feat(platform): close AI expense value loop

Add tenant-safe value, telemetry, connector, commercial, and production-readiness foundations.

web/tests/workbench-ai-mode-switch.test.mjs

@@ -28,128 +28,130 @@ function countGifFrameBlocks(buffer) {
   return count
 }
 
+function readAssetFrames(assetPath, options = {}) {
+  const metadata = execFileSync('identify', ['-format', '%w %h %n\n', assetPath], {
+    encoding: 'utf8'
+  }).trim().split('\n')[0].split(/\s+/).map(Number)
+  const sourceWidth = metadata[0]
+  const sourceHeight = metadata[1]
+  const frameCount = metadata[2] || 1
+  const width = options.width || sourceWidth
+  const height = options.height || sourceHeight
+  const convertArgs = [assetPath, '-coalesce']
+  if (options.width || options.height) {
+    convertArgs.push('-resize', `${width}x${height}!`)
+  }
+  convertArgs.push('-alpha', 'off', '-depth', '8', 'rgb:-')
+  const pixels = execFileSync('convert', convertArgs, {
+    maxBuffer: 64 * 1024 * 1024
+  })
+  const frameSize = width * height * 3
+  return {
+    frameCount: Math.min(frameCount, Math.floor(pixels.length / frameSize)),
+    frameSize,
+    height,
+    pixels,
+    width
+  }
+}
+
 function measureGifMotion(assetPath) {
-  const script = `
-from PIL import Image, ImageSequence
-import json
-import sys
-
-image = Image.open(sys.argv[1])
-frames = [frame.convert("RGB").resize((64, 64)) for frame in ImageSequence.Iterator(image)]
-
-def delta(left, right):
-    left_pixels = left.load()
-    right_pixels = right.load()
-    total = 0
-    for y in range(64):
-        for x in range(64):
-            a = left_pixels[x, y]
-            b = right_pixels[x, y]
-            total += abs(a[0] - b[0]) + abs(a[1] - b[1]) + abs(a[2] - b[2])
-    return total / (64 * 64 * 3)
-
-adjacent = [delta(frames[index], frames[index + 1]) for index in range(len(frames) - 1)]
-adjacent_sorted = sorted(adjacent)
-median = adjacent_sorted[len(adjacent_sorted) // 2]
-print(json.dumps({
-    "medianAdjacentDelta": median,
-    "seamDelta": delta(frames[-1], frames[0])
-}))
-`
-  return JSON.parse(execFileSync('python3', ['-', assetPath], {
-    encoding: 'utf8',
-    input: script
-  }).trim())
+  const { frameCount, frameSize, pixels } = readAssetFrames(assetPath, { width: 64, height: 64 })
+  const delta = (leftFrame, rightFrame) => {
+    const leftOffset = leftFrame * frameSize
+    const rightOffset = rightFrame * frameSize
+    let total = 0
+    for (let index = 0; index < frameSize; index += 1) {
+      total += Math.abs(pixels[leftOffset + index] - pixels[rightOffset + index])
+    }
+    return total / frameSize
+  }
+  const adjacent = Array.from(
+    { length: Math.max(0, frameCount - 1) },
+    (_, index) => delta(index, index + 1)
+  ).sort((left, right) => left - right)
+  return {
+    medianAdjacentDelta: adjacent[Math.floor(adjacent.length / 2)] || 0,
+    seamDelta: delta(frameCount - 1, 0)
+  }
 }
 
 function measureGifDuration(assetPath) {
-  const script = `
-from PIL import Image
-import sys
-
-image = Image.open(sys.argv[1])
-total = 0
-for index in range(getattr(image, "n_frames", 1)):
-    image.seek(index)
-    total += image.info.get("duration", 0)
-print(total)
-`
-  return Number(execFileSync('python3', ['-', assetPath], {
-    encoding: 'utf8',
-    input: script
-  }).trim())
+  const delays = execFileSync('identify', ['-format', '%T\n', assetPath], {
+    encoding: 'utf8'
+  }).trim().split('\n').map(Number)
+  return delays.reduce((total, delay) => total + delay * 10, 0)
 }
 
