include dist in build

services/our/.dockerignore

@@ -1,4 +1,3 @@
-dist
 vibeui
 venv
 src/test

services/our/src/tests/runInferenceOnFrame.unit.test.ts

@@ -1,107 +0,0 @@
-// import { describe, it, expect, vi } from 'vitest';
-// import { runInferenceOnFrame } from '../utils/vibeui';
-// import {
-//   type InferenceSession,
-//   type Tensor,
-// } from 'onnxruntime-node'
-
-// type DetectionOutput = {
-//   bbox: [number, number, number, number];
-//   confidence: number;
-//   classIndex: number;
-// };
-
-// describe('runInferenceOnFrame', () => {
-//   it('parses detections and filters by confidence, rounds classIndex, includes out-of-range classes', async () => {
-//     // Mock session
-//     const mockSession = {
-//       inputNames: ['input'],
-//       outputNames: ['output'],
-//       run: vi.fn().mockResolvedValue({
-//         output: {
-//           data: new Float32Array([
-//             0.1, 0.2, 0.3, 0.4, 0.5, 10,  // valid detection
-//             0.2, 0.3, 0.4, 0.5, 0.2, 5,   // confidence too low, filtered out
-//             0.3, 0.4, 0.5, 0.6, 0.9, 54   // class 54 out of range, but included
-//           ]),
-//           dims: [3, 6]
-//         }
-//       }),
-//     } as unknown as InferenceSession;
-
-//     // Mock tensor input, content irrelevant here
-//     const mockTensor = {} as Tensor;
-
-//     const detections = await runInferenceOnFrame(mockSession, mockTensor);
-
-//     expect(detections).toHaveLength(2);
-//     expect(detections[0]).toEqual({
-//       bbox: [0.1, 0.2, 0.3, 0.4],
-//       confidence: 0.5,
-//       classIndex: 10,
-//     });
-//     expect(detections[1]).toEqual({
-//       bbox: [0.3, 0.4, 0.5, 0.6],
-//       confidence: 0.9,
-//       classIndex: 54,
-//     });
-//   });
-
-//   it('throws if output missing or data is wrong type', async () => {
-//     const badSession = {
-//       inputNames: ['input'],
-//       outputNames: ['output'],
-//       run: vi.fn().mockResolvedValue({
-//         output: {
-//           data: [1, 2, 3],  // not Float32Array
-//           dims: [1, 6]
-//         }
-//       }),
-//     } as unknown as InferenceSession;
-
-//     await expect(runInferenceOnFrame(badSession, {} as Tensor)).rejects.toThrow(
-//       'Unexpected model output format'
-//     );
-//   });
-// });
-
-
-
-import { describe, it, expect } from 'vitest';
-import fs from 'fs/promises';
-import path, { resolve } from 'path';
-import ort from 'onnxruntime-node';
-import sharp from 'sharp';
-import { preprocessImage, runModelInference } from '../utils/vibeui';
-
-
-const __dirname = import.meta.dirname;
-const FIXTURE_DIR = resolve(__dirname, 'fixtures');
-const DIST_DIR = resolve(__dirname, '..', '..', 'dist');
-const VIBEUI_DIR = resolve(DIST_DIR, 'vibeui');
-const VIDEO = resolve(FIXTURE_DIR, 'sample.mp4');
-const MODEL_PATH = resolve(VIBEUI_DIR, 'vibeui.onnx');
-const IMAGE_PATH = resolve(FIXTURE_DIR, 'prediction/frames/000001.jpg');
-
-
-describe.skip('runInferenceOnFrame integration', () => {
-  it('runs inference on real image and returns valid detections', async () => {
-    // Load ONNX model session
-    const session = await ort.InferenceSession.create(MODEL_PATH);
-
-    // Prepare input tensor from JPG image
-    const inputTensor = await preprocessImage(IMAGE_PATH);
-
-    // Run inference
-    const detections = await runModelInference(session, inputTensor);
-
-    // Check output is not empty and class indices are within range
-    expect(detections.length).toBeGreaterThan(0);
-    for (const det of detections) {
-      expect(det.confidence).toBeGreaterThan(0.3);
-      expect(det.classIndex).toBeGreaterThanOrEqual(0);
-      expect(det.classIndex).toBeLessThan(19); // since you have 19 classes
-      expect(det.bbox).toHaveLength(4);
-    }
-  });
-});

services/our/src/tests/vibeuiInference.integration.test.ts

@@ -0,0 +1,26 @@
+import { describe, it, expect } from 'vitest';
+import fs from 'fs/promises';
+import path, { resolve } from 'path';
+import ort from 'onnxruntime-node';
+import sharp from 'sharp';
+import { preprocessImage, inference } from '../utils/vibeui';
+
+
+const __dirname = import.meta.dirname;
+const FIXTURE_DIR = resolve(__dirname, 'fixtures');
+const DIST_DIR = resolve(__dirname, '..', '..', 'dist');
+const VIBEUI_DIR = resolve(DIST_DIR, 'vibeui');
+const VIDEO = resolve(FIXTURE_DIR, 'sample-short.mp4');
+
+
+describe('inference integration', () => {
+  it('runs inference on real video and returns valid detections', { timeout: 60000 }, async () => {
+
+    // Run inference
+    const output = await inference(VIDEO);
+
+    // Check output is not empty and class indices are within range
+    expect(output).toContain('/runs/')
+
+  });
+});