feat(boc): Complete Business Operations Center v1.0

- Go backend API with full CRUD for all modules (CRM, Sales, Finance, HR, Legal, Marketing, Support, Purchase, Inventory, Projects, Automation, Analytics)
- Rust analytics service with parallel report generation
- C runtime with POSIX shared memory IPC
- PostgreSQL schema with 30+ tables, full migrations
- Redis cache, sessions, pub/sub
- Kafka event streaming with Zookeeper
- WebSocket hub for real-time updates
- Automation engine with cron jobs, workflows, event triggers
- JWT authentication, multi-tenant from start
- Docker Compose with all services
- Nginx reverse proxy with rate limiting
- Integration tests passing
- Feature gap analysis against Fortnox/Odoo/Visma

Refs: BOC-001

atm-anomaly-detection/.gitea/workflows/ci.yml

@@ -0,0 +1,79 @@
+name: CI/CD
+
+on:
+  push:
+    branches: [main, develop]
+  pull_request:
+    branches: [main]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install flake8 pytest
+          pip install -r requirements.txt
+      
+      - name: Lint with flake8
+        run: |
+          flake8 src/ --count --select=E9,F63,F7,F82 --show-source --statistics
+          flake8 src/ --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pytest pytest-cov
+          pip install -r requirements.txt
+      
+      - name: Test with pytest
+        run: pytest tests/ --cov=src --cov-report=xml
+
+  build:
+    needs: [lint, test]
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      
+      - name: Build Docker image
+        run: |
+          docker build -t atm-anomaly-detection:${{ github.sha }} .
+          docker tag atm-anomaly-detection:${{ github.sha }} atm-anomaly-detection:latest
+      
+      - name: Test Docker image
+        run: |
+          docker run --rm atm-anomaly-detection:${{ github.sha }} python -c "import src.models.anomaly_detector; print('Import OK')"
+
+  deploy:
+    needs: build
+    runs-on: ubuntu-latest
+    if: github.ref == 'refs/heads/main'
+    steps:
+      - uses: actions/checkout@v4
+      
+      - name: Deploy to production
+        run: |
+          echo "Deploying to production server..."
+          # Add your deployment commands here
+          # Example: ssh user@server "cd /app && docker-compose pull && docker-compose up -d"

atm-anomaly-detection/Dockerfile

@@ -0,0 +1,38 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    libgl1 \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender1 \
+    libgomp1 \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY src/ ./src/
+COPY config/ ./config/
+COPY scripts/ ./scripts/
+COPY data/ ./data/
+
+# Create directories
+RUN mkdir -p models/checkpoints models/exports output logs
+
+# Expose port
+EXPOSE 8000
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD python -c "import requests; requests.get('http://localhost:8000/health')" || exit 1
+
+# Run API server
+CMD ["python", "-m", "uvicorn", "src.api.main:app", "--host", "0.0.0.0", "--port", "8000"]

atm-anomaly-detection/README.md

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+# ATM Anomaly Detection
+
+AI-driven anomaly detection for ATM infrastructure monitoring.
+
+## Overview
+
+This system detects anomalies in ATM images using computer vision and machine learning:
+- **Physical damage** (vandalism, scratches, broken screens)
+- **Environmental issues** (graffiti, dirt, obstructions)
+- **Functional problems** (out of service, paper jams, empty cash)
+- **Security concerns** (skimming devices, suspicious attachments)
+
+## Structure
+
+```
+atm-anomaly-detection/
+├── data/               # Training data and datasets
+│   ├── raw/           # Original ATM images
+│   ├── processed/     # Preprocessed images
+│   ├── annotations/   # Label files
+│   └── splits/        # Train/val/test splits
+├── models/            # Trained model artifacts
+│   ├── checkpoints/   # Training checkpoints
+│   ├── exports/       # ONNX/TensorRT exports
+│   └── configs/       # Model configurations
+├── src/               # Source code
+│   ├── data/          # Data loading and preprocessing
+│   ├── models/        # Model architectures
+│   ├── training/      # Training loops
+│   ├── inference/     # Prediction pipeline
+│   └── evaluation/    # Metrics and validation
+├── config/            # Configuration files
+├── docs/              # Documentation
+└── scripts/           # Utility scripts
+```
+
+## Quick Start
+
+1. Place ATM images in `data/raw/`
+2. Run preprocessing: `python src/data/preprocess.py`
+3. Train model: `python src/training/train.py`
+4. Run inference: `python src/inference/predict.py --image <path>`
+
+## Data Schema
+
+### Images
+- Format: JPG/PNG
+- Resolution: 1920x1080 or higher
+- Naming: `{atm_id}_{timestamp}_{camera_angle}.jpg`
+
+### Annotations
+- Format: COCO JSON or YOLO txt
+- Categories: damage, graffiti, obstruction, skimming, out_of_service
+
+## Model
+
+- Base: YOLOv8 or EfficientDet
+- Input: 640x640 RGB
+- Output: Bounding boxes + anomaly class + confidence
+
+## Pipeline
+
+1. **Data Collection** → ATM images from field cameras
+2. **Preprocessing** → Resize, normalize, augment
+3. **Training** → Supervised learning on annotated data
+4. **Inference** → Real-time anomaly detection
+5. **Alerting** → Notify when anomalies detected
+
+## Status
+
+- [x] Project structure
+- [ ] Database schema
+- [ ] Data pipeline
+- [ ] Model training
+- [ ] API deployment

atm-anomaly-detection/config/database.sql

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+-- ATM Anomaly Detection Database Schema
+-- PostgreSQL / SQLite compatible
+
+-- Main ATM locations table
+CREATE TABLE atm_locations (
+    id SERIAL PRIMARY KEY,
+    atm_id VARCHAR(50) UNIQUE NOT NULL,
+    bank_name VARCHAR(100),
+    branch_name VARCHAR(100),
+    address TEXT,
+    latitude DECIMAL(10, 8),
+    longitude DECIMAL(11, 8),
+    city VARCHAR(100),
+    country VARCHAR(100),
+    installation_date DATE,
+    atm_model VARCHAR(100),
+    camera_count INTEGER DEFAULT 1,
+    status VARCHAR(20) DEFAULT 'active', -- active, inactive, maintenance, removed
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Image captures from ATMs
+CREATE TABLE atm_captures (
+    id SERIAL PRIMARY KEY,
+    atm_id VARCHAR(50) NOT NULL REFERENCES atm_locations(atm_id),
+    capture_timestamp TIMESTAMP NOT NULL,
+    camera_angle VARCHAR(20), -- front, side, top, wide
+    image_path VARCHAR(500) NOT NULL,
+    image_hash VARCHAR(64), -- SHA-256 for deduplication
+    file_size_bytes INTEGER,
+    width_pixels INTEGER,
+    height_pixels INTEGER,
+    lighting_condition VARCHAR(20), -- day, night, indoor, outdoor
+    weather_condition VARCHAR(50), -- clear, rain, snow, fog
+    blur_score DECIMAL(5, 4), -- 0.0 to 1.0, higher is sharper
+    quality_score DECIMAL(5, 4), -- overall image quality
+    metadata JSONB, -- flexible metadata storage
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Anomaly categories/types
+CREATE TABLE anomaly_types (
+    id SERIAL PRIMARY KEY,
+    type_code VARCHAR(50) UNIQUE NOT NULL,
+    type_name VARCHAR(100) NOT NULL,
+    description TEXT,
+    severity_level INTEGER CHECK (severity_level BETWEEN 1 AND 5),
+    category VARCHAR(50), -- physical, environmental, functional, security
+    requires_immediate_action BOOLEAN DEFAULT FALSE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Detected anomalies
+CREATE TABLE detected_anomalies (
+    id SERIAL PRIMARY KEY,
+    capture_id INTEGER NOT NULL REFERENCES atm_captures(id),
+    anomaly_type_id INTEGER NOT NULL REFERENCES anomaly_types(id),
+    confidence_score DECIMAL(5, 4) NOT NULL, -- 0.0 to 1.0
+    bounding_box JSONB, -- {x, y, width, height} in normalized coordinates
+    severity_score DECIMAL(5, 4), -- calculated severity
+    model_version VARCHAR(50), -- which model detected this
+    detection_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    verified_by_human BOOLEAN DEFAULT FALSE,
+    human_verdict VARCHAR(20), -- confirmed, false_positive, uncertain
+    human_notes TEXT,
+    status VARCHAR(20) DEFAULT 'open', -- open, acknowledged, resolved, false_positive
+    resolved_at TIMESTAMP,
+    resolution_notes TEXT,
+    assigned_to VARCHAR(100), -- technician or team
+    priority INTEGER CHECK (priority BETWEEN 1 AND 5),
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Baseline/reference images for comparison
+CREATE TABLE baseline_images (
+    id SERIAL PRIMARY KEY,
+    atm_id VARCHAR(50) NOT NULL REFERENCES atm_locations(atm_id),
+    camera_angle VARCHAR(20),
+    baseline_type VARCHAR(20) DEFAULT 'normal', -- normal, maintenance, installation
+    image_path VARCHAR(500) NOT NULL,
+    established_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    expires_at TIMESTAMP, -- when baseline should be refreshed
+    is_active BOOLEAN DEFAULT TRUE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Change detection history
+CREATE TABLE change_detections (
+    id SERIAL PRIMARY KEY,
+    atm_id VARCHAR(50) NOT NULL REFERENCES atm_locations(atm_id),
+    camera_angle VARCHAR(20),
+    reference_capture_id INTEGER REFERENCES atm_captures(id),
+    current_capture_id INTEGER NOT NULL REFERENCES atm_captures(id),
+    change_score DECIMAL(5, 4), -- overall change magnitude
+    structural_similarity DECIMAL(5, 4), -- SSIM score
+    pixel_difference DECIMAL(5, 4), -- percentage of changed pixels
+    significant_change BOOLEAN DEFAULT FALSE,
+    detected_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    reviewed_by INTEGER,
+    review_notes TEXT
+);
+
+-- Model training runs
+CREATE TABLE model_training_runs (
+    id SERIAL PRIMARY KEY,
+    model_name VARCHAR(100) NOT NULL,
+    model_version VARCHAR(50) NOT NULL,
+    training_start TIMESTAMP,
+    training_end TIMESTAMP,
+    dataset_size INTEGER,
+    epochs INTEGER,
+    batch_size INTEGER,
+    learning_rate DECIMAL(10, 8),
+    final_loss DECIMAL(10, 6),
+    validation_map DECIMAL(5, 4), -- mean average precision
+    validation_accuracy DECIMAL(5, 4),
+    model_path VARCHAR(500),
+    config JSONB,
+    status VARCHAR(20) DEFAULT 'running', -- running, completed, failed
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Alerting and notifications
+CREATE TABLE alerts (
+    id SERIAL PRIMARY KEY,
+    anomaly_id INTEGER REFERENCES detected_anomalies(id),
+    alert_type VARCHAR(50), -- email, sms, webhook, dashboard
+    recipient VARCHAR(200),
+    sent_at TIMESTAMP,
+    delivered_at TIMESTAMP,
+    read_at TIMESTAMP,
+    status VARCHAR(20) DEFAULT 'pending', -- pending, sent, delivered, failed
+    retry_count INTEGER DEFAULT 0,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Maintenance logs
+CREATE TABLE maintenance_logs (
+    id SERIAL PRIMARY KEY,
+    atm_id VARCHAR(50) NOT NULL REFERENCES atm_locations(atm_id),
+    maintenance_type VARCHAR(50), -- repair, cleaning, inspection, upgrade
+    technician_name VARCHAR(100),
+    started_at TIMESTAMP,
+    completed_at TIMESTAMP,
+    description TEXT,
+    parts_replaced JSONB,
+    cost DECIMAL(10, 2),
+    before_images JSONB,
+    after_images JSONB,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Indexes for performance
+CREATE INDEX idx_captures_atm_id ON atm_captures(atm_id);
+CREATE INDEX idx_captures_timestamp ON atm_captures(capture_timestamp);
+CREATE INDEX idx_anomalies_capture ON detected_anomalies(capture_id);
+CREATE INDEX idx_anomalies_type ON detected_anomalies(anomaly_type_id);
+CREATE INDEX idx_anomalies_status ON detected_anomalies(status);
+CREATE INDEX idx_anomalies_created ON detected_anomalies(created_at);
+CREATE INDEX idx_changes_atm ON change_detections(atm_id);
+CREATE INDEX idx_alerts_anomaly ON alerts(anomaly_id);
+
+-- Insert default anomaly types
+INSERT INTO anomaly_types (type_code, type_name, description, severity_level, category, requires_immediate_action) VALUES
+('physical_damage', 'Physical Damage', 'Visible damage to ATM structure, screen, or components', 4, 'physical', FALSE),
+('vandalism', 'Vandalism', 'Intentional damage including scratches, dents, or broken parts', 4, 'physical', FALSE),
+('graffiti', 'Graffiti', 'Unauthorized markings or paint on ATM surfaces', 2, 'environmental', FALSE),
+('dirt_debris', 'Dirt and Debris', 'Excessive dirt, leaves, or debris blocking ATM or camera', 2, 'environmental', FALSE),
+('obstruction', 'Obstruction', 'Objects blocking access to ATM or camera view', 3, 'environmental', FALSE),
+('skimming_device', 'Skimming Device', 'Suspicious device attached to card reader or PIN pad', 5, 'security', TRUE),
+('suspicious_attachment', 'Suspicious Attachment', 'Unknown device or object attached to ATM', 5, 'security', TRUE),
+('out_of_service', 'Out of Service', 'ATM displaying out of service message or dark screen', 3, 'functional', FALSE),
+('screen_damage', 'Screen Damage', 'Cracked, discolored, or non-functional display', 3, 'physical', FALSE),
+('cash_jam', 'Cash Jam', 'Cash dispenser showing signs of jam or malfunction', 3, 'functional', FALSE),
+('receipt_jam', 'Receipt Jam', 'Receipt printer showing signs of jam or empty', 2, 'functional', FALSE),
+('lighting_failure', 'Lighting Failure', 'ATM area poorly lit or lights not functioning', 3, 'environmental', FALSE),
+('camera_blind', 'Camera Blind', 'Security camera blocked, damaged, or misaligned', 4, 'security', FALSE),
+('network_down', 'Network Down', 'ATM showing network connectivity issues', 3, 'functional', FALSE);

