Monitor Assembly Line Health with Evidently and YOLO26

                      
                     
import os
import cv2
import numpy as np
from evidently import ColumnMapping
from evidently.dashboard import Dashboard
from evidently.dashboard.tabs import DataQualityTab
from typing import List, Dict

# Configuration
MODEL_PATH = os.getenv('MODEL_PATH', 'path/to/yolo_model.weights')
VIDEO_SOURCE = os.getenv('VIDEO_SOURCE', '0')  # default to webcam

# Initialize YOLO model
class YOLOModel:
    def __init__(self, model_path: str):
        self.net = cv2.dnn.readNetFromDarknet('path/to/yolo.cfg', model_path)

    def detect(self, frame: np.ndarray) -> List[Dict]:
        # Process frame for YOLO detection
        blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
        self.net.setInput(blob)
        output_layers = self.net.getUnconnectedOutLayersNames()
        detections = self.net.forward(output_layers)
        return detections

# Health monitoring logic
class AssemblyLineMonitor:
    def __init__(self, model: YOLOModel):
        self.model = model

    def monitor(self):
        cap = cv2.VideoCapture(VIDEO_SOURCE)
        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            detections = self.model.detect(frame)
            # Process detections and log health metrics
            # TODO: Implement health metrics logging

        cap.release()

# Main execution
if __name__ == '__main__':
    try:
        yolo_model = YOLOModel(MODEL_PATH)
        monitor = AssemblyLineMonitor(yolo_model)
        monitor.monitor()
    except Exception as e:
        print(f'Error: {str(e)}')