demo: real synthetic headshots — fetch pool + serve route + UI wire
Three layers shipped:
1. SCRIPT — scripts/staffing/fetch_face_pool.py
Pulls N synthetic StyleGAN faces from thispersondoesnotexist.com
into data/headshots/face_NNNN.jpg, writes manifest.jsonl. Idempotent:
re-running skips existing files. Optional gender tagging via deepface
(currently unavailable on this box; the script handles ImportError
gracefully and tags everything as untagged). Fetched 198 faces with
concurrency=3 in ~67s.
2. SERVER — /headshots/:key route in mcp-server/index.ts
Loads manifest at first hit, caches in globalThis._faces. Hashes the
key with djb2-style mixing → pool index → returns the JPG. Same
key always gets the same face (deterministic). Accepts
?g=man|woman&e=caucasian|black|hispanic|south_asian|east_asian|middle_eastern
to bias pool selection — the gender/ethnicity buckets fall back to
the full pool when no tagged matches exist. Cache-Control:
86400 immutable so faces ride the browser cache after first hit.
/headshots/__reload re-reads the manifest without restart.
3. UI — search.html + console.html worker cards
Re-added overlay <img> on top of the monogram .av circle. img.src
= /headshots/<encoded-key>?g=<hint>&e=<hint>. img.onerror removes
the failed image so the monogram stays visible if the face pool
isn't fetched / CDN is blocked. .av now has overflow:hidden +
position:relative to clip the img to a perfect circle.
Forced-confident name resolution (J: "we're CREATING the profile,
created as though you truly have the information Xavier is more
likely Hispanic and he's a male"):
genderFor(name) — looks up MALE_NAMES + FEMALE_NAMES,
falls back to a deterministic hash split
so unknown names spread ~50/50. Sets now
include cross-cultural names: Alejandro/
Andres/Mateo/Santiago/Joaquin/Cesar/Hugo/
Felipe/Gerardo/Salvador/Ramon (Hispanic),
Raj/Anil/Vikram/Krishna/Pradeep (South
Asian), Wei/Yi/Hiroshi/Akira/Hyun (East
Asian), Demetrius/Kareem/DaQuan/Khalil
(Black), Omar/Khalid/Hassan/Ahmed/Bilal
(Middle Eastern). FEMALE_NAMES extended
in parallel.
guessEthnicityFromFirstName(name)
— confident default of 'caucasian' for any
name not in the cultural buckets so every
worker resolves to a category the face
pool can be biased toward. Order: ME → Black
→ Hispanic → South Asian → East Asian →
Caucasian (matters where names overlap,
e.g. Aisha appears in ME + Black, biases
toward ME for visual fit).
Both helpers also ported into console.html so the triage backfills
and try-it-yourself rendering get the same hint stack.
Privacy note in the script + route comments: the synthetic data uses
the worker's name as the seed; production should hash worker_id (not
name) to avoid leaking PII to a third-party CDN. The fetch URL itself
is referenced once per pool build, not per-worker.
.gitignore — added data/headshots/face_*.jpg (~100MB for 198 faces;
the manifest + script are tracked). Re-running the script on a fresh
checkout rebuilds the pool from scratch.
Verified end-to-end via playwright on devop.live/lakehouse:
forklift query → 10 worker cards
10/10 with face images (real synthetic headshots, not monograms)
0/10 broken
Alejandro G. Nelson → ?g=man&e=hispanic
Patricia K. Garcia → ?g=woman&e=caucasian
Each name → unique face, deterministic across loads.
Console triage backfills get the same treatment.
This commit is contained in:
root
parent
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commit
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.gitignore
vendored
4
.gitignore
vendored
@ -4,3 +4,7 @@
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.env
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__pycache__/
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*.pyc
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# Headshot pool — binary face JPGs are fetched by scripts/staffing/fetch_face_pool.py
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# (synthetic StyleGAN, ~100MB for 200 faces). Manifest + fetch script are tracked.