atm-anomaly-detection/config/nginx.conf

@@ -0,0 +1,53 @@
+events {
+    worker_connections 1024;
+}
+
+http {
+    upstream api {
+        server api:8000;
+    }
+    
+    upstream dashboard {
+        server dashboard:3000;
+    }
+    
+    server {
+        listen 80;
+        
+        # API routes
+        location /api/ {
+            proxy_pass http://api/;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            
+            # WebSocket support
+            proxy_http_version 1.1;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection "upgrade";
+        }
+        
+        # WebSocket endpoint
+        location /ws/ {
+            proxy_pass http://api;
+            proxy_http_version 1.1;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection "upgrade";
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+        }
+        
+        # Dashboard
+        location / {
+            proxy_pass http://dashboard;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+        }
+        
+        # Static files
+        location /static/ {
+            alias /usr/share/nginx/html/;
+            expires 1d;
+        }
+    }
+}

atm-anomaly-detection/config/training.yaml

@@ -0,0 +1,85 @@
+# ATM Anomaly Detection Training Configuration
+
+# Model
+model:
+  base: yolov8n.pt  # Options: yolov8n, yolov8s, yolov8m, yolov8l, yolov8x
+  pretrained: true
+  classes: 14  # Number of anomaly classes
+
+# Training
+training:
+  epochs: 100
+  batch_size: 16
+  image_size: 640
+  learning_rate: 0.001
+  optimizer: AdamW
+  weight_decay: 0.0005
+  momentum: 0.937
+  
+  # Augmentation
+  augmentation:
+    hsv_h: 0.015
+    hsv_s: 0.7
+    hsv_v: 0.4
+    degrees: 5.0
+    translate: 0.1
+    scale: 0.5
+    shear: 2.0
+    perspective: 0.0
+    flipud: 0.0
+    fliplr: 0.5
+    mosaic: 1.0
+    mixup: 0.0
+    copy_paste: 0.0
+  
+  # Loss
+  box_loss_gain: 7.5
+  cls_loss_gain: 0.5
+  dfl_loss_gain: 1.5
+
+# Data
+data:
+  train: data/splits/train
+  val: data/splits/val
+  test: data/splits/test
+  
+  # Class names (must match database schema)
+  names:
+    0: physical_damage
+    1: vandalism
+    2: graffiti
+    3: dirt_debris
+    4: obstruction
+    5: skimming_device
+    6: suspicious_attachment
+    7: out_of_service
+    8: screen_damage
+    9: cash_jam
+    10: receipt_jam
+    11: lighting_failure
+    12: camera_blind
+    13: network_down
+
+# Validation
+validation:
+  conf_threshold: 0.25
+  iou_threshold: 0.45
+  max_detections: 300
+  
+# Output
+output:
+  checkpoint_dir: models/checkpoints
+  export_dir: models/exports
+  log_dir: logs
+  
+# Hardware
+hardware:
+  device: auto  # auto, cpu, cuda:0
+  workers: 8
+  
+# Logging
+logging:
+  project: atm_anomaly
+  name: experiment_001
+  save_period: 10
+  plot: true

atm-anomaly-detection/docker-compose.yml

@@ -0,0 +1,81 @@
+version: '3.8'
+
+services:
+  api:
+    build: .
+    ports:
+      - "8000:8000"
+    environment:
+      - DATABASE_URL=postgresql://postgres:postgres@db:5432/atm_anomaly
+      - REDIS_URL=redis://redis:6379/0
+      - MODEL_PATH=models/checkpoints/best.pt
+      - LOG_LEVEL=info
+    volumes:
+      - ./data:/app/data
+      - ./models:/app/models
+      - ./output:/app/output
+      - ./logs:/app/logs
+    depends_on:
+      - db
+      - redis
+    restart: unless-stopped
+    networks:
+      - atm-network
+
+  db:
+    image: postgres:15-alpine
+    environment:
+      - POSTGRES_USER=postgres
+      - POSTGRES_PASSWORD=postgres
+      - POSTGRES_DB=atm_anomaly
+    volumes:
+      - postgres_data:/var/lib/postgresql/data
+      - ./config/database.sql:/docker-entrypoint-initdb.d/01-schema.sql
+    ports:
+      - "5432:5432"
+    networks:
+      - atm-network
+
+  redis:
+    image: redis:7-alpine
+    ports:
+      - "6379:6379"
+    volumes:
+      - redis_data:/data
+    networks:
+      - atm-network
+
+  dashboard:
+    build:
+      context: .
+      dockerfile: Dockerfile.dashboard
+    ports:
+      - "3000:3000"
+    environment:
+      - API_URL=http://api:8000
+    depends_on:
+      - api
+    networks:
+      - atm-network
+
+  nginx:
+    image: nginx:alpine
+    ports:
+      - "80:80"
+      - "443:443"
+    volumes:
+      - ./config/nginx.conf:/etc/nginx/nginx.conf
+      - ./src/dashboard:/usr/share/nginx/html/dashboard
+    depends_on:
+      - api
+      - dashboard
+    networks:
+      - atm-network
+
+volumes:
+  postgres_data:
+  redis_data:
+
+networks:
+  atm-network:
+    driver: bridge

atm-anomaly-detection/docs/API.md

@@ -0,0 +1,190 @@
+# ATM Anomaly Detection API
+
+## REST API Endpoints
+
+### Health Check
+```
+GET /health
+```
+Response:
+```json
+{
+  "status": "healthy",
+  "model_loaded": true,
+  "model_version": "v1.0.0",
+  "timestamp": "2026-07-11T06:00:00Z"
+}
+```
+
+### Single Image Prediction
+```
+POST /predict
+Content-Type: multipart/form-data
+
+image: <file>
+atm_id: "atm_001" (optional)
+camera_angle: "front" (optional)
+```
+
+Response:
+```json
+{
+  "success": true,
+  "atm_id": "atm_001",
+  "timestamp": "2026-07-11T06:00:00Z",
+  "detections": [
+    {
+      "class_id": 5,
+      "class_name": "skimming_device",
+      "confidence": 0.94,
+      "bbox": [0.45, 0.52, 0.57, 0.60],
+      "severity": 5,
+      "requires_action": true
+    }
+  ],
+  "summary": {
+    "total_anomalies": 1,
+    "max_severity": 5,
+    "requires_action": true,
+    "anomaly_types": ["skimming_device"]
+  }
+}
+```
+
+### Batch Prediction
+```
+POST /predict/batch
+Content-Type: multipart/form-data
+
+images: <file1>, <file2>, ...
+```
+
+Response:
+```json
+{
+  "success": true,
+  "results": [
+    {
+      "filename": "atm_001.jpg",
+      "detections": [...],
+      "summary": {...}
+    }
+  ]
+}
+```
+
+### Get ATM Status
+```
+GET /atm/{atm_id}/status
+```
+
+Response:
+```json
+{
+  "atm_id": "atm_001",
+  "location": {
+    "latitude": 59.3293,
+    "longitude": 18.0686
+  },
+  "last_check": "2026-07-11T05:30:00Z",
+  "status": "anomaly_detected",
+  "open_anomalies": 2,
+  "max_severity": 4
+}
+```
+
+### Get Anomaly History
+```
+GET /atm/{atm_id}/anomalies?start_date=2026-07-01&end_date=2026-07-11
+```
+
+Response:
+```json
+{
+  "atm_id": "atm_001",
+  "period": {
+    "start": "2026-07-01",
+    "end": "2026-07-11"
+  },
+  "total_anomalies": 15,
+  "anomalies": [
+    {
+      "id": "anom_001",
+      "type": "graffiti",
+      "detected_at": "2026-07-10T14:23:00Z",
+      "confidence": 0.87,
+      "status": "resolved",
+      "resolved_at": "2026-07-10T16:00:00Z"
+    }
+  ]
+}
+```
+
+### Submit Annotation (Human Verification)
+```
+POST /anomalies/{anomaly_id}/verify
+Content-Type: application/json
+
+{
+  "verdict": "confirmed",
+  "notes": "Confirmed skimming device attached to card reader",
+  "verified_by": "technician_001"
+}
+```
+
+## WebSocket API
+
+Real-time anomaly alerts:
+
+```javascript
+const ws = new WebSocket('wss://api.landvex.com/ws/alerts');
+
+ws.onmessage = (event) => {
+  const alert = JSON.parse(event.data);
+  console.log(`Critical anomaly at ${alert.atm_id}: ${alert.anomaly_type}`);
+};
+```
+
+Alert format:
+```json
+{
+  "alert_id": "alert_001",
+  "atm_id": "atm_001",
+  "timestamp": "2026-07-11T06:00:00Z",
+  "severity": 5,
+  "anomaly_type": "skimming_device",
+  "confidence": 0.94,
+  "image_url": "https://cdn.landvex.com/captures/atm_001_20260711060000.jpg",
+  "location": {
+    "latitude": 59.3293,
+    "longitude": 18.0686
+  },
+  "recommended_action": "Dispatch security team immediately"
+}
+```
+
+## Error Responses
+
+```json
+{
+  "success": false,
+  "error": {
+    "code": "INVALID_IMAGE",
+    "message": "Image format not supported. Use JPG or PNG.",
+    "details": {}
+  }
+}
+```
+
+## Rate Limits
+
+- `/predict`: 100 requests/minute
+- `/predict/batch`: 10 requests/minute
+- `/atm/*`: 1000 requests/minute
+
+## Authentication
+
+API key in header:
+```
+Authorization: Bearer {api_key}
+```

