $? -eq 0 ]; then + echo -e "${GREEN}Scene Intelligence Services started successfully!${NC}" + + echo "" + echo -e "${BLUE}Services:${NC}" + echo -e " • Traffic Intelligence API: ${YELLOW}http://${HOST_IP}:${TRAFFIC_INTELLIGENCE_PORT}${NC}" + echo -e " • Traffic Intelligence UI: ${YELLOW}http://${HOST_IP}:${TRAFFIC_INTELLIGENCE_UI_PORT}${NC}" + echo -e " • VLM Service: ${YELLOW}http://${HOST_IP}:${VLM_SERVICE_PORT}${NC}" + echo "" + echo -e "${BLUE}To view logs:${NC}" + echo -e " ${YELLOW}docker compose -f docker/compose.yaml logs -f${NC}" + echo -e "${BLUE}To stop the services:${NC}" + echo -e " ${YELLOW}source setup.sh --stop${NC}" + else + echo -e "${RED}Failed to start Scene Intelligence Services${NC}" + return 1 + fi +} + +# Function to restart the services (for env var changes) +restart_service() { + local SERVICE_TYPE="${1:-all}" + + case "$SERVICE_TYPE" in + agent) + echo -e "${BLUE}==> Restarting Scene Intelligence Services with updated environment variables...${NC}" + + # Stop the Scene Intelligence services + docker compose -f docker/compose.yaml down + + if [ $? -ne 0 ]; then + echo -e "${RED}Failed to stop Scene Intelligence services${NC}" + return 1 + fi + + # Start with force-recreate to ensure env vars are picked up + docker compose -f docker/compose.yaml up -d --force-recreate + + if [ $? -eq 0 ]; then + echo -e "${GREEN}Scene Intelligence Services restarted successfully with updated configuration!${NC}" + + echo "" + echo -e "${BLUE}Services:${NC}" + echo -e " • Traffic Intelligence API: ${YELLOW}http://${HOST_IP}:${TRAFFIC_INTELLIGENCE_PORT}${NC}" + echo -e " • Traffic Intelligence UI: ${YELLOW}http://${HOST_IP}:${TRAFFIC_INTELLIGENCE_UI_PORT}${NC}" + echo -e " • VLM Service: ${YELLOW}http://${HOST_IP}:${VLM_SERVICE_PORT}${NC}" + echo "" + echo -e "${BLUE}To view logs:${NC}" + echo -e " ${YELLOW}docker compose -f docker/compose.yaml logs -f${NC}" + else + echo -e "${RED}Failed to restart Scene Intelligence Services${NC}" + return 1 + fi + ;; + + prerequisite) + echo -e "${BLUE}==> Restarting Prerequisite Services (edge-ai-suites)...${NC}" + + local METRO_DIR="edge-ai-suites/metro-ai-suite/metro-vision-ai-app-recipe" + + if [ ! -d "$METRO_DIR" ]; then + echo -e "${RED}Directory $METRO_DIR not found${NC}" + echo -e "${YELLOW}Please run 'source setup.sh --setup' first to set up prerequisites${NC}" + return 1 + fi + + cd "$METRO_DIR" + + # Stop the prerequisite services + echo -e "${BLUE}==> Stopping prerequisite services...${NC}" + docker compose down + + if [ $? -ne 0 ]; then + echo -e "${RED}Failed to stop prerequisite services${NC}" + cd - > /dev/null + return 1 + fi + + # Start with force-recreate to ensure env vars are picked up + echo -e "${BLUE}==> Starting prerequisite services with updated configuration...${NC}" + docker compose up -d --force-recreate + + if [ $? -eq 0 ]; then + echo -e "${GREEN}Prerequisite Services restarted successfully with updated configuration!${NC}" + + echo "" + echo -e "${BLUE}Edge AI Suites Services:${NC}" + echo -e " • SceneScape Web UI: ${YELLOW}https://${HOST_IP}:443${NC}" + echo -e " • DLStreamer Pipeline Server API: ${YELLOW}http://${HOST_IP}:8080${NC}" + echo -e " • InfluxDB UI: ${YELLOW}http://${HOST_IP}:8086${NC}" + echo -e " • Grafana Dashboard: ${YELLOW}http://${HOST_IP}:3000${NC}" + echo -e " • Node-RED UI: ${YELLOW}http://${HOST_IP}:1880${NC}" + echo "" + else + echo -e "${RED}Failed to restart Prerequisite Services${NC}" + cd - > /dev/null + return 1 + fi + + cd - > /dev/null + ;; + + all) + echo -e "${BLUE}==> Restarting All Services with updated environment variables...${NC}" + + # Restart prerequisite services first + local METRO_DIR="edge-ai-suites/metro-ai-suite/metro-vision-ai-app-recipe" + + if [ -d "$METRO_DIR" ]; then + cd "$METRO_DIR" + + echo -e "${BLUE}==> Restarting prerequisite services...${NC}" + docker compose down + docker compose up -d --force-recreate + + if [ $? -eq 0 ]; then + echo -e "${GREEN}Prerequisite Services restarted successfully!${NC}" + else + echo -e "${RED}Failed to restart Prerequisite Services${NC}" + cd - > /dev/null + return 1 + fi + + cd - > /dev/null + else + echo -e "${YELLOW}Prerequisite services directory not found, skipping...${NC}" + fi + + # Restart Scene Intelligence services + echo -e "${BLUE}==> Restarting Scene Intelligence Services...${NC}" + docker compose -f docker/compose.yaml down + docker compose -f docker/compose.yaml up -d --force-recreate + + if [ $? -eq 0 ]; then + echo -e "${GREEN}All services restarted successfully with updated configuration!${NC}" + + echo "" + echo -e "${BLUE}Edge AI Suites Services:${NC}" + echo -e " • SceneScape Web UI: ${YELLOW}https://${HOST_IP}:443${NC}" + echo -e " • DLStreamer Pipeline Server API: ${YELLOW}http://${HOST_IP}:8080${NC}" + echo -e " • InfluxDB UI: ${YELLOW}http://${HOST_IP}:8086${NC}" + echo -e " • Grafana Dashboard: ${YELLOW}http://${HOST_IP}:3000${NC}" + echo -e " • Node-RED UI: ${YELLOW}http://${HOST_IP}:1880${NC}" + echo "" + echo -e "${BLUE}Scene Intelligence Services:${NC}" + echo -e " • Traffic Intelligence API: ${YELLOW}http://${HOST_IP}:${TRAFFIC_INTELLIGENCE_PORT}${NC}" + echo -e " • Traffic Intelligence UI: ${YELLOW}http://${HOST_IP}:${TRAFFIC_INTELLIGENCE_UI_PORT}${NC}" + echo -e " • VLM Service: ${YELLOW}http://${HOST_IP}:${VLM_SERVICE_PORT}${NC}" + echo "" + else + echo -e "${RED}Failed to restart Scene Intelligence Services${NC}" + return 1 + fi + ;; + esac +} + +# if only base environment variables are to be set without deploying application, exit here +if [ "$1" = "--setenv" ]; then + echo -e "${BLUE}Done setting up all environment variables. ${NC}" + return 0 +fi + +# Main logic based on command +case $1 in + --setup) + build_and_start_service + ;; + --restart) + restart_service "$2" + ;; + --run|*) + start_service + ;; +esac + +if [ $? -eq 0 ]; then + echo -e "${GREEN}Done!${NC}" +else + echo -e "${RED}Setup failed. Check the logs above for details.${NC}" + return 1 +fi diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/.dockerignore b/metro-ai-suite/smart-traffic-intersection-agent/src/.dockerignore new file mode 100644 index 000000000..870dc97b4 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/.dockerignore @@ -0,0 +1,13 @@ +# Copyright (C) 2026 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +.python-version +__pycache__/ +.gitignore +.dockerignore +docker-compose.yml +Dockerfile +README.md +compose.yaml +.venv/ +venv/ \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/Dockerfile b/metro-ai-suite/smart-traffic-intersection-agent/src/Dockerfile new file mode 100644 index 000000000..7cb002ba5 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/Dockerfile @@ -0,0 +1,43 @@ +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +FROM python:3.11-slim + +# Set working directory +WORKDIR /app + +# Install system dependencies +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + curl \ + ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +# Install uv package manager +RUN pip install --no-cache-dir uv==0.9.22 + +COPY . . + +# Create virtual environment and install dependencies using uv +RUN uv sync && uv pip install -r ui/requirements.txt + +# Create non-root user and adjust permissions +RUN useradd -m -u 1000 traffic && \ + chmod +x docker-entrypoint.sh && \ + chown -R traffic:traffic /app + +# Switch to non-root user +USER traffic + +# Update PATH to use venv +ENV PATH="/app/.venv/bin:$PATH" + +# Expose API port and UI port +EXPOSE 8081 7860 + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --retries=3 --start-period=10s \ + CMD curl -f http://localhost:8081/health || exit 1 + +# Run both API and UI services using entrypoint script +CMD ["bash", "docker-entrypoint.sh"] diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/README.md b/metro-ai-suite/smart-traffic-intersection-agent/src/README.md new file mode 100644 index 000000000..744cb8dc6 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/README.md @@ -0,0 +1,481 @@ +# Traffic Intelligence Service + +A lightweight microservice for real-time traffic analysis at individual intersections. This service provides weather-aware traffic intelligence with VLM-powered insights and structured alerts. + +## Features + +### 🚦 Real-time Traffic Monitoring +- Subscribes to MQTT camera data from intersection cameras +- Monitors traffic density across multiple directions (North, South, East, West) +- Provides real-time traffic snapshots and historical data + +### 🌤️ Weather Integration +- Fetches real-time weather data from National Weather Service API +- Analyzes weather impact on traffic patterns +- Correlates road conditions with traffic congestion + +### 🤖 AI-Powered Analysis +- Enhanced VLM (Vision Language Model) integration for traffic analysis +- Structured prompts for traffic pattern recognition +- Weather-aware alert generation with confidence scoring +- Contextual recommendations for traffic management + +### 📊 Structured Data Output +- API responses match the existing `data.json` schema +- Real-time camera image synchronization +- Comprehensive traffic intelligence reports + +## Architecture + +``` +┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ +│ MQTT Broker │────│ Traffic Intel │────│ VLM Service │ +│ (Camera Data) │ │ Service │ │ (Analysis) │ +└─────────────────┘ └──────────────────┘ └─────────────────┘ + │ + │ + ┌──────────────────┐ ┌─────────────────┐ + │ Weather Service │────│ NWS API │ + │ (Conditions) │ │ (gov data) │ + └──────────────────┘ └─────────────────┘ +``` + +## Quick Start + +### UV Development (Recommended) + +1. **Install uv (if not already installed):** +```bash +curl -LsSf https://astral.sh/uv/install.sh | sh +``` + +2. **Quick setup and run:** +```bash +git clone +cd traffic-intelligence + +# Using the development script +./dev.sh setup # Create venv and install dependencies +./dev.sh run # Start the service + +# Or using Make +make setup # Setup environment +make run # Start the service +make help # Show all available commands +``` + +3. **Development mode:** +```bash +./dev.sh dev # Run with debug logging +./dev.sh test # Run tests +./dev.sh example # Run example client + +# Or with Make +make dev # Development mode +make test # Run tests +make example # Run example client +make lint # Run linting +``` + +### Docker Deployment + +1. **Clone the repository:** +```bash +git clone +cd traffic-intelligence +``` + +2. **Configure environment:** +```bash +cp config/traffic_intelligence.json.example config/traffic_intelligence.json +# Edit configuration for your intersection +``` + +3. **Start with Docker Compose:** +```bash +docker-compose up -d +``` + +4. **Verify service:** +```bash +curl http://localhost:8081/health +curl http://localhost:8081/api/v1/traffic/current +``` + +### Local Development with UV + +1. **Install uv (if not already installed):** +```bash +curl -LsSf https://astral.sh/uv/install.sh | sh +# or +pip install uv +``` + +2. **Create virtual environment and install dependencies:** +```bash +uv venv +source .venv/bin/activate # On Windows: .venv\Scripts\activate +uv pip install -r requirements.tx +``` + +3. **Set environment variables:** +```bash +export INTERSECTION_ID="97781c36-b53a-4749-87e6-8815da99bac7" +export INTERSECTION_NAME="Intersection-Demo" +export MQTT_HOST="broker.scenescape.intel.com" +export VLM_BASE_URL="http://vlm-service:8080" +``` + +4. **Run the service:** +```bash +uv run python -m traffic_intelligence.main +# or with activated venv: +python -m traffic_intelligence.main +``` + +## API Documentation + +### Base URL +- Local: `http://localhost:8081/api/v1` +- Docker: `http://localhost:8081/api/v1` + +### Endpoints + +#### Get Current Traffic Intelligence +```http +GET /traffic/current +``` + +Returns complete traffic intelligence data matching the `data.json` schema: + +```json +{ + "timestamp": "2025-09-16T10:30:00.123456", + "intersection_id": "cb1cf1a0-b936-4d47-9221-3fd5cf24857d", + "data": { + "intersection_id": "cb1cf1a0-b936-4d47-9221-3fd5cf24857d", + "intersection_name": "Intersection-1", + "latitude": 37.55336, + "longitude": -122.29627, + "timestamp": "2025-09-16T10:30:00.123456", + "north_camera": 3, + "south_camera": 7, + "east_camera": 2, + "west_camera": 4, + "total_density": 16 + }, + "camera_images": { + "north_camera": { + "camera_id": "intersection-1-cam1", + "direction": "north", + "timestamp": "2025-09-16T10:29:45.000000", + "image_base64": "...", + "image_size_bytes": 45678 + } + }, + "weather_data": { + "name": "This Afternoon", + "temperature": 72, + "temperature_unit": "F", + "detailed_forecast": "Partly cloudy with light winds", + "fetched_at": "2025-09-16T10:25:00.000000", + "is_precipitation": false, + }, + "vlm_analysis": { + "traffic_summary": "Moderate traffic with high density in south direction", + "alerts": [ + { + "alert_type": "congestion", + "level": "warning", + "description": "High traffic density detected in south direction", + "weather_related": false + } + ], + "recommendations": [ + "Monitor south-bound signal timing", + "Consider traffic flow optimization" + ], + "analysis_timestamp": "2025-09-16T10:30:00.000000" + } +} +``` + +#### Get Traffic History +```http +GET /traffic/history?minutes=30 +``` + +Returns historical traffic data for the specified time period. + +#### Get Current Weather +```http +GET /weather/current +``` + +Returns current weather conditions for the intersection location. + +#### Get VLM Analysis +```http +GET /analysis/current +``` + +Returns the latest VLM traffic analysis with structured alerts. + +#### Trigger Manual Analysis +```http +POST /analysis/trigger +``` + +Manually triggers VLM analysis of current traffic conditions. + +#### Service Status +```http +GET /status +``` + +Returns service health and operational statistics. + +#### Configuration +```http +GET /config +PUT /config/threshold?threshold=10 +``` + +Get/update service configuration parameters. + +## Configuration + +### Environment Variables + +| Variable | Default | Description | +|----------|---------|-------------| +| `INTERSECTION_ID` | `cb1cf1a0-b936-4d47-9221-3fd5cf24857d` | Unique intersection identifier | +| `INTERSECTION_NAME` | `Intersection-1` | Human-readable intersection name | +| `INTERSECTION_LATITUDE` | `37.55336` | Intersection GPS latitude | +| `INTERSECTION_LONGITUDE` | `-122.29627` | Intersection GPS longitude | +| `MQTT_HOST` | `broker.scenescape.intel.com` | MQTT broker hostname | +| `MQTT_PORT` | `1883` | MQTT broker port | +| `CAMERA_1_TOPIC` | `scenescape/data/camera/camera1` | MQTT topic for camera 1 | +| `CAMERA_2_TOPIC` | `scenescape/data/camera/camera2` | MQTT topic for camera 2 | +| `CAMERA_3_TOPIC` | `scenescape/data/camera/camera3` | MQTT topic for camera 3 | +| `VLM_BASE_URL` | `http://vlm-service:8080` | VLM service endpoint | +| `HIGH_DENSITY_THRESHOLD` | `5` | Vehicle count threshold for high density | +| `WEATHER_CACHE_MINUTES` | `15` | Weather data cache duration | + +### Configuration File + +Create `config/traffic_intelligence.json`: + +```json +{ + "intersection": { + "id": "cb1cf1a0-b936-4d47-9221-3fd5cf24857d", + "name": "Intersection-1", + "latitude": 37.55336, + "longitude": -122.29627 + }, + "mqtt": { + "host": "broker.scenescape.intel.com", + "port": 1883, + "use_tls": true, + "camera_topics": [ + "scenescape/data/camera/camera1", + "scenescape/data/camera/camera2", + "scenescape/data/camera/camera3", + "scenescape/data/camera/camera4" + ] + }, + "traffic": { + "high_density_threshold": 5, + "analysis_window_seconds": 30, + } +} +``` + +## MQTT Data Format + +The service expects camera data on MQTT topics in this format: + +```json +{ + "camera_id": "camera1", + "vehicle_count": 5, + "pedestrian_count": 2, + "timestamp": "2025-09-16T10:30:00.000000Z", + "image_data": "base64-encoded-image-data" +} +``` + +### Camera-to-Direction Mapping + +| Camera | MQTT Topic | Direction | +|--------|------------|-----------| +| Camera 1 | `scenescape/data/camera/camera1` | North | +| Camera 2 | `scenescape/data/camera/camera2` | South | +| Camera 3 | `scenescape/data/camera/camera3` | East | + +## VLM Integration + +### Enhanced Prompts + +The service uses structured prompts that include: +- Real-time traffic density by direction +- Current weather conditions and road impact +- Historical traffic patterns +- Request for structured JSON responses + +### Alert Types + +- `congestion`: Traffic density alerts +- `weather_related`: Weather-impacted traffic +- `road_condition`: Road surface/visibility issues +- `accident`: Detected incidents +- `maintenance`: Construction/maintenance impacts + +### Alert Levels + +- `info`: Informational notices +- `warning`: Elevated traffic conditions +- `critical`: Severe congestion or safety issues + +## Weather Analysis + +### Road Condition Detection + +The service analyzes weather data to determine: +- **Dry**: Normal conditions +- **Wet**: Rain or recent precipitation +- **Icy**: Freezing conditions or ice formation +- **Low Visibility**: Fog or severe weather + +### Traffic Correlation + +Weather impact analysis includes: +- Precipitation effects on traffic flow +- Temperature impacts on vehicle performance +- Visibility conditions affecting driver behavior +- Road surface conditions and safety + +## Monitoring and Observability + +### Health Checks + +```bash +# Service health +curl http://localhost:8081/health + +# Service status with metrics +curl http://localhost:8081/api/v1/status +``` + +### Logging + +Structured JSON logging with configurable levels: +- Service operations and errors +- MQTT message processing +- VLM analysis triggers and results +- Weather data updates + +### Metrics + +Key operational metrics available via `/status`: +- Current traffic density +- Active camera count +- VLM analysis frequency +- Data processing rates + +## Development + +### Project Structure + +``` +traffic-intelligence/ +├── main.py # Service entry point +├── models/ # Data models and schemas +│ └── __init__.py +├── services/ # Business logic services +│ ├── config.py # Configuration management +│ ├── mqtt_service.py # MQTT data ingestion +│ ├── weather_service.py # Weather API integration +│ ├── vlm_service.py # VLM analysis service +│ └── data_aggregator.py # Data processing and coordination +├── api/ # REST API endpoints +│ └── routes.py +├── config/ # Configuration files +│ └── traffic_intelligence.json +├── requirements.txt # Python dependencies +├── Dockerfile # Container build +├── docker-compose.yml # Multi-service deployment +└── start.sh # Service startup script +``` + +### Adding New Features + +1. **New Alert Types**: Extend `AlertType` enum in `models/__init__.py` +2. **Additional Cameras**: Update camera topics in configuration +3. **Custom Analysis**: Modify VLM prompts in `vlm_service.py` +4. **New Endpoints**: Add routes to `api/routes.py` + +### Testing + +```bash +# Run tests with uv +uv run pytest + +# Test with mock data +uv run python -m pytest tests/ -v + +# Integration tests +docker-compose -f docker-compose.test.yml up +``` + +## Troubleshooting + +### Common Issues + +**MQTT Connection Failed** +- Verify broker hostname and TLS configuration +- Check certificate paths and permissions +- Ensure network connectivity to broker + +**VLM Analysis Not Triggering** +- Check traffic density against threshold +- Verify VLM service connectivity +- Review analysis timing configuration + +**Weather Data Unavailable** +- Confirm intersection coordinates are valid +- Check National Weather Service API accessibility +- Verify user agent configuration + +**Missing Camera Data** +- Verify MQTT topic subscriptions +- Check camera data format +- Review message processing logs + +### Debug Mode + +Enable debug logging: +```bash +export LOG_LEVEL=DEBUG +uv run python -m traffic_intelligence.main +``` + +## License + +This project is licensed under the MIT License - see the LICENSE file for details. + +## Contributing + +1. Fork the repository +2. Create a feature branch +3. Make your changes with tests +4. Submit a pull request + +## Support + +For issues and questions: +- Review logs with debug level enabled +- Check service status endpoints +- Verify configuration parameters +- Consult API documentation \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/__init__.py b/metro-ai-suite/smart-traffic-intersection-agent/src/__init__.py new file mode 100644 index 000000000..2bcd291fd --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/__init__.py @@ -0,0 +1,4 @@ +"""Package initialization for traffic intelligence service.""" + +__version__ = "1.0.0" +__description__ = "Lightweight traffic intelligence service for single intersection monitoring" \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/api/__init__.py b/metro-ai-suite/smart-traffic-intersection-agent/src/api/__init__.py new file mode 100644 index 000000000..690fedf84 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/api/__init__.py @@ -0,0 +1 @@ +"""Package initialization for API routes.""" \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/api/routes.py b/metro-ai-suite/smart-traffic-intersection-agent/src/api/routes.py new file mode 100644 index 000000000..3eccf36d5 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/api/routes.py @@ -0,0 +1,113 @@ +"""API routes for traffic intelligence service.""" + +from datetime import datetime, timedelta +from typing import Dict, Any + +from fastapi import APIRouter, HTTPException, Depends, Query, Request +from fastapi.responses import JSONResponse +import structlog + +from services.data_aggregator import DataAggregatorService + +logger = structlog.get_logger(__name__) + +router = APIRouter() + + +def get_data_aggregator(request): + """Dependency to get data aggregator service from app state.""" + return request.app.state.data_aggregator + + +def get_weather_service(request): + """Dependency to get weather service from app state.""" + return request.app.state.weather_service + + +@router.get("/traffic/current", response_model=Dict[str, Any]) +async def get_current_traffic_intelligence( + request: Request, + images: bool = Query(default=True, description="Include camera images in response") +) -> Dict[str, Any]: + """ + Get current traffic intelligence data for the intersection. + + Returns complete traffic intelligence response using weather data and VLM analysis. + + Args: + images: If False, camera_images will be excluded from response to reduce size + """ + try: + data_aggregator: DataAggregatorService = get_data_aggregator(request) + + # Get current traffic intelligence + traffic_response = await data_aggregator.get_current_traffic_intelligence() + + if not traffic_response: + raise HTTPException(status_code=404, detail="No traffic data available") + + # Get current weather data + weather_service = get_weather_service(request) + weather_data = await weather_service.get_current_weather() + + # Convert to dict for JSON response + response_dict = { + "timestamp": traffic_response.timestamp, + "response_age": traffic_response.response_age if traffic_response.response_age else None, + "intersection_id": traffic_response.intersection_id, + "data": { + "intersection_id": traffic_response.data.intersection_id, + "intersection_name": traffic_response.data.intersection_name, + "latitude": traffic_response.data.latitude, + "longitude": traffic_response.data.longitude, + "timestamp": traffic_response.data.timestamp.isoformat(), + "north_camera": traffic_response.data.north_camera, + "south_camera": traffic_response.data.south_camera, + "east_camera": traffic_response.data.east_camera, + "west_camera": traffic_response.data.west_camera, + "total_density": traffic_response.data.total_density, + "intersection_status": traffic_response.data.intersection_status, + "north_pedestrian": traffic_response.data.north_pedestrian, + "south_pedestrian": traffic_response.data.south_pedestrian, + "east_pedestrian": traffic_response.data.east_pedestrian, + "west_pedestrian": traffic_response.data.west_pedestrian, + "total_pedestrian_count": traffic_response.data.total_pedestrian_count, + "north_timestamp": traffic_response.data.north_timestamp, + "south_timestamp": traffic_response.data.south_timestamp, + "east_timestamp": traffic_response.data.east_timestamp, + "west_timestamp": traffic_response.data.west_timestamp, + }, + "weather_data": weather_data.