import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers@4.2.0';

export const MODEL_OPTIONS = [
  {
    key: 'rtdetr',
    label: 'RT-DETR R18',
    modelId: 'onnx-community/rtdetr_r18vd',
    note: 'Recommended classroom default: modern real-time transformer-style detector.',
    dtype: 'q8',
  },
  {
    key: 'detr',
    label: 'DETR ResNet-50',
    modelId: 'Xenova/detr-resnet-50',
    note: 'Representative DETR-family detector. Larger download; useful for comparison.',
    dtype: 'q8',
  },
  {
    key: 'yolos',
    label: 'YOLOS Tiny',
    modelId: 'Xenova/yolos-tiny',
    note: 'Small browser-friendly detector. Useful for showing that model choice changes results.',
    dtype: 'q8',
  },
];

const detectorCache = new Map();
export function getModelOption(key) {
  return MODEL_OPTIONS.find((model) => model.key === key) || MODEL_OPTIONS[0];
}

export async function loadDetector(modelKey, progressCallback) {
  const option = getModelOption(modelKey);
  if (detectorCache.has(option.key)) return detectorCache.get(option.key);

  const detector = await pipeline('object-detection', option.modelId, {
    dtype: option.dtype,
    progress_callback: progressCallback,
  });

  detectorCache.set(option.key, detector);
  return detector;
}

export async function detectObjects(imageDataUrl, options = {}) {
  const modelKey = options.modelKey || MODEL_OPTIONS[0].key;
  const threshold = Number.isFinite(options.threshold) ? options.threshold : 0.5;
  const detector = await loadDetector(modelKey, options.progressCallback);
  return detector(imageDataUrl, { threshold });
}