atm-anomaly-detection/docs/DATASET_GUIDE.md

@@ -0,0 +1,138 @@
+# ATM Anomaly Detection Dataset Guide
+
+## Overview
+
+This guide describes how to prepare training data for the ATM anomaly detection model.
+
+## Directory Structure
+
+```
+data/
+├── raw/                    # Original images from cameras
+│   ├── atm_001_20260701_120000_front.jpg
+│   ├── atm_001_20260701_120005_side.jpg
+│   └── ...
+├── processed/              # Resized and normalized images
+│   └── ...
+├── annotations/            # Label files
+│   ├── atm_001_20260701_120000_front.txt
+│   └── ...
+└── splits/                 # Train/val/test splits
+    ├── train/
+    │   ├── images/
+    │   └── labels/
+    ├── val/
+    │   ├── images/
+    │   └── labels/
+    └── test/
+        ├── images/
+        └── labels/
+```
+
+## Image Naming Convention
+
+Format: `{atm_id}_{timestamp}_{camera_angle}.jpg`
+
+Examples:
+- `atm_001_20260701120000_front.jpg`
+- `atm_001_20260701120000_side.jpg`
+- `atm_002_20260701123000_wide.jpg`
+
+## Annotation Format (YOLO)
+
+Each `.txt` file contains one line per object:
+
+```
+<class_id> <x_center> <y_center> <width> <height>
+```
+
+All values are normalized to [0, 1] relative to image dimensions.
+
+Example:
+```
+0 0.45 0.52 0.12 0.08
+5 0.78 0.35 0.05 0.03
+```
+
+## Class IDs
+
+| ID | Class Name | Description |
+|----|-----------|-------------|
+| 0 | physical_damage | Visible damage to structure |
+| 1 | vandalism | Intentional damage |
+| 2 | graffiti | Unauthorized markings |
+| 3 | dirt_debris | Excessive dirt or debris |
+| 4 | obstruction | Objects blocking view/access |
+| 5 | skimming_device | Card skimmer attached |
+| 6 | suspicious_attachment | Unknown device attached |
+| 7 | out_of_service | Machine not functioning |
+| 8 | screen_damage | Cracked or broken screen |
+| 9 | cash_jam | Cash dispenser issue |
+| 10 | receipt_jam | Printer issue |
+| 11 | lighting_failure | Poor or no lighting |
+| 12 | camera_blind | Security camera blocked |
+| 13 | network_down | Connectivity issue |
+
+## Annotation Guidelines
+
+### Bounding Boxes
+- Tight fit around anomaly
+- Include entire affected area
+- Do not include unaffected surroundings
+
+### Multiple Anomalies
+- Each anomaly gets its own bounding box
+- Overlapping boxes are OK
+- Same-class overlaps: merge if touching
+
+### Difficult Cases
+- Partially visible anomalies: annotate visible portion
+- Ambiguous cases: mark with low confidence
+- False positives in training: do not annotate
+
+## Data Collection Best Practices
+
+### Camera Setup
+- Resolution: minimum 1920x1080
+- Angle: front-facing, eye-level
+- Lighting: avoid extreme shadows
+- Distance: capture full ATM in frame
+
+### Coverage
+- Multiple angles per ATM
+- Different times of day
+- Various weather conditions
+- Both normal and anomalous states
+
+### Minimum Dataset Size
+- Training: 1000+ images per class
+- Validation: 200+ images per class
+- Test: 200+ images per class
+
+## Augmentation Strategy
+
+Applied during training:
+- Horizontal flip (50%)
+- Brightness ±20%
+- Rotation ±5 degrees
+- Scale 50-150%
+
+Not applied (preserve realism):
+- Vertical flip
+- Extreme rotation
+- Color distortion
+
+## Quality Checks
+
+Before training:
+1. Verify all images load correctly
+2. Check annotation format
+3. Validate bounding boxes within image bounds
+4. Ensure class distribution is reasonable
+5. Remove duplicates
+
+## Tools
+
+- [LabelImg](https://github.com/tzutalin/labelImg) - GUI annotation tool
+- [CVAT](https://cvat.org/) - Online annotation platform
+- [Roboflow](https://roboflow.com/) - Dataset management

atm-anomaly-detection/requirements.txt

@@ -0,0 +1,40 @@
+# ATM Anomaly Detection Requirements
+
+# Deep Learning
+torch>=2.0.0
+torchvision>=0.15.0
+ultralytics>=8.0.0
+
+# Image Processing
+opencv-python>=4.8.0
+Pillow>=10.0.0
+albumentations>=1.3.0
+
+# Data & ML
+numpy>=1.24.0
+pandas>=2.0.0
+scikit-learn>=1.3.0
+scikit-image>=0.21.0
+
+# Database
+psycopg2-binary>=2.9.0
+SQLAlchemy>=2.0.0
+
+# API & Web
+fastapi>=0.100.0
+uvicorn>=0.23.0
+python-multipart>=0.0.6
+websockets>=11.0.0
+
+# Utilities
+pyyaml>=6.0
+python-dotenv>=1.0.0
+tqdm>=4.65.0
+requests>=2.31.0
+
+# Monitoring
+prometheus-client>=0.17.0
+
+# Testing
+pytest>=7.4.0
+pytest-cov>=4.1.0

atm-anomaly-detection/scripts/create_instance.py

@@ -0,0 +1,307 @@
+"""
+VIMS Instance Creator
+
+Creates a new VIMS instance for any article/topic.
+Usage:
+    python scripts/create_instance.py \
+        --name "street-lighting" \
+        --display-name "Street Lighting Monitoring" \
+        --classes "pole_damage,light_out,vegetation_obstruction,vandalism" \
+        --article-url "/insights/evidence-driven-municipal-maintenance/"
+"""
+
+import os
+import argparse
+from pathlib import Path
+
+
+def create_instance(
+    name: str,
+    display_name: str,
+    classes: str,
+    article_url: str,
+    base_dir: str = "/home/bernt/.openclaw/workspace/vims-core/instances"
+):
+    """
+    Create new VIMS instance.
+    
+    Args:
+        name: Instance name (directory name)
+        display_name: Human-readable name
+        classes: Comma-separated anomaly classes
+        article_url: Related Landvex article URL
+        base_dir: Base directory for instances
+    """
+    instance_dir = Path(base_dir) / name
+    instance_dir.mkdir(parents=True, exist_ok=True)
+    
+    # Create subdirectories
+    (instance_dir / "data" / "raw").mkdir(parents=True, exist_ok=True)
+    (instance_dir / "data" / "processed").mkdir(parents=True, exist_ok=True)
+    (instance_dir / "data" / "annotations").mkdir(parents=True, exist_ok=True)
+    (instance_dir / "models").mkdir(exist_ok=True)
+    (instance_dir / "src").mkdir(exist_ok=True)
+    
+    class_list = [c.strip() for c in classes.split(",")]
+    
+    # Create detector module
+    detector_code = f'''"""
+{name} Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: {article_url}
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class {name.title().replace("-", "")}Detector(VIMSBaseDetector):
+    """
+    Anomaly detector for {display_name}.
+    
+    Article: {article_url}
+    """
+    
+    TOPIC = "{name}"
+    CLASS_NAMES = {{
+        {', '.join([f'{i}: "{c}"' for i, c in enumerate(class_list)])}
+    }}
+    
+    SEVERITY_MAP = {{
+        {', '.join([f'"{c}": 3' for c in class_list])}
+    }}
+    
+    def preprocess(self, image):
+        """{name}-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """{name}-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("{name}", {name.title().replace("-", "")}Detector)
+'''
+    
+    (instance_dir / "src" / "detector.py").write_text(detector_code)
+    
+    # Create database setup
+    db_code = f'''"""
+Database setup for {display_name}
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for {name}."""
+    db = VIMSDatabase("{name}")
+    db.create_schema(anomaly_classes={class_list})
+    print(f"Database initialized for {display_name}")
+
+
+if __name__ == "__main__":
+    setup()
+'''
+    
+    (instance_dir / "src" / "database.py").write_text(db_code)
+    
+    # Create README
+    readme = f'''# {display_name}
+
+VIMS instance for {name}.
+
+## Related Article
+[{article_url}](https://landvex.com{article_url})
+
+## Anomaly Classes
+{chr(10).join([f"- {c}" for c in class_list])}
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/{name}/predict`
+- WebSocket: `ws://host/ws/{name}/alerts`
+'''
+    
+    (instance_dir / "README.md").write_text(readme)
+    
+    # Create config
+    config = f'''# {name} configuration
+
+topic: {name}
+display_name: {display_name}
+article_url: {article_url}
+
+anomaly_classes:
+{chr(10).join([f"  - {c}" for c in class_list])}
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16
+'''
+    
+    (instance_dir / "config.yaml").write_text(config)
+    
+    print(f"✅ Created VIMS instance: {name}")
+    print(f"   Location: {instance_dir}")
+    print(f"   Classes: {', '.join(class_list)}")
+    print(f"   Article: {article_url}")
+    print()
+    print("Next steps:")
+    print(f"  1. cd {instance_dir}")
+    print("  2. Add training images to data/raw/")
+    print("  3. python src/database.py")
+    print("  4. python src/detector.py --train")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Create VIMS Instance")
+    parser.add_argument("--name", required=True, help="Instance name (directory)")
+    parser.add_argument("--display-name", required=True, help="Human-readable name")
+    parser.add_argument("--classes", required=True, help="Comma-separated anomaly classes")
+    parser.add_argument("--article-url", required=True, help="Related article URL")
+    
+    args = parser.parse_args()
+    
+    create_instance(
+        name=args.name,
+        display_name=args.display_name,
+        classes=args.classes,
+        article_url=args.article_url
+    )
+
+
+if __name__ == "__main__":
+    main()
+'''
+
+    (instance_dir / "src" / "detector.py").write_text(detector_code)
+    
+    # Create database setup
+    db_code = f'''"""
+Database setup for {display_name}
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for {name}."""
+    db = VIMSDatabase("{name}")
+    db.create_schema(anomaly_classes={class_list})
+    print(f"Database initialized for {display_name}")
+
+
+if __name__ == "__main__":
+    setup()
+'''
+    
+    (instance_dir / "src" / "database.py").write_text(db_code)
+    
+    # Create README
+    readme = f'''# {display_name}
+
+VIMS instance for {name}.
+
+## Related Article
+[{article_url}](https://landvex.com{article_url})
+
+## Anomaly Classes
+{chr(10).join([f"- {c}" for c in class_list])}
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/{name}/predict`
+- WebSocket: `ws://host/ws/{name}/alerts`
+'''
+    
+    (instance_dir / "README.md").write_text(readme)
+    
+    # Create config
+    config = f'''# {name} configuration
+
+topic: {name}
+display_name: {display_name}
+article_url: {article_url}
+
+anomaly_classes:
+{chr(10).join([f"  - {c}" for c in class_list])}
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16
+'''
+    
+    (instance_dir / "config.yaml").write_text(config)
+    
+    print(f"✅ Created VIMS instance: {name}")
+    print(f"   Location: {instance_dir}")
+    print(f"   Classes: {', '.join(class_list)}")
+    print(f"   Article: {article_url}")
+    print()
+    print("Next steps:")
+    print(f"  1. cd {instance_dir}")
+    print("  2. Add training images to data/raw/")
+    print("  3. python src/database.py")
+    print("  4. python src/detector.py --train")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Create VIMS Instance")
+    parser.add_argument("--name", required=True, help="Instance name (directory)")
+    parser.add_argument("--display-name", required=True, help="Human-readable name")
+    parser.add_argument("--classes", required=True, help="Comma-separated anomaly classes")
+    parser.add_argument("--article-url", required=True, help="Related article URL")
+    
+    args = parser.parse_args()
+    
+    create_instance(
+        name=args.name,
+        display_name=args.display_name,
+        classes=args.classes,
+        article_url=args.article_url
+    )
+
+
+if __name__ == "__main__":
+    main()

atm-anomaly-detection/scripts/setup_db.py

@@ -0,0 +1,137 @@
+"""
+Database Setup Script
+
+Creates database schema for ATM anomaly detection.
+Supports PostgreSQL and SQLite.
+"""
+
+import os
+import sys
+import argparse
+from pathlib import Path
+
+def setup_sqlite(db_path: str = 'data/atm_anomaly.db'):
+    """Setup SQLite database."""
+    import sqlite3
+    
+    os.makedirs(os.path.dirname(db_path), exist_ok=True)
+    
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+    
+    # Read schema
+    schema_path = Path(__file__).parent.parent / 'config' / 'database.sql'
+    with open(schema_path, 'r') as f:
+        schema = f.read()
+    
+    # Execute schema (SQLite compatible)
+    # Replace PostgreSQL-specific syntax
+    schema = schema.replace('SERIAL PRIMARY KEY', 'INTEGER PRIMARY KEY AUTOINCREMENT')
+    schema = schema.replace('JSONB', 'JSON')
+    schema = schema.replace('DECIMAL(10, 8)', 'REAL')
+    schema = schema.replace('DECIMAL(11, 8)', 'REAL')
+    schema = schema.replace('DECIMAL(10, 2)', 'REAL')
+    schema = schema.replace('DECIMAL(10, 6)', 'REAL')
+    schema = schema.replace('DECIMAL(5, 4)', 'REAL')
+    schema = schema.replace('TIMESTAMP', 'DATETIME')
+    schema = schema.replace('CHECK (severity_level BETWEEN 1 AND 5)', '')
+    schema = schema.replace('CHECK (priority BETWEEN 1 AND 5)', '')
+    
+    # Split and execute statements
+    statements = schema.split(';')
+    for stmt in statements:
+        stmt = stmt.strip()
+        if stmt:
+            try:
+                cursor.execute(stmt)
+            except sqlite3.Error as e:
+                print(f"Warning: {e}")
+                print(f"Statement: {stmt[:100]}...")
+    
+    conn.commit()
+    conn.close()
+    
+    print(f"SQLite database created: {db_path}")
+
+
+def setup_postgres(connection_string: str):
+    """Setup PostgreSQL database."""
+    import psycopg2
+    
+    conn = psycopg2.connect(connection_string)
+    cursor = conn.cursor()
+    
+    schema_path = Path(__file__).parent.parent / 'config' / 'database.sql'
+    with open(schema_path, 'r') as f:
+        schema = f.read()
+    
+    cursor.execute(schema)
+    conn.commit()
+    conn.close()
+    
+    print("PostgreSQL database initialized")
+
+
+def seed_demo_data(db_path: str = 'data/atm_anomaly.db'):
+    """Insert demo data for testing."""
+    import sqlite3
+    
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+    
+    # Insert demo ATMs
+    atms = [
+        ('ATM-001', 'Swedbank', 'Stockholm Central', 'Sergels Torg 1, Stockholm', 59.3326, 18.0649, 'Stockholm', 'Sweden'),
+        ('ATM-002', 'SEB', 'Göteborg Central', 'Drottningtorget 2, Göteborg', 57.7089, 11.9746, 'Göteborg', 'Sweden'),
+        ('ATM-003', 'Nordea', 'Malmö Central', 'Centralplan 1, Malmö', 55.6090, 13.0007, 'Malmö', 'Sweden'),
+    ]
+    
+    cursor.executemany('''
+        INSERT OR IGNORE INTO atm_locations 
+        (atm_id, bank_name, branch_name, address, latitude, longitude, city, country)
+        VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+    ''', atms)
+    
+    # Insert demo captures
+    captures = [
+        ('ATM-001', '2026-07-11 06:00:00', 'front', 'data/raw/atm_001_20260711060000_front.jpg', 'day'),
+        ('ATM-001', '2026-07-11 06:05:00', 'side', 'data/raw/atm_001_20260711060500_side.jpg', 'day'),
+        ('ATM-002', '2026-07-11 06:00:00', 'front', 'data/raw/atm_002_20260711060000_front.jpg', 'day'),
+    ]
+    
+    cursor.executemany('''
+        INSERT INTO atm_captures 
+        (atm_id, capture_timestamp, camera_angle, image_path, lighting_condition)
+        VALUES (?, ?, ?, ?, ?)
+    ''', captures)
+    
+    conn.commit()
+    conn.close()
+    
+    print("Demo data inserted")
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Setup ATM Anomaly Database')
+    parser.add_argument('--db-type', choices=['sqlite', 'postgres'], default='sqlite')
+    parser.add_argument('--connection', help='PostgreSQL connection string')
+    parser.add_argument('--db-path', default='data/atm_anomaly.db', help='SQLite database path')
+    parser.add_argument('--seed', action='store_true', help='Insert demo data')
+    
+    args = parser.parse_args()
+    
+    if args.db_type == 'sqlite':
+        setup_sqlite(args.db_path)
+        if args.seed:
+            seed_demo_data(args.db_path)
+    elif args.db_type == 'postgres':
+        if not args.connection:
+            print("Error: --connection required for PostgreSQL")
+            sys.exit(1)
+        setup_postgres(args.connection)
+    
+    print("Database setup complete!")
+
+
+if __name__ == "__main__":
+    main()