__dict__, + "vlm_analysis": { + "traffic_summary": traffic_response.vlm_analysis.traffic_summary, + "alerts": [ + { + "alert_type": alert.alert_type.value, + "level": alert.level.value, + "description": alert.description, + "weather_related": alert.weather_related + } + for alert in traffic_response.vlm_analysis.alerts + ], + "recommendations": traffic_response.vlm_analysis.recommendations or [], + "analysis_timestamp": traffic_response.vlm_analysis.analysis_timestamp.isoformat() if traffic_response.vlm_analysis.analysis_timestamp else None + } + } + + # Add camera images only if requested + if images: + response_dict["camera_images"] = traffic_response.camera_images + + logger.info("Current traffic intelligence served", + intersection_id=traffic_response.intersection_id, + total_density=traffic_response.data.total_density, + total_pedestrian_count=traffic_response.data.total_pedestrian_count, + alerts_count=len(traffic_response.vlm_analysis.alerts)) + + return response_dict + + except HTTPException: + raise + except Exception as e: + logger.error("Failed to get current traffic intelligence", error=str(e)) + raise HTTPException(status_code=500, detail="Internal server error") diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/config/traffic_intelligence.json b/metro-ai-suite/smart-traffic-intersection-agent/src/config/traffic_intelligence.json new file mode 100644 index 000000000..92f172fd0 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/config/traffic_intelligence.json @@ -0,0 +1,50 @@ +{ + "intersection": { + "name": "Intersection-1", + "latitude": 33.3091336, + "longitude": -111.9353095 + }, + "mqtt": { + "host": "broker.scenescape.intel.com", + "port": 1883, + "use_tls": true, + "cert_required": false, + "verify_hostname": false, + "ca_cert_path": "secrets/certs/scenescape-ca.pem", + "camera_topics": [ + "scenescape/data/camera/camera1", + "scenescape/data/camera/camera2", + "scenescape/data/camera/camera3", + "scenescape/data/camera/camera4" + ], + "image_topics": [ + "scenescape/image/camera/camera1", + "scenescape/image/camera/camera2", + "scenescape/image/camera/camera3", + "scenescape/image/camera/camera4" + ], + "max_connect_attempts": -1, + "retry_delay_seconds": 5, + "max_retry_delay": 60, + "image_command_interval_seconds": 30, + "rate_limit_seconds": 10.0 + }, + "weather": { + "api_base_url": "https://api.weather.gov", + "user_agent": "traffic-intelligence (admin@example.com)", + "cache_duration_minutes": 11, + "enable_markers": false + }, + "vlm": { + "base_url": "http://vlm-openvino-serving:8000", + "model": "Qwen/Qwen2.5-VL-3B-Instruct", + "timeout_seconds": 300, + "max_completion_tokens": 1500, + "temperature": 0.1, + "top_p": 0.1 + }, + "traffic": { + "high_density_threshold": 10, + "analysis_window_seconds": 30 + } +} \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/config/weather.json b/metro-ai-suite/smart-traffic-intersection-agent/src/config/weather.json new file mode 100644 index 000000000..e2eb72f29 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/config/weather.json @@ -0,0 +1,23 @@ +{ + "clear": { + "name": "Mock Weather Period", + "temperature": 78, + "temperature_unit": "F", + "detailed_forecast": "Clear conditions with 9 mph winds from the N.", + "short_forecast": "Clear", + "wind_speed": "9 mph", + "wind_direction": "N", + "wind_info": "9mph/N", + "fetched_at": "2025-10-16T07:50:59.478546", + "is_precipitation": false, + "is_mock": true, + "precipitation_prob": 0, + "dewpoint": 37, + "dewpoint_unit": "F", + "relative_humidity": 65, + "is_daytime": true, + "start_time": "2025-10-16T07:00:00-07:00", + "end_time": "2025-10-16T08:00:00-07:00", + "is_cached": false + } +} \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/docker-entrypoint.sh b/metro-ai-suite/smart-traffic-intersection-agent/src/docker-entrypoint.sh new file mode 100644 index 000000000..1ecc6b914 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/docker-entrypoint.sh @@ -0,0 +1,59 @@ +#!/bin/bash +# Docker entrypoint script to run both API and UI services + +set -e + +# Colors for output +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +NC='\033[0m' # No Color + +echo -e "${GREEN}Starting Traffic Intelligence Services${NC}" +echo "========================================" + +# Set default environment variables if not provided +export TRAFFIC_INTELLIGENCE_PORT=${TRAFFIC_INTELLIGENCE_PORT:-8081} +export TRAFFIC_INTELLIGENCE_HOST=${TRAFFIC_INTELLIGENCE_HOST:-0.0.0.0} +export TRAFFIC_INTELLIGENCE_UI_PORT=${TRAFFIC_INTELLIGENCE_UI_PORT:-7860} +export USE_API=${USE_API:-true} +export API_URL=${API_URL:-"http://localhost:${TRAFFIC_INTELLIGENCE_PORT}/api/v1/traffic/current"} + +echo "Configuration:" +echo " Backend API Port: $TRAFFIC_INTELLIGENCE_PORT" +echo " UI Dashboard Port: $TRAFFIC_INTELLIGENCE_UI_PORT" +echo " Host: $TRAFFIC_INTELLIGENCE_HOST" +echo " Log Level: ${LOG_LEVEL:-INFO}" + +# Function to cleanup on exit +cleanup() { + echo -e "${YELLOW}Shutting down services...${NC}" + kill $BACKEND_PID 2>/dev/null || true + kill $UI_PID 2>/dev/null || true + exit 0 +} + +# Set up signal handling +trap cleanup SIGTERM SIGINT + +# Start the backend API service +echo -e "${GREEN}Starting Backend API on port $TRAFFIC_INTELLIGENCE_PORT...${NC}" +python run.py & +BACKEND_PID=$! + +# Wait a moment for backend to start +sleep 3 + +# Start the UI dashboard +echo -e "${GREEN}Starting UI Dashboard on port $TRAFFIC_INTELLIGENCE_UI_PORT...${NC}" +cd ui && python app.py & +UI_PID=$! +cd .. + +echo -e "${GREEN}Both services started successfully!${NC}" +echo " Backend API: http://$TRAFFIC_INTELLIGENCE_HOST:$TRAFFIC_INTELLIGENCE_PORT" +echo " UI Dashboard: http://$TRAFFIC_INTELLIGENCE_HOST:$TRAFFIC_INTELLIGENCE_UI_PORT" +echo " Health Check: http://$TRAFFIC_INTELLIGENCE_HOST:$TRAFFIC_INTELLIGENCE_PORT/health" +echo " API Docs: http://$TRAFFIC_INTELLIGENCE_HOST:$TRAFFIC_INTELLIGENCE_PORT/docs" + +# Wait for both processes +wait $BACKEND_PID $UI_PID diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/main.py b/metro-ai-suite/smart-traffic-intersection-agent/src/main.py new file mode 100644 index 000000000..0ec9dcaad --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/main.py @@ -0,0 +1,180 @@ +""" +Traffic Intelligence Service - Lightweight traffic analysis for a single intersection + +This service reads camera data from a single intersection via MQTT topics and provides +real-time traffic analysis with weather-enriched insights and VLM-powered alerts. + +Key Features: +- MQTT subscription to scenescape/data/camera/camera<1,2,3> topics +- Weather-aware traffic analysis +- Structured VLM prompts for traffic and alert analysis +- Camera image synchronization with data readings +- Modular, well-documented codebase +""" + +import asyncio +import logging +import os +from contextlib import asynccontextmanager +from datetime import datetime + +import structlog +import uvicorn +from fastapi import FastAPI + +from api.routes import router +from services.config import ConfigService +from services.mqtt_service import MQTTService +from services.weather_service import WeatherService +from services.vlm_service import VLMService +from services.data_aggregator import DataAggregatorService + + +# Configure structured logging +structlog.configure( + processors=[ + structlog.stdlib.filter_by_level, + structlog.stdlib.add_logger_name, + structlog.stdlib.add_log_level, + structlog.stdlib.PositionalArgumentsFormatter(), + structlog.processors.TimeStamper(fmt="iso"), + structlog.processors.StackInfoRenderer(), + structlog.processors.format_exc_info, + structlog.processors.UnicodeDecoder(), + structlog.processors.JSONRenderer() + ], + context_class=dict, + logger_factory=structlog.stdlib.LoggerFactory(), + wrapper_class=structlog.stdlib.BoundLogger, + cache_logger_on_first_use=True, +) + +logger = structlog.get_logger(__name__) + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """FastAPI lifespan context manager for traffic intelligence service.""" + logger.info("Starting Traffic Intelligence service") + + try: + # Load configuration + config_service = ConfigService() + app.state.config = config_service + + # Initialize weather service + weather_service = WeatherService(config_service) + app.state.weather_service = weather_service + + # Start weather service with periodic updates + await weather_service.start() + + # Initialize VLM service for traffic analysis + vlm_service = VLMService(config_service, weather_service) + app.state.vlm_service = vlm_service + + # Initialize data aggregator service + data_aggregator = DataAggregatorService(config_service, vlm_service) + app.state.data_aggregator = data_aggregator + + # Initialize and start MQTT service for camera data + mqtt_service = MQTTService(config_service, data_aggregator, vlm_service) + await mqtt_service.initialize() + + # Set the current event loop for async task scheduling + current_loop = asyncio.get_running_loop() + mqtt_service.set_event_loop(current_loop) + + app.state.mqtt = mqtt_service + + # Start MQTT service in background + mqtt_task = asyncio.create_task(mqtt_service.start()) + app.state.mqtt_task = mqtt_task + + logger.info("Traffic Intelligence service started successfully", + intersection_id=config_service.get_intersection_id(), + mqtt_topics=config_service.get_camera_topics()) + + yield + + except Exception as e: + logger.error("Failed to start Traffic Intelligence service", error=str(e)) + raise + + finally: + # Cleanup + logger.info("Shutting down Traffic Intelligence service") + + # Stop MQTT service + if hasattr(app.state, 'mqtt_task'): + app.state.mqtt_task.cancel() + try: + await app.state.mqtt_task + except asyncio.CancelledError: + pass + + if hasattr(app.state, 'mqtt'): + await app.state.mqtt.stop() + + # Stop weather service + if hasattr(app.state, 'weather_service'): + await app.state.weather_service.stop() + + logger.info("Traffic Intelligence service stopped") + + +def create_app() -> FastAPI: + """Create and configure FastAPI application for traffic intelligence.""" + api_name = os.getenv("API_NAME", "Traffic Intelligence Service") + + app = FastAPI( + title=api_name, + description="Single intersection monitoring Agent API for traffic analysis and alerts", + version="1.0.0", + lifespan=lifespan + ) + + # Include API routes + app.include_router(router, prefix="/api/v1") + + # Health check endpoint + @app.get("/health") + async def health_check(): + """Health check endpoint.""" + return { + "status": "healthy", + "service": "traffic-intelligence", + "timestamp": datetime.utcnow().isoformat() + } + + return app + + +def main(): + """Main entry point for traffic intelligence service.""" + # Set up logging level + log_level = os.getenv("LOG_LEVEL", "INFO").upper() + logging.basicConfig(level=getattr(logging, log_level)) + + # Create FastAPI app + app = create_app() + + # Get configuration + port = int(os.getenv("TRAFFIC_INTELLIGENCE_PORT", "8081")) + host = os.getenv("TRAFFIC_INTELLIGENCE_HOST", "0.0.0.0") + + logger.info("Starting Traffic Intelligence service", + host=host, port=port, log_level=log_level) + + # Run the application + uvicorn.run( + app, + host=host, + port=port, + log_level=log_level.lower(), + access_log=True + ) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/models/__init__.py b/metro-ai-suite/smart-traffic-intersection-agent/src/models/__init__.py new file mode 100644 index 000000000..2f30843e7 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/models/__init__.py @@ -0,0 +1,181 @@ +"""Data models for traffic intelligence service.""" + +from dataclasses import dataclass +from datetime import datetime +from typing import Dict, List, Optional, Any +from enum import Enum + + +class AlertLevel(Enum): + """Alert severity levels.""" + INFO = "info" + WARNING = "warning" + CRITICAL = "critical" + + +class AlertType(Enum): + """Types of traffic alerts.""" + CONGESTION = "congestion" + WEATHER_RELATED = "weather_related" + ROAD_CONDITION = "road_condition" + ACCIDENT = "accident" + MAINTENANCE = "maintenance" + NORMAL = "normal" + +class WeatherType(Enum): + """Types of weather conditions.""" + CLEAR = "clear" + +@dataclass +class WeatherData: + """Weather information for traffic analysis.""" + name: str # Current weather period name (e.g., "This Afternoon") + temperature: int # Temperature value + temperature_unit: str # Temperature unit (F/C) + detailed_forecast: str # Detailed weather description + fetched_at: datetime # When weather data was retrieved + is_precipitation: bool = False # Whether there's rain/snow + is_mock: bool = False # Whether this is mock data + wind_speed: str = "0 mph" # Wind speed (e.g., "5 mph", "0 to 5 mph") + wind_direction: str = "N" # Wind direction (e.g., "NE", "SW") + short_forecast: str = "Clear" # Short weather description (e.g., "Sunny") + wind_info: str = "0mph/N" # Combined wind speed and direction (e.g., "3mph/W") + precipitation_prob: float = 0.0 # Precipitation probability percentage (0-100) + dewpoint: Optional[float] = None # Dewpoint in Celsius + relative_humidity: Optional[float] = None # Relative humidity percentage (0-100) + is_daytime: Optional[bool] = None # Whether it's daytime + start_time: Optional[str] = None # Start time of the forecast period + end_time: Optional[str] = None # End time of the forecast period + is_cached: bool = False # Whether this data was served from cache + weather_type: WeatherType = WeatherType.CLEAR # Type of weather condition + + +@dataclass +class VLMAlert: + """Individual alert from VLM analysis.""" + alert_type: AlertType + level: AlertLevel + description: str + weather_related: bool = False + + +@dataclass +class VLMAnalysisData: + """VLM analysis results with structured traffic and alert data.""" + traffic_summary: str # General traffic conditions summary + alerts: List[VLMAlert] # Structured alerts list + recommendations: List[str] = None # Traffic management recommendations + analysis_timestamp: Optional[datetime] = None + + +@dataclass +class CameraImage: + """Camera image data from MQTT.""" + camera_id: str + direction: str # north, south, east, west + image_base64: str + timestamp: Optional[datetime] = None + image_size_bytes: Optional[int] = None + + +@dataclass +class IntersectionData: + """Core intersection traffic data structure based on data.json schema.""" + intersection_id: str # UUID of the intersection + intersection_name: str # Human-readable name + latitude: float # GPS coordinates + longitude: float + timestamp: datetime # Data timestamp + + # Camera density counts by direction + north_camera: int = 0 + south_camera: int = 0 + east_camera: int = 0 + west_camera: int = 0 + total_density: int = 0 + + # Intersection-level traffic status (HIGH, MODERATE, NORMAL) + intersection_status: str = "NORMAL" + + # Pedestrian counts by direction + north_pedestrian: int = 0 + south_pedestrian: int = 0 + east_pedestrian: int = 0 + west_pedestrian: int = 0 + total_pedestrian_count: int = 0 + + # Timestamp of each directional count + north_timestamp: Optional[datetime] = None + south_timestamp: Optional[datetime] = None + east_timestamp: Optional[datetime] = None + west_timestamp: Optional[datetime] = None + + +@dataclass +class TrafficIntelligenceResponse: + """Complete traffic intelligence response matching data.json structure.""" + timestamp: str # ISO format timestamp + intersection_id: str # UUID of intersection + data: IntersectionData # Core intersection data + camera_images: Dict[str, Dict[str, Any]] # Camera images by direction + weather_data: WeatherData # Weather information + vlm_analysis: VLMAnalysisData # VLM analysis with alerts + response_age: Optional[float] = None + + +@dataclass +class CameraDataMessage: + """MQTT camera data message structure.""" + camera_id: str + intersection_id: str + direction: str + vehicle_count: int + pedestrian_count: int = 0 + timestamp: Optional[datetime] = None + image_data: Optional[str] = None # Base64 encoded image + + def __post_init__(self): + if self.timestamp is None: + self.timestamp = datetime.utcnow() + + +@dataclass +class TrafficSnapshot: + """Snapshot of traffic data at a point in time.""" + timestamp: datetime + intersection_id: str + directional_counts: Dict[str, int] # camera direction -> count + total_count: int + camera_images: Optional[Dict[str, CameraImage]] = None + weather_conditions: Optional[WeatherData] = None + intersection_data: Optional[IntersectionData] = None + + def calculate_total(self) -> int: + """Calculate total traffic count.""" + return sum(self.directional_counts.values()) + + +class TrafficState(Enum): + """Traffic state enumeration.""" + NORMAL = "normal" + MODERATE = "moderate" + HIGH = "high" + CRITICAL = "critical" + + +@dataclass +class TrafficTrend: + """Traffic trend analysis over time.""" + intersection_id: str + current_state: TrafficState + trend_direction: str # "increasing", "decreasing", "stable" + change_percentage: float # Percentage change from previous period + peak_time: Optional[datetime] = None + peak_count: Optional[int] = None + duration_minutes: int = 0 # How long in current state + + +# Type aliases for better code readability +CameraTopics = List[str] +DirectionalCounts = Dict[str, int] # direction -> count +CameraImagesDict = Dict[str, CameraImage] # direction -> image \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/pyproject.toml b/metro-ai-suite/smart-traffic-intersection-agent/src/pyproject.toml new file mode 100644 index 000000000..951c23487 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/pyproject.toml @@ -0,0 +1,73 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +version = "1.0.0" +name="Smart-Traffic-Intersection-Agent" +requires-python = ">=3.10" +dependencies = [ + "fastapi==0.104.1", + "uvicorn[standard]==0.24.0", + "paho-mqtt==1.6.1", + "aiohttp==3.9.1", + "structlog==23.2.0", + "pydantic==2.5.0", + "python-dateutil==2.8.2", + "python-dotenv==1.0.0", + "gradio>=4.0.0", + "pillow>=9.0.0", + "markdown>=3.4.0", + "requests>=2.25.0", + "huggingface_hub==0.36.0", +] + +[project.optional-dependencies] +dev = [ + "pytest==7.4.3", + "pytest-asyncio==0.21.1", + "httpx==0.25.2", + "black==23.11.0", + "flake8==6.1.0", + "mypy==1.7.1", +] + +[project.scripts] +traffic-intelligence = "traffic_intelligence.main:main" + +[tool.hatch.build.targets.wheel] +packages = ["traffic_intelligence"] + +[tool.black] +line-length = 88 +target-version = ['py310'] +include = '\.pyi?$' +extend-exclude = ''' +/( + # directories + \.eggs + | \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | _build + | buck-out + | build + | dist +)/ +''' + +[tool.mypy] +python_version = "3.10" +warn_return_any = true +warn_unused_configs = true +disallow_untyped_defs = true + +[tool.pytest.ini_options] +minversion = "6.0" +addopts = "-ra -q --strict-markers" +testpaths = [ + "tests", +] +asyncio_mode = "auto" diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/run.py b/metro-ai-suite/smart-traffic-intersection-agent/src/run.py new file mode 100644 index 000000000..021373fd9 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/run.py @@ -0,0 +1,18 @@ +#!