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2026-07-18 01:34:52 +05:30
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+import type { AIProvider, DetectedObject, MediaItem } from '@photo-gallery/sdk';
+
+/**
+ * Free, in-browser AI provider using TensorFlow.js + COCO-SSD object detection.
+ * No API key, no server — the model (~6 MB) downloads from the public TF model
+ * CDN on first use and runs on the GPU via the WebGL backend.
+ *
+ * Detects 80 COCO classes (person, dog, cat, car, laptop, chair, dining table,
+ * tv, bottle, cup, …) which power search, find-similar and the Objects browser.
+ */
+export function createTensorflowProvider(): AIProvider {
+  // Loaded lazily and memoized so the heavy bundle/model is fetched only once.
+  let modelPromise: Promise<CocoModel> | null = null;
+
+  async function getModel(): Promise<CocoModel> {
+    if (!modelPromise) {
+      modelPromise = (async () => {
+        const tf = await import('@tensorflow/tfjs');
+        try {
+          await tf.setBackend('webgl');
+        } catch {
+          // Fall back to the default backend if WebGL is unavailable.
+        }
+        await tf.ready();
+        const cocoSsd = await import('@tensorflow-models/coco-ssd');
+        return cocoSsd.load({ base: 'lite_mobilenet_v2' }) as unknown as CocoModel;
+      })();
+    }
+    return modelPromise;
+  }
+
+  return {
+    name: 'tensorflow-coco-ssd',
+
+    async detectObjects(item: MediaItem, image): Promise<DetectedObject[]> {
+      const model = await getModel();
+      const el = image as HTMLImageElement;
+      const w = el.naturalWidth || el.width || item.width || 1;
+      const h = el.naturalHeight || el.height || item.height || 1;
+      const predictions = await model.detect(el, 20, 0.4);
+      return predictions.map((p) => ({
+        label: p.class,
+        confidence: p.score,
+        box: {
+          x: p.bbox[0] / w,
+          y: p.bbox[1] / h,
+          width: p.bbox[2] / w,
+          height: p.bbox[3] / h,
+        },
+      }));
+    },
+  };
+}
+
+interface CocoPrediction {
+  bbox: [number, number, number, number];
+  class: string;
+  score: number;
+}
+interface CocoModel {
+  detect(
+    img: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement,
+    maxNumBoxes?: number,
+    minScore?: number,
+  ): Promise<CocoPrediction[]>;
+}