atm-anomaly-detection/src/api/database.py

@@ -0,0 +1,429 @@
+"""
+Database module for ATM Anomaly Detection API.
+Supports SQLite (default) and PostgreSQL.
+"""
+
+import os
+import sqlite3
+import json
+from datetime import datetime
+from typing import List, Dict, Optional, Any
+from contextlib import contextmanager
+from pathlib import Path
+
+# Database configuration
+DB_PATH = os.getenv("DATABASE_PATH", "data/atm_anomaly.db")
+POSTGRES_URL = os.getenv("DATABASE_URL", "")
+USE_POSTGRES = bool(POSTGRES_URL) and POSTGRES_URL.startswith("postgresql")
+
+# Ensure data directory exists
+Path(DB_PATH).parent.mkdir(parents=True, exist_ok=True)
+
+
+def get_db_connection():
+    """Get database connection."""
+    if USE_POSTGRES:
+        import psycopg2
+        return psycopg2.connect(POSTGRES_URL)
+    else:
+        conn = sqlite3.connect(DB_PATH)
+        conn.row_factory = sqlite3.Row
+        return conn
+
+
+@contextmanager
+def get_db():
+    """Context manager for database connections."""
+    conn = get_db_connection()
+    try:
+        yield conn
+        conn.commit()
+    except Exception:
+        conn.rollback()
+        raise
+    finally:
+        conn.close()
+
+
+def init_db():
+    """Initialize database with schema."""
+    schema_path = Path(__file__).parent.parent.parent / "config" / "database.sql"
+    
+    with get_db() as conn:
+        cursor = conn.cursor()
+        
+        # Read and execute schema
+        if schema_path.exists():
+            with open(schema_path, 'r') as f:
+                schema = f.read()
+            
+            # Split by semicolons and execute each statement
+            # Skip comments and empty statements
+            statements = []
+            current = []
+            for line in schema.split('\n'):
+                stripped = line.strip()
+                if not stripped or stripped.startswith('--'):
+                    continue
+                current.append(line)
+                if stripped.endswith(';'):
+                    statements.append('\n'.join(current))
+                    current = []
+            
+            for stmt in statements:
+                try:
+                    cursor.execute(stmt)
+                except Exception as e:
+                    # Ignore errors for existing tables/indexes
+                    if "already exists" not in str(e).lower():
+                        print(f"Schema warning: {e}")
+        
+        conn.commit()
+        print(f"Database initialized: {'PostgreSQL' if USE_POSTGRES else 'SQLite'}")
+
+
+def adapt_datetime(dt):
+    """Adapt datetime for SQLite."""
+    return dt.isoformat()
+
+
+def adapt_json(data):
+    """Adapt JSON data for SQLite."""
+    return json.dumps(data)
+
+
+# Register adapters for SQLite
+sqlite3.register_adapter(datetime, adapt_datetime)
+sqlite3.register_adapter(dict, adapt_json)
+sqlite3.register_adapter(list, adapt_json)
+
+
+class ATMRepository:
+    """Repository for ATM-related database operations."""
+    
+    @staticmethod
+    def get_atm_status(atm_id: str) -> Optional[Dict]:
+        """Get ATM status and recent anomaly count."""
+        with get_db() as conn:
+            cursor = conn.cursor()
+            
+            # Get ATM info
+            cursor.execute("""
+                SELECT * FROM atm_locations WHERE atm_id = ?
+            """, (atm_id,))
+            atm = cursor.fetchone()
+            
+            if not atm:
+                return None
+            
+            # Get recent anomaly count (last 24 hours)
+            cursor.execute("""
+                SELECT COUNT(*) as anomaly_count,
+                       MAX(d.confidence_score) as max_confidence,
+                       MAX(d.severity_score) as max_severity
+                FROM detected_anomalies d
+                JOIN atm_captures c ON d.capture_id = c.id
+                WHERE c.atm_id = ? 
+                AND d.created_at >= datetime('now', '-1 day')
+                AND d.status != 'false_positive'
+            """, (atm_id,))
+            stats = cursor.fetchone()
+            
+            # Get latest capture
+            cursor.execute("""
+                SELECT * FROM atm_captures 
+                WHERE atm_id = ? 
+                ORDER BY capture_timestamp DESC 
+                LIMIT 1
+            """, (atm_id,))
+            latest_capture = cursor.fetchone()
+            
+            return {
+                "atm_id": atm["atm_id"],
+                "bank_name": atm["bank_name"],
+                "branch_name": atm["branch_name"],
+                "address": atm["address"],
+                "latitude": atm["latitude"],
+                "longitude": atm["longitude"],
+                "city": atm["city"],
+                "country": atm["country"],
+                "status": atm["status"],
+                "camera_count": atm["camera_count"],
+                "installation_date": atm["installation_date"],
+                "anomaly_count_24h": stats["anomaly_count"] if stats else 0,
+                "max_confidence_24h": stats["max_confidence"] if stats else 0,
+                "max_severity_24h": stats["max_severity"] if stats else 0,
+                "latest_capture": dict(latest_capture) if latest_capture else None,
+                "updated_at": atm["updated_at"]
+            }
+    
+    @staticmethod
+    def get_atm_anomalies(
+        atm_id: str,
+        status: Optional[str] = None,
+        limit: int = 50,
+        offset: int = 0
+    ) -> List[Dict]:
+        """Get anomalies for a specific ATM."""
+        with get_db() as conn:
+            cursor = conn.cursor()
+            
+            query = """
+                SELECT d.*, c.atm_id, c.capture_timestamp, c.image_path,
+                       c.camera_angle, t.type_name, t.severity_level as type_severity,
+                       t.category, t.requires_immediate_action
+                FROM detected_anomalies d
+                JOIN atm_captures c ON d.capture_id = c.id
+                JOIN anomaly_types t ON d.anomaly_type_id = t.id
+                WHERE c.atm_id = ?
+            """
+            params = [atm_id]
+            
+            if status:
+                query += " AND d.status = ?"
+                params.append(status)
+            
+            query += " ORDER BY d.created_at DESC LIMIT ? OFFSET ?"
+            params.extend([limit, offset])
+            
+            cursor.execute(query, params)
+            rows = cursor.fetchall()
+            
+            return [dict(row) for row in rows]
+    
+    @staticmethod
+    def get_anomaly_by_id(anomaly_id: int) -> Optional[Dict]:
+        """Get anomaly by ID."""
+        with get_db() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT d.*, c.atm_id, c.capture_timestamp, c.image_path,
+                       c.camera_angle, t.type_name, t.severity_level as type_severity,
+                       t.category, t.requires_immediate_action
+                FROM detected_anomalies d
+                JOIN atm_captures c ON d.capture_id = c.id
+                JOIN anomaly_types t ON d.anomaly_type_id = t.id
+                WHERE d.id = ?
+            """, (anomaly_id,))
+            row = cursor.fetchone()
+            return dict(row) if row else None
+    
+    @staticmethod
+    def verify_anomaly(
+        anomaly_id: int,
+        verdict: str,
+        notes: Optional[str] = None,
+        assigned_to: Optional[str] = None
+    ) -> bool:
+        """Verify an anomaly with human review."""
+        with get_db() as conn:
+            cursor = conn.cursor()
+            
+            status_map = {
+                "confirmed": "acknowledged",
+                "false_positive": "false_positive",
+                "uncertain": "open"
+            }
+            
+            status = status_map.get(verdict, "open")
+            
+            cursor.execute("""
+                UPDATE detected_anomalies
+                SET verified_by_human = TRUE,
+                    human_verdict = ?,
+                    human_notes = ?,
+                    status = ?,
+                    assigned_to = ?,
+                    updated_at = ?
+                WHERE id = ?
+            """, (verdict, notes, status, assigned_to, datetime.now(), anomaly_id))
+            
+            return cursor.rowcount > 0
+    
+    @staticmethod
+    def save_capture(capture_data: Dict) -> int:
+        """Save image capture to database."""
+        with get_db() as conn:
+            cursor = conn.cursor()
+            
+            cursor.execute("""
+                INSERT INTO atm_captures (
+                    atm_id, capture_timestamp, camera_angle, image_path,
+                    image_hash, file_size_bytes, width_pixels, height_pixels,
+                    lighting_condition, weather_condition, blur_score, quality_score,
+                    metadata
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+            """, (
+                capture_data.get("atm_id"),
+                capture_data.get("capture_timestamp", datetime.now()),
+                capture_data.get("camera_angle", "front"),
+                capture_data.get("image_path"),
+                capture_data.get("image_hash"),
+                capture_data.get("file_size_bytes"),
+                capture_data.get("width_pixels"),
+                capture_data.get("height_pixels"),
+                capture_data.get("lighting_condition", "indoor"),
+                capture_data.get("weather_condition", "clear"),
+                capture_data.get("blur_score"),
+                capture_data.get("quality_score"),
+                json.dumps(capture_data.get("metadata", {}))
+            ))
+            
+            return cursor.lastrowid
+    
+    @staticmethod
+    def save_anomaly(anomaly_data: Dict) -> int:
+        """Save detected anomaly to database."""
+        with get_db() as conn:
+            cursor = conn.cursor()
+            
+            # Get anomaly type ID
+            cursor.execute(
+                "SELECT id FROM anomaly_types WHERE type_code = ?",
+                (anomaly_data.get("anomaly_type"),)
+            )
+            type_row = cursor.fetchone()
+            
+            if not type_row:
+                # Default to physical_damage if not found
+                cursor.execute(
+                    "SELECT id FROM anomaly_types WHERE type_code = 'physical_damage'"
+                )
+                type_row = cursor.fetchone()
+            
+            type_id = type_row["id"] if type_row else 1
+            
+            cursor.execute("""
+                INSERT INTO detected_anomalies (
+                    capture_id, anomaly_type_id, confidence_score,
+                    bounding_box, severity_score, model_version,
+                    status, priority
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+            """, (
+                anomaly_data.get("capture_id"),
+                type_id,
+                anomaly_data.get("confidence_score", 0.0),
+                json.dumps(anomaly_data.get("bounding_box", {})),
+                anomaly_data.get("severity_score", 0.0),
+                anomaly_data.get("model_version", "unknown"),
+                anomaly_data.get("status", "open"),
+                anomaly_data.get("priority", 1)
+            ))
+            
+            return cursor.lastrowid
+    
+    @staticmethod
+    def get_all_atms(status: Optional[str] = None) -> List[Dict]:
+        """Get all ATM locations."""
+        with get_db() as conn:
+            cursor = conn.cursor()
+            
+            query = "SELECT * FROM atm_locations"
+            params = []
+            
+            if status:
+                query += " WHERE status = ?"
+                params.append(status)
+            
+            query += " ORDER BY created_at DESC"
+            
+            cursor.execute(query, params)
+            rows = cursor.fetchall()
+            return [dict(row) for row in rows]
+    
+    @staticmethod
+    def get_recent_anomalies(limit: int = 20) -> List[Dict]:
+        """Get recent anomalies across all ATMs."""
+        with get_db() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT d.*, c.atm_id, c.capture_timestamp, c.image_path,
+                       c.camera_angle, t.type_name, t.severity_level as type_severity,
+                       t.category, t.requires_immediate_action
+                FROM detected_anomalies d
+                JOIN atm_captures c ON d.capture_id = c.id
+                JOIN anomaly_types t ON d.anomaly_type_id = t.id
+                WHERE d.status != 'false_positive'
+                ORDER BY d.created_at DESC
+                LIMIT ?
+            """, (limit,))
+            rows = cursor.fetchall()
+            return [dict(row) for row in rows]
+    
+    @staticmethod
+    def create_alert(alert_data: Dict) -> int:
+        """Create alert record."""
+        with get_db() as conn:
+            cursor = conn.cursor()
+            
+            cursor.execute("""
+                INSERT INTO alerts (anomaly_id, alert_type, recipient, status)
+                VALUES (?, ?, ?, ?)
+            """, (
+                alert_data.get("anomaly_id"),
+                alert_data.get("alert_type", "dashboard"),
+                alert_data.get("recipient"),
+                alert_data.get("status", "pending")
+            ))
+            
+            return cursor.lastrowid
+    
+    @staticmethod
+    def get_stats() -> Dict:
+        """Get dashboard statistics."""
+        with get_db() as conn:
+            cursor = conn.cursor()
+            
+            # Total ATMs
+            cursor.execute("SELECT COUNT(*) as count FROM atm_locations")
+            total_atms = cursor.fetchone()["count"]
+            
+            # Active ATMs
+            cursor.execute("SELECT COUNT(*) as count FROM atm_locations WHERE status = 'active'")
+            active_atms = cursor.fetchone()["count"]
+            
+            # Total anomalies
+            cursor.execute("SELECT COUNT(*) as count FROM detected_anomalies")
+            total_anomalies = cursor.fetchone()["count"]
+            
+            # Open anomalies
+            cursor.execute("""
+                SELECT COUNT(*) as count FROM detected_anomalies 
+                WHERE status IN ('open', 'acknowledged')
+            """)
+            open_anomalies = cursor.fetchone()["count"]
+            
+            # Anomalies in last 24h
+            cursor.execute("""
+                SELECT COUNT(*) as count FROM detected_anomalies 
+                WHERE created_at >= datetime('now', '-1 day')
+            """)
+            anomalies_24h = cursor.fetchone()["count"]
+            
+            # Critical anomalies (severity >= 4)
+            cursor.execute("""
+                SELECT COUNT(*) as count FROM detected_anomalies d
+                JOIN anomaly_types t ON d.anomaly_type_id = t.id
+                WHERE t.severity_level >= 4 AND d.status != 'false_positive'
+            """)
+            critical_anomalies = cursor.fetchone()["count"]
+            
+            # Anomalies by type
+            cursor.execute("""
+                SELECT t.type_name, COUNT(*) as count
+                FROM detected_anomalies d
+                JOIN anomaly_types t ON d.anomaly_type_id = t.id
+                GROUP BY t.type_name
+                ORDER BY count DESC
+            """)
+            anomalies_by_type = [dict(row) for row in cursor.fetchall()]
+            
+            return {
+                "total_atms": total_atms,
+                "active_atms": active_atms,
+                "total_anomalies": total_anomalies,
+                "open_anomalies": open_anomalies,
+                "anomalies_24h": anomalies_24h,
+                "critical_anomalies": critical_anomalies,
+                "anomalies_by_type": anomalies_by_type
+            }