/usr/bin/env python3 +""" +Simple launcher script for traffic intelligence service. +This script handles the module path setup and launches the main application. +""" + +import sys +import os +from pathlib import Path + +# Add the current directory to Python path so relative imports work +current_dir = Path(__file__).parent +sys.path.insert(0, str(current_dir)) + +# Now we can import and run the main function +if __name__ == "__main__": + from main import main + main() \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/services/__init__.py b/metro-ai-suite/smart-traffic-intersection-agent/src/services/__init__.py new file mode 100644 index 000000000..11d4c670e --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/services/__init__.py @@ -0,0 +1 @@ +"""Package initialization for services.""" \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/services/config.py b/metro-ai-suite/smart-traffic-intersection-agent/src/services/config.py new file mode 100644 index 000000000..30328df23 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/services/config.py @@ -0,0 +1,197 @@ +"""Configuration service for traffic intelligence.""" + +import os +import json +import hashlib +from typing import Dict, List, Optional +import structlog + +logger = structlog.get_logger(__name__) + + +def hash_intersection_name(name: str, length: int = 16) -> str: + hash_object = hashlib.sha256(name.encode('utf-8')) + hex_digest = hash_object.hexdigest() + return hex_digest[:length] + + +class ConfigService: + """ + Configuration service for traffic intelligence. + + Manages configuration for single intersection monitoring, + MQTT topics, weather API, and VLM service settings. + """ + + def __init__(self): + """Initialize configuration service.""" + self.config = self._load_config() + logger.info("Configuration service initialized", + intersection_id=self.get_intersection_id()) + + def _load_config(self) -> dict: + """Load configuration from environment and file.""" + config = {} + + # Load from config file if specified + config_file = os.getenv("TRAFFIC_INTELLIGENCE_CONFIG", "config/traffic_intelligence.json") + if os.path.exists(config_file): + try: + with open(config_file, 'r') as f: + file_config = json.load(f) + config.update(file_config) + logger.info("Loaded configuration from file", path=config_file) + except Exception as e: + logger.warning("Failed to load config file", path=config_file, error=str(e)) + + # Override with environment variables + # Intersection configuration + if os.getenv("INTERSECTION_NAME"): + if "intersection" not in config: + config["intersection"] = {} + config["intersection"]["name"] = os.getenv("INTERSECTION_NAME") + if os.getenv("INTERSECTION_LATITUDE"): + if "intersection" not in config: + config["intersection"] = {} + config["intersection"]["latitude"] = float(os.getenv("INTERSECTION_LATITUDE")) + if os.getenv("INTERSECTION_LONGITUDE"): + if "intersection" not in config: + config["intersection"] = {} + config["intersection"]["longitude"] = float(os.getenv("INTERSECTION_LONGITUDE")) + + # MQTT configuration + if os.getenv("MQTT_HOST"): + if "mqtt" not in config: + config["mqtt"] = {} + config["mqtt"]["host"] = os.getenv("MQTT_HOST") + if os.getenv("MQTT_PORT"): + if "mqtt" not in config: + config["mqtt"] = {} + config["mqtt"]["port"] = int(os.getenv("MQTT_PORT")) + + # Weather configuration + if os.getenv("WEATHER_MOCK"): + if "weather" not in config: + config["weather"] = {} + config["weather"]["use_mock"] = os.getenv("WEATHER_MOCK").lower() in ["true", "1", "yes"] + + if os.getenv("ENABLE_FIRE_MARKERS"): + if "weather" not in config: + config["weather"] = {} + config["weather"]["enable_fire_markers"] = os.getenv("ENABLE_FIRE_MARKERS").lower() in ["true", "1", "yes"] + + if os.getenv("ENABLE_STORM_MARKERS"): + if "weather" not in config: + config["weather"] = {} + config["weather"]["enable_storm_markers"] = os.getenv("ENABLE_STORM_MARKERS").lower() in ["true", "1", "yes"] + + if os.getenv("ENABLE_FLOOD_MARKERS"): + if "weather" not in config: + config["weather"] = {} + config["weather"]["enable_flood_markers"] = os.getenv("ENABLE_FLOOD_MARKERS").lower() in ["true", "1", "yes"] + + # VLM configuration + if os.getenv("VLM_BASE_URL"): + if "vlm" not in config: + config["vlm"] = {} + config["vlm"]["base_url"] = os.getenv("VLM_BASE_URL") + if os.getenv("VLM_MODEL"): + if "vlm" not in config: + config["vlm"] = {} + config["vlm"]["model"] = os.getenv("VLM_MODEL") + if os.getenv("VLM_TIMEOUT_SECONDS"): + if "vlm" not in config: + config["vlm"] = {} + config["vlm"]["timeout_seconds"] = int(os.getenv("VLM_TIMEOUT_SECONDS")) + if os.getenv("VLM_MAX_COMPLETION_TOKENS"): + if "vlm" not in config: + config["vlm"] = {} + config["vlm"]["max_completion_tokens"] = int(os.getenv("VLM_MAX_COMPLETION_TOKENS")) + if os.getenv("VLM_TEMPERATURE"): + if "vlm" not in config: + config["vlm"] = {} + config["vlm"]["temperature"] = float(os.getenv("VLM_TEMPERATURE")) + if os.getenv("VLM_TOP_P"): + if "vlm" not in config: + config["vlm"] = {} + config["vlm"]["top_p"] = float(os.getenv("VLM_TOP_P")) + + # Traffic configuration + if os.getenv("HIGH_DENSITY_THRESHOLD"): + if "traffic" not in config: + config["traffic"] = {} + config["traffic"]["high_density_threshold"] = float(os.getenv("HIGH_DENSITY_THRESHOLD")) + if os.getenv("TRAFFIC_BUFFER_DURATION"): + if "traffic" not in config: + config["traffic"] = {} + config["traffic"]["analysis_window_seconds"] = int(os.getenv("TRAFFIC_BUFFER_DURATION")) + return config + + def get_intersection_id(self) -> str: + name = self.get_intersection_name() + return hash_intersection_name(name) + + def get_intersection_name(self) -> str: + """Get the intersection name.""" + return self.config.get("intersection", {}).get("name", "Intersection-1") + + def get_intersection_coordinates(self) -> tuple: + """Get intersection coordinates (lat, lon).""" + intersection = self.config.get("intersection", {}) + return ( + intersection.get("latitude", 33.3091336), + intersection.get("longitude", -111.9353095) + ) + + def get_camera_topics(self) -> List[str]: + """Get MQTT camera topics.""" + return self.config.get("mqtt", {}).get("camera_topics", [ + "scenescape/data/camera/camera1", + "scenescape/data/camera/camera2", + "scenescape/data/camera/camera3", + "scenescape/data/camera/camera4" + ]) + + def get_image_topics(self) -> List[str]: + """Get MQTT image topics.""" + return self.config.get("mqtt", {}).get("image_topics", [ + "scenescape/image/camera/camera1", + "scenescape/image/camera/camera2", + "scenescape/image/camera/camera3", + "scenescape/image/camera/camera4" + ]) + + def get_mqtt_config(self) -> dict: + """Get MQTT configuration.""" + return self.config.get("mqtt", {}) + + def get_weather_config(self) -> dict: + """Get weather API configuration.""" + return self.config.get("weather", {}) + + def get_vlm_config(self) -> dict: + """Get VLM service configuration.""" + return self.config.get("vlm", {}) + + def get_traffic_config(self) -> dict: + """Get traffic analysis configuration.""" + return self.config.get("traffic", {}) + + def get_high_density_threshold(self) -> int: + """Get high density threshold for traffic analysis.""" + return self.config.get("traffic", {}).get("high_density_threshold", 5) + + def update_config(self, key: str, value: any) -> None: + """Update configuration value.""" + keys = key.split('.') + config_ref = self.config + + # Navigate to the nested key + for k in keys[:-1]: + if k not in config_ref: + config_ref[k] = {} + config_ref = config_ref[k] + + # Set the value + config_ref[keys[-1]] = value + logger.info("Configuration updated", key=key, value=value) \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/services/data_aggregator.py b/metro-ai-suite/smart-traffic-intersection-agent/src/services/data_aggregator.py new file mode 100644 index 000000000..4629b57b1 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/services/data_aggregator.py @@ -0,0 +1,375 @@ +"""Data aggregator service for traffic intelligence.""" + +import asyncio +from datetime import datetime, timezone +from typing import Dict, List, Optional, Any +from collections import deque +import structlog + +from models import ( + CameraDataMessage, CameraImage, TrafficSnapshot, IntersectionData, + TrafficIntelligenceResponse, WeatherData, VLMAnalysisData +) +from .config import ConfigService +from .vlm_service import VLMService + + +logger = structlog.get_logger(__name__) + + +class DataAggregatorService: + """ + Data aggregator service for traffic intelligence. + + Aggregates camera data, coordinates with weather and VLM services, + and maintains current traffic state for API responses. + """ + + def __init__(self, config_service: ConfigService, vlm_service: VLMService): + """ + Initialize data aggregator service. + + Args: + config_service: Configuration service + vlm_service: VLM service for traffic analysis + """ + self.config = config_service + self.vlm_service = vlm_service + + # Data storage - separate temporary and VLM-analyzed data + self.temp_camera_data: Dict[str, CameraDataMessage] = {} # direction -> latest temp data + self.temp_camera_images: Dict[str, CameraImage] = {} # direction -> latest temp image + self.temp_intersection_data: Optional[IntersectionData] = None + + # VLM-analyzed data storage (only data that was part of VLM analysis) + self.vlm_analyzed_camera_images: Dict[str, CameraImage] = {} # direction -> VLM-analyzed images + self.vlm_analyzed_intersection_data: Optional[IntersectionData] = None + self.vlm_analyzed_weather_data: Optional[WeatherData] = None + + # Current state + self.current_vlm_analysis: Optional[VLMAnalysisData] = None + self.last_analysis_time: Optional[float] = 0.0 + + + logger.info("Data aggregator service initialized") + + + async def process_camera_image(self, camera_image: CameraImage) -> None: + """ + Process incoming camera image separately from data. + + Args: + camera_image: Camera image data from MQTT + """ + try: + direction = camera_image.direction + + # Update temporary camera image + self.temp_camera_images[direction] = camera_image + + logger.info("Camera image updated (temporary)", + direction=direction, + camera_id=camera_image.camera_id, + image_size=camera_image.image_size_bytes, + has_image_data=bool(camera_image.image_base64), + total_temp_images_stored=len(self.temp_camera_images)) + + except Exception as e: + logger.error("Failed to process camera image", error=str(e)) + + async def process_camera_data(self, camera_message: CameraDataMessage) -> None: + """ + Process incoming camera data and update current state. + + Args: + camera_message: Camera data message from MQTT + """ + try: + direction = camera_message.direction + + # Update temporary camera data + self.temp_camera_data[direction] = camera_message + + logger.info("Camera data updated (temporary)") + + # Update temporary intersection data + await self._update_temp_intersection_data() + + if len(self.temp_camera_data) == 4: + # Check if VLM analysis should be triggered + self.temp_camera_data = {} # Clear after processing all directions + await self._check_analysis_trigger() + + + except Exception as e: + logger.error("Failed to process camera data", error=str(e)) + + async def _update_temp_intersection_data(self) -> None: + """Update temporary intersection data from camera inputs.""" + intersection_id = self.config.get_intersection_id() + intersection_name = self.config.get_intersection_name() + lat, lon = self.config.get_intersection_coordinates() + + # Calculate directional counts from temporary data + north_count = self.temp_camera_data.get('north', CameraDataMessage('', '', 'north', 0)).vehicle_count + south_count = self.temp_camera_data.get('south', CameraDataMessage('', '', 'south', 0)).vehicle_count + east_count = self.temp_camera_data.get('east', CameraDataMessage('', '', 'east', 0)).vehicle_count + west_count = self.temp_camera_data.get('west', CameraDataMessage('', '', 'west', 0)).vehicle_count + + # Calculate pedestrian counts from temporary data + north_pedestrian = self.temp_camera_data.get('north', CameraDataMessage('', '', 'north', 0, 0)).pedestrian_count + south_pedestrian = self.temp_camera_data.get('south', CameraDataMessage('', '', 'south', 0, 0)).pedestrian_count + east_pedestrian = self.temp_camera_data.get('east', CameraDataMessage('', '', 'east', 0, 0)).pedestrian_count + west_pedestrian = self.temp_camera_data.get('west', CameraDataMessage('', '', 'west', 0, 0)).pedestrian_count + + # Get Traffic data timestamps + north_timestamp = self.temp_camera_data.get('north').timestamp if 'north' in self.temp_camera_data else None + south_timestamp = self.temp_camera_data.get('south').timestamp if 'south' in self.temp_camera_data else None + east_timestamp = self.temp_camera_data.get('east').timestamp if 'east' in self.temp_camera_data else None + west_timestamp = self.temp_camera_data.get('west').timestamp if 'west' in self.temp_camera_data else None + + total_count = north_count + south_count + east_count + west_count + total_pedestrian_count = north_pedestrian + south_pedestrian + east_pedestrian + west_pedestrian + + # Calculate intersection-level traffic status based on total density + high_density_threshold = self.config.get_high_density_threshold() + + if total_count >= (high_density_threshold * 2/3): + intersection_status = "HIGH" + elif total_count >= (high_density_threshold * 1/3): + intersection_status = "MODERATE" + else: + intersection_status = "NORMAL" + + self.temp_intersection_data = IntersectionData( + intersection_id=intersection_id, + intersection_name=intersection_name, + latitude=lat, + longitude=lon, + timestamp=datetime.now(timezone.utc), + north_camera=north_count, + south_camera=south_count, + east_camera=east_count, + west_camera=west_count, + total_density=total_count, + intersection_status=intersection_status, + north_pedestrian=north_pedestrian, + south_pedestrian=south_pedestrian, + east_pedestrian=east_pedestrian, + west_pedestrian=west_pedestrian, + total_pedestrian_count=total_pedestrian_count, + north_timestamp=north_timestamp, + south_timestamp=south_timestamp, + east_timestamp=east_timestamp, + west_timestamp=west_timestamp, + ) + + logger.info("Temporary intersection data updated", + total_density=total_count, + intersection_status=intersection_status, + total_pedestrian_count=total_pedestrian_count, + north=north_count, south=south_count, + east=east_count, west=west_count, + north_ped=north_pedestrian, south_ped=south_pedestrian, + east_ped=east_pedestrian, west_ped=west_pedestrian, + north_timestamp=north_timestamp, + south_timestamp=south_timestamp, + east_timestamp=east_timestamp, + west_timestamp=west_timestamp) + + def _create_temp_traffic_snapshot(self) -> Optional[TrafficSnapshot]: + """Create a traffic snapshot from temporary data for VLM analysis.""" + if not self.temp_intersection_data: + return None + + directional_counts = { + 'north': self.temp_intersection_data.north_camera, + 'south': self.temp_intersection_data.south_camera, + 'east': self.temp_intersection_data.east_camera, + 'west': self.temp_intersection_data.west_camera + } + + return TrafficSnapshot( + timestamp=datetime.now(timezone.utc), + intersection_id=self.temp_intersection_data.intersection_id, + directional_counts=directional_counts, + total_count=self.temp_intersection_data.total_density, + camera_images=self.temp_camera_images.copy(), + intersection_data=self.temp_intersection_data, + ) + + def _save_vlm_analyzed_data(self, vlm_analysis: VLMAnalysisData, traffic_snapshot: TrafficSnapshot) -> None: + """Save data that was used in VLM analysis as the current analyzed data.""" + + self.current_vlm_analysis = vlm_analysis + + # Copy temporary camera data to VLM-analyzed storage + self.vlm_analyzed_camera_images = traffic_snapshot.camera_images + self.vlm_analyzed_intersection_data = traffic_snapshot.intersection_data + self.vlm_analyzed_weather_data = self.vlm_service.get_weather_details() + + + # Add to historical snapshots (only VLM-analyzed data) + + logger.info("VLM-analyzed data saved", + total_density=traffic_snapshot.total_count, + analyzed_cameras=list(self.vlm_analyzed_camera_images.keys()), + intersection_id=traffic_snapshot.intersection_id) + + async def _check_analysis_trigger(self) -> None: + """Check if VLM analysis should be triggered based on traffic conditions.""" + + if not self.temp_intersection_data: + logger.debug("No intersection data available for analysis trigger check") + return + + # Get current threshold dynamically from config + high_density_threshold = self.config.get_high_density_threshold() + + logger.info("Checking if VLM analysis should be triggered", + total_density=self.temp_intersection_data.total_density, + threshold=high_density_threshold, + last_analysis_time=self.last_analysis_time) + + # High traffic - always analyze + if self.temp_intersection_data.total_density >= high_density_threshold: + logger.info("High traffic detected, triggering VLM analysis", + total_density=self.temp_intersection_data.total_density, + threshold=high_density_threshold) + await self._trigger_vlm_analysis() + return + + # Low traffic - check if enough time has passed since last analysis + if self.last_analysis_time == 0.0: + logger.info("No previous analysis, triggering VLM analysis for low traffic", + total_density=self.temp_intersection_data.total_density) + await self._trigger_vlm_analysis() + return + + analysis_window_seconds = self.config.get_traffic_config().get("analysis_window_seconds", 30) + time_since_last_analysis = datetime.now().timestamp() - self.last_analysis_time + + logger.info("Low traffic - checking analysis window", + total_density=self.temp_intersection_data.total_density, + time_since_last_analysis=time_since_last_analysis, + analysis_window_seconds=analysis_window_seconds) + + if time_since_last_analysis >= analysis_window_seconds: + logger.info("Analysis window expired, triggering VLM analysis for low traffic", + total_density=self.temp_intersection_data.total_density, + time_since_last=time_since_last_analysis, + window_seconds=analysis_window_seconds) + await self._trigger_vlm_analysis() + else: + logger.info("Skipping VLM analysis - within analysis window", + total_density=self.temp_intersection_data.total_density, + time_since_last=time_since_last_analysis, + window_seconds=analysis_window_seconds) + + async def _trigger_vlm_analysis(self) -> None: + """Trigger VLM analysis with current traffic and weather data.""" + try: + logger.info("Starting VLM analysis trigger") + traffic_snapshot = self._create_temp_traffic_snapshot() + + if not traffic_snapshot: + logger.warning("Cannot trigger VLM analysis: no traffic snapshot available") + return + + # Trigger VLM analysis + try: + vlm_analysis: VLMAnalysisData = await self.vlm_service.analyze_traffic_non_blocking( + traffic_snapshot=traffic_snapshot + ) + + if vlm_analysis: + self._save_vlm_analyzed_data(vlm_analysis, traffic_snapshot) + self.last_analysis_time = datetime.now().timestamp() + + logger.info("VLM analysis completed successfully and data saved", + alerts_count=len(vlm_analysis.alerts), + analyzed_total_density=traffic_snapshot.total_count) + else: + logger.warning("VLM analysis returned no result - temporary data not saved") + + + except Exception as vlm_error: + logger.error("VLM analysis failed - temporary data not saved", error=str(vlm_error)) + # Don't update analysis on error + + except Exception as e: + logger.error("Failed to trigger VLM analysis", error=str(e)) + + async def get_current_traffic_intelligence(self) -> Optional[TrafficIntelligenceResponse]: + """ + Get current traffic intelligence response. + + Returns: + Complete traffic intelligence response or None if no VLM-analyzed data available + """ + # Only return data that was part of VLM analysis + if not self.vlm_analyzed_intersection_data or not self.current_vlm_analysis: + logger.info("No VLM-analyzed data available for API response", + has_vlm_intersection_data=self.vlm_analyzed_intersection_data is not None, + has_vlm_analysis=self.current_vlm_analysis is not None) + return None + + try: + + # Prepare camera images for response (only VLM-analyzed images) + camera_images_dict = {} + for direction, camera_image in self.vlm_analyzed_camera_images.items(): + camera_images_dict[f"{direction}_camera"] = { + 'camera_id': camera_image.camera_id, + 'direction': camera_image.direction, + 'timestamp': camera_image.timestamp, + 'image_base64': camera_image.image_base64, # Include full base64 image + 'image_size_bytes': camera_image.image_size_bytes + } + + # Create response with VLM-analyzed data only + response = TrafficIntelligenceResponse( + timestamp=datetime.now(timezone.utc).isoformat(), + intersection_id=self.vlm_analyzed_intersection_data.intersection_id, + data=self.vlm_analyzed_intersection_data, + camera_images=camera_images_dict, + weather_data=self.vlm_analyzed_weather_data, + vlm_analysis=self.current_vlm_analysis, + response_age=(datetime.now(timezone.utc).timestamp() - self.last_analysis_time), + ) + + return response + + except Exception as e: + logger.error("Failed to create traffic intelligence response", error=str(e)) + return None + + + def _get_default_weather(self) -> WeatherData: + """Get default weather data when none is available.""" + return WeatherData( + name="Unknown", + temperature=72, + temperature_unit="F", + detailed_forecast="Weather data unavailable", + fetched_at=datetime.now(timezone.utc), + is_precipitation=False, + is_mock=True + ) + + + def get_service_status(self) -> Dict[str, Any]: + """Get current service status and statistics.""" + return { + "intersection_id": self.config.get_intersection_id(), + "intersection_name": self.config.get_intersection_name(), + "current_traffic_density": self.vlm_analyzed_intersection_data.total_density if self.vlm_analyzed_intersection_data else 0, + "current_pedestrian_count": self.vlm_analyzed_intersection_data.total_pedestrian_count if self.vlm_analyzed_intersection_data else 0, + "analyzed_camera_directions": list(self.vlm_analyzed_camera_images.keys()), + "active_analyzed_cameras": len(self.vlm_analyzed_camera_images), + "has_weather_data": self.vlm_analyzed_weather_data is not None, + "has_vlm_analysis": self.current_vlm_analysis is not None, + "last_analysis_time": self.last_analysis_time.isoformat() if self.last_analysis_time else None, + # "vlm_analyzed_history_count": len(self.traffic_history), + # "analysis_tasks_running": self.analysis_task is not None and not self.analysis_task.done() + } \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/services/mqtt_service.py b/metro-ai-suite/smart-traffic-intersection-agent/src/services/mqtt_service.py new file mode 100644 index 000000000..d28f714e0 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/services/mqtt_service.py @@ -0,0 +1,570 @@ +"""MQTT service for traffic intelligence camera data subscription.""" + +import asyncio +import json +import os +import re +from