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data/headshots/face_*.jpg
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198
data/headshots/manifest.jsonl
Normal file
198
data/headshots/manifest.jsonl
Normal file
@ -0,0 +1,198 @@
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{"id": 0, "file": "face_0000.jpg", "gender": null}
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{"id": 1, "file": "face_0001.jpg", "gender": null}
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{"id": 2, "file": "face_0002.jpg", "gender": null}
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{"id": 3, "file": "face_0003.jpg", "gender": null}
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{"id": 4, "file": "face_0004.jpg", "gender": null}
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{"id": 5, "file": "face_0005.jpg", "gender": null}
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{"id": 6, "file": "face_0006.jpg", "gender": null}
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{"id": 7, "file": "face_0007.jpg", "gender": null}
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{"id": 8, "file": "face_0008.jpg", "gender": null}
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{"id": 9, "file": "face_0009.jpg", "gender": null}
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{"id": 10, "file": "face_0010.jpg", "gender": null}
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{"id": 11, "file": "face_0011.jpg", "gender": null}
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{"id": 12, "file": "face_0012.jpg", "gender": null}
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{"id": 13, "file": "face_0013.jpg", "gender": null}
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{"id": 14, "file": "face_0014.jpg", "gender": null}
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{"id": 15, "file": "face_0015.jpg", "gender": null}
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{"id": 16, "file": "face_0016.jpg", "gender": null}
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{"id": 17, "file": "face_0017.jpg", "gender": null}
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{"id": 18, "file": "face_0018.jpg", "gender": null}
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{"id": 19, "file": "face_0019.jpg", "gender": null}
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{"id": 20, "file": "face_0020.jpg", "gender": null}
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{"id": 21, "file": "face_0021.jpg", "gender": null}
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{"id": 22, "file": "face_0022.jpg", "gender": null}
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{"id": 23, "file": "face_0023.jpg", "gender": null}
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{"id": 24, "file": "face_0024.jpg", "gender": null}
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{"id": 25, "file": "face_0025.jpg", "gender": null}
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{"id": 26, "file": "face_0026.jpg", "gender": null}
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{"id": 27, "file": "face_0027.jpg", "gender": null}
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{"id": 28, "file": "face_0028.jpg", "gender": null}
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{"id": 29, "file": "face_0029.jpg", "gender": null}
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{"id": 30, "file": "face_0030.jpg", "gender": null}
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{"id": 31, "file": "face_0031.jpg", "gender": null}
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{"id": 32, "file": "face_0032.jpg", "gender": null}
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{"id": 33, "file": "face_0033.jpg", "gender": null}
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{"id": 34, "file": "face_0034.jpg", "gender": null}
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{"id": 35, "file": "face_0035.jpg", "gender": null}
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{"id": 36, "file": "face_0036.jpg", "gender": null}
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{"id": 37, "file": "face_0037.jpg", "gender": null}
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{"id": 38, "file": "face_0038.jpg", "gender": null}
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{"id": 39, "file": "face_0039.jpg", "gender": null}
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{"id": 40, "file": "face_0040.jpg", "gender": null}
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{"id": 41, "file": "face_0041.jpg", "gender": null}
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{"id": 42, "file": "face_0042.jpg", "gender": null}
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{"id": 43, "file": "face_0043.jpg", "gender": null}
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{"id": 44, "file": "face_0044.jpg", "gender": null}
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{"id": 45, "file": "face_0045.jpg", "gender": null}
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{"id": 46, "file": "face_0046.jpg", "gender": null}
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{"id": 47, "file": "face_0047.jpg", "gender": null}
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{"id": 48, "file": "face_0048.jpg", "gender": null}
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{"id": 49, "file": "face_0049.jpg", "gender": null}
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{"id": 50, "file": "face_0050.jpg", "gender": null}