 function measureOrbAssetPresentation(assetPath) {
-  const script = `
-from PIL import Image
-import json
-import sys
+  const { frameCount, frameSize, height, pixels, width } = readAssetFrames(assetPath)
+  let minimumCornerLuma = 255
+  let maximumCornerLuma = 0
+  let minimumBackgroundSimilarityRatio = 1
+  let minimumForegroundWidthRatio = 1
+  let minimumForegroundHeightRatio = 1
 
-image = Image.open(sys.argv[1])
-frame_count = getattr(image, "n_frames", 1)
-width, height = image.size
-minimum_corner_luma = 255
-maximum_corner_luma = 0
-minimum_background_similarity_ratio = 1
-minimum_foreground_width_ratio = 1
-minimum_foreground_height_ratio = 1
-
-for index in range(frame_count):
-    if frame_count > 1:
-        image.seek(index)
-    rgb = image.convert("RGB")
-    corners = [
-        rgb.getpixel((0, 0)),
-        rgb.getpixel((width - 1, 0)),
-        rgb.getpixel((0, height - 1)),
-        rgb.getpixel((width - 1, height - 1)),
+  for (let frame = 0; frame < frameCount; frame += 1) {
+    const frameOffset = frame * frameSize
+    const pixelAt = (x, y) => {
+      const offset = frameOffset + (y * width + x) * 3
+      return [pixels[offset], pixels[offset + 1], pixels[offset + 2]]
+    }
+    const corners = [
+      pixelAt(0, 0),
+      pixelAt(width - 1, 0),
+      pixelAt(0, height - 1),
+      pixelAt(width - 1, height - 1)
     ]
-    corner_lumas = [sum(pixel) / 3 for pixel in corners]
-    minimum_corner_luma = min(minimum_corner_luma, min(corner_lumas))
-    maximum_corner_luma = max(maximum_corner_luma, max(corner_lumas))
-    background = tuple(round(sum(pixel[channel] for pixel in corners) / len(corners)) for channel in range(3))
-    foreground_mask = Image.new("L", (width, height), 0)
-    foreground_pixels = foreground_mask.load()
-    background_similarity = 0
-    rgb_pixels = rgb.load()
-    for y in range(height):
-        for x in range(width):
-            pixel = rgb_pixels[x, y]
-            diff = sum(abs(pixel[channel] - background[channel]) for channel in range(3))
-            if diff > 22:
-                foreground_pixels[x, y] = 255
-            if diff <= 12:
-                background_similarity += 1
-    foreground_box = foreground_mask.getbbox()
-    if foreground_box:
-        minimum_foreground_width_ratio = min(
-            minimum_foreground_width_ratio,
-            (foreground_box[2] - foreground_box[0]) / width
-        )
-        minimum_foreground_height_ratio = min(
-            minimum_foreground_height_ratio,
-            (foreground_box[3] - foreground_box[1]) / height
-        )
-    minimum_background_similarity_ratio = min(
-        minimum_background_similarity_ratio,
-        background_similarity / (width * height)
-    )
+    const cornerLumas = corners.map((pixel) => (pixel[0] + pixel[1] + pixel[2]) / 3)
+    minimumCornerLuma = Math.min(minimumCornerLuma, ...cornerLumas)
+    maximumCornerLuma = Math.max(maximumCornerLuma, ...cornerLumas)
+    const background = [0, 1, 2].map((channel) => Math.round(
+      corners.reduce((total, pixel) => total + pixel[channel], 0) / corners.length
+    ))
+    let backgroundSimilarity = 0
+    let minX = width
+    let minY = height
+    let maxX = -1
+    let maxY = -1
 