datetime import datetime, timezone +from typing import Optional, Dict, Any +from queue import Queue +import threading + +import paho.mqtt.client as mqtt +import structlog + +from models import CameraDataMessage, CameraImage +from .config import ConfigService +from .data_aggregator import DataAggregatorService +from .vlm_service import VLMService + + +logger = structlog.get_logger(__name__) + + +class MQTTService: + """ + MQTT service for subscribing to camera data topics. + + Subscribes to scenescape/data/camera/camera<1,2,3,4> topics and processes + incoming camera data for traffic intelligence analysis. + """ + + direction_mapping = { + '1': 'south', + '2': 'west', + '3': 'north', + '4': 'east', + } + + def __init__(self, config_service: ConfigService, data_aggregator: DataAggregatorService, vlm_service: VLMService): + """ + Initialize MQTT service. + + Args: + config_service: Configuration service instance + data_aggregator: Data aggregator service for processing messages + """ + self.config = config_service + self.data_aggregator = data_aggregator + self.mqtt_config = config_service.get_mqtt_config() + self.vlm_service = vlm_service + + # MQTT connection settings - use config or fallback to localhost + self.host = self.mqtt_config.get("host", "localhost") + self.port = self.mqtt_config.get("port", 1883) + self.use_tls = self.mqtt_config.get("use_tls", False) + self.ca_cert_path = self.mqtt_config.get("ca_cert_path", "../secrets/certs/scenescape-ca.pem") + self.cert_required = self.mqtt_config.get("cert_required", True) + self.verify_hostname = self.mqtt_config.get("verify_hostname", False) + self.username = self.mqtt_config.get("username") + self.password = self.mqtt_config.get("password") + + # Camera topics + self.camera_topics = config_service.get_camera_topics() + self.image_topics = config_service.get_image_topics() + + # MQTT client and connection state + self.client = None + self.connected = False + self.loop = None + + # Message processing + self.shutdown_event = asyncio.Event() + + # Rate limiting - process data every 1 second per camera + self.last_processed_time = {} # camera_number -> timestamp + self.rate_limit_seconds = self.mqtt_config.get("rate_limit_seconds", 10.0) + + # Topic patterns for camera data and images + # Pattern: scenescape/data/camera/camera{1,2,3,4} + self.camera_data_pattern = re.compile(r'scenescape/data/camera/camera(\d+)') + # Pattern: scenescape/image/camera/camera{1,2,3,4} + self.camera_image_pattern = re.compile(r'scenescape/image/camera/camera(\d+)') + + logger.info("MQTT service initialized", + host=self.host, + port=self.port, + use_tls=self.use_tls, + has_auth=bool(self.username), + cert_required=self.cert_required, + verify_hostname=self.verify_hostname, + ca_cert_path=self.ca_cert_path, + camera_topics=self.camera_topics, + image_topics=self.image_topics) + + async def initialize(self) -> None: + """Initialize MQTT client.""" + try: + self.client = mqtt.Client() + + # Configure authentication + if self.username and self.password: + self.client.username_pw_set(self.username, self.password) + logger.info("MQTT username/password authentication configured", username=self.username) + + # Configure TLS + if self.use_tls: + logger.info("Configuring MQTT TLS", + ca_cert=self.ca_cert_path, + cert_required=self.cert_required, + verify_hostname=self.verify_hostname) + + try: + import ssl + + # Resolve certificate paths relative to the app root + # Get the app root directory (parent of services directory) + script_dir = os.path.dirname(os.path.abspath(__file__)) + app_root = os.path.dirname(script_dir) # Go up one level from services/ + + def resolve_cert_path(cert_path): + if os.path.isabs(cert_path): + return cert_path + return os.path.join(app_root, cert_path) + + ca_cert_full_path = resolve_cert_path(self.ca_cert_path) + + logger.info("Certificate path resolved", + ca_cert=ca_cert_full_path) + + if self.cert_required: + # Check if CA certificate file exists + if not os.path.exists(ca_cert_full_path): + logger.error("Missing CA certificate file", ca_cert=ca_cert_full_path) + raise FileNotFoundError(f"Missing CA certificate file: {ca_cert_full_path}") + + # Use CA certificate only for server verification + self.client.tls_set(ca_certs=ca_cert_full_path, + certfile=None, + keyfile=None, + cert_reqs=ssl.CERT_REQUIRED, + tls_version=ssl.PROTOCOL_TLS) + + # Set hostname verification based on config + if not self.verify_hostname: + self.client.tls_insecure_set(True) + logger.info("TLS configured with CA certificate (hostname verification disabled)") + else: + logger.info("TLS configured with CA certificate (full verification)") + + else: + # TLS without certificate verification (insecure but matches some UI settings) + try: + # First try with CA cert only + if os.path.exists(ca_cert_full_path): + self.client.tls_set(ca_certs=ca_cert_full_path, + certfile=None, + keyfile=None, + cert_reqs=ssl.CERT_NONE, + tls_version=ssl.PROTOCOL_TLS) + logger.info("TLS with CA cert but no verification") + else: + # No certificates, just TLS + self.client.tls_set(ca_certs=None, + certfile=None, + keyfile=None, + cert_reqs=ssl.CERT_NONE, + tls_version=ssl.PROTOCOL_TLS) + logger.info("TLS without certificates") + + self.client.tls_insecure_set(True) + + except Exception as e: + logger.warning("Advanced TLS failed, trying basic TLS", error=str(e)) + # Fallback to most basic TLS + self.client.tls_set() + self.client.tls_insecure_set(True) + logger.info("Basic TLS enabled") + + except Exception as e: + logger.error("Failed to configure TLS", error=str(e)) + raise + + # Set callbacks + self.client.on_connect = self._on_connect + self.client.on_disconnect = self._on_disconnect + self.client.on_message = self._on_message + + logger.info("MQTT client initialized") + + except Exception as e: + logger.error("Failed to initialize MQTT client", error=str(e)) + raise + + def _on_connect(self, client, userdata, flags, rc): + """MQTT connection callback.""" + if rc == 0: + self.connected = True + logger.info("MQTT connected successfully") + + # Subscribe to camera data topics + for topic in self.camera_topics: + client.subscribe(topic, qos=1) + logger.info("Subscribed to camera topic", topic=topic) + for topic in self.image_topics: + client.subscribe(topic, qos=1) + logger.info("Subscribed to image topic", topic=topic) + + # Automatically trigger getimage commands once connected and subscribed + # if self.loop: + # asyncio.run_coroutine_threadsafe( + # self._trigger_initial_getimage_commands(), + # self.loop + # ) + # else: + # logger.warning("No event loop set, cannot trigger initial getimage commands") + + else: + self.connected = False + logger.error("MQTT connection failed", return_code=rc) + + def _on_disconnect(self, client, userdata, rc): + """MQTT disconnection callback.""" + self.connected = False + if rc != 0: + logger.warning("MQTT disconnected unexpectedly", return_code=rc) + else: + logger.info("MQTT disconnected") + + def _on_message(self, client, userdata, msg): + """MQTT message callback - process messages directly with rate limiting.""" + + try: + current_ts = datetime.now(timezone.utc).timestamp() + if self.vlm_service.get_vlm_semaphore().locked(): + # logger.info("VLM analysis currently running, skipping message receipt") + return + + try: + payload = json.loads(msg.payload.decode()) + except json.JSONDecodeError: + logger.error("Failed to parse MQTT message payload", + topic=msg.topic, payload=msg.payload.decode()[:100]) + return + + # Check if this is a camera data or camera image topic + camera_data_match = self.camera_data_pattern.match(msg.topic) + camera_image_match = self.camera_image_pattern.match(msg.topic) + + if camera_data_match: + # Handle camera data message + camera_number = camera_data_match.group(1) + # if last processing less than rate_limit_seconds ago, skip + last_time = self.last_processed_time.get(f"data_{camera_number}", 0) + if current_ts - last_time < self.rate_limit_seconds: + return + + self.last_processed_time[f"data_{camera_number}"] = current_ts + + # Schedule async processing for camera data + if self.loop: + asyncio.run_coroutine_threadsafe( + self._process_camera_data_message( + camera_number=camera_number, + payload=payload, + topic=msg.topic + ), + self.loop + ) + else: + logger.warning("No event loop set, cannot process camera data message") + + elif camera_image_match: + # Handle camera image message + camera_number = camera_image_match.group(1) + + # Schedule async processing for camera image + if self.loop: + asyncio.run_coroutine_threadsafe( + self._process_camera_image_message( + camera_number=camera_number, + payload=payload, + topic=msg.topic + ), + self.loop + ) + else: + logger.warning("No event loop set, cannot process camera image message") + + else: + logger.debug("Ignoring unrecognized topic", topic=msg.topic) + + except Exception as e: + logger.error("Error processing MQTT message", error=str(e), topic=msg.topic) + + async def start(self) -> None: + """Start MQTT service.""" + try: + if not self.client: + await self.initialize() + + logger.info("Starting MQTT connection", host=self.host, port=self.port, use_tls=self.use_tls) + + try: + self.client.connect(self.host, self.port, 60) + except Exception as e: + error_str = str(e).lower() + if "ssl" in error_str or "tls" in error_str: + logger.error("SSL/TLS connection failed", error=str(e), host=self.host, port=self.port) + + # Provide specific suggestions based on the error + if "wrong version number" in error_str: + logger.error("SSL version mismatch detected.") + elif "certificate" in error_str: + logger.error("Certificate validation failed. Try: MQTT_CERT_REQUIRED=false") + + # If TLS is enabled and we get SSL errors, suggest trying without TLS + if self.use_tls: + logger.error("Consider trying connection without TLS first to test basic connectivity") + + else: + logger.error("MQTT connection failed", error=str(e), host=self.host, port=self.port) + raise + + # Start the network loop in a separate thread + self.client.loop_start() + + # Wait for connection to establish + await asyncio.sleep(2) + + logger.info("MQTT service started successfully") + + # Keep the service running + while not self.shutdown_event.is_set(): + await asyncio.sleep(1) + + except Exception as e: + logger.error("Failed to start MQTT service", error=str(e)) + raise + + async def stop(self) -> None: + """Stop MQTT service.""" + try: + # Signal the shutdown event + self.shutdown_event.set() + + if self.client: + self.client.loop_stop() + self.client.disconnect() + logger.info("MQTT service stopped") + + except Exception as e: + logger.error("Error stopping MQTT service", error=str(e)) + + def is_connected(self) -> bool: + """Check if MQTT client is connected.""" + return self.connected + + def set_event_loop(self, loop): + """Set the event loop reference for async task scheduling.""" + self.loop = loop + logger.info("Event loop reference set for MQTT service") + + async def send_getimage_commands(self, camera_number: Optional[str] = None) -> bool: + """Send 'getimage' command to all cameras.""" + if not self.connected: + logger.warning("MQTT Publisher not connected, cannot send commands") + return False + + try: + all_cameras = ["camera1", "camera2", "camera3", "camera4"] + camera_ids = [f"camera{camera_number}"] if camera_number else all_cameras + + if not camera_ids: + logger.warning("No camera IDs found") + return False + + success_count = 0 + for camera_id in camera_ids: + try: + topic = f"scenescape/cmd/camera/{camera_id}" + message = "getimage" + + result = self.client.publish(topic, message) + if result.rc == mqtt.MQTT_ERR_SUCCESS: + success_count += 1 + logger.debug("Sent getimage command", camera_id=camera_id, topic=topic) + else: + logger.warning("Failed to send getimage command", + camera_id=camera_id, + result_code=result.rc) + + # Small delay between commands to avoid overwhelming the broker + # await asyncio.sleep(0.1) + + except Exception as e: + logger.error("Error sending getimage command", + camera_id=camera_id, + error=str(e)) + + logger.info("Sent getimage commands", + total_cameras=len(camera_ids), + successful=success_count) + + return success_count > 0 + + except Exception as e: + logger.error("Failed to send getimage commands", error=str(e)) + return False + + async def _trigger_initial_getimage_commands(self) -> None: + """ + Trigger initial getimage commands after MQTT connection is established. + + This method is called automatically when the MQTT connection is established + to request initial images from all cameras. + """ + try: + # Small delay to ensure subscriptions are fully established + await asyncio.sleep(1.0) + + logger.info("Triggering initial getimage commands for all cameras") + success = await self.send_getimage_commands() + + if success: + logger.info("Initial getimage commands sent successfully") + else: + logger.warning("Failed to send initial getimage commands") + + except Exception as e: + logger.error("Error triggering initial getimage commands", error=str(e)) + + async def _process_camera_image_message(self, + camera_number: str, + payload: Dict[str, Any], + # timestamp: datetime, + topic: str) -> None: + """ + Process camera image message from MQTT. + + Args: + camera_number: Camera number (1, 2, 3, 4) + payload: Message payload with image data + timestamp: Message timestamp + topic: MQTT topic + """ + try: + logger.info("Processing camera image message", camera_number=camera_number, topic=topic) + + # Extract data from payload + camera_id = payload.get('id', f'camera{camera_number}') + image_data = payload.get('image') # Base64 encoded image + if not image_data: + logger.warning("No image data in image message", camera_id=camera_id, topic=topic) + return + + image_timestamp = payload.get('timestamp', None) + if image_timestamp: + try: + image_timestamp = datetime.fromisoformat(image_timestamp) + except: + pass + + # Map camera number to direction (configurable mapping) + direction = MQTTService.direction_mapping.get(camera_number, f'camera{camera_number}') + + # Create camera image + camera_image = CameraImage( + camera_id=camera_id, + direction=direction, + image_base64=image_data, + timestamp=image_timestamp, + image_size_bytes=len(image_data) * 3 // 4 if image_data else None # Approximate base64 decode size + ) + + # Send to data aggregator for processing (image only) + await self.data_aggregator.process_camera_image(camera_image) + + logger.debug("Camera image processed successfully", + camera_id=camera_id, + direction=direction, + image_size=len(image_data)) + except Exception as e: + logger.error("Failed to process camera image message", + error=str(e), + camera_number=camera_number, + topic=topic) + + async def _process_camera_data_message(self, + camera_number: str, + payload: Dict[str, Any], + # timestamp: datetime, + topic: str) -> None: + """ + Process camera data message from MQTT. + + Args: + camera_number: Camera number (1, 2, 3, 4) + payload: Message payload + timestamp: Message timestamp + topic: MQTT topic + """ + try: + logger.info("Processing camera data message", camera_number=camera_number, topic=topic) + # Bring Image corresponding to current camera data + success = await self.send_getimage_commands(camera_number=camera_number) + if success: + logger.info(f"Getimage commands for camera {camera_number} sent successfully") + else: + logger.warning(f"Failed to send getimage command for camera {camera_number}") + + await asyncio.sleep(1) # Small wait to allow image to arrive + + # Extract data from payload + camera_id = payload.get('id', f'camera{camera_number}') + intersection_id = self.config.get_intersection_id() + + # Map camera number to direction (configurable mapping) + direction = MQTTService.direction_mapping.get(camera_number, f'camera{camera_number}') + + # Extract traffic counts from objects array + objects = payload.get('objects', {}) + vehicle_count = len(objects.get('vehicle', [])) + pedestrian_count = len(objects.get('pedestrian', [])) + + data_timestamp = payload.get('timestamp', None) + if data_timestamp: + try: + data_timestamp = datetime.fromisoformat(data_timestamp) + except: + pass + + # Create camera data message + camera_message = CameraDataMessage( + camera_id=camera_id, + intersection_id=intersection_id, + direction=direction, + vehicle_count=vehicle_count, + pedestrian_count=pedestrian_count, + timestamp=data_timestamp, + ) + + + # Send to data aggregator for processing + await self.data_aggregator.process_camera_data(camera_message) + + logger.debug("Camera data processed successfully", + camera_id=camera_id, + direction=direction, + vehicle_count=vehicle_count, + has_image=False) + + except Exception as e: + logger.error("Failed to process camera data message", + error=str(e), + camera_number=camera_number, + topic=topic) + + def get_connection_status(self) -> Dict[str, Any]: + """Get MQTT connection status information.""" + return { + "connected": self.connected, + "host": self.host, + "port": self.port, + "use_tls": self.use_tls, + "has_authentication": bool(self.username), + "cert_required": self.cert_required, + "subscribed_topics": self.camera_topics, + "rate_limit_seconds": self.rate_limit_seconds, + "cameras_being_tracked": list(self.last_processed_time.keys()) + } \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/services/vlm_service.py b/metro-ai-suite/smart-traffic-intersection-agent/src/services/vlm_service.py new file mode 100644 index 000000000..590b1527a --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/services/vlm_service.py @@ -0,0 +1,623 @@ +"""Enhanced VLM service for traffic intelligence with weather-aware analysis.""" + +import asyncio +import json +from datetime import datetime, timezone +from typing import Dict, List, Optional, Any + +from click import prompt +import aiohttp +import structlog + +from models import ( + WeatherData, VLMAnalysisData, VLMAlert, AlertLevel, AlertType, + CameraImage, TrafficSnapshot, WeatherType +) +from .config import ConfigService +from .weather_service import WeatherService + + +# Update logger to show debug level logs for development +logger = structlog.get_logger(__name__) + + +class VLMService: + """ + Enhanced VLM service for traffic intelligence with weather-aware analysis. + + Features: + - Structured prompts for traffic analysis and alerts + - Weather integration for contextual analysis + - Alert generation with severity levels + - Traffic pattern recognition + """ + + def __init__(self, config_service: ConfigService, weather_service: WeatherService): + """Initialize VLM service with configuration and weather integration.""" + self.config = config_service + self.weather_service = weather_service + self.vlm_config = config_service.get_vlm_config() + + self.weather_data: Optional[WeatherData] = None + + # VLM service configuration + self.base_url = self.vlm_config.get("base_url", "http://vlm-service:8080") + self.model = self.vlm_config.get("model", "gpt-4-vision-preview") + self.timeout = self.vlm_config.get("timeout_seconds", 300) # Reduced from 300 to 30 seconds + self.max_tokens = self.vlm_config.get("max_completion_tokens", 2000) + self.temperature = self.vlm_config.get("temperature", 0.1) + self.top_p = self.vlm_config.get("top_p", 0.1) + + # Store config service for dynamic threshold access + self.config_service = config_service + + # Cache for recent analyses + self._analysis_cache: Dict[str, VLMAnalysisData] = {} + + # Semaphore to ensure only one VLM request at a time + self._vlm_semaphore = asyncio.Semaphore(1) + + # Store last successful analysis to reuse when service is busy + self._last_analysis: Optional[VLMAnalysisData] = None + self._last_analysis_timestamp: Optional[datetime] = None + + logger.info("Enhanced VLM service initialized", + base_url=self.base_url, + model=self.model, + threshold=self.config_service.get_high_density_threshold()) + + def get_vlm_semaphore(self) -> asyncio.Semaphore: + """Get the VLM semaphore for external use.""" + return self._vlm_semaphore + + def get_weather_details(self) -> Optional[WeatherData]: + """Get the last fetched weather data.""" + return self.weather_data or self.weather_service.get_default_weather() + + async def analyze_traffic_with_weather( + self, + traffic_snapshot: TrafficSnapshot, + camera_images: List[CameraImage] + ) -> Optional[VLMAnalysisData]: + """ + Perform comprehensive traffic analysis with weather context. + + Args: + traffic_snapshot: Current traffic data + camera_images: List of camera images from intersection + weather_data: Current weather data (optional) + + Returns: + VLMAnalysisData with structured analysis and alerts + """ + try: + try: + self.weather_data = await self.weather_service.get_current_weather() + except Exception as e: + logger.warning("Weather fetch failed during VLM analysis, using cached or default data", error=str(e)) + self.weather_data = self.weather_data or self.weather_service.get_default_weather() + + # Create structured prompt with weather context + prompt = self._create_structured_prompt(traffic_snapshot, self.weather_data) + + logger.info("Generated VLM prompt", prompt=prompt) + + # Prepare VLM request with images + logger.info("Preparing VLM request", + camera_images_available=len(camera_images), + camera_directions=[img.direction for img in camera_images], + image_data_present=[bool(img.image_base64) for img in camera_images]) + vlm_request = self._build_vlm_request(prompt, camera_images) + + # Call VLM service + analysis_result = await self._call_vlm_service(vlm_request) + + if analysis_result: + # Parse structured response + structured_analysis = self._parse_vlm_response( + analysis_result, traffic_snapshot, self.weather_data + ) + + # Cache the analysis + self._analysis_cache[traffic_snapshot.intersection_id] = structured_analysis + + # Store as last successful analysis for reuse when service is busy + self._last_analysis = structured_analysis + self._last_analysis_timestamp = datetime.now(timezone.utc) + + logger.info("VLM analysis completed successfully", + intersection_id=traffic_snapshot.intersection_id, + alerts_count=len(structured_analysis.alerts)) + + return