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{"id": 51, "file": "face_0051.jpg", "gender": null}
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{"id": 52, "file": "face_0052.jpg", "gender": null}
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{"id": 53, "file": "face_0053.jpg", "gender": null}
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{"id": 54, "file": "face_0054.jpg", "gender": null}
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{"id": 55, "file": "face_0055.jpg", "gender": null}
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{"id": 56, "file": "face_0056.jpg", "gender": null}
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{"id": 57, "file": "face_0057.jpg", "gender": null}
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{"id": 58, "file": "face_0058.jpg", "gender": null}
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{"id": 59, "file": "face_0059.jpg", "gender": null}
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{"id": 60, "file": "face_0060.jpg", "gender": null}
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{"id": 61, "file": "face_0061.jpg", "gender": null}
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{"id": 62, "file": "face_0062.jpg", "gender": null}
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{"id": 63, "file": "face_0063.jpg", "gender": null}
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{"id": 64, "file": "face_0064.jpg", "gender": null}
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{"id": 65, "file": "face_0065.jpg", "gender": null}
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{"id": 66, "file": "face_0066.jpg", "gender": null}
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{"id": 67, "file": "face_0067.jpg", "gender": null}
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{"id": 68, "file": "face_0068.jpg", "gender": null}
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{"id": 69, "file": "face_0069.jpg", "gender": null}
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{"id": 70, "file": "face_0070.jpg", "gender": null}
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{"id": 71, "file": "face_0071.jpg", "gender": null}
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{"id": 72, "file": "face_0072.jpg", "gender": null}
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{"id": 73, "file": "face_0073.jpg", "gender": null}
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{"id": 74, "file": "face_0074.jpg", "gender": null}
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{"id": 75, "file": "face_0075.jpg", "gender": null}
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{"id": 76, "file": "face_0076.jpg", "gender": null}
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{"id": 77, "file": "face_0077.jpg", "gender": null}
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{"id": 78, "file": "face_0078.jpg", "gender": null}
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{"id": 79, "file": "face_0079.jpg", "gender": null}
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{"id": 80, "file": "face_0080.jpg", "gender": null}
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{"id": 81, "file": "face_0081.jpg", "gender": null}
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{"id": 82, "file": "face_0082.jpg", "gender": null}
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{"id": 83, "file": "face_0083.jpg", "gender": null}
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{"id": 84, "file": "face_0084.jpg", "gender": null}
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{"id": 85, "file": "face_0085.jpg", "gender": null}
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{"id": 86, "file": "face_0086.jpg", "gender": null}
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{"id": 87, "file": "face_0087.jpg", "gender": null}
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{"id": 88, "file": "face_0088.jpg", "gender": null}
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{"id": 89, "file": "face_0089.jpg", "gender": null}
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{"id": 90, "file": "face_0090.jpg", "gender": null}
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{"id": 91, "file": "face_0091.jpg", "gender": null}
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{"id": 92, "file": "face_0092.jpg", "gender": null}
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{"id": 93, "file": "face_0093.jpg", "gender": null}
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{"id": 94, "file": "face_0094.jpg", "gender": null}
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{"id": 95, "file": "face_0095.jpg", "gender": null}
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{"id": 96, "file": "face_0096.jpg", "gender": null}
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{"id": 97, "file": "face_0097.jpg", "gender": null}
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{"id": 98, "file": "face_0098.jpg", "gender": null}
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{"id": 99, "file": "face_0099.jpg", "gender": null}
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{"id": 100, "file": "face_0100.jpg", "gender": null}
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{"id": 101, "file": "face_0101.jpg", "gender": null}
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{"id": 102, "file": "face_0102.jpg", "gender": null}
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{"id": 103, "file": "face_0103.jpg", "gender": null}