-print(json.dumps({
-    "minimumCornerLuma": minimum_corner_luma,
-    "maximumCornerLuma": maximum_corner_luma,
-    "minimumBackgroundSimilarityRatio": minimum_background_similarity_ratio,
-    "minimumForegroundWidthRatio": minimum_foreground_width_ratio,
-    "minimumForegroundHeightRatio": minimum_foreground_height_ratio,
-    "width": width,
-    "height": height
-}))
-`
-  return JSON.parse(execFileSync('python3', ['-', assetPath], {
-    encoding: 'utf8',
-    input: script
-  }).trim())
+    for (let y = 0; y < height; y += 1) {
+      for (let x = 0; x < width; x += 1) {
+        const pixel = pixelAt(x, y)
+        const diff = pixel.reduce(
+          (total, value, channel) => total + Math.abs(value - background[channel]),
+          0
+        )
+        if (diff > 22) {
+          minX = Math.min(minX, x)
+          minY = Math.min(minY, y)
+          maxX = Math.max(maxX, x)
+          maxY = Math.max(maxY, y)
+        }
+        if (diff <= 12) {
+          backgroundSimilarity += 1
+        }
+      }
+    }
+    if (maxX >= minX && maxY >= minY) {
+      minimumForegroundWidthRatio = Math.min(minimumForegroundWidthRatio, (maxX - minX + 1) / width)
+      minimumForegroundHeightRatio = Math.min(minimumForegroundHeightRatio, (maxY - minY + 1) / height)
+    }
+    minimumBackgroundSimilarityRatio = Math.min(
+      minimumBackgroundSimilarityRatio,
+      backgroundSimilarity / (width * height)
+    )
+  }
+
+  return {
+    minimumCornerLuma,
+    maximumCornerLuma,
+    minimumBackgroundSimilarityRatio,
+    minimumForegroundWidthRatio,
+    minimumForegroundHeightRatio,
+    width,
+    height
+  }
 }
 