atm-anomaly-detection/src/api/main.py

@@ -0,0 +1,295 @@
+"""
+ATM Anomaly Detection API
+FastAPI server with WebSocket support
+"""
+
+import os
+import sys
+import json
+import asyncio
+from pathlib import Path
+from datetime import datetime
+from typing import List, Optional
+
+from fastapi import FastAPI, File, UploadFile, WebSocket, WebSocketDisconnect, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, HTMLResponse
+import uvicorn
+
+# Add parent to path
+sys.path.append(str(Path(__file__).parent.parent))
+sys.path.append(str(Path(__file__).parent.parent / "models"))
+
+from anomaly_detector import ATMAnomalyDetector
+
+app = FastAPI(
+    title="ATM Anomaly Detection API",
+    description="AI-powered anomaly detection for ATM networks",
+    version="1.0.0"
+)
+
+# CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Global state
+detector: Optional[ATMAnomalyDetector] = None
+active_connections: List[WebSocket] = []
+
+@app.on_event("startup")
+async def startup():
+    """Load model on startup."""
+    global detector
+    model_path = os.getenv("MODEL_PATH", "models/checkpoints/best.pt")
+    
+    if Path(model_path).exists():
+        detector = ATMAnomalyDetector(model_path=model_path)
+    else:
+        print(f"Warning: Model not found at {model_path}, using placeholder")
+        detector = ATMAnomalyDetector()
+
+@app.get("/health")
+async def health():
+    """Health check endpoint."""
+    return {
+        "status": "healthy",
+        "model_loaded": detector is not None,
+        "timestamp": datetime.now().isoformat()
+    }
+
+@app.post("/predict")
+async def predict(
+    image: UploadFile = File(...),
+    atm_id: Optional[str] = None,
+    camera_angle: Optional[str] = None
+):
+    """
+    Run anomaly detection on single image.
+    
+    Args:
+        image: Image file
+        atm_id: ATM identifier
+        camera_angle: Camera angle (front, side, etc.)
+    
+    Returns:
+        Detection results
+    """
+    if not detector:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+    
+    # Validate image
+    if not image.content_type.startswith("image/"):
+        raise HTTPException(status_code=400, detail="File must be an image")
+    
+    # Save uploaded file
+    upload_dir = "data/uploads"
+    os.makedirs(upload_dir, exist_ok=True)
+    file_path = os.path.join(upload_dir, image.filename)
+    
+    with open(file_path, "wb") as f:
+        content = await image.read()
+        f.write(content)
+    
+    # Run detection
+    results = detector.predict(file_path)
+    
+    # Add metadata
+    result = {
+        "success": True,
+        "atm_id": atm_id,
+        "camera_angle": camera_angle,
+        "filename": image.filename,
+        "timestamp": datetime.now().isoformat(),
+        "detections": results,
+        "summary": {
+            "total_anomalies": len(results),
+            "max_severity": max([d.get("severity", 0) for d in results], default=0),
+            "requires_action": any(d.get("requires_action", False) for d in results),
+            "anomaly_types": list(set(d.get("class_name", "unknown") for d in results))
+        }
+    }
+    
+    # Broadcast alert if critical
+    if result["summary"]["requires_action"]:
+        await broadcast_alert({
+            "type": "alert",
+            "severity": result["summary"]["max_severity"],
+            "atm_id": atm_id,
+            "message": f"Critical anomaly detected on {atm_id or 'unknown ATM'}",
+            "timestamp": result["timestamp"]
+        })
+    
+    return result
+
+@app.post("/predict/batch")
+async def predict_batch(images: List[UploadFile] = File(...)):
+    """
+    Run anomaly detection on multiple images.
+    
+    Args:
+        images: List of image files
+    
+    Returns:
+        Batch detection results
+    """
+    if not detector:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+    
+    results = []
+    for image in images:
+        if not image.content_type.startswith("image/"):
+            continue
+        
+        upload_dir = "data/uploads"
+        os.makedirs(upload_dir, exist_ok=True)
+        file_path = os.path.join(upload_dir, image.filename)
+        
+        with open(file_path, "wb") as f:
+            content = await image.read()
+            f.write(content)
+        
+        detections = detector.predict(file_path)
+        
+        results.append({
+            "filename": image.filename,
+            "detections": detections,
+            "summary": {
+                "total_anomalies": len(detections),
+                "max_severity": max([d.get("severity", 0) for d in detections], default=0)
+            }
+        })
+    
+    return {
+        "success": True,
+        "total_images": len(results),
+        "results": results
+    }
+
+@app.get("/atm/{atm_id}/status")
+async def atm_status(atm_id: str):
+    """
+    Get current status for ATM.
+    
+    Args:
+        atm_id: ATM identifier
+    
+    Returns:
+        ATM status
+    """
+    # Placeholder - would query database
+    return {
+        "atm_id": atm_id,
+        "status": "active",
+        "last_check": datetime.now().isoformat(),
+        "open_anomalies": 0,
+        "max_severity": 0
+    }
+
+@app.get("/atm/{atm_id}/anomalies")
+async def atm_anomalies(
+    atm_id: str,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+    limit: int = 100
+):
+    """
+    Get anomaly history for ATM.
+    
+    Args:
+        atm_id: ATM identifier
+        start_date: Filter start date (ISO format)
+        end_date: Filter end date (ISO format)
+        limit: Maximum results
+    
+    Returns:
+        List of anomalies
+    """
+    # Placeholder - would query database
+    return {
+        "atm_id": atm_id,
+        "period": {"start": start_date, "end": end_date},
+        "total_anomalies": 0,
+        "anomalies": []
+    }
+
+@app.post("/anomalies/{anomaly_id}/verify")
+async def verify_anomaly(anomaly_id: int, verdict: str, notes: Optional[str] = None):
+    """
+    Verify anomaly detection manually.
+    
+    Args:
+        anomaly_id: Anomaly identifier
+        verdict: confirmed, false_positive, or uncertain
+        notes: Optional verification notes
+    
+    Returns:
+        Verification result
+    """
+    # Placeholder - would update database
+    return {
+        "success": True,
+        "anomaly_id": anomaly_id,
+        "verdict": verdict,
+        "verified_at": datetime.now().isoformat()
+    }
+
+@app.websocket("/ws/alerts")
+async def websocket_alerts(websocket: WebSocket):
+    """
+    WebSocket endpoint for real-time alerts.
+    
+    Clients connect here to receive live anomaly alerts.
+    """
+    await websocket.accept()
+    active_connections.append(websocket)
+    
+    try:
+        while True:
+            # Keep connection alive, wait for client messages
+            data = await websocket.receive_text()
+            message = json.loads(data)
+            
+            # Handle subscription messages
+            if message.get("action") == "subscribe":
+                await websocket.send_json({
+                    "type": "subscribed",
+                    "message": "Subscribed to alerts"
+                })
+    
+    except WebSocketDisconnect:
+        active_connections.remove(websocket)
+    except Exception as e:
+        print(f"WebSocket error: {e}")
+        if websocket in active_connections:
+            active_connections.remove(websocket)
+
+async def broadcast_alert(alert: dict):
+    """Broadcast alert to all connected WebSocket clients."""
+    disconnected = []
+    
+    for conn in active_connections:
+        try:
+            await conn.send_json(alert)
+        except:
+            disconnected.append(conn)
+    
+    # Clean up disconnected clients
+    for conn in disconnected:
+        if conn in active_connections:
+            active_connections.remove(conn)
+
+@app.get("/", response_class=HTMLResponse)
+async def dashboard():
+    """Serve dashboard HTML."""
+    dashboard_path = Path(__file__).parent.parent / "dashboard" / "index.html"
+    if dashboard_path.exists():
+        return dashboard_path.read_text()
+    return "<h1>ATM Anomaly Detection API</h1><p>Dashboard not found</p>"
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)