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{"id": 104, "file": "face_0104.jpg", "gender": null}
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{"id": 105, "file": "face_0105.jpg", "gender": null}
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{"id": 106, "file": "face_0106.jpg", "gender": null}
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{"id": 107, "file": "face_0107.jpg", "gender": null}
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{"id": 108, "file": "face_0108.jpg", "gender": null}
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{"id": 109, "file": "face_0109.jpg", "gender": null}
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{"id": 110, "file": "face_0110.jpg", "gender": null}
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{"id": 111, "file": "face_0111.jpg", "gender": null}
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{"id": 112, "file": "face_0112.jpg", "gender": null}
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{"id": 113, "file": "face_0113.jpg", "gender": null}
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{"id": 114, "file": "face_0114.jpg", "gender": null}
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{"id": 115, "file": "face_0115.jpg", "gender": null}
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{"id": 116, "file": "face_0116.jpg", "gender": null}
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{"id": 117, "file": "face_0117.jpg", "gender": null}
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{"id": 118, "file": "face_0118.jpg", "gender": null}
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{"id": 119, "file": "face_0119.jpg", "gender": null}
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{"id": 121, "file": "face_0121.jpg", "gender": null}
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{"id": 122, "file": "face_0122.jpg", "gender": null}
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{"id": 123, "file": "face_0123.jpg", "gender": null}
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{"id": 124, "file": "face_0124.jpg", "gender": null}
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{"id": 125, "file": "face_0125.jpg", "gender": null}
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{"id": 126, "file": "face_0126.jpg", "gender": null}
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{"id": 127, "file": "face_0127.jpg", "gender": null}
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{"id": 128, "file": "face_0128.jpg", "gender": null}
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{"id": 129, "file": "face_0129.jpg", "gender": null}
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{"id": 130, "file": "face_0130.jpg", "gender": null}
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{"id": 131, "file": "face_0131.jpg", "gender": null}
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{"id": 132, "file": "face_0132.jpg", "gender": null}
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{"id": 133, "file": "face_0133.jpg", "gender": null}
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{"id": 134, "file": "face_0134.jpg", "gender": null}
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{"id": 135, "file": "face_0135.jpg", "gender": null}
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{"id": 136, "file": "face_0136.jpg", "gender": null}
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{"id": 137, "file": "face_0137.jpg", "gender": null}
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{"id": 138, "file": "face_0138.jpg", "gender": null}
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{"id": 139, "file": "face_0139.jpg", "gender": null}
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{"id": 140, "file": "face_0140.jpg", "gender": null}
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{"id": 141, "file": "face_0141.jpg", "gender": null}
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{"id": 142, "file": "face_0142.jpg", "gender": null}
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{"id": 143, "file": "face_0143.jpg", "gender": null}
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{"id": 144, "file": "face_0144.jpg", "gender": null}
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{"id": 145, "file": "face_0145.jpg", "gender": null}
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{"id": 146, "file": "face_0146.jpg", "gender": null}
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{"id": 147, "file": "face_0147.jpg", "gender": null}
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{"id": 148, "file": "face_0148.jpg", "gender": null}
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{"id": 149, "file": "face_0149.jpg", "gender": null}
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{"id": 150, "file": "face_0150.jpg", "gender": null}
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{"id": 151, "file": "face_0151.jpg", "gender": null}
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{"id": 152, "file": "face_0152.jpg", "gender": null}
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{"id": 153, "file": "face_0153.jpg", "gender": null}
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{"id": 154, "file": "face_0154.jpg", "gender": null}
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{"id": 155, "file": "face_0155.jpg", "gender": null}
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{"id": 156, "file": "face_0156.jpg", "gender": null}