 const appShell = readSource('../src/views/AppShellRouteView.vue')
@@ -230,7 +232,7 @@ test('AI mode screen follows the approved reference structure', () => {
   assert.match(aiModeSurface, /费用测算中，请稍等/)
   assert.match(aiModeSurface, /rows="3"/)
   assert.match(aiModeSurface, /workbench-ai-composer-toolbar/)
-  assert.match(aiModeSurface, /<article v-for="file in runtime\.selectedFileCards"[\s\S]*class="workbench-ai-file-card"/)
+  assert.match(aiModeSurface, /<article[\s\S]{0,140}v-for="file in runtime\.selectedFileCards"[\s\S]{0,140}class="workbench-ai-file-card"/)
   assert.match(aiModeSurface, /class="workbench-ai-file-card__ocr"/)
   assert.match(aiModeSurface, /file\.ocrState\?\.label/)
   assert.match(aiModeSurface, /mdi mdi-text-recognition/)
@@ -239,7 +241,7 @@ test('AI mode screen follows the approved reference structure', () => {
   assert.match(aiModeSurface, /:aria-label="`移除附件 \$\{file\.name\}`"/)
   assert.match(aiModeSurface, /function removeAiModeFile\(fileKey\)/)
   assert.match(aiModeSurface, /const selectedFileCards = computed/)
-  assert.match(aiModeSurface, /resolveAiComposerFileType\(file\)/)
+  assert.match(aiModeSurface, /resolveAiComposerFileType\(file, previewAsset\)/)
   assert.match(aiModeSurface, /AI_COMPOSER_FILE_TYPE_META = \{[\s\S]*pdf:\s*\{ label:\s*'PDF'/)
   assert.match(aiModeSurface, /buildFileIdentity,[\s\S]*collectReceiptFiles[\s\S]*travelReimbursementAttachmentModel\.js/)
   assert.match(aiModeSurface, /MAX_ATTACHMENTS,[\s\S]*mergeFilesWithLimit[\s\S]*travelReimbursementAttachmentModel\.js/)
@@ -345,7 +347,8 @@ test('AI mode screen follows the approved reference structure', () => {
   assert.match(aiModeStyles, /\.workbench-ai-answer-markdown :deep\(\.ai-document-query-summary\)/)
   assert.match(aiModeStyles, /\.workbench-ai-answer-markdown :deep\(\.ai-document-query-summary__scope\)/)
   assert.match(aiModeStyles, /\.workbench-ai-answer-markdown :deep\(\.ai-document-card-list\) \{[\s\S]*gap:\s*16px;/)
-  assert.match(aiModeStyles, /\.workbench-ai-answer-markdown :deep\(\.ai-document-card\) \{[\s\S]*url\("\.\.\/\.\.\/ai-document-card-bg\.png"\);/)
+  assert.match(aiModeStyles, /\.workbench-ai-answer-markdown :deep\(\.ai-document-card\) \{[\s\S]*background-color:\s*#ffffff;/)
+  assert.doesNotMatch(aiModeStyles, /ai-document-card-bg\.png/)
   assert.doesNotMatch(aiModeStyles, /\.workbench-ai-answer-markdown :deep\(\.ai-document-card\)::before/)
   assert.match(aiModeStyles, /\.workbench-ai-answer-markdown :deep\(\.ai-document-card__head\) \{[\s\S]*background: var\(--ai-document-card-head-bg\);/)
   assert.match(aiModeStyles, /\.workbench-ai-answer-markdown :deep\(\.ai-document-card\.is-success \.ai-document-card__head\)/)
@@ -422,7 +425,8 @@ test('AI mode screen follows the approved reference structure', () => {
   assert.match(aiModeStyles, /\.workbench-ai-mode\s*\{[\s\S]*min-height:\s*100%;[\s\S]*background:/)
   assert.match(aiModeStyles, /\.workbench-ai-mode\.has-conversation\s*\{[\s\S]*place-items:\s*stretch;[\s\S]*padding:\s*0;/)
   assert.match(aiModeStyles, /\.workbench-ai-composer\s*\{[\s\S]*border-radius:\s*20px;[\s\S]*box-shadow:/)
-  assert.match(fileStripRule, /flex-wrap:\s*wrap;/)
+  assert.match(fileStripRule, /flex-wrap:\s*nowrap;/)
+  assert.match(fileStripRule, /overflow-x:\s*auto;/)
   assert.match(fileStripRule, /justify-content:\s*flex-start;/)
   assert.match(fileCardRule, /grid-template-columns:\s*48px minmax\(0,\s*1fr\) 30px;/)
   assert.match(fileCardRule, /border-radius:\s*16px;/)
@@ -579,7 +583,7 @@ test('AI mode normal assistant requests include OCR context for uploaded receipt
   assert.match(aiModeSurface, /function buildAiModeReceiptContextCacheKey\(ocrFiles = \[\]\)/)
   assert.match(aiModeSurface, /applyAiModeReceiptRecognitionResult\(ocrFiles, context\)/)
   assert.match(aiModeSurface, /buildFileIdentity\(file\)/)
-  assert.match(aiModeSurface, /watch\(selectedFiles, \(files\) => \{[\s\S]*attachmentFlow\.primeAiModeReceiptContext\(files\)/)
+  assert.match(aiModeSurface, /watch\(selectedFiles, \(files(?:, previousFiles = \[\])?\) => \{[\s\S]*attachmentFlow\.primeAiModeReceiptContext\(files\)/)
   assert.match(aiModeSurface, /async function collectAiModeReceiptContext\(files = \[\]\)/)
   assert.match(aiModeSurface, /cached\?\.status === 'pending'[\s\S]*await cached\.promise/)
   assert.match(aiModeSurface, /collectReceiptFiles\(\{[\s\S]*files:\s*ocrFiles,[\s\S]*recognizeOcrFiles[\s\S]*\}\)/)