structured_analysis + else: + logger.warning("VLM service returned no result, using fallback analysis") + # Use fallback analysis instead of returning None + fallback_analysis = self._create_fallback_analysis( + "VLM service returned no result", traffic_snapshot, self.weather_data + ) + logger.info("Using fallback analysis", + alerts_count=len(fallback_analysis.alerts)) + return fallback_analysis + + except Exception as e: + logger.error("VLM analysis failed", error=str(e)) + fallback_analysis = self._create_fallback_analysis( + "VLM service returned no result", traffic_snapshot, self.weather_data + ) + logger.info("Using fallback analysis", + alerts_count=len(fallback_analysis.alerts)) + return fallback_analysis + + async def analyze_traffic_non_blocking(self, traffic_snapshot: TrafficSnapshot) -> Optional[VLMAnalysisData]: + """ + Non-blocking traffic analysis that uses semaphore to prevent concurrent requests. + Returns cached analysis if VLM service is busy. + + Args: + traffic_snapshot: Current traffic data + + Returns: + VLMAnalysisData - either new analysis or cached result if service is busy + """ + try: + # Try to acquire semaphore without blocking + if self._vlm_semaphore.locked(): + current_time = datetime.now(timezone.utc) + logger.info("VLM service is busy, returning cached analysis", + has_cached_analysis=self._last_analysis is not None, + last_analysis_age_minutes=(current_time - self._last_analysis_timestamp).total_seconds() / 60 + if self._last_analysis_timestamp else None) + + # Return last successful analysis if available + if self._last_analysis: + # Update timestamp to current time for the cached result + cached_analysis = VLMAnalysisData( + traffic_summary=self._last_analysis.traffic_summary, + alerts=self._last_analysis.alerts, + recommendations=self._last_analysis.recommendations, + analysis_timestamp=current_time # Update to current time + ) + logger.debug("Returning cached VLM analysis", + original_timestamp=self._last_analysis.analysis_timestamp, + cached_alerts_count=len(cached_analysis.alerts)) + return cached_analysis + else: + logger.warning("VLM service busy and no cached analysis available") + return None + + # Semaphore is available, acquire it and perform analysis + async with self._vlm_semaphore: + logger.info("VLM service is free, performing new analysis") + + # Perform the actual analysis + camera_images = list(traffic_snapshot.camera_images.values()) + analysis_result = await self.analyze_traffic_with_weather( + traffic_snapshot, camera_images + ) + + # Cache successful result + if analysis_result: + self._last_analysis = analysis_result + self._last_analysis_timestamp = datetime.now(timezone.utc) + logger.info("New VLM analysis completed and cached", + alerts_count=len(analysis_result.alerts)) + else: + logger.warning("New VLM analysis returned no result") + + return analysis_result + + except Exception as e: + logger.error("Non-blocking VLM analysis failed", error=str(e)) + return self._last_analysis # Return cached result on error if available + + + def _create_structured_prompt(self, + traffic_snapshot: TrafficSnapshot, + weather_data: Optional[WeatherData]) -> str: + """ + Create structured prompt for VLM analysis with weather context. + + Args: + traffic_snapshot: Current traffic data + weather_data: Current weather conditions + + Returns: + Structured prompt string + """ + intersection_name = self.config.get_intersection_name() + timestamp = traffic_snapshot.timestamp.strftime("%H:%M:%S") + + # Traffic density information + high_density_threshold = self.config_service.get_high_density_threshold() + density_info = [] + for direction, count in traffic_snapshot.directional_counts.items(): + if count >= (high_density_threshold * 2/3): + status = "HIGH" + elif count >= (high_density_threshold * 1/3) and count < (high_density_threshold * 2/3): + status = "MODERATE" + else: + status = "NORMAL" + density_info.append(f"{direction.title()}: {count} vehicles ({status})") + + density_summary = "\n".join(density_info) + + # Weather context + weather_context = "Weather conditions at the intersection: Clear" + if weather_data: + weather_context = f"""Weather conditions at the intersection: +- Temperature: {weather_data.temperature}°{weather_data.temperature_unit} +- Conditions: {weather_data.detailed_forecast}""" + + # Create structured prompt + prompt = f"""Analyze traffic conditions at a traffic intersection with 4 cameras in each of the 4 directions. + +TRAFFIC DATA: +Total number of vehicles on intersection : {traffic_snapshot.total_count} +Directional traffic breakdown - Number of vehicles per direction: +{density_summary} + +{weather_context} + +ANALYSIS REQUIREMENTS: +Please provide a structured analysis in JSON format with the following key details about the traffic situation: + +1. "analysis": Detailed human like overview of current traffic conditions based on the image data for the intersection's current state, referencing specific traffic image observations, traffic patterns and weather impacts. +2. "alerts": This field is an array of alert objects based on the traffic analysis. Each alert object should strictly contain: + - "alert_type": value should be strictly one of the following: [congestion, weather_related, road_condition, accident, maintenance, normal] + - "level": value should be strictly one of the following: [info, warning, critical] + - "description": detailed context-rich alert description. This is based on the detailed traffic analysis. + - "weather_related": strictly a boolean value. If weather is a factor for the traffic situation value should be True, otherwise False + +3. "recommendations": Array of recommendation objects helping to make decisions while travelling through this intersection: + - "recommendation": Clear advice for traffic management or safety. +Strictly respond ONLY with valid JSON format enclosed in markdown code blocks like: +```json +{{ + "analysis": "...", + "alerts": [...], + "recommendations": [...] +}} +```""" + + return prompt + + def _build_vlm_request(self, prompt: str, camera_images: List[CameraImage]) -> Dict[str, Any]: + """Build VLM API request with multiple images.""" + + logger.debug("Building VLM request", + prompt_length=len(prompt), + camera_images_count=len(camera_images)) + + # Prepare content with text prompt and images + content = [ + { + "type": "text", + "text": prompt + } + ] + + # Add up to 4 camera images (one per direction) + for i, camera_image in enumerate(camera_images[:4]): + content.append({ + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{camera_image.image_base64}" + } + }) + + # Use configurable parameters - match working curl structure + request = { + "model": self.model, + "max_completion_tokens": self.max_tokens, + "top_p": self.top_p, + "temperature": self.temperature, + "messages": [ + { + "role": "user", + "content": content + } + ] + } + + logger.debug("VLM request built", + request) + + return request + + async def _call_vlm_service(self, request_data: Dict[str, Any]) -> Optional[str]: + """ + Call VLM service API with error handling. + + Args: + request_data: VLM API request payload + + Returns: + VLM response text or None if failed + """ + try: + url = f"{self.base_url}/v1/chat/completions" + + async with aiohttp.ClientSession() as session: + async with session.post(url, json=request_data) as response: + if response.status == 200: + result = await response.json() + + logger.debug("VLM service raw response", response_keys=list(result.keys()) if isinstance(result, dict) else "non-dict") + + if 'choices' in result and len(result['choices']) > 0: + choice = result['choices'][0] + if 'message' in choice and 'content' in choice['message']: + content = choice['message']['content'] + logger.info("VLM service response received successfully", + content_length=len(content)) + return content.strip() + else: + logger.error("Missing message.content in VLM response", choice_keys=list(choice.keys()) if isinstance(choice, dict) else "non-dict") + return None + else: + logger.error("Invalid VLM response format - missing choices", response_keys=list(result.keys()) if isinstance(result, dict) else "non-dict") + return None + else: + error_text = await response.text() + logger.error("VLM service error", + status=response.status, error=error_text) + return None + except aiohttp.ClientConnectorError as e: + logger.warning("VLM service connection failed", error=str(e)) + return None + except Exception as e: + logger.error("VLM service call failed", error=str(e)) + return None + + def _parse_vlm_response(self, + response_text: str, + traffic_snapshot: TrafficSnapshot, + weather_data: Optional[WeatherData]) -> VLMAnalysisData: + """ + Parse VLM response into structured VLMAnalysisData. + + Args: + response_text: Raw VLM response + traffic_snapshot: Traffic data used for analysis + weather_data: Weather data used for analysis + + Returns: + Structured VLMAnalysisData object + """ + try: + # Extract JSON from markdown code blocks if present + logger.debug(response_text) + json_content = self._extract_json_from_response(response_text) + + + # If extraction failed or returned None, use fallback + if json_content is None or len(json_content.strip()) == 0: + logger.warning("JSON extraction failed or returned empty content, using fallback") + return self._create_fallback_analysis(response_text, traffic_snapshot, weather_data) + + # Parse JSON response + logger.info("Attempting to parse JSON content") + response_data = json.loads(json_content) + + logger.info("JSON parsing successful", + response_type=type(response_data).__name__, + response_keys=list(response_data.keys()) if isinstance(response_data, dict) else "non-dict") + + # Parse alerts + alerts = [] + for alert_data in response_data.get('alerts', []): + try: + alert = VLMAlert( + alert_type=AlertType(alert_data.get('alert_type', 'congestion')), + level=AlertLevel(alert_data.get('level', 'info')), + description=alert_data.get('description', ''), + weather_related=alert_data.get('weather_related', False) + ) + alerts.append(alert) + except (ValueError, KeyError) as e: + logger.warning("Failed to parse alert", error=str(e), alert_data=alert_data) + continue + + # Parse recommendations - handle both array of objects and array of strings + recommendations = [] + raw_recommendations = response_data.get('recommendations', []) + for rec in raw_recommendations: + if isinstance(rec, dict): + # Extract recommendation text from object + rec_text = rec.get('recommendation', rec.get('description', str(rec))) + recommendations.append(rec_text) + else: + # Handle string recommendations directly + recommendations.append(str(rec)) + + # Create structured analysis - map LLM response fields to our data model + analysis_text = response_data.get('analysis', '') + + analysis = VLMAnalysisData( + traffic_summary=analysis_text, # Use the "analysis" field from LLM response + alerts=alerts, + recommendations=recommendations, + analysis_timestamp=datetime.utcnow() + ) + + logger.debug("VLM response parsed successfully", + alerts_count=len(alerts), + has_analysis=bool(analysis_text), + recommendations_count=len(analysis.recommendations)) + + return analysis + + except json.JSONDecodeError as e: + logger.error("Failed to parse VLM JSON response, using fallback", + error=str(e), + response_preview=response_text[:200]) + # Create fallback analysis + fallback = self._create_fallback_analysis(response_text, traffic_snapshot, weather_data) + return fallback + except Exception as e: + logger.error("Failed to parse VLM response, using fallback", + error=str(e), + error_type=type(e).__name__) + fallback = self._create_fallback_analysis(response_text, traffic_snapshot, weather_data) + return fallback + + def _extract_json_from_response(self, response_text: str) -> Optional[str]: + """ + Extract JSON content from VLM response, handling markdown code blocks. + + Args: + response_text: Raw VLM response text + + Returns: + JSON string content or None if extraction fails + """ + # Check if response contains markdown code blocks + if "```json" in response_text: + # Extract content between ```json and ``` + start_marker = "```json" + end_marker = "```" + + start_index = response_text.find(start_marker) + if start_index != -1: + start_index += len(start_marker) + end_index = response_text.find(end_marker, start_index) + if end_index != -1: + json_content = response_text[start_index:end_index].strip() + logger.debug("Extracted JSON from markdown code block", json_length=len(json_content)) + return json_content + + # If no markdown code blocks, try to find JSON-like content + # Look for content between { and } + start_brace = response_text.find('{') + if start_brace != -1: + # Find the matching closing brace + brace_count = 0 + end_brace = start_brace + for i, char in enumerate(response_text[start_brace:]): + if char == '{': + brace_count += 1 + elif char == '}': + brace_count -= 1 + if brace_count == 0: + end_brace = start_brace + i + break + + if end_brace > start_brace: + json_content = response_text[start_brace:end_brace + 1] + logger.info("Extracted JSON from response text", json_length=len(json_content)) + return json_content + + # Return None if no valid JSON structure found + logger.warning("No valid JSON structure found in response") + return None + + def _create_fallback_analysis(self, + response_text: str, + traffic_snapshot: TrafficSnapshot, + weather_data: Optional[WeatherData]) -> VLMAnalysisData: + """ + Create fallback analysis when JSON parsing fails. + + Args: + response_text: Raw VLM response + traffic_snapshot: Traffic data + weather_data: Weather data + + Returns: + Basic VLMAnalysisData with extracted information + """ + try: + # Create basic alert + alerts = [] + weather_impact = False + + # Basic traffic analysis from data + high_density_threshold = self.config_service.get_high_density_threshold() + + # Use high traffic statement only if total count exceeds threshold + if traffic_snapshot.total_count > high_density_threshold: + traffic_summary = f"High traffic detected with total {traffic_snapshot.total_count} vehicles at the Intersection" + alert = VLMAlert( + alert_type=AlertType.CONGESTION, + level=AlertLevel.WARNING if traffic_snapshot.total_count > high_density_threshold * 1.5 else AlertLevel.INFO, + description=f"High traffic detected at the Intersection" if traffic_snapshot.total_count > high_density_threshold * 1.5 else f"Normal Traffic at the Intersection", + weather_related=weather_impact + ) + alerts.append(alert) + else: + traffic_summary = f"Traffic conditions monitored with {traffic_snapshot.total_count} vehicles at the Intersection" + + # Weather alert + try: + if weather_data: + weather_impact = True + + # Handle both WeatherType enum and string (defensive programming) + weather_type = weather_data.weather_type + if isinstance(weather_type, str): + weather_type = WeatherType(weather_type) + + CRITICAL_WEATHER = {WeatherType.FIRES, WeatherType.STORM, WeatherType.FLOOD} + alert_level = AlertLevel.CRITICAL if weather_type in CRITICAL_WEATHER else AlertLevel.WARNING + + alert = VLMAlert( + alert_type=AlertType.WEATHER_RELATED, + level=alert_level, + description=self.weather_service.get_current_weather_description(weather_type), + weather_related=weather_impact + ) + alerts.append(alert) + except Exception as e: + logger.warning("Failed to create weather alert in fallback analysis", error=str(e)) + + fallback_data = VLMAnalysisData( + traffic_summary=traffic_summary, + alerts=alerts, + recommendations=["Monitor traffic flow", "Consider traffic signal optimization"], + analysis_timestamp=datetime.utcnow() + ) + + logger.info("Fallback analysis created successfully", + summary=traffic_summary, + alerts_count=len(alerts), + alert_types=[alert.alert_type.value for alert in alerts]) + + return fallback_data + + except Exception as e: + logger.error("Critical error in fallback analysis, returning minimal analysis", error=str(e)) + # Return absolute minimum fallback in case of complete failure + return VLMAnalysisData( + traffic_summary=f"Traffic analysis unavailable. Total vehicles: {traffic_snapshot.total_count}", + alerts=[VLMAlert( + alert_type=AlertType.NORMAL, + level=AlertLevel.INFO, + description="Traffic monitoring active", + weather_related=False + )], + recommendations=["Monitor traffic conditions"], + analysis_timestamp=datetime.utcnow() + ) + + + def clear_cache(self) -> None: + """Clear all cached analyses.""" + self._analysis_cache.clear() + logger.info("VLM analysis cache cleared") + + def get_service_status(self) -> Dict[str, Any]: + """ + Get current VLM service status including semaphore state and cached analysis info. + + Returns: + Dictionary with service status information + """ + return { + "is_busy": self._vlm_semaphore.locked(), + "has_cached_analysis": self._last_analysis is not None, + "last_analysis_timestamp": self._last_analysis_timestamp.isoformat() if self._last_analysis_timestamp else None, + "cached_analysis_age_minutes": (datetime.utcnow() - self._last_analysis_timestamp).total_seconds() / 60 + if self._last_analysis_timestamp else None, + "analysis_cache_size": len(self._analysis_cache) + } \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/services/weather_service.py b/metro-ai-suite/smart-traffic-intersection-agent/src/services/weather_service.py new file mode 100644 index 000000000..b7bdfb2f2 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/services/weather_service.py @@ -0,0 +1,414 @@ +"""Weather service for traffic intelligence with caching and error handling.""" + +import asyncio +import json +import os +import requests +from datetime import datetime, timedelta, timezone +from typing import Optional, Dict, Any +import structlog + +from models import WeatherData, WeatherType +from .config import ConfigService + + +logger = structlog.get_logger(__name__) + + +class WeatherService: + """ + Weather service for traffic intelligence. + + Fetches weather data from National Weather Service API with caching + and provides weather analysis for traffic correlation. + """ + + def __init__(self, config_service: ConfigService): + """Initialize weather service with configuration.""" + self.config_service = config_service + self.weather_config = config_service.get_weather_config() + + # Weather API configuration + self.api_base_url = self.weather_config.get("api_base_url", "https://api.weather.gov") + self.user_agent = self.weather_config.get("user_agent", "traffic-intelligence (admin@example.com)") + self.cache_duration = timedelta(minutes=self.weather_config.get("cache_duration_minutes", 15)) + self.update_interval = timedelta(minutes=self.weather_config.get("update_interval_minutes", 10)) + + # Caching + self._cached_weather: Optional[WeatherData] = None + self._cache_timestamp: Optional[datetime] = None + + # Periodic update task + self._update_task: Optional[asyncio.Task] = None + self._running = False + + # Mock mode configuration + self.use_mock = self.weather_config.get("use_mock", False) + self.mock_data_file = "config/weather.json" + self.mock_weather_descriptions = { + "clear": "Clear conditions with 9 mph winds from the N." + } + + logger.info("Weather service initialized", + api_base_url=self.api_base_url, + cache_duration_minutes=self.cache_duration.total_seconds() / 60, + update_interval_minutes=self.update_interval.total_seconds() / 60) + + async def start(self): + """Start the weather service with periodic updates.""" + if self._running: + logger.warning("Weather service already running") + return + + self._running = True + + logger.info("Weather service starting - fetching initial weather data") + + # Fetch initial weather data + try: + initial_weather = await self.get_current_weather(force_refresh=True) + if initial_weather: + logger.info("Initial weather data fetched successfully", + temperature=initial_weather.temperature, + conditions=initial_weather.detailed_forecast) + else: + logger.warning("Failed to fetch initial weather data") + except Exception as e: + logger.error("Error fetching initial weather data", error=str(e)) + + # Start periodic update task + self._update_task = asyncio.create_task(self._periodic_update_loop()) + logger.info("Weather service started with periodic updates") + + async def stop(self): + """Stop the weather service and cancel periodic updates.""" + self._running = False + + if self._update_task and not self._update_task.done(): + self._update_task.cancel() + try: + await self._update_task + except asyncio.CancelledError: + pass + + logger.info("Weather service stopped") + + async def _periodic_update_loop(self): + """Periodic update loop that fetches weather data at intervals.""" + logger.info("Starting periodic weather update loop", + interval_minutes=self.update_interval.total_seconds() / 60) + + while self._running: + try: + await asyncio.sleep(self.update_interval.total_seconds()) + + if self._running: # Check again after sleep + logger.debug("Performing periodic weather update") + await self.get_current_weather(force_refresh=True) + + except asyncio.CancelledError: + logger.info("Periodic weather update loop cancelled") + break + except Exception as e: + logger.error("Error in periodic weather update loop", error=str(e)) + # Continue loop on error, but wait a bit before retrying + if self._running: + await asyncio.sleep(60) # Wait 1 minute before retrying + + async def get_current_weather(self, force_refresh: bool = False) -> Optional[WeatherData]: + """ + Get current weather data with caching. + + Args: + force_refresh: Force refresh of cached data + + Returns: + WeatherData object or None if unavailable + """ + logger.info("Getting current weather data", force_refresh=force_refresh, has_cached=self._cached_weather is not None, use_mock=self.use_mock) + + if not force_refresh and self._is_cache_valid(): + logger.debug("Returning cached weather data") + self._cached_weather.is_cached = True + return self._cached_weather + + if self.use_mock: + weather_data = self._load_mock_weather_from_file() + else: + # Get intersection coordinates and fetch live weather data + lat, lon = self.config_service.get_intersection_coordinates() + logger.info("Fetching weather for coordinates", lat=lat, lon=lon) + + weather_data = await self._fetch_weather_data(lat, lon) + if not weather_data: + logger.warning("Failed to fetch weather data, returning cached if available") + return self._cached_weather + + # Update cache + self._cached_weather = weather_data + self._cache_timestamp = datetime.now(timezone.utc) + + if not self.use_mock: + logger.info("Weather data updated", + temperature=weather_data.temperature, + conditions=weather_data.detailed_forecast, + precipitation=weather_data.is_precipitation) + + return weather_data + + async def _fetch_weather_data(self, lat: float, lon: float) -> Optional[WeatherData]: + """ + Fetch weather data from National Weather Service API using synchronous requests. + + Args: + lat: Latitude + lon: Longitude + + Returns: + WeatherData object or None if failed + """ + logger.debug("Starting weather data fetch", lat=lat, lon=lon, api_base_url=self.api_base_url) + + def _sync_fetch(): + """Synchronous fetch operation to run in thread pool.""" + try: + # Step 1: Get the gridpoint endpoint for the given lat/long + points_url = f"{self.api_base_url}/points/{lat},{lon}" + logger.debug("Making request to points API", url=points_url) + + headers = { + "Accept": "application/geo+json", + "Cache-Control": "max-age=0", + "Pragma": "no-cache", + "Feature-Flags": f"forecast_wind_speed_{self._cache_timestamp or ''}", + "Accept-Language": "en-US,en;q=0.9" + } + points_resp = requests.get(points_url, headers=headers) + points_resp.raise_for_status() + points_data = points_resp.json() + + logger.debug("Points API response successful", status_code=points_resp.status_code) + + # Step 2: Get hourly forecast URL from the gridpoint response + forecast_url = points_data["properties"]["forecastHourly"] + logger.debug("Making request to hourly forecast API", url=forecast_url) + + # Step 3: Fetch forecast data + forecast_resp = requests.get(forecast_url, headers=headers) + forecast_resp.raise_for_status() + forecast_data = forecast_resp.json() + + logger.debug("Forecast API response successful", status_code=forecast_resp.status_code) + + # Step 4: Process the first forecast period (most current hour) and format according to specified structure + first_period = forecast_data["properties"]["periods"][0] + logger.info("First forecast period data", period=first_period) + + # Process weather data into WeatherData object + weather_data = self._process_weather_data(first_period) + + logger.debug("Weather data fetched successfully", + conditions=weather_data.detailed_forecast, + temperature=weather_data.temperature, + precipitation=weather_data.is_precipitation) + + return weather_data + + except requests.exceptions.RequestException as e: + logger.error("HTTP request error when fetching weather data", error=str(e)) + return self._load_mock_weather_from_file() + except KeyError as e: + logger.error("Missing key in weather API response", key=str(e)) + return self._load_mock_weather_from_file() + except Exception as e: + logger.error("Failed to fetch weather data", error=str(e)) + return self._load_mock_weather_from_file() + + # Run the synchronous operation in a thread pool to avoid blocking + try: + loop = asyncio.get_event_loop() + result = await loop.run_in_executor(None, _sync_fetch) + return result + except Exception as e: + logger.error("Error in async weather fetch wrapper", error=str(e)) + return self._load_mock_weather_from_file() + except KeyError as e: + logger.error("Missing key in weather API response", key=str(e)) + return self._load_mock_weather_from_file() + except Exception as e: + logger.error("Failed to fetch weather data", error=str(e)) + return self._load_mock_weather_from_file() + + def _load_mock_weather_from_file(self, weather_type: WeatherType = WeatherType.CLEAR) -> WeatherData: + """Load mock weather data from json file. + + Args: + weather_type: Type of weather to load from mock data + Returns: + WeatherData object with mock data + """ + logger.info(f"Using mock weather data", weather_type=weather_type.value, file=self.mock_data_file) + + try: + if os.path.exists(self.mock_data_file): + with open(self.mock_data_file, 'r') as f: + data = json.load(f) + data = data.get(weather_type.value, {}) + + # Parse fetched_at if it's a string + fetched_at = data.get("fetched_at") + if isinstance(fetched_at, str): + try: + fetched_at = datetime.fromisoformat(fetched_at.replace('Z', '+00:00')) + except: + fetched_at = datetime.now(timezone.utc) + else: + fetched_at = datetime.now(timezone.utc) + + weather_data = WeatherData( + name=data.get("name", "Mock Data"), + temperature=data.get("temperature", 72), + temperature_unit=data.get("temperature_unit", "F"), + detailed_forecast=data.get("detailed_forecast", "Mock weather data"), + fetched_at=fetched_at, + is_precipitation=data.get("is_precipitation", False), + is_mock=True, + wind_speed=data.get("wind_speed", "0 mph"), + wind_direction=data.get("wind_direction", "N"), + short_forecast=data.get("short_forecast", "Clear"), + wind_info=data.get("wind_info", "0mph/N"), + precipitation_prob=float(data.get("precipitation_prob", 0)), + dewpoint=data.get("dewpoint"), + relative_humidity=data.get("relative_humidity"), + is_daytime=data.get("is_daytime"), + start_time=data.get("start_time"), + end_time=data.get("end_time"), + weather_type=weather_type + ) + logger.info("Loaded mock weather data from file", + weather_type=weather_type.value, + temperature=weather_data.temperature, + conditions=weather_data.detailed_forecast) + return weather_data + else: + logger.warning("Mock weather file not found, using fallback", file=self.mock_data_file) + return self.get_default_weather() + except Exception as e: + logger.error("Error loading mock weather data from file", error=str(e), file=self.mock_data_file) + return self.get_default_weather() + + def get_default_weather(self) -> WeatherData: + """Get default weather data when none is available.""" + return WeatherData( + name="Unknown", + temperature=72, + temperature_unit="F", + detailed_forecast="Weather data unavailable", + fetched_at=datetime.now(timezone.utc), + is_precipitation=False, + is_mock=True + ) + + def _process_weather_data(self, forecast_period: Dict[str, Any]) -> WeatherData: + """ + Process raw hourly forecast data into WeatherData object. + + Args: + forecast_period: Raw forecast period from NWS hourly API + + Returns: + WeatherData object with processed weather information + """ + # Get temperature and keep in Fahrenheit (as expected by WeatherData) + temp_f = forecast_period.get("temperature", 72) + temp_unit = forecast_period.get("temperatureUnit", "F") + + # Get precipitation probability for road conditions + precipitation_prob = forecast_period.get("probabilityOfPrecipitation", {}) + if isinstance(precipitation_prob, dict): + prob_value = precipitation_prob.get("value", 0) or 0 + else: + prob_value = 0 + + # Determine precipitation status and road conditions + is_precipitation = prob_value > 30 # Consider > 30% as likely precipitation + + # Get period name (hourly forecast usually doesn't have a name, so use a default) + period_name = forecast_period.get("name", "Current Hour") + + # Get detailed forecast (for hourly, this might be empty) + detailed_forecast = forecast_period.get("detailedForecast", + forecast_period.get("shortForecast", "Clear conditions")) + + # If detailed forecast is empty, create one from available data + if not detailed_forecast.strip(): + short_forecast = forecast_period.get("shortForecast", "Clear") + wind_speed = forecast_period.get("windSpeed", "0 mph") + wind_direction = forecast_period.get("windDirection", "N") + detailed_forecast = f"{short_forecast} conditions with {wind_speed} winds from the {wind_direction}." + if prob_value > 0: + detailed_forecast += f" {prob_value}% chance of precipitation." + + # Get short forecast + short_forecast = forecast_period.get("shortForecast", "Clear") + + # Get wind speed and direction + wind_speed = forecast_period.get("windSpeed", "0 mph") + wind_direction = forecast_period.get("windDirection", "N") + + # Create wind_info field by combining speed and direction (e.g., "3mph/W") + # Extract numeric speed from wind_speed string + import re + speed_match = re.search(r'(\d+)', wind_speed) + speed_number = speed_match.group(1) if speed_match else "0" + wind_info = f"{speed_number}mph/{wind_direction}" + + # Get dewpoint from hourly data + dewpoint_data = forecast_period.get("dewpoint", {}) + dewpoint = None + if isinstance(dewpoint_data, dict): + dewpoint = dewpoint_data.get("value") + + # Get relative humidity from hourly data + humidity_data = forecast_period.get("relativeHumidity", {}) + relative_humidity = None + if isinstance(humidity_data, dict): + relative_humidity = humidity_data.get("value") + + # Get daytime status + is_daytime = forecast_period.get("isDaytime", None) + + # Get time periods + start_time = forecast_period.get("startTime") + end_time = forecast_period.get("endTime") + + return WeatherData( + name=period_name, + temperature=temp_f, + temperature_unit=temp_unit, + detailed_forecast=detailed_forecast, + fetched_at=datetime.utcnow(), + is_precipitation=is_precipitation, + is_mock=False, + wind_speed=wind_speed, + wind_direction=wind_direction, + short_forecast=short_forecast, + wind_info=wind_info, + precipitation_prob=float(prob_value), + dewpoint=dewpoint, + relative_humidity=relative_humidity, + is_daytime=is_daytime, + start_time=start_time, + end_time=end_time + ) + + def _is_cache_valid(self) -> bool: + """Check if cached weather data is still valid.""" + if not self._cached_weather or not self._cache_timestamp: + return False + + age = datetime.now(timezone.utc) - self._cache_timestamp + return age < self.cache_duration + + def get_current_weather_description(self) -> str: + return self._cached_weather.detailed_forecast if self._cached_weather else "Unknown weather conditions" \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/ui/README.md b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/README.md new file mode 100644 index 000000000..aabfbb6f2 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/README.md @@ -0,0 +1,180 @@ +# RSU Monitoring System + +A real-time web dashboard for monitoring Road Side Unit (RSU) traffic and environmental data using Gradio. + +## Features + +- **Real-time Dashboard**: Live monitoring of traffic density, environmental conditions, and system alerts +- **Auto-refresh**: Configurable automatic data refresh (default: 5 seconds) +- **Modern UI**: Dark theme with responsive design inspired by professional monitoring systems +- **Modular Architecture**: Clean separation of concerns with dedicated modules for data, UI, and configuration +- **Environmental Configuration**: All settings configurable via environment variables + +## Quick Start + +### Using UV (Recommended) + +```bash +# Clone and navigate to the project +cd /path/to/agent_ui + +# Run the startup script (creates venv, installs dependencies, and starts the app) +chmod +x start.sh +./start.sh +``` + +### Manual Setup + +```bash +# Create virtual environment with uv +uv venv +source .venv/bin/activate + +# Install dependencies +uv pip install -r requirements.txt + +# Run the application +python app.py +``` + +## Configuration + +Configure the system using environment variables or by editing `config.py`: + +```bash +# Data refresh interval (seconds) +export REFRESH_INTERVAL=5 + +# API endpoint configuration +export API_URL="http://localhost:8081/api/v1/traffic/current" + +# UI settings +export UI_THEME="dark" # or "light" +export APP_TITLE="RSU MONITORING SYSTEM" + +# Alert thresholds +export HIGH_DENSITY_THRESHOLD=5 +export MODERATE_DENSITY_THRESHOLD=3 +export HIGH_WIND_THRESHOLD=25.0 +export HEAVY_RAIN_THRESHOLD=5.0 +``` + +## Application Structure + +``` +agent_ui/ +├── app.py # Main Gradio application +├── config.py # Configuration management +├── models.py # Data models and classes +├── data_loader.py # Data loading and parsing +├── ui_components.py # UI component generators +├── auto_refresh.py # Auto-refresh functionality +├── requirements.txt # Python dependencies +├── start.sh # Startup script +└── README.md # This file +``` + +## Data Source + +The system fetches data from the Traffic Intelligence API endpoint. The API returns data in this structure: + +```json +{ + "timestamp": "2025-09-15T20:30:45Z", + "intersection_id": "INT_001", + "data": { + "intersection_name": "Highway South & Pedestrian Crossing", + "latitude": 40.7128, + "longitude": -74.0060, + "northbound_density": 3, + "southbound_density": 2, + "eastbound_density": 1, + "westbound_density": 0, + "total_density": 6 + }, + "camera_images": { + "CAM_1": { + "camera_id": "INTERSECTION_NORTH", + "direction": "North", + "timestamp": "2025-09-15T20:30:45Z" + } + }, + "vlm_analysis": { + "analysis": "Traffic flow optimized. Pedestrian activity normal.", + "alerts": ["URGENT: UNIDENTIFIED OBJECT DETECTED NEAR CAM 4"] + }, + "weather_data": { + "temperature_fahrenheit": 28, + "humidity_percent": 55, + "precipitation_prob": 0.0, + "wind_speed_mph": 12.0, + "wind_direction_degrees": 180, + "conditions": "Clear Sky" + } +} +``` + +## Auto-Refresh + +The dashboard automatically refreshes every X seconds (configurable). Features include: + +- **Visual Indicator**: Green dot showing auto-refresh is active +- **Manual Refresh**: Backup refresh button +- **Error Handling**: Graceful handling of data loading errors +- **Background Processing**: Non-blocking refresh mechanism + +## UI Components + +### Traffic Summary +- Real-time traffic density by direction (North, South, East, West) +- Total vehicle count +- Active camera information + +### Environmental Panel +- Temperature, humidity, wind speed and direction +- Precipitation levels +- Air quality assessment based on conditions + +### Alerts & Analysis +- VLM (Vision Language Model) analysis results +- System alerts with severity levels (Urgent, Advisory, Info) +- Analysis confidence and age indicators + +### System Information +- Last update timestamp +- Intersection location and ID +- System status indicators + +## Dependencies + +- **gradio**: Web UI framework +- **pydantic**: Data validation and models +- **python-dateutil**: Date/time handling + +## Development + +To extend the system: + +1. **Add new data fields**: Update `models.py` with new data classes +2. **Modify UI**: Update `ui_components.py` component generators +3. **Change data source**: Modify `data_loader.py` for different data formats +4. **Adjust configuration**: Add new settings to `config.py` + +## Troubleshooting + +### Common Issues + +1. **Port already in use**: Change `APP_PORT` in configuration +2. **API connection failed**: Ensure the Traffic Intelligence API is running at the configured endpoint +3. **Import errors**: Make sure virtual environment is activated and dependencies are installed + +### Logs + +The application provides detailed logging. Check console output for: +- Data loading errors +- Configuration issues +- Network problems + +## License + +This project is part of the RSU monitoring system implementation. \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/ui/app.py b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/app.py new file mode 100644 index 000000000..b09d87849 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/app.py @@ -0,0 +1,291 @@ +#!/usr/bin/env python3 +""" +RSU Monitoring System - Main Application +A Gradio-based web interface for monitoring RSU data +""" + +import gradio as gr +import logging +import sys +import time + +# Import our modules +from config import Config +from data_loader import load_monitoring_data +from ui_components import UIComponents +from auto_refresh import create_status_indicator_html + +# Configure logging +logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def update_dashboard(debug_mode=False): + """Update all dashboard components with fresh data""" + try: + # Load fresh data using API only + data = load_monitoring_data(api_url=Config.API_URL) + + if not data: + error_msg = "
❌ No data available
" + return error_msg, [], error_msg, error_msg, error_msg, error_msg, gr.HTML(visible=False) + + # Generate UI components + header = UIComponents.create_header(data) + #camera_images_html = UIComponents.create_camera_grid_html(data) + camera_gallery = UIComponents.create_camera_images(data) + traffic = UIComponents.create_traffic_summary(data) + environmental = UIComponents.create_environmental_panel(data) + alerts = UIComponents.create_alerts_panel(data) + system_info = UIComponents.create_system_info(data) + debug_panel = UIComponents.create_debug_panel(data) + + return header, camera_gallery, traffic, environmental, alerts, system_info, gr.HTML(value=debug_panel, visible=debug_mode) + + except Exception as e: + logger.error(f"Error updating dashboard: {e}") + error_msg = f"
❌ Error: {str(e)}
" + return error_msg, [], error_msg, error_msg, error_msg, error_msg, gr.HTML(visible=False) + +def create_dashboard_interface(): + """Create the main dashboard interface""" + + # Custom CSS for better styling - theme-aware + is_light_theme = Config.UI_THEME == "light" + + # Define theme colors + bg_primary = "#ffffff" if is_light_theme else "#1f2937" + bg_secondary = "#f8fafc" if is_light_theme else "#374151" + border_color = "#e2e8f0" if is_light_theme else "#4b5563" + text_primary = "#1f2937" if is_light_theme else "#f3f4f6" + + css = f""" + .gradio-container {{ + max-width: 1400px !important; + margin: auto !important; + padding: 10px !important; + background: {bg_primary} !important; + font-family: Arial, sans-serif !important; + }} + + .block {{ + border-radius: 12px !important; + border: 1px solid {border_color} !important; + box-shadow: 0 2px 4px rgba(0,0,0,0.1) !important; + background: {bg_secondary} !important; + }} + + .alert-urgent {{ + background: linear-gradient(135deg, #ff4444, #cc0000) !important; + color: white !important; + border-radius: 8px !important; + padding: 12px !important; + margin: 4px !important; + border-left: 4px solid #ff0000 !important; + }} + + .alert-advisory {{ + background: linear-gradient(135deg, #ff8800, #cc6600) !important; + color: white !important; + border-radius: 8px !important; + padding: 12px !important; + margin: 4px !important; + border-left: 4px solid #ff6600 !important; + }} + + .status-good {{ + color: #10b981 !important; + font-weight: bold !important; + }} + + .status-warning {{ + color: #f59e0b !important; + font-weight: bold !important; + }} + + .status-critical {{ + color: #ef4444 !important; + font-weight: bold !important; + }} + + .metric-card {{ + background: {bg_secondary} !important; + border-radius: 12px !important; + padding: 16px !important; + margin: 8px !important; + border: 1px solid {border_color} !important; + box-shadow: 0 2px 4px rgba(0,0,0,0.2) !important; + }} + + .metric-value {{ + font-size: 2em !important; + font-weight: bold !important; + margin: 8px 0 !important; + color: {text_primary} !important; + }} + + .debug {{ + padding: 5px; + background: #4b5563; + border-radius: 4px; + margin-top: 5px; + text-align: center; + }} + + /* Gallery styling */ + .gallery {{ + border-radius: 12px !important; + overflow: hidden !important; + }} + + /* Button styling */ + .primary {{ + background: linear-gradient(135deg, #3b82f6, #1e40af) !important; + border: none !important; + border-radius: 8px !important; + font-weight: 600 !important; + padding: 10px 20px !important; + }} + """ + + with gr.Blocks( + css=css, + title=Config.APP_TITLE, + theme=gr.themes.Base() if Config.UI_THEME == "light" else gr.themes.Monochrome() + ) as interface: + + # Header component + header_component = gr.HTML() + + # Main content grid + with gr.Row(): + with gr.Column(scale=2): + camera_gallery = gr.Gallery( + label="📹 Camera Feeds", + show_label=True, + columns=2, + rows=2, + height="450px", + container=True, + object_fit="cover" + ) + + with gr.Column(scale=1): + traffic_component = gr.HTML() + + with gr.Column(scale=1): + environmental_component = gr.HTML() + debug_panel_component = gr.HTML(visible=False) + + + # Alerts section + alerts_component = gr.HTML() + + # System information footer + system_info_component = gr.HTML() + + # Auto-refresh status indicator + gr.HTML(create_status_indicator_html()) + + # Refresh function for the interface + def refresh_data(debug_mode_checked): + """Refresh dashboard data""" + return update_dashboard(debug_mode=debug_mode_checked) + + # Manual refresh button and debug toggle + with gr.Row(elem_id="footer-actions"): + with gr.Column(scale=3): + pass + with gr.Column(scale=1): + with gr.Row(): + refresh_btn = gr.Button("🔄 Refresh Data", variant="primary", elem_id="refresh-data-btn") + with gr.Row(): + debug_mode = gr.Checkbox(label="🐞 Show Debug Info", value=False, container=False, visible=False) + + # Initial load of data + interface.load( + fn=refresh_data, + inputs=[debug_mode], + outputs=[ + header_component, + camera_gallery, + traffic_component, + environmental_component, + alerts_component, + system_info_component, + debug_panel_component + ] + ) + # Set up manual refresh handler + refresh_btn.click( + fn=refresh_data, + inputs=[debug_mode], + outputs=[ + header_component, + camera_gallery, + traffic_component, + environmental_component, + alerts_component, + system_info_component, + debug_panel_component + ] + ) + + # Show/hide debug panel + debug_mode.change( + fn=lambda x: gr.update(visible=x), + inputs=debug_mode, + outputs=debug_panel_component + ) + + # Auto refresh using Gradio Timer (server side) + try: + auto_timer = gr.Timer(value=Config.REFRESH_INTERVAL_SECONDS) + auto_timer.tick( + fn=refresh_data, + inputs=[debug_mode], + outputs=[ + header_component, + camera_gallery, + traffic_component, + environmental_component, + alerts_component, + system_info_component, + debug_panel_component + ] + ) + logger.info("Gradio Timer auto-refresh enabled (value=%ss)" % Config.REFRESH_INTERVAL_SECONDS) + except Exception as e: + logger.warning(f"Unable to initialize Gradio Timer auto-refresh: {e}") + + return interface + +def main(): + """Main application entry point""" + logger.info("Starting RSU Monitoring Dashboard...") + logger.info(f"API URL: {Config.API_URL}") + logger.info(f"Refresh interval: {Config.REFRESH_INTERVAL_SECONDS} seconds") + logger.info(f"Server: {Config.APP_HOST}:{Config.APP_PORT}") + logger.info("Configured to use API endpoint for data") + + try: + # Create and launch the interface + interface = create_dashboard_interface() + + interface.launch( + server_name=Config.APP_HOST, + server_port=Config.APP_PORT, + share=False, + show_error=True, + show_api=False, + quiet=False + ) + + except KeyboardInterrupt: + logger.info("Application stopped by user") + sys.exit(0) + except Exception as e: + logger.error(f"Failed to start application: {e}") + sys.exit(1) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/ui/auto_refresh.py b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/auto_refresh.py new file mode 100644 index 000000000..ebf9e1df2 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/auto_refresh.py @@ -0,0 +1,102 @@ +""" +Auto-refresh enhancement for the Gradio UI +This creates a periodic refresh mechanism using gradio's built-in capabilities +""" +import gradio as gr +import time +import threading +import logging +from datetime import datetime + +logger = logging.getLogger(__name__) + +class AutoRefreshManager: + """Manages auto-refresh functionality for the dashboard""" + + def __init__(self, refresh_interval: int = 5): + self.refresh_interval = refresh_interval + self.is_running = False + self.refresh_thread = None + self.update_callbacks = [] + + def add_update_callback(self, callback): + """Add a callback function to be called on refresh""" + self.update_callbacks.append(callback) + + def start_auto_refresh(self): + """Start the auto-refresh mechanism""" + if self.is_running: + return + + self.is_running = True + self.refresh_thread = threading.Thread(target=self._refresh_loop, daemon=True) + self.refresh_thread.start() + logger.info(f"Auto-refresh started with {self.refresh_interval}s interval") + + def stop_auto_refresh(self): + """Stop the auto-refresh mechanism""" + self.is_running = False + if self.refresh_thread: + self.refresh_thread.join(timeout=1.0) + logger.info("Auto-refresh stopped") + + def _refresh_loop(self): + """Internal refresh loop that runs in a separate thread""" + while self.is_running: + try: + time.sleep(self.refresh_interval) + if self.is_running: # Check again after sleep + for callback in self.update_callbacks: + try: + callback() + except Exception as e: + logger.error(f"Error in refresh callback: {e}") + except Exception as e: + logger.error(f"Error in refresh loop: {e}") + + +def create_status_indicator_html() -> str: + """Create a status indicator that shows the auto-refresh status""" + return """ + +