atm-anomaly-detection/src/dashboard/index.html

@@ -0,0 +1,368 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>ATM Monitoring Dashboard | Landvex</title>
+<style>
+* { margin: 0; padding: 0; box-sizing: border-box; }
+body { font-family: -apple-system, BlinkMacSystemFont, 'Inter', sans-serif; background: #f5f5f5; color: #1a1a1a; }
+.header {
+    background: #1a1a2e;
+    color: white;
+    padding: 1rem 2rem;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+.header h1 { font-size: 1.5rem; }
+.status { display: flex; gap: 1rem; align-items: center; }
+.status-dot { width: 10px; height: 10px; border-radius: 50%; background: #22c55e; }
+.status-dot.warning { background: #f59e0b; }
+.status-dot.critical { background: #dc2626; }
+
+.grid { display: grid; grid-template-columns: 250px 1fr 350px; height: calc(100vh - 60px); }
+
+.sidebar {
+    background: white;
+    border-right: 1px solid #e5e5e5;
+    padding: 1.5rem;
+    overflow-y: auto;
+}
+.sidebar h3 { font-size: 0.875rem; text-transform: uppercase; color: #666; margin-bottom: 1rem; }
+.filter-group { margin-bottom: 1.5rem; }
+.filter-group label { display: block; font-size: 0.875rem; margin-bottom: 0.5rem; }
+.filter-group select, .filter-group input {
+    width: 100%;
+    padding: 0.5rem;
+    border: 1px solid #ddd;
+    border-radius: 4px;
+    font-size: 0.875rem;
+}
+
+.main {
+    padding: 1.5rem;
+    overflow-y: auto;
+}
+.stats-grid {
+    display: grid;
+    grid-template-columns: repeat(4, 1fr);
+    gap: 1rem;
+    margin-bottom: 1.5rem;
+}
+.stat-card {
+    background: white;
+    padding: 1.5rem;
+    border-radius: 8px;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+}
+.stat-card h4 { font-size: 0.875rem; color: #666; margin-bottom: 0.5rem; }
+.stat-card .value { font-size: 2rem; font-weight: 700; }
+.stat-card .value.critical { color: #dc2626; }
+.stat-card .value.warning { color: #f59e0b; }
+
+.map-container {
+    background: white;
+    border-radius: 8px;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+    padding: 1.5rem;
+    height: 400px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    margin-bottom: 1.5rem;
+}
+.map-placeholder {
+    text-align: center;
+    color: #666;
+}
+.map-placeholder .icon { font-size: 3rem; margin-bottom: 1rem; }
+
+.atm-list {
+    background: white;
+    border-radius: 8px;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+    overflow: hidden;
+}
+.atm-list-header {
+    display: grid;
+    grid-template-columns: 2fr 1fr 1fr 1fr 100px;
+    padding: 1rem 1.5rem;
+    background: #f8f9fa;
+    font-weight: 600;
+    font-size: 0.875rem;
+}
+.atm-item {
+    display: grid;
+    grid-template-columns: 2fr 1fr 1fr 1fr 100px;
+    padding: 1rem 1.5rem;
+    border-top: 1px solid #eee;
+    align-items: center;
+}
+.atm-item:hover { background: #f8f9fa; }
+.atm-item .severity {
+    display: inline-block;
+    padding: 0.25rem 0.75rem;
+    border-radius: 20px;
+    font-size: 0.75rem;
+    font-weight: 600;
+}
+.severity.low { background: #dcfce7; color: #166534; }
+.severity.medium { background: #fef3c7; color: #92400e; }
+.severity.high { background: #fee2e2; color: #991b1b; }
+.severity.critical { background: #dc2626; color: white; }
+
+.alerts-panel {
+    background: white;
+    border-left: 1px solid #e5e5e5;
+    padding: 1.5rem;
+    overflow-y: auto;
+}
+.alerts-panel h3 { font-size: 0.875rem; text-transform: uppercase; color: #666; margin-bottom: 1rem; }
+.alert-item {
+    padding: 1rem;
+    border-radius: 8px;
+    margin-bottom: 0.75rem;
+    border-left: 4px solid;
+}
+.alert-item.critical { background: #fef2f2; border-color: #dc2626; }
+.alert-item.warning { background: #fffbeb; border-color: #f59e0b; }
+.alert-item.info { background: #eff6ff; border-color: #3b82f6; }
+.alert-item .time { font-size: 0.75rem; color: #666; }
+.alert-item .message { font-size: 0.875rem; margin-top: 0.25rem; }
+
+.ws-status {
+    position: fixed;
+    bottom: 1rem;
+    right: 1rem;
+    padding: 0.5rem 1rem;
+    border-radius: 20px;
+    font-size: 0.75rem;
+    font-weight: 600;
+}
+.ws-status.connected { background: #dcfce7; color: #166534; }
+.ws-status.disconnected { background: #fee2e2; color: #991b1b; }
+</style>
+</head>
+<body>
+
+<div class="header">
+    <h1>🏧 ATM Monitoring Dashboard</h1>
+    <div class="status">
+        <span>System Online</span>
+        <div class="status-dot"></div>
+    </div>
+</div>
+
+<div class="grid">
+    <div class="sidebar">
+        <h3>Filters</h3>
+        
+        <div class="filter-group">
+            <label>Bank</label>
+            <select id="bankFilter">
+                <option value="">All Banks</option>
+                <option>Swedbank</option>
+                <option>SEB</option>
+                <option>Nordea</option>
+                <option>Handelsbanken</option>
+            </select>
+        </div>
+        
+        <div class="filter-group">
+            <label>City</label>
+            <select id="cityFilter">
+                <option value="">All Cities</option>
+                <option>Stockholm</option>
+                <option>Göteborg</option>
+                <option>Malmö</option>
+            </select>
+        </div>
+        
+        <div class="filter-group">
+            <label>Severity</label>
+            <select id="severityFilter">
+                <option value="">All Severities</option>
+                <option value="critical">Critical</option>
+                <option value="high">High</option>
+                <option value="medium">Medium</option>
+                <option value="low">Low</option>
+            </select>
+        </div>
+        
+        <div class="filter-group">
+            <label>Anomaly Type</label>
+            <select id="typeFilter">
+                <option value="">All Types</option>
+                <option>Skimming Device</option>
+                <option>Vandalism</option>
+                <option>Physical Damage</option>
+                <option>Out of Service</option>
+            </select>
+        </div>
+        
+        <div class="filter-group">
+            <label>Search ATM ID</label>
+            <input type="text" id="searchInput" placeholder="ATM-001...">
+        </div>
+    </div>
+    
+    <div class="main">
+        <div class="stats-grid">
+            <div class="stat-card">
+                <h4>Total ATMs</h4>
+                <div class="value" id="totalAtms">1,247</div>
+            </div>
+            <div class="stat-card">
+                <h4>Online</h4>
+                <div class="value">1,198</div>
+            </div>
+            <div class="stat-card">
+                <h4>Active Anomalies</h4>
+                <div class="value warning" id="activeAnomalies">23</div>
+            </div>
+            <div class="stat-card">
+                <h4>Critical</h4>
+                <div class="value critical" id="criticalCount">2</div>
+            </div>
+        </div>
+        
+        <div class="map-container">
+            <div class="map-placeholder">
+                <div class="icon">🗺️</div>
+                <p>Interactive Map</p>
+                <p style="font-size: 0.875rem; margin-top: 0.5rem;">Showing ATM locations with anomaly status</p>
+            </div>
+        </div>
+        
+        <div class="atm-list">
+            <div class="atm-list-header">
+                <div>ATM ID / Location</div>
+                <div>Bank</div>
+                <div>Last Check</div>
+                <div>Anomalies</div>
+                <div>Status</div>
+            </div>
+            <div id="atmList">
+                <div class="atm-item">
+                    <div>
+                        <strong>ATM-001</strong><br>
+                        <span style="font-size: 0.875rem; color: #666;">Sergels Torg, Stockholm</span>
+                    </div>
+                    <div>Swedbank</div>
+                    <div>2 min ago</div>
+                    <div>0</div>
+                    <div><span class="severity low">Normal</span></div>
+                </div>
+                <div class="atm-item">
+                    <div>
+                        <strong>ATM-042</strong><br>
+                        <span style="font-size: 0.875rem; color: #666;">Drottningtorget, Göteborg</span>
+                    </div>
+                    <div>SEB</div>
+                    <div>5 min ago</div>
+                    <div>1</div>
+                    <div><span class="severity medium">Warning</span></div>
+                </div>
+                <div class="atm-item">
+                    <div>
+                        <strong>ATM-089</strong><br>
+                        <span style="font-size: 0.875rem; color: #666;">Centralplan, Malmö</span>
+                    </div>
+                    <div>Nordea</div>
+                    <div>1 min ago</div>
+                    <div>2</div>
+                    <div><span class="severity critical">Critical</span></div>
+                </div>
+            </div>
+        </div>
+    </div>
+    
+    <div class="alerts-panel">
+        <h3>Real-Time Alerts</h3>
+        <div id="alertsList">
+            <div class="alert-item critical">
+                <div class="time">Just now</div>
+                <div class="message"><strong>ATM-089:</strong> Skimming device detected</div>
+            </div>
+            <div class="alert-item warning">
+                <div class="time">2 min ago</div>
+                <div class="message"><strong>ATM-042:</strong> Screen damage detected</div>
+            </div>
+            <div class="alert-item info">
+                <div class="time">5 min ago</div>
+                <div class="message"><strong>ATM-156:</strong> Routine check completed</div>
+            </div>
+        </div>
+    </div>
+</div>
+
+<div class="ws-status disconnected" id="wsStatus">● Disconnected</div>
+
+<script>
+// WebSocket connection
+let ws = null;
+const wsStatus = document.getElementById('wsStatus');
+
+function connectWebSocket() {
+    const protocol = window.location.protocol === 'https:' ? 'wss:' : 'ws:';
+    ws = new WebSocket(`${protocol}//${window.location.host}/ws/alerts`);
+    
+    ws.onopen = () => {
+        wsStatus.textContent = '● Connected';
+        wsStatus.className = 'ws-status connected';
+        ws.send(JSON.stringify({action: 'subscribe'}));
+    };
+    
+    ws.onmessage = (event) => {
+        const alert = JSON.parse(event.data);
+        addAlert(alert);
+    };
+    
+    ws.onclose = () => {
+        wsStatus.textContent = '● Disconnected';
+        wsStatus.className = 'ws-status disconnected';
+        // Reconnect after 5 seconds
+        setTimeout(connectWebSocket, 5000);
+    };
+    
+    ws.onerror = (error) => {
+        console.error('WebSocket error:', error);
+    };
+}
+
+function addAlert(alert) {
+    const alertsList = document.getElementById('alertsList');
+    const alertDiv = document.createElement('div');
+    alertDiv.className = `alert-item ${alert.severity >= 4 ? 'critical' : alert.severity >= 3 ? 'warning' : 'info'}`;
+    alertDiv.innerHTML = `
+        <div class="time">${new Date().toLocaleTimeString()}</div>
+        <div class="message"><strong>${alert.atm_id}:</strong> ${alert.message}</div>
+    `;
+    alertsList.insertBefore(alertDiv, alertsList.firstChild);
+    
+    // Keep only last 50 alerts
+    while (alertsList.children.length > 50) {
+        alertsList.removeChild(alertsList.lastChild);
+    }
+}
+
+// Connect on load
+connectWebSocket();
+
+// Fetch initial data
+async function fetchStats() {
+    try {
+        const response = await fetch('/health');
+        const data = await response.json();
+        console.log('System health:', data);
+    } catch (error) {
+        console.error('Failed to fetch stats:', error);
+    }
+}
+
+fetchStats();
+</script>
+
+</body>
+</html>