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{"id": 157, "file": "face_0157.jpg", "gender": null}
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{"id": 158, "file": "face_0158.jpg", "gender": null}
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{"id": 159, "file": "face_0159.jpg", "gender": null}
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{"id": 160, "file": "face_0160.jpg", "gender": null}
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{"id": 161, "file": "face_0161.jpg", "gender": null}
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{"id": 162, "file": "face_0162.jpg", "gender": null}
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{"id": 163, "file": "face_0163.jpg", "gender": null}
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{"id": 164, "file": "face_0164.jpg", "gender": null}
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{"id": 165, "file": "face_0165.jpg", "gender": null}
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{"id": 166, "file": "face_0166.jpg", "gender": null}
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{"id": 167, "file": "face_0167.jpg", "gender": null}
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{"id": 168, "file": "face_0168.jpg", "gender": null}
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{"id": 169, "file": "face_0169.jpg", "gender": null}
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{"id": 170, "file": "face_0170.jpg", "gender": null}
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{"id": 171, "file": "face_0171.jpg", "gender": null}
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{"id": 172, "file": "face_0172.jpg", "gender": null}
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{"id": 173, "file": "face_0173.jpg", "gender": null}
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{"id": 174, "file": "face_0174.jpg", "gender": null}
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{"id": 175, "file": "face_0175.jpg", "gender": null}
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{"id": 176, "file": "face_0176.jpg", "gender": null}
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{"id": 177, "file": "face_0177.jpg", "gender": null}
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{"id": 178, "file": "face_0178.jpg", "gender": null}
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{"id": 179, "file": "face_0179.jpg", "gender": null}
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{"id": 180, "file": "face_0180.jpg", "gender": null}
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{"id": 181, "file": "face_0181.jpg", "gender": null}
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{"id": 182, "file": "face_0182.jpg", "gender": null}
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{"id": 183, "file": "face_0183.jpg", "gender": null}
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{"id": 184, "file": "face_0184.jpg", "gender": null}
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{"id": 185, "file": "face_0185.jpg", "gender": null}
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{"id": 186, "file": "face_0186.jpg", "gender": null}
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{"id": 187, "file": "face_0187.jpg", "gender": null}
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{"id": 188, "file": "face_0188.jpg", "gender": null}
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{"id": 189, "file": "face_0189.jpg", "gender": null}
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{"id": 191, "file": "face_0191.jpg", "gender": null}
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{"id": 192, "file": "face_0192.jpg", "gender": null}
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{"id": 193, "file": "face_0193.jpg", "gender": null}
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{"id": 194, "file": "face_0194.jpg", "gender": null}
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{"id": 195, "file": "face_0195.jpg", "gender": null}
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{"id": 196, "file": "face_0196.jpg", "gender": null}
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{"id": 197, "file": "face_0197.jpg", "gender": null}
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{"id": 198, "file": "face_0198.jpg", "gender": null}
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{"id": 199, "file": "face_0199.jpg", "gender": null}
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@ -55,7 +55,8 @@ details .body{padding-top:10px;font-size:12px;color:#8b949e}
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.accent-r{border-left:3px solid #f85149}
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.worker{display:flex;align-items:center;gap:10px;padding:8px 10px;background:#161b22;border-radius:6px;margin-bottom:4px;font-size:12px;border-left:3px solid #30363d}
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.worker .av{width:32px;height:32px;border-radius:50%;background:#0d1117;border:1px solid #21262d;display:flex;align-items:center;justify-content:center;font-weight:600;color:#c9d1d9;font-size:11px;flex-shrink:0;letter-spacing:0.5px}
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.worker .av{width:32px;height:32px;border-radius:50%;background:#0d1117;border:1px solid #21262d;display:flex;align-items:center;justify-content:center;font-weight:600;color:#c9d1d9;font-size:11px;flex-shrink:0;letter-spacing:0.5px;overflow:hidden;position:relative}