+
+
+ Auto-refresh active +
+
+ + """ \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/ui/config.py b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/config.py new file mode 100644 index 000000000..3af0ef01a --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/config.py @@ -0,0 +1,56 @@ +""" +Configuration module for the RSU Monitoring System +""" +import os +from typing import Dict, Any + + +class Config: + """Configuration settings for the monitoring dashboard""" + + # Refresh settings + REFRESH_INTERVAL_SECONDS: float = float(os.getenv("REFRESH_INTERVAL", "10.0")) + + # API settings + API_URL: str = os.getenv("API_URL", "http://localhost:8081/api/v1/traffic/current") + + # UI settings + APP_TITLE: str = os.getenv("APP_TITLE", "TRAFFIC MONITORING SYSTEM") + APP_PORT: int = int(os.getenv("TRAFFIC_INTELLIGENCE_UI_PORT", "7860")) + APP_HOST: str = "0.0.0.0" + + # Theme settings + UI_THEME: str = os.getenv("UI_THEME", "light") + + # Alert thresholds + HIGH_DENSITY_THRESHOLD: int = int(float(os.getenv("HIGH_DENSITY_THRESHOLD", "5"))) + MODERATE_DENSITY_THRESHOLD: int = int(float(os.getenv("MODERATE_DENSITY_THRESHOLD", "3"))) + + # Weather alert thresholds + HIGH_WIND_THRESHOLD: float = float(os.getenv("HIGH_WIND_THRESHOLD", "25.0")) + HEAVY_RAIN_THRESHOLD: float = float(os.getenv("HEAVY_RAIN_THRESHOLD", "5.0")) + + @classmethod + def get_all_settings(cls) -> Dict[str, Any]: + """Get all configuration settings as a dictionary""" + return { + "refresh_interval": cls.REFRESH_INTERVAL_SECONDS, + "api_url": cls.API_URL, + "app_title": cls.APP_TITLE, + "app_port": cls.APP_PORT, + "app_host": cls.APP_HOST, + "ui_theme": cls.UI_THEME, + "high_density_threshold": cls.HIGH_DENSITY_THRESHOLD, + "moderate_density_threshold": cls.MODERATE_DENSITY_THRESHOLD, + "high_wind_threshold": cls.HIGH_WIND_THRESHOLD, + "heavy_rain_threshold": cls.HEAVY_RAIN_THRESHOLD + } + + @classmethod + def print_settings(cls): + """Print current configuration settings""" + print("=== RSU Monitoring System Configuration ===") + settings = cls.get_all_settings() + for key, value in settings.items(): + print(f"{key.upper()}: {value}") + print("=" * 45) \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/ui/data_loader.py b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/data_loader.py new file mode 100644 index 000000000..beea28a33 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/data_loader.py @@ -0,0 +1,294 @@ +""" +Data loader module for the RSU Monitoring System +""" +import logging +import requests +from typing import Optional +from datetime import datetime, timezone + +from models import ( + MonitoringData, IntersectionData, RegionCount, + VLMAnalysis, WeatherData, CameraData, TrafficContext +) + +logger = logging.getLogger(__name__) + + +def load_monitoring_data_from_api(api_url: str = "http://localhost:8081/api/v1/traffic/current") -> Optional[MonitoringData]: + """ + Load monitoring data from the traffic intelligence API + + Args: + api_url: URL of the traffic intelligence API endpoint + + Returns: + MonitoringData object or None if loading fails + """ + try: + logger.info(f"Fetching data from API: {api_url}") + response = requests.get(api_url, timeout=10) + response.raise_for_status() + + raw_data = response.json() + + # Parse the API response and convert to UI data structure + return parse_api_response(raw_data) + + except requests.exceptions.RequestException as e: + logger.error(f"Error fetching data from API: {str(e)}") + return None + except Exception as e: + logger.error(f"Error processing API response: {str(e)}") + return None + + +def parse_api_response(raw_data: dict) -> Optional[MonitoringData]: + """ + Parse the traffic intelligence API response and convert to MonitoringData + + Args: + raw_data: Raw API response dictionary + + Returns: + MonitoringData object or None if parsing fails + """ + try: + # Extract traffic data + traffic_data = raw_data.get("data", {}) + + # Create region counts mapping pedestrian data from API + # Map directional pedestrian counts to regions + region_counts = { + "region_1": RegionCount( # North + vehicle=0, # API doesn't provide region-specific vehicle counts + pedestrian=traffic_data.get("north_pedestrian", 0) + ), + "region_2": RegionCount( # South + vehicle=0, + pedestrian=traffic_data.get("south_pedestrian", 0) + ), + "region_3": RegionCount( # East + vehicle=0, + pedestrian=traffic_data.get("east_pedestrian", 0) + ), + "region_4": RegionCount( # West + vehicle=0, + pedestrian=traffic_data.get("west_pedestrian", 0) + ) + } + + # Parse intersection data + # Create IntersectionData from the main data + # Map API field names to model field names + intersection_data = IntersectionData( + intersection_id=traffic_data["intersection_id"], + intersection_name=traffic_data["intersection_name"], + latitude=traffic_data["latitude"], + longitude=traffic_data["longitude"], + timestamp=traffic_data["timestamp"], + northbound_density=traffic_data.get("north_camera", 0), # Map north_camera to northbound_density + southbound_density=traffic_data.get("south_camera", 0), # Map south_camera to southbound_density + eastbound_density=traffic_data.get("east_camera", 0), # Map east_camera to eastbound_density + westbound_density=traffic_data.get("west_camera", 0), # Map west_camera to westbound_density + total_density=traffic_data.get("total_density", 0), + region_counts=region_counts, # Use the region_counts created above + total_pedestrian_count=traffic_data.get("total_pedestrian_count", 0), # Get total pedestrian count from API + north_timestamp=traffic_data.get("north_timestamp"), + south_timestamp=traffic_data.get("south_timestamp"), + east_timestamp=traffic_data.get("east_timestamp"), + west_timestamp=traffic_data.get("west_timestamp"), + ) + + # Parse camera data - handle the new API structure + camera_images = {} + camera_data = raw_data.get("camera_images", {}) + + # Handle the new API format where cameras are named like "west_camera", "north_camera", etc. + for camera_key, camera_info in sorted(camera_data.items()): + if isinstance(camera_info, dict): + camera_images[camera_key] = camera_info # Store as dict for UI compatibility + else: + # Fallback: create CameraData object if needed for old format + camera_images[camera_key] = CameraData( + camera_id=camera_info.get("camera_id", camera_key), + direction=camera_info.get("direction", "unknown"), + timestamp=camera_info.get("timestamp", ""), + image_base64=camera_info.get("image_base64") + ) + # Parse VLM analysis + vlm_data = raw_data.get("vlm_analysis", {}) + + # Create traffic context (simplified for API data) + traffic_context = TrafficContext( + analysis_period={"start": "", "end": ""}, + avg_densities={ + "northbound": traffic_data.get("north_camera", 0), + "southbound": traffic_data.get("south_camera", 0), + "eastbound": traffic_data.get("east_camera", 0), + "westbound": traffic_data.get("west_camera", 0) + }, + peak_densities={ + "northbound": traffic_data.get("north_camera", 0), + "southbound": traffic_data.get("south_camera", 0), + "eastbound": traffic_data.get("east_camera", 0), + "westbound": traffic_data.get("west_camera", 0) + } + ) + + # Process alerts from the new API format + alerts = [] + api_alerts = vlm_data.get("alerts", []) + for alert in api_alerts: + if isinstance(alert, dict): + # Store the full alert structure for UI processing + alerts.append(alert) + else: + # Fallback for string format + alerts.append(str(alert)) + + # Process recommendations from the new API format + recommendations = vlm_data.get("recommendations", []) + + # Calculate high density directions (threshold of 3+ vehicles) + high_density_directions = [] + current_high_directions = [] + if traffic_data.get("north_camera", 0) >= 3: + high_density_directions.append("northbound") + current_high_directions.append("northbound") + if traffic_data.get("south_camera", 0) >= 3: + high_density_directions.append("southbound") + current_high_directions.append("southbound") + if traffic_data.get("east_camera", 0) >= 3: + high_density_directions.append("eastbound") + current_high_directions.append("eastbound") + if traffic_data.get("west_camera", 0) >= 3: + high_density_directions.append("westbound") + current_high_directions.append("westbound") + + current_time = datetime.now(timezone.utc).timestamp() + vlm_analysis = VLMAnalysis( + analysis=vlm_data.get("traffic_summary", "No analysis available"), + high_density_directions=high_density_directions, + analysis_timestamp=vlm_data.get("analysis_timestamp", ""), + current_high_directions=current_high_directions, + analysis_age_minutes=0.0, + traffic_context=traffic_context, + alerts=alerts, + recommendations=recommendations + ) + + # Parse weather data + weather_data_raw = raw_data.get("weather_data", {}) + + # Extract wind information from the new API format + wind_speed_str = weather_data_raw.get("wind_speed", "0 mph") + wind_direction_str = weather_data_raw.get("wind_direction", "N") + + # Parse wind speed to mph + import re + speed_match = re.search(r'(\d+)', wind_speed_str) + wind_speed_mph = float(speed_match.group(1)) if speed_match else 0.0 + + # Convert wind direction to degrees + direction_map = { + "N": 0, "NNE": 22, "NE": 45, "ENE": 67, + "E": 90, "ESE": 112, "SE": 135, "SSE": 157, + "S": 180, "SSW": 202, "SW": 225, "WSW": 247, + "W": 270, "WNW": 292, "NW": 315, "NNW": 337 + } + wind_direction_degrees = direction_map.get(wind_direction_str.upper(), 0) + + # Get precipitation probability directly from API instead of estimating + precipitation_prob = weather_data_raw.get("precipitation_prob", 0.0) + + # Convert temperature to Fahrenheit if needed (API provides in F) + temperature_f = weather_data_raw.get("temperature", 70) + + # Use relative humidity from API if available, otherwise estimate + humidity = weather_data_raw.get("relative_humidity", 50) + if humidity is None: + # Fallback estimation if API doesn't provide humidity + if weather_data_raw.get("is_precipitation", False): + humidity = 75 + elif "clear" in weather_data_raw.get("short_forecast", "").lower(): + humidity = 40 + else: + humidity = 50 + + # Use short_forecast for conditions if available, otherwise detailed_forecast + conditions = weather_data_raw.get("short_forecast", + weather_data_raw.get("detailed_forecast", "Unknown")) + + weather_data = WeatherData( + timestamp=weather_data_raw.get("fetched_at", ""), + temperature_fahrenheit=temperature_f, + humidity_percent=int(humidity), + precipitation_prob=precipitation_prob, + wind_speed_mph=wind_speed_mph, + wind_direction_degrees=wind_direction_degrees, + conditions=conditions, + # New hourly forecast fields + dewpoint=weather_data_raw.get("dewpoint"), + relative_humidity=weather_data_raw.get("relative_humidity"), + is_daytime=weather_data_raw.get("is_daytime"), + start_time=weather_data_raw.get("start_time"), + end_time=weather_data_raw.get("end_time"), + detailed_forecast=weather_data_raw.get("detailed_forecast"), + temperature_unit=weather_data_raw.get("temperature_unit", "F") + ) + + # Create complete monitoring data object + monitoring_data = MonitoringData( + timestamp=traffic_data.get("timestamp", ""), + intersection_id=traffic_data.get("intersection_id", "intersection_1"), + data=intersection_data, + camera_images=camera_images, + vlm_analysis=vlm_analysis, + weather_data=weather_data + ) + + return monitoring_data + + except Exception as e: + logger.error(f"Error parsing API response: {str(e)}") + return None + + +def convert_fahrenheit_to_celsius(fahrenheit: float) -> float: + """Convert Fahrenheit to Celsius""" + return (fahrenheit - 32) * 5.0 / 9.0 +def load_monitoring_data(api_url: str = None) -> Optional[MonitoringData]: + """ + Load monitoring data from API endpoint only + + Args: + api_url: API endpoint URL (if None, uses default) + + Returns: + MonitoringData object or None if loading fails + """ + # Load from API endpoint only + api_data = load_monitoring_data_from_api(api_url) if api_url else load_monitoring_data_from_api() + if api_data: + return api_data + + logger.error("Failed to load data from API endpoint. Please check API connectivity and try again.") + return None + + +def get_last_update_time(monitoring_data: MonitoringData) -> str: + """ + Get formatted last update time + + Args: + monitoring_data: MonitoringData object + + Returns: + Formatted timestamp string + """ + try: + from datetime import datetime + timestamp = datetime.fromisoformat(monitoring_data.timestamp.replace('Z', '+00:00')) + return timestamp.strftime("%Y-%m-%d %H:%M:%S UTC") + except: + return monitoring_data.timestamp \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/ui/models.py b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/models.py new file mode 100644 index 000000000..7f360a505 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/models.py @@ -0,0 +1,134 @@ +""" +Data classes for the RSU Monitoring System +""" +from dataclasses import dataclass +from typing import Dict, List, Optional +from datetime import datetime + + +@dataclass +class RegionCount: + """Count data for a specific region""" + vehicle: int + pedestrian: int + + +@dataclass +class TrafficContext: + """Traffic analysis context information""" + analysis_period: Dict[str, str] + avg_densities: Dict[str, int] + peak_densities: Dict[str, int] + + +@dataclass +class VLMAnalysis: + """Vehicle Learning Model analysis data""" + analysis: str + high_density_directions: List[str] + analysis_timestamp: str + current_high_directions: List[str] + analysis_age_minutes: float + traffic_context: TrafficContext + alerts: List[str] # Can contain strings or structured alert objects + recommendations: Optional[List[str]] = None + + +@dataclass +class WeatherData: + """Weather information""" + timestamp: str + temperature_fahrenheit: float + humidity_percent: int + precipitation_prob: float + wind_speed_mph: float + wind_direction_degrees: int + conditions: str + # New hourly forecast fields + dewpoint: Optional[float] = None # Dewpoint in Celsius + relative_humidity: Optional[float] = None # Relative humidity percentage from API + is_daytime: Optional[bool] = None # Whether it's daytime + start_time: Optional[str] = None # Start time of the forecast period + end_time: Optional[str] = None # End time of the forecast period + detailed_forecast: Optional[str] = None # Detailed forecast description + temperature_unit: str = "F" # Temperature unit + + +@dataclass +class CameraData: + """Camera feed information""" + camera_id: str + direction: str + timestamp: str + image_base64: Optional[str] = None + + +@dataclass +class IntersectionData: + """Main intersection traffic data""" + intersection_id: str + intersection_name: str + latitude: float + longitude: float + timestamp: str + northbound_density: int + southbound_density: int + eastbound_density: int + westbound_density: int + total_density: int + region_counts: Dict[str, RegionCount] + total_pedestrian_count: Optional[int] = None # Direct from API + north_timestamp: Optional[str] = None + south_timestamp: Optional[str] = None + east_timestamp: Optional[str] = None + west_timestamp: Optional[str] = None + + +@dataclass +class MonitoringData: + """Complete monitoring system data""" + timestamp: str + intersection_id: str + data: IntersectionData + camera_images: Dict[str, CameraData] + vlm_analysis: VLMAnalysis + weather_data: WeatherData + + def get_total_vehicles(self) -> int: + """Get total vehicle count across all regions""" + return sum(region.vehicle for region in self.data.region_counts.values()) + + def get_total_pedestrians(self) -> int: + """Get total pedestrian count - prefer API count if available, otherwise sum regions""" + if self.data.total_pedestrian_count is not None: + return self.data.total_pedestrian_count + return sum(region.pedestrian for region in self.data.region_counts.values()) + + def get_traffic_status(self) -> str: + """Get overall traffic status based on density""" + max_density = max( + self.data.northbound_density, + self.data.southbound_density, + self.data.eastbound_density, + self.data.westbound_density + ) + + if max_density >= 5: + return "HEAVY" + elif max_density >= 3: + return "MODERATE" + else: + return "LIGHT" + + def get_busy_directions(self) -> List[str]: + """Get directions with high traffic density (>= 3)""" + directions = [] + if self.data.northbound_density >= 3: + directions.append("Northbound") + if self.data.southbound_density >= 3: + directions.append("Southbound") + if self.data.eastbound_density >= 3: + directions.append("Eastbound") + if self.data.westbound_density >= 3: + directions.append("Westbound") + return directions \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/ui/requirements.txt b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/requirements.txt new file mode 100644 index 000000000..74aa65b40 --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/requirements.txt @@ -0,0 +1,4 @@ +gradio>=4.0.0 +pillow>=9.0.0 +markdown>=3.4.0 +requests>=2.25.0 \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/ui/ui_components.py b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/ui_components.py new file mode 100644 index 000000000..31d07bd0b --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/ui_components.py @@ -0,0 +1,597 @@ +""" +UI Components for the RSU Monitoring System +""" +import base64 +import io +from PIL import Image +from typing import Optional, List, Tuple + +from models import MonitoringData +from config import Config + + +class ThemeColors: + """Theme-aware color configuration""" + + @staticmethod + def get_colors(): + """Get theme colors based on UI_THEME setting""" + is_light = Config.UI_THEME == "light" + + return { + 'bg_primary': '#ffffff' if is_light else '#1f2937', + 'bg_secondary': '#f8fafc' if is_light else '#374151', + 'bg_card': '#ffffff' if is_light else '#374151', + 'text_primary': '#1f2937' if is_light else '#f3f4f6', + 'text_secondary': '#64748b' if is_light else '#d1d5db', + 'border': '#e2e8f0' if is_light else '#4b5563', + 'header_bg': 'linear-gradient(135deg, #1e3a8a, #3b82f6)' if is_light else 'linear-gradient(135deg, #1e3a8a, #3b82f6)', + 'shadow': '0 2px 4px rgba(0,0,0,0.1)' if is_light else '0 2px 4px rgba(0,0,0,0.3)' + } + + +class UIComponents: + """UI component generator class""" + + @staticmethod + def _render_markdown(md_text: str) -> str: + """Render markdown text to HTML. Falls back to simple replacements if markdown package not installed.""" + if md_text is None: + return "" + + try: + import markdown + return markdown.markdown(md_text, extensions=["extra", "sane_lists", "tables"]) + except Exception: + pass + + @staticmethod + def _get_traffic_density_color(density: int) -> str: + """Get background color based on traffic density + + Args: + density: Number of vehicles in the direction + + Returns: + CSS background color string + """ + if density > 8: + return "#ecb3b3" # Light red for >8 vehicles + elif density >= 5: + return "#ffff99" # Yellow for 5-8 vehicles + else: + return "#ffffff" # Default white for <5 vehicles + @staticmethod + def create_header(monitoring_data: Optional[MonitoringData] = None) -> str: + """Create the header section with system title and status""" + colors = ThemeColors.get_colors() + + if not monitoring_data: + return f""" +
+