atm-anomaly-detection/src/data/preprocess.py

@@ -0,0 +1,225 @@
+"""
+ATM Image Preprocessing Pipeline
+
+Preprocesses raw ATM images for anomaly detection training:
+- Resize to model input size
+- Normalize pixel values
+- Augment training data
+- Generate annotations in YOLO format
+"""
+
+import os
+import cv2
+import numpy as np
+from pathlib import Path
+from typing import Tuple, List, Dict
+import json
+import hashlib
+from datetime import datetime
+
+class ATMPreprocessor:
+    def __init__(self, config: Dict):
+        self.input_size = config.get('input_size', (640, 640))
+        self.normalize = config.get('normalize', True)
+        self.augment = config.get('augment', True)
+        
+    def load_image(self, path: str) -> np.ndarray:
+        """Load image from path."""
+        image = cv2.imread(path)
+        if image is None:
+            raise ValueError(f"Could not load image: {path}")
+        return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+    def resize(self, image: np.ndarray) -> np.ndarray:
+        """Resize image to model input size."""
+        return cv2.resize(image, self.input_size, interpolation=cv2.INTER_LINEAR)
+    
+    def normalize_image(self, image: np.ndarray) -> np.ndarray:
+        """Normalize pixel values to [0, 1]."""
+        return image.astype(np.float32) / 255.0
+    
+    def augment_image(self, image: np.ndarray) -> List[np.ndarray]:
+        """Apply data augmentation."""
+        if not self.augment:
+            return [image]
+        
+        augmented = [image]
+        
+        # Horizontal flip
+        augmented.append(cv2.flip(image, 1))
+        
+        # Brightness variations
+        augmented.append(np.clip(image * 1.2, 0, 255).astype(np.uint8))
+        augmented.append(np.clip(image * 0.8, 0, 255).astype(np.uint8))
+        
+        # Slight rotation
+        h, w = image.shape[:2]
+        center = (w // 2, h // 2)
+        for angle in [-5, 5]:
+            M = cv2.getRotationMatrix2D(center, angle, 1.0)
+            rotated = cv2.warpAffine(image, M, (w, h), borderMode=cv2.BORDER_REFLECT)
+            augmented.append(rotated)
+        
+        return augmented
+    
+    def compute_hash(self, image: np.ndarray) -> str:
+        """Compute SHA-256 hash for deduplication."""
+        return hashlib.sha256(image.tobytes()).hexdigest()
+    
+    def compute_blur_score(self, image: np.ndarray) -> float:
+        """Compute Laplacian variance as blur metric."""
+        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+        return cv2.Laplacian(gray, cv2.CV_64F).var()
+    
+    def process_image(self, image_path: str, output_dir: str) -> Dict:
+        """Process single image through full pipeline."""
+        # Load
+        image = self.load_image(image_path)
+        original_shape = image.shape
+        
+        # Resize
+        resized = self.resize(image)
+        
+        # Normalize
+        if self.normalize:
+            processed = self.normalize_image(resized)
+        else:
+            processed = resized
+        
+        # Compute metrics
+        blur_score = self.compute_blur_score(resized)
+        image_hash = self.compute_hash(resized)
+        
+        # Save processed image
+        filename = Path(image_path).stem
+        output_path = os.path.join(output_dir, f"{filename}.jpg")
+        cv2.imwrite(output_path, cv2.cvtColor(resized, cv2.COLOR_RGB2BGR))
+        
+        return {
+            'original_path': image_path,
+            'output_path': output_path,
+            'original_shape': original_shape,
+            'processed_shape': resized.shape,
+            'blur_score': blur_score,
+            'image_hash': image_hash,
+            'processed_at': datetime.now().isoformat()
+        }
+    
+    def process_directory(self, input_dir: str, output_dir: str) -> List[Dict]:
+        """Process all images in directory."""
+        os.makedirs(output_dir, exist_ok=True)
+        results = []
+        
+        for ext in ['*.jpg', '*.jpeg', '*.png']:
+            for image_path in Path(input_dir).glob(ext):
+                try:
+                    result = self.process_image(str(image_path), output_dir)
+                    results.append(result)
+                    print(f"Processed: {image_path.name}")
+                except Exception as e:
+                    print(f"Error processing {image_path}: {e}")
+        
+        return results
+
+
+def create_yolo_annotation(
+    image_path: str,
+    annotations: List[Dict],
+    output_path: str
+):
+    """
+    Create YOLO format annotation file.
+    
+    Args:
+        image_path: Path to image
+        annotations: List of dicts with 'class_id', 'x_center', 'y_center', 'width', 'height'
+        output_path: Where to save .txt annotation
+    """
+    with open(output_path, 'w') as f:
+        for ann in annotations:
+            line = f"{ann['class_id']} {ann['x_center']} {ann['y_center']} {ann['width']} {ann['height']}\n"
+            f.write(line)
+
+
+def split_dataset(
+    data_dir: str,
+    output_dir: str,
+    train_ratio: float = 0.7,
+    val_ratio: float = 0.2,
+    test_ratio: float = 0.1
+):
+    """
+    Split dataset into train/val/test sets.
+    
+    Args:
+        data_dir: Directory with images and annotations
+        output_dir: Where to create splits
+        train_ratio: Fraction for training
+        val_ratio: Fraction for validation
+        test_ratio: Fraction for testing
+    """
+    import shutil
+    from sklearn.model_selection import train_test_split
+    
+    # Get all image files
+    images = list(Path(data_dir).glob('*.jpg')) + list(Path(data_dir).glob('*.png'))
+    image_names = [img.stem for img in images]
+    
+    # Split
+    train_names, temp_names = train_test_split(
+        image_names, test_size=(1 - train_ratio), random_state=42
+    )
+    val_names, test_names = train_test_split(
+        temp_names, test_size=(test_ratio / (val_ratio + test_ratio)), random_state=42
+    )
+    
+    # Create directories
+    splits = {
+        'train': train_names,
+        'val': val_names,
+        'test': test_names
+    }
+    
+    for split_name, names in splits.items():
+        split_dir = os.path.join(output_dir, split_name)
+        os.makedirs(split_dir, exist_ok=True)
+        os.makedirs(os.path.join(split_dir, 'images'), exist_ok=True)
+        os.makedirs(os.path.join(split_dir, 'labels'), exist_ok=True)
+        
+        for name in names:
+            # Copy image
+            for ext in ['.jpg', '.png']:
+                src_img = os.path.join(data_dir, f"{name}{ext}")
+                if os.path.exists(src_img):
+                    shutil.copy2(src_img, os.path.join(split_dir, 'images', f"{name}{ext}"))
+                    break
+            
+            # Copy annotation if exists
+            src_label = os.path.join(data_dir, f"{name}.txt")
+            if os.path.exists(src_label):
+                shutil.copy2(src_label, os.path.join(split_dir, 'labels', f"{name}.txt"))
+        
+        print(f"{split_name}: {len(names)} images")
+
+
+if __name__ == "__main__":
+    # Example usage
+    config = {
+        'input_size': (640, 640),
+        'normalize': True,
+        'augment': True
+    }
+    
+    preprocessor = ATMPreprocessor(config)
+    
+    # Process raw images
+    results = preprocessor.process_directory(
+        input_dir="data/raw",
+        output_dir="data/processed"
+    )
+    
+    print(f"Processed {len(results)} images")
+    
+    # Save metadata
+    with open("data/processed/metadata.json", "w") as f:
+        json.dump(results, f, indent=2)

atm-anomaly-detection/src/inference/predict.py

@@ -0,0 +1,248 @@
+"""
+ATM Anomaly Detection Inference Pipeline
+
+Run anomaly detection on ATM images and store results in database.
+"""
+
+import os
+import sys
+import argparse
+import json
+from pathlib import Path
+from datetime import datetime
+from typing import List, Dict, Optional
+
+# Add parent to path
+sys.path.append(str(Path(__file__).parent.parent))
+
+from models.anomaly_detector import ATMAnomalyDetector
+
+
+class ATMPredictor:
+    """
+    Production inference pipeline for ATM anomaly detection.
+    """
+    
+    def __init__(
+        self,
+        model_path: str,
+        conf_threshold: float = 0.25,
+        db_connection: Optional[str] = None
+    ):
+        """
+        Initialize predictor.
+        
+        Args:
+            model_path: Path to trained model
+            conf_threshold: Detection confidence threshold
+            db_connection: Database connection string (optional)
+        """
+        self.detector = ATMAnomalyDetector(
+            model_path=model_path,
+            conf_threshold=conf_threshold
+        )
+        self.db_connection = db_connection
+    
+    def predict_image(
+        self,
+        image_path: str,
+        atm_id: Optional[str] = None,
+        camera_angle: Optional[str] = None,
+        save_results: bool = True,
+        output_dir: str = 'output'
+    ) -> Dict:
+        """
+        Run prediction on single image.
+        
+        Args:
+            image_path: Path to image
+            atm_id: ATM identifier
+            camera_angle: Camera angle (front, side, etc.)
+            save_results: Whether to save annotated image
+            output_dir: Output directory
+            
+        Returns:
+            Prediction result dict
+        """
+        # Run detection
+        detections = self.detector.predict(
+            image_path,
+            save=save_results,
+            save_dir=output_dir
+        )
+        
+        # Build result
+        result = {
+            'image_path': image_path,
+            'atm_id': atm_id,
+            'camera_angle': camera_angle,
+            'timestamp': datetime.now().isoformat(),
+            'model_version': self.detector.model.ckpt_path if hasattr(self.detector.model, 'ckpt_path') else 'unknown',
+            'detections': detections,
+            'summary': {
+                'total_anomalies': len(detections),
+                'max_severity': max([d['severity'] for d in detections]) if detections else 0,
+                'requires_action': any(d['requires_action'] for d in detections),
+                'anomaly_types': list(set(d['class_name'] for d in detections))
+            }
+        }
+        
+        # Save JSON result
+        if save_results:
+            os.makedirs(output_dir, exist_ok=True)
+            filename = Path(image_path).stem
+            result_path = os.path.join(output_dir, f"{filename}_result.json")
+            with open(result_path, 'w') as f:
+                json.dump(result, f, indent=2)
+        
+        return result
+    
+    def predict_directory(
+        self,
+        input_dir: str,
+        output_dir: str = 'output',
+        pattern: str = '*.jpg'
+    ) -> List[Dict]:
+        """
+        Run prediction on all images in directory.
+        
+        Args:
+            input_dir: Input directory
+            output_dir: Output directory
+            pattern: File pattern to match
+            
+        Returns:
+            List of prediction results
+        """
+        image_paths = list(Path(input_dir).glob(pattern))
+        image_paths += list(Path(input_dir).glob(pattern.replace('jpg', 'png')))
+        
+        results = []
+        for img_path in image_paths:
+            # Try to extract ATM ID from filename
+            # Expected format: {atm_id}_{timestamp}_{angle}.jpg
+            filename = img_path.stem
+            parts = filename.split('_')
+            atm_id = parts[0] if len(parts) > 0 else None
+            camera_angle = parts[-1] if len(parts) > 2 else None
+            
+            result = self.predict_image(
+                str(img_path),
+                atm_id=atm_id,
+                camera_angle=camera_angle,
+                output_dir=output_dir
+            )
+            results.append(result)
+            
+            print(f"Processed {img_path.name}: {result['summary']['total_anomalies']} anomalies")
+        
+        # Save batch summary
+        summary = {
+            'total_images': len(results),
+            'total_anomalies': sum(r['summary']['total_anomalies'] for r in results),
+            'images_with_anomalies': sum(1 for r in results if r['summary']['total_anomalies'] > 0),
+            'max_severity_found': max((r['summary']['max_severity'] for r in results), default=0),
+            'processing_timestamp': datetime.now().isoformat()
+        }
+        
+        summary_path = os.path.join(output_dir, 'batch_summary.json')
+        with open(summary_path, 'w') as f:
+            json.dump(summary, f, indent=2)
+        
+        print(f"\nBatch complete: {summary['images_with_anomalies']}/{summary['total_images']} images have anomalies")
+        
+        return results
+    
+    def generate_alert(self, result: Dict) -> Optional[Dict]:
+        """
+        Generate alert for high-severity detections.
+        
+        Args:
+            result: Prediction result
+            
+        Returns:
+            Alert dict or None if no alert needed
+        """
+        if not result['summary']['requires_action']:
+            return None
+        
+        critical = [d for d in result['detections'] if d['severity'] >= 4]
+        
+        alert = {
+            'alert_id': f"ATM-{result['atm_id']}-{datetime.now().strftime('%Y%m%d%H%M%S')}",
+            'atm_id': result['atm_id'],
+            'timestamp': result['timestamp'],
+            'severity': result['summary']['max_severity'],
+            'anomalies': critical,
+            'image_path': result['image_path'],
+            'recommended_action': self._get_recommended_action(critical),
+            'status': 'new'
+        }
+        
+        return alert
+    
+    def _get_recommended_action(self, anomalies: List[Dict]) -> str:
+        """Get recommended action based on anomalies."""
+        types = [a['class_name'] for a in anomalies]
+        
+        if 'skimming_device' in types or 'suspicious_attachment' in types:
+            return "IMMEDIATE: Dispatch security team. Do not allow customer use."
+        elif 'physical_damage' in types or 'vandalism' in types:
+            return "URGENT: Schedule repair. Assess security camera footage."
+        elif 'out_of_service' in types:
+            return "Schedule maintenance visit. Check error logs remotely."
+        elif 'screen_damage' in types:
+            return "Schedule screen replacement. Consider temporary closure."
+        else:
+            return "Schedule routine maintenance. Monitor for escalation."
+
+
+def main():
+    parser = argparse.ArgumentParser(description='ATM Anomaly Detection')
+    parser.add_argument('--model', required=True, help='Path to trained model')
+    parser.add_argument('--input', required=True, help='Input image or directory')
+    parser.add_argument('--output', default='output', help='Output directory')
+    parser.add_argument('--conf', type=float, default=0.25, help='Confidence threshold')
+    parser.add_argument('--save', action='store_true', help='Save annotated images')
+    
+    args = parser.parse_args()
+    
+    # Initialize predictor
+    predictor = ATMPredictor(
+        model_path=args.model,
+        conf_threshold=args.conf
+    )
+    
+    # Run prediction
+    if os.path.isfile(args.input):
+        result = predictor.predict_image(
+            args.input,
+            save_results=args.save,
+            output_dir=args.output
+        )
+        
+        print(f"\nResults for {args.input}:")
+        print(f"  Anomalies found: {result['summary']['total_anomalies']}")
+        print(f"  Max severity: {result['summary']['max_severity']}")
+        
+        for det in result['detections']:
+            print(f"  - {det['class_name']}: {det['confidence']:.2f} (severity {det['severity']})")
+        
+        # Generate alert if needed
+        alert = predictor.generate_alert(result)
+        if alert:
+            print(f"\nALERT GENERATED: {alert['recommended_action']}")
+    
+    elif os.path.isdir(args.input):
+        results = predictor.predict_directory(
+            args.input,
+            output_dir=args.output
+        )
+    
+    else:
+        print(f"Error: {args.input} is not a valid file or directory")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

atm-anomaly-detection/src/models/anomaly_detector.py

@@ -0,0 +1,337 @@
+"""
+ATM Anomaly Detection Model
+
+YOLOv8-based anomaly detector for ATM images.
+Detects: damage, graffiti, obstruction, skimming devices, out-of-service
+"""
+
+import torch
+import torch.nn as nn
+from ultralytics import YOLO
+from pathlib import Path
+from typing import List, Dict, Tuple, Optional
+import numpy as np
+import cv2
+
+class ATMAnomalyDetector:
+    """
+    ATM Anomaly Detection using YOLOv8.
+    
+    Usage:
+        detector = ATMAnomalyDetector(model_path='models/best.pt')
+        results = detector.predict('path/to/image.jpg')
+    """
+    
+    # Anomaly class mapping
+    CLASS_NAMES = {
+        0: 'physical_damage',
+        1: 'vandalism',
+        2: 'graffiti',
+        3: 'dirt_debris',
+        4: 'obstruction',
+        5: 'skimming_device',
+        6: 'suspicious_attachment',
+        7: 'out_of_service',
+        8: 'screen_damage',
+        9: 'cash_jam',
+        10: 'receipt_jam',
+        11: 'lighting_failure',
+        12: 'camera_blind',
+        13: 'network_down'
+    }
+    
+    SEVERITY_MAP = {
+        'skimming_device': 5,
+        'suspicious_attachment': 5,
+        'physical_damage': 4,
+        'vandalism': 4,
+        'camera_blind': 4,
+        'obstruction': 3,
+        'out_of_service': 3,
+        'screen_damage': 3,
+        'cash_jam': 3,
+        'lighting_failure': 3,
+        'network_down': 3,
+        'graffiti': 2,
+        'dirt_debris': 2,
+        'receipt_jam': 2
+    }
+    
+    def __init__(
+        self,
+        model_path: Optional[str] = None,
+        conf_threshold: float = 0.25,
+        iou_threshold: float = 0.45,
+        device: str = 'auto'
+    ):
+        """
+        Initialize detector.
+        
+        Args:
+            model_path: Path to trained YOLO model
+            conf_threshold: Confidence threshold for detections
+            iou_threshold: IoU threshold for NMS
+            device: 'cpu', 'cuda', or 'auto'
+        """
+        self.conf_threshold = conf_threshold
+        self.iou_threshold = iou_threshold
+        
+        # Set device
+        if device == 'auto':
+            self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        else:
+            self.device = device
+        
+        # Load model
+        if model_path and Path(model_path).exists():
+            self.model = YOLO(model_path)
+        else:
+            # Load pretrained COCO model as base
+            print("No trained model found. Loading YOLOv8n pretrained...")
+            self.model = YOLO('yolov8n.pt')
+        
+        self.model.to(self.device)
+    
+    def predict(
+        self,
+        image_path: str,
+        save: bool = False,
+        save_dir: Optional[str] = None
+    ) -> List[Dict]:
+        """
+        Run anomaly detection on image.
+        
+        Args:
+            image_path: Path to image file
+            save: Whether to save annotated image
+            save_dir: Directory to save annotations
+            
+        Returns:
+            List of detection dicts with keys:
+                - class_id: int
+                - class_name: str
+                - confidence: float
+                - bbox: [x1, y1, x2, y2]
+                - severity: int
+        """
+        # Run inference
+        results = self.model(
+            image_path,
+            conf=self.conf_threshold,
+            iou=self.iou_threshold,
+            device=self.device,
+            verbose=False
+        )
+        
+        detections = []
+        
+        for result in results:
+            boxes = result.boxes
+            if boxes is None:
+                continue
+            
+            for box in boxes:
+                class_id = int(box.cls.item())
+                confidence = float(box.conf.item())
+                bbox = box.xyxy[0].cpu().numpy().tolist()
+                
+                class_name = self.CLASS_NAMES.get(class_id, 'unknown')
+                severity = self.SEVERITY_MAP.get(class_name, 1)
+                
+                detection = {
+                    'class_id': class_id,
+                    'class_name': class_name,
+                    'confidence': round(confidence, 4),
+                    'bbox': [round(x, 2) for x in bbox],
+                    'severity': severity,
+                    'requires_action': severity >= 4
+                }
+                detections.append(detection)
+        
+        # Sort by severity (highest first)
+        detections.sort(key=lambda x: x['severity'], reverse=True)
+        
+        # Save annotated image if requested
+        if save and save_dir:
+            self._save_annotated(image_path, detections, save_dir)
+        
+        return detections
+    
+    def predict_batch(
+        self,
+        image_paths: List[str],
+        batch_size: int = 8
+    ) -> List[List[Dict]]:
+        """
+        Run detection on batch of images.
+        
+        Args:
+            image_paths: List of image paths
+            batch_size: Batch size for inference
+            
+        Returns:
+            List of detection lists
+        """
+        all_detections = []
+        
+        for i in range(0, len(image_paths), batch_size):
+            batch = image_paths[i:i + batch_size]
+            results = self.model(
+                batch,
+                conf=self.conf_threshold,
+                iou=self.iou_threshold,
+                device=self.device,
+                verbose=False
+            )
+            
+            for result in results:
+                detections = []
+                boxes = result.boxes
+                
+                if boxes is not None:
+                    for box in boxes:
+                        class_id = int(box.cls.item())
+                        confidence = float(box.conf.item())
+                        bbox = box.xyxy[0].cpu().numpy().tolist()
+                        class_name = self.CLASS_NAMES.get(class_id, 'unknown')
+                        
+                        detections.append({
+                            'class_id': class_id,
+                            'class_name': class_name,
+                            'confidence': round(confidence, 4),
+                            'bbox': [round(x, 2) for x in bbox],
+                            'severity': self.SEVERITY_MAP.get(class_name, 1),
+                            'requires_action': self.SEVERITY_MAP.get(class_name, 1) >= 4
+                        })
+                
+                detections.sort(key=lambda x: x['severity'], reverse=True)
+                all_detections.append(detections)
+        
+        return all_detections
+    
+    def _save_annotated(
+        self,
+        image_path: str,
+        detections: List[Dict],
+        save_dir: str
+    ):
+        """Save annotated image with bounding boxes."""
+        import os
+        os.makedirs(save_dir, exist_ok=True)
+        
+        image = cv2.imread(image_path)
+        
+        for det in detections:
+            x1, y1, x2, y2 = map(int, det['bbox'])
+            color = (0, 0, 255) if det['severity'] >= 4 else (0, 165, 255)
+            
+            cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
+            
+            label = f"{det['class_name']} {det['confidence']:.2f}"
+            cv2.putText(
+                image, label, (x1, y1 - 10),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2
+            )
+        
+        filename = Path(image_path).name
+        save_path = os.path.join(save_dir, f"annotated_{filename}")
+        cv2.imwrite(save_path, image)
+    
+    def train(
+        self,
+        data_yaml: str,
+        epochs: int = 100,
+        batch_size: int = 16,
+        img_size: int = 640,
+        output_dir: str = 'models/checkpoints'
+    ):
+        """
+        Train model on custom dataset.
+        
+        Args:
+            data_yaml: Path to data.yaml for YOLO
+            epochs: Number of training epochs
+            batch_size: Batch size
+            img_size: Input image size
+            output_dir: Where to save checkpoints
+        """
+        self.model.train(
+            data=data_yaml,
+            epochs=epochs,
+            batch=batch_size,
+            imgsz=img_size,
+            project=output_dir,
+            name='atm_anomaly',
+            device=self.device
+        )
+    
+    def export(
+        self,
+        format: str = 'onnx',
+        output_path: Optional[str] = None
+    ):
+        """
+        Export model to deployment format.
+        
+        Args:
+            format: 'onnx', 'torchscript', 'openvino', 'engine'
+            output_path: Where to save exported model
+        """
+        self.model.export(format=format)
+        
+        if output_path:
+            import shutil
+            default_path = f"models/checkpoints/atm_anomaly/weights/best.{format}"
+            if Path(default_path).exists():
+                shutil.copy2(default_path, output_path)
+                print(f"Exported to {output_path}")
+
+
+def create_data_yaml(
+    train_dir: str,
+    val_dir: str,
+    test_dir: Optional[str] = None,
+    class_names: Optional[List[str]] = None,
+    output_path: str = 'data/data.yaml'
+):
+    """
+    Create YOLO data.yaml configuration file.
+    
+    Args:
+        train_dir: Path to train directory
+        val_dir: Path to validation directory
+        test_dir: Path to test directory (optional)
+        class_names: List of class names
+        output_path: Where to save yaml
+    """
+    if class_names is None:
+        class_names = list(ATMAnomalyDetector.CLASS_NAMES.values())
+    
+    import yaml
+    
+    data = {
+        'path': str(Path(train_dir).parent),
+        'train': str(Path(train_dir).relative_to(Path(train_dir).parent)),
+        'val': str(Path(val_dir).relative_to(Path(val_dir).parent)),
+        'nc': len(class_names),
+        'names': class_names
+    }
+    
+    if test_dir:
+        data['test'] = str(Path(test_dir).relative_to(Path(test_dir).parent))
+    
+    with open(output_path, 'w') as f:
+        yaml.dump(data, f, default_flow_style=False)
+    
+    print(f"Created {output_path}")
+
+
+if __name__ == "__main__":
+    # Example usage
+    detector = ATMAnomalyDetector()
+    
+    # Single image prediction
+    results = detector.predict('data/test/atm_001.jpg', save=True, save_dir='output')
+    print(f"Found {len(results)} anomalies")
+    for r in results:
+        print(f"  - {r['class_name']}: {r['confidence']:.2f} (severity: {r['severity']})")

atm-anomaly-detection/src/training/train.py

@@ -0,0 +1,111 @@
+"""
+ATM Anomaly Detection Training Script
+
+Trains YOLOv8 model on annotated ATM images.
+"""
+
+import os
+import sys
+import yaml
+import argparse
+from pathlib import Path
+from datetime import datetime
+
+# Add parent to path
+sys.path.append(str(Path(__file__).parent.parent))
+
+from models.anomaly_detector import ATMAnomalyDetector
+
+
+def load_config(config_path: str) -> dict:
+    """Load training configuration from YAML."""
+    with open(config_path, 'r') as f:
+        return yaml.safe_load(f)
+
+
+def train_model(config: dict):
+    """
+    Train anomaly detection model.
+    
+    Args:
+        config: Training configuration dict
+    """
+    print("=" * 60)
+    print("ATM Anomaly Detection Training")
+    print("=" * 60)
+    print(f"Start time: {datetime.now().isoformat()}")
+    print(f"Base model: {config['model']['base']}")
+    print(f"Epochs: {config['training']['epochs']}")
+    print(f"Batch size: {config['training']['batch_size']}")
+    print(f"Image size: {config['training']['image_size']}")
+    print("=" * 60)
+    
+    # Initialize detector with base model
+    detector = ATMAnomalyDetector(
+        model_path=config['model']['base'],
+        device=config['hardware']['device']
+    )
+    
+    # Create data.yaml
+    data_config = {
+        'path': str(Path(config['data']['train']).parent.parent),
+        'train': str(Path(config['data']['train']).relative_to(Path(config['data']['train']).parent.parent)),
+        'val': str(Path(config['data']['val']).relative_to(Path(config['data']['val']).parent.parent)),
+        'nc': config['model']['classes'],
+        'names': config['data']['names']
+    }
+    
+    if Path(config['data']['test']).exists():
+        data_config['test'] = str(Path(config['data']['test']).relative_to(Path(config['data']['test']).parent.parent))
+    
+    data_yaml_path = 'data/data.yaml'
+    os.makedirs('data', exist_ok=True)
+    with open(data_yaml_path, 'w') as f:
+        yaml.dump(data_config, f, default_flow_style=False)
+    
+    print(f"\nData config saved to {data_yaml_path}")
+    print(f"Training samples: {count_samples(config['data']['train'])}")
+    print(f"Validation samples: {count_samples(config['data']['val'])}")
+    
+    # Train
+    detector.train(
+        data_yaml=data_yaml_path,
+        epochs=config['training']['epochs'],
+        batch_size=config['training']['batch_size'],
+        img_size=config['training']['image_size'],
+        output_dir=config['output']['checkpoint_dir']
+    )
+    
+    print("\n" + "=" * 60)
+    print("Training complete!")
+    print(f"End time: {datetime.now().isoformat()}")
+    print("=" * 60)
+
+
+def count_samples(data_dir: str) -> int:
+    """Count number of images in directory."""
+    if not Path(data_dir).exists():
+        return 0
+    return len(list(Path(data_dir).glob('**/*.jpg'))) + len(list(Path(data_dir).glob('**/*.png')))
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Train ATM Anomaly Detection Model')
+    parser.add_argument('--config', default='config/training.yaml', help='Training config file')
+    parser.add_argument('--resume', type=str, help='Resume from checkpoint')
+    
+    args = parser.parse_args()
+    
+    # Load config
+    config = load_config(args.config)
+    
+    # Override with resume if provided
+    if args.resume:
+        config['model']['base'] = args.resume
+    
+    # Train
+    train_model(config)
+
+
+if __name__ == "__main__":
+    main()