🚦 Traffic MONITORING SYSTEM

+

⚠️ DATA UNAVAILABLE

+
+ """ + + return f""" +
+

🚦 {Config.APP_TITLE} | {monitoring_data.data.intersection_name}

+
+ """ + + @staticmethod + def create_traffic_summary(monitoring_data: Optional[MonitoringData]) -> str: + """Create traffic summary cards""" + if not monitoring_data: + return "

No traffic data available

" + + colors = ThemeColors.get_colors() + data = monitoring_data.data + total_pedestrians = monitoring_data.get_total_pedestrians() + + # Get background colors for each direction based on traffic density + north_bg_color = UIComponents._get_traffic_density_color(data.northbound_density) + south_bg_color = UIComponents._get_traffic_density_color(data.southbound_density) + east_bg_color = UIComponents._get_traffic_density_color(data.eastbound_density) + west_bg_color = UIComponents._get_traffic_density_color(data.westbound_density) + + return f""" +
+

🚦 TRAFFIC SUMMARY

+ + +
+
+
{data.eastbound_density}
+
→ EAST
+
+
+
{data.northbound_density}
+
↑ NORTH
+
+
+
{data.southbound_density}
+
↓ SOUTH
+
+
+
{data.westbound_density}
+
← WEST
+
+
+ + +
+
+
{data.total_density}
+
TOTAL VEHICLES
+
+
+
{total_pedestrians}
+
TOTAL PEDESTRIANS
+
+
+
+ """ + + @staticmethod + def create_debug_panel(monitoring_data: Optional[MonitoringData]) -> str: + """ + Create a hidden debug panel which is shown only when the debug checkbox is enabled. + Contains timestamp info about data and images for each direction. + """ + + if not monitoring_data: + return "

No traffic data available

" + + data = monitoring_data.data + image = monitoring_data.camera_images + + return f""" +
+

Debug Timestamps

+
+
+
→ EAST
+
Data: {data.east_timestamp}
+
Image: {image.get("east_camera", {}).get("timestamp")}
+
+
+
→ NORTH
+
Data: {data.north_timestamp}
+
Image: {image.get("north_camera", {}).get("timestamp")}
+
+
+
→ SOUTH
+
Data: {data.south_timestamp}
+
Image: {image.get("south_camera", {}).get("timestamp")}
+
+
+
→ WEST
+
Data: {data.west_timestamp}
+
Image: {image.get("west_camera", {}).get("timestamp")}
+
+
+
+ """ + + + + @staticmethod + def create_environmental_panel(monitoring_data: Optional[MonitoringData]) -> str: + """Create environmental data panel""" + if not monitoring_data: + return "

No environmental data available

" + + colors = ThemeColors.get_colors() + weather = monitoring_data.weather_data + + # Determine air quality status (simulated) + temp = weather.temperature_fahrenheit + humidity = weather.humidity_percent + + if temp < 0 or temp > 35 or humidity > 80: + air_quality = "POOR" + air_color = "#ef4444" + elif temp < 10 or temp > 30 or humidity > 60: + air_quality = "MODERATE" + air_color = "#f59e0b" + else: + air_quality = "GOOD" + air_color = "#10b981" + + # Wind direction + wind_dir = weather.wind_direction_degrees + if wind_dir < 45 or wind_dir >= 315: + wind_text = "N" + elif wind_dir < 135: + wind_text = "E" + elif wind_dir < 225: + wind_text = "S" + else: + wind_text = "W" + + # Format dewpoint for display + dewpoint_display = "N/A" + if weather.dewpoint is not None: + # Convert from Celsius to Fahrenheit for consistency + dewpoint_f = (weather.dewpoint * 9/5) + 32 + dewpoint_display = f"{dewpoint_f:.0f}°F" + + # Day/Night status + daytime_status = "Unknown" + daytime_icon = "🌅" + if weather.is_daytime is not None: + if weather.is_daytime: + daytime_status = "Day time" + daytime_icon = "☀️" + else: + daytime_status = "Night time" + daytime_icon = "🌙" + + # Format forecast period + forecast_period = "Current" + if weather.start_time and weather.end_time: + try: + from datetime import datetime + start_dt = datetime.fromisoformat(weather.start_time.replace('Z', '+00:00')) + end_dt = datetime.fromisoformat(weather.end_time.replace('Z', '+00:00')) + forecast_period = f"{start_dt.strftime('%H:%M')} - {end_dt.strftime('%H:%M')}" + except: + forecast_period = "Current Hour" + + # Use relative humidity from API if available, otherwise use the estimated value + display_humidity = weather.relative_humidity if weather.relative_humidity is not None else weather.humidity_percent + + return f""" +
+

🌡️ ENVIRONMENTAL DATA

+ + +
+
+
{int(weather.temperature_fahrenheit)}°{weather.temperature_unit}
+
TEMPERATURE
+
+
+
{display_humidity:.0f}%
+
HUMIDITY
+
+
+
{weather.wind_speed_mph:.1f} mph
+
WIND {wind_text}
+
+
+
{weather.precipitation_prob:.0f}%
+
RAINFALL (P)
+
+
+ + +
+
+
{dewpoint_display}
+
DEWPOINT
+
+
+
{daytime_icon}
+
{daytime_status.upper()}
+
+
+ +
+
{weather.conditions}
+
CONDITIONS
+
+
+ """ + + @staticmethod + def create_alerts_panel(monitoring_data: Optional[MonitoringData]) -> str: + """Create alerts panel with structured alerts and recommendations""" + if not monitoring_data: + return "

No alerts data available

" + + colors = ThemeColors.get_colors() + alerts = monitoring_data.vlm_analysis.alerts + recommendations = monitoring_data.vlm_analysis.recommendations or [] + + if not alerts and not recommendations: + return f""" +
+

🚨 Traffic Status and Alerts

+
+
✅ ALL SYSTEMS OPERATIONAL
+
No active alerts or recommendations
+
+
+ """ + + # Process alerts with new structured format + alerts_html = "" + for alert in alerts: + if isinstance(alert, dict): + # New structured alert format + alert_type = alert.get("alert_type", "general") + level = alert.get("level", "info") + description = alert.get("description", "") + weather_related = alert.get("weather_related", False) + + # Determine styling based on level + if level.lower() == "critical": + bg_color = "#c75959" # Dark red + text_color = "#f6e4e4" + icon = "🚨" + border_color = "#dc2626" + alt_color = "#802222" + elif level.lower() == "warning": + bg_color = "#b3724a" # Dark orange + text_color = "#ffe7cc" + icon = "⚠️" + border_color = "#f59e0b" + alt_color = "#66361A" + elif level.lower() == "advisory": + bg_color = "#3959BF" # Dark blue + text_color = "#dde4ed" + icon = "ℹ️" + border_color = "#3b82f6" + alt_color = "#7EA8F1" + else: + bg_color = "#D6DEEB" + text_color = "#25354E" + icon = "ℹ️" + border_color = "#6b7280" + alt_color = "#919CAD" + + # Add weather icon if weather-related + if weather_related: + icon = "🌦️" + + alerts_html += f""" +
+
+ {icon} {level.upper()} ALERT + {alert_type.replace('_', ' ').upper()} +
+
{description}
+
+ """ + else: + # Fallback for string format (legacy) + alerts_html += f""" +
+ ℹ️ INFO: {alert} +
+ """ + + # Process recommendations + recommendations_html = "" + if recommendations: + for idx, recommendation in enumerate(recommendations, 1): + recommendations_html += f""" +
+
+ 💡 Recommendation {idx}: {recommendation} +
+
+ """ + + analysis_html = UIComponents._render_markdown(monitoring_data.vlm_analysis.analysis) + + return f""" +
+

🚨 Traffic Status and Alerts

+ + {alerts_html} + + {f''' +
+

💡 Recommendations

+ {recommendations_html} +
+ ''' if recommendations_html else ''} + +
+

🔎 Analysis Summary:

+
{analysis_html}
+
+
+ """ + + @staticmethod + def create_camera_images(monitoring_data: Optional[MonitoringData]) -> List[Tuple[str, str]]: + """Create camera images for display in Gradio Gallery""" + if not monitoring_data or not monitoring_data.camera_images: + return [] + + image_list = [] + + for camera_name, camera_data in monitoring_data.camera_images.items(): + # Handle both CameraData objects and dict structures from API + if hasattr(camera_data, 'image_base64'): + # CameraData object + image_base64 = camera_data.image_base64 + direction = camera_data.direction + camera_id = camera_data.camera_id + elif isinstance(camera_data, dict): + # Dict from API + image_base64 = camera_data.get('image_base64') + direction = camera_data.get('direction', 'unknown') + camera_id = camera_data.get('camera_id', 'unknown') + else: + continue + + if image_base64: + try: + # Decode base64 image + image_bytes = base64.b64decode(image_base64) + + # Create PIL Image from bytes + image = Image.open(io.BytesIO(image_bytes)) + + # Create a caption with camera info + caption = f"{direction.upper()} - {camera_id}" + + # For Gradio Gallery, we need to save the image temporarily or use base64 + # We'll return the image object and caption + image_list.append((image, caption)) + + except Exception as e: + print(f"Error processing image for {camera_name}: {e}") + continue + + return image_list + + @staticmethod + def create_camera_grid_html(monitoring_data: Optional[MonitoringData]) -> str: + """Create an HTML grid display of camera images""" + if not monitoring_data or not monitoring_data.camera_images: + return "

No camera images available

" + + colors = ThemeColors.get_colors() + cameras_html = "" + + # Define camera order for consistent layout - updated for new API format + camera_order = ["north_camera", "east_camera", "south_camera", "west_camera"] + + # If the expected camera keys don't exist, use whatever keys are available + available_cameras = list(monitoring_data.camera_images.keys()) + cameras_to_display = [cam for cam in camera_order if cam in available_cameras] or available_cameras + + for camera_key in cameras_to_display: + if camera_key in monitoring_data.camera_images: + camera_data = monitoring_data.camera_images[camera_key] + + # Handle both CameraData objects and dict structures from API + if hasattr(camera_data, 'image_base64'): + # CameraData object + image_base64 = camera_data.image_base64 + direction = camera_data.direction + camera_id = camera_data.camera_id + elif isinstance(camera_data, dict): + # Dict from API + image_base64 = camera_data.get('image_base64') + direction = camera_data.get('direction', 'unknown') + camera_id = camera_data.get('camera_id', 'unknown') + else: + continue + + if image_base64: + # Create data URL for image + image_src = f"data:image/jpeg;base64,{image_base64}" + border_color = colors['border'] + + cameras_html += f""" +
+

+ {direction.upper()} VIEW - {camera_id} +

+
+ {direction} view +
+ ● LIVE +
+
+
+ """ + else: + cameras_html += f""" +
+

+ {direction.upper()} VIEW - {camera_id} +

+
+
📷
+
No image available
+
+
+ """ + + return f""" +
+

📹 Camera Feeds

+
+ {cameras_html} +
+
+ """ + + @staticmethod + def create_system_info(monitoring_data: Optional[MonitoringData] = None) -> str: + """Create system information footer with current status""" + # Use UTC and consistent formatting for both current time and last update + from datetime import datetime, timezone + from data_loader import get_last_update_time + + colors = ThemeColors.get_colors() + + # Current time in UTC with date and time (consistent format) + current_time = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC") + + # Get system status information + last_update = "N/A" + system_status = "OFFLINE" + + if monitoring_data: + # Prefer a nicely formatted last update using the data loader helper + try: + last_update = get_last_update_time(monitoring_data) or current_time + except Exception: + # Fallback to raw timestamp or current time + last_update = monitoring_data.timestamp or current_time + system_status = "ONLINE" + + return f""" +
+
+
+ System Status: + + {'🟢' if system_status == 'ONLINE' else '🔴'} {system_status} + +
+
+ Last Update: + {last_update} +
+
+ Current Time: + {current_time} +
+
+ Dashboard: + RSU Monitor v1.0 +
+
+
+ """ \ No newline at end of file diff --git a/metro-ai-suite/smart-traffic-intersection-agent/src/ui/weather.py b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/weather.py new file mode 100644 index 000000000..cd2d9b23c --- /dev/null +++ b/metro-ai-suite/smart-traffic-intersection-agent/src/ui/weather.py @@ -0,0 +1,129 @@ +import requests +import re +from datetime import datetime + +def get_weather(lat, lon): + # Step 1: Get the gridpoint endpoint for the given lat/long + points_url = f"https://api.weather.gov/points/{lat},{lon}" + points_resp = requests.get(points_url, headers={"User-Agent": "weather-app (your_email@example.com)"}) + print("\n") + points_resp.raise_for_status() + points_data = points_resp.json() + + print(points_data) + # Step 2: Get forecast URL from the gridpoint response + forecast_url = points_data["properties"]["forecast"] + + # Step 3: Fetch forecast data + forecast_resp = requests.get(forecast_url, headers={"User-Agent": "weather-app (your_email@example.com)"}) + forecast_resp.raise_for_status() + forecast_data = forecast_resp.json() + + print(forecast_data) + # Step 4: Process the first forecast period and format according to specified structure + first_period = forecast_data["properties"]["periods"][0] + + # Convert temperature to Celsius + temp_f = first_period["temperature"] + temp_c = round((temp_f - 32) * 5/9, 1) + + # Parse wind speed and convert to mph + wind_speed_str = first_period.get("windSpeed", "0 mph") + wind_speed_mph = _parse_wind_speed(wind_speed_str) + + # Parse wind direction to degrees + wind_direction = first_period.get("windDirection", "") + wind_direction_degrees = _parse_wind_direction(wind_direction) + + # Get precipitation probability and convert to estimated mm + precipitation_prob = first_period.get("probabilityOfPrecipitation", {}) + if isinstance(precipitation_prob, dict): + prob_value = precipitation_prob.get("value", 0) or 0 + else: + prob_value = 0 + # Use probability directly instead of converting to mm + precipitation_prob = prob_value + + # Simplify conditions + conditions = _simplify_conditions(first_period.get("shortForecast", "Clear")) + + # Generate timestamp + start_time = first_period.get("startTime", "") + timestamp = _parse_timestamp(start_time) if start_time else datetime.utcnow().isoformat() + "Z" + + return { + "timestamp": timestamp, + "temperature_fahrenheit": temp_c, + "humidity_percent": 60, # NWS API doesn't provide humidity, using default + "precipitation_prob": precipitation_prob, + "wind_speed_mph": wind_speed_mph, + "wind_direction_degrees": wind_direction_degrees, + "conditions": conditions + } + +def _parse_wind_speed(wind_speed_str: str) -> float: + """Parse wind speed string and convert to mph.""" + numbers = re.findall(r'\d+', wind_speed_str) + if not numbers: + return 0.0 + + # If range (e.g., "5 to 10 mph"), take average + if len(numbers) >= 2: + avg_mph = (int(numbers[0]) + int(numbers[1])) / 2 + else: + avg_mph = int(numbers[0]) + + # Convert mph to mph + return round(avg_mph * 1, 1) + +def _parse_wind_direction(direction_str: str) -> int: + """Parse wind direction string to degrees.""" + direction_map = { + "N": 0, "NNE": 22, "NE": 45, "ENE": 67, + "E": 90, "ESE": 112, "SE": 135, "SSE": 157, + "S": 180, "SSW": 202, "SW": 225, "WSW": 247, + "W": 270, "WNW": 292, "NW": 315, "NNW": 337 + } + + direction_str = direction_str.strip().upper() + return direction_map.get(direction_str, 0) + +def _simplify_conditions(short_forecast: str) -> str: + """Simplify weather conditions to basic categories.""" + forecast_lower = short_forecast.lower() + + if any(word in forecast_lower for word in ["sunny", "clear"]): + return "Clear" + elif any(word in forecast_lower for word in ["partly cloudy", "partly sunny", "mostly sunny"]): + return "Partly Cloudy" + elif "cloudy" in forecast_lower or "overcast" in forecast_lower: + return "Cloudy" + elif any(word in forecast_lower for word in ["rain", "shower", "drizzle"]): + return "Rain" + elif any(word in forecast_lower for word in ["snow", "sleet", "freezing"]): + return "Snow" + elif any(word in forecast_lower for word in ["storm", "thunder"]): + return "Storm" + elif "fog" in forecast_lower: + return "Fog" + else: + return "Clear" + +def _parse_timestamp(timestamp_str: str) -> str: + """Parse NWS timestamp and convert to ISO format.""" + try: + # Parse the timestamp and convert to UTC + dt = datetime.fromisoformat(timestamp_str.replace('Z', '+00:00')) + return dt.strftime('%Y-%m-%dT%H:%M:%SZ') + except: + return datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ') + +# Example usage: +if __name__ == "__main__": + lat, lon = 33.3091336,-111.9353095 # Example coordinates + weather = get_weather(lat, lon) + print(weather) + print(f"Temperature: {weather['temperature_fahrenheit']}°F") + print(f"Conditions: {weather['conditions']}") + print(f"Wind: {weather['wind_speed_mph']} mph from {weather['wind_direction_degrees']}°") + 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