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.worker .av img{position:absolute;inset:0;width:100%;height:100%;object-fit:cover;display:block}
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.worker[data-role-band="warehouse"]{border-left-color:#58a6ff}
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.worker[data-role-band="production"]{border-left-color:#d29922}
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.worker[data-role-band="trades"]{border-left-color:#bc8cff}
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// the left edge + uppercase role pill in the detail line. Used by
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// every chapter that renders worker rows. `name` and `role` drive the
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// classification; `detail` is the full text after the pill.
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// Quick first-name → gender hint for face-pool selection. Same lookup
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// idea as the dashboard; if the name is unknown, the server falls back
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// to the full pool. Trimmed table — covers the most common names that
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// appear in the synthetic worker data.
|
||||
var FEMALE_NAMES = new Set(['Mary','Patricia','Jennifer','Linda','Elizabeth','Barbara','Susan','Jessica','Sarah','Karen','Lisa','Nancy','Betty','Sandra','Margaret','Ashley','Kimberly','Emily','Donna','Michelle','Carol','Amanda','Melissa','Deborah','Stephanie','Dorothy','Rebecca','Sharon','Laura','Cynthia','Amy','Kathleen','Angela','Shirley','Brenda','Emma','Anna','Pamela','Nicole','Samantha','Katherine','Christine','Helen','Debra','Rachel','Carolyn','Janet','Maria','Catherine','Heather','Diane','Olivia','Julie','Joyce','Victoria','Ruth','Virginia','Lauren','Kelly','Christina','Joan','Evelyn','Judith','Andrea','Hannah','Megan','Cheryl','Jacqueline','Martha','Madison','Teresa','Gloria','Sara','Janice','Ann','Kathryn','Abigail','Sophia','Frances','Jean','Alice','Judy','Isabella','Julia','Grace','Amber','Denise','Danielle','Marilyn','Beverly','Charlotte','Natalie','Theresa','Diana','Brittany','Kayla','Alexis','Lori','Marie','Carmen','Aisha','Rosa','Mia','Audrey','Erin','Tina','Vanessa','Tara','Wendy','Tanya','Maya','Crystal','Yvonne','Kara','Shannon','Brianna','Faith','Caroline','Carla','Tracey','Tracy','Rita','Dawn','Tiffany','Stacy','Stacey','Gina','Bonnie','Tammy','Joanne','Jamie','Tonya','Alyssa','Ariana','Elena','Ellie','Erica','Erika','Felicia','Holly','Jenna','Jenny','Krista','Kristen','Kristin','Krystal','Lana','Leah','Lucy','Mallory','Melinda','Meredith','Misty','Monica','Naomi','Paige','Paula','Renee','Rhonda','Robin','Roxanne','Selena','Sierra','Skylar','Sonia','Stella','Tamara','Veronica','Vivian','Whitney','Yolanda','Zoe']);
|
||||
var MALE_NAMES = new Set(['James','Robert','John','Michael','David','William','Richard','Joseph','Thomas','Charles','Christopher','Daniel','Matthew','Anthony','Mark','Donald','Steven','Paul','Andrew','Joshua','Kenneth','Kevin','Brian','George','Edward','Ronald','Timothy','Jason','Jeffrey','Ryan','Jacob','Gary','Nicholas','Eric','Jonathan','Stephen','Larry','Justin','Scott','Brandon','Benjamin','Samuel','Gregory','Frank','Alexander','Raymond','Patrick','Jack','Dennis','Jerry','Tyler','Aaron','Jose','Adam','Henry','Nathan','Douglas','Zachary','Peter','Kyle','Walter','Ethan','Jeremy','Harold','Keith','Christian','Roger','Noah','Gerald','Carl','Terry','Sean','Austin','Arthur','Lawrence','Jesse','Dylan','Bryan','Joe','Jordan','Billy','Bruce','Albert','Willie','Gabriel','Logan','Alan','Juan','Wayne','Roy','Ralph','Randy','Eugene','Vincent','Russell','Elijah','Louis','Bobby','Philip','Johnny','Marcus','Antonio','Carlos','Diego','Hector','Jorge','Julio','Manuel','Miguel','Pedro','Raul','Ricardo','Roberto','Sergio','Victor','Jamal','Xavier','DeShawn','Dwayne','Jermaine','Malik','Tyrone','Devon','Andre','Brent','Calvin','Casey','Cody','Cole','Cory','Dale','Damon','Darius','Darrell','Dean','Derek','Drew','Earl','Eddie','Floyd','Glenn','Greg','Howard','Ivan','Jared','Jay','Jeff','Joel','Lance','Lee','Leonard','Lloyd','Mario','Martin','Mason','Maurice','Max','Mitchell','Morgan','Nick','Norman','Oliver','Owen','Pete','Quincy','Rafael','Reggie','Rex','Ricky','Russ','Shane','Shaun','Stanley','Steve','Theodore','Todd','Travis','Trevor','Troy','Wade','Warren','Wesley']);
|
||||
function guessGenderFromFirstName(n){
|
||||
if(!n) return null;
|
||||
var clean=n.replace(/[^A-Za-z]/g,'');
|
||||
if(!clean) return null;
|
||||
var c=clean[0].toUpperCase()+clean.slice(1).toLowerCase();
|
||||
if(FEMALE_NAMES.has(c)) return 'woman';
|
||||
if(MALE_NAMES.has(c)) return 'man';
|
||||
return null;
|
||||
}
|
||||
function genderFor(name){
|
||||
var g = guessGenderFromFirstName(name);
|
||||
if(g) return g;
|
||||
if(!name) return 'man';
|
||||
var s=String(name); var h=0;
|
||||
for(var i=0;i<s.length;i++) h=(h*31+s.charCodeAt(i))|0;
|
||||
return (Math.abs(h)&1)?'man':'woman';
|
||||
}
|
||||
// Confident first-name → ethnicity. Synthetic data — we own the call.
|
||||
var NAMES_SOUTH_ASIAN_C=new Set(['Raj','Anil','Rohan','Vikram','Arjun','Sanjay','Ravi','Krishna','Pradeep','Sunil','Amit','Deepak','Ashok','Manoj','Rahul','Vijay','Suresh','Naveen','Anand','Nikhil','Aditya','Karan','Rajesh','Priya','Anjali','Neha','Kavya','Pooja','Divya','Meera','Lakshmi','Rani','Asha','Saanvi','Aanya','Aaradhya','Shreya','Riya','Tanvi','Ishita','Aarav','Ishaan','Shivani']);
|
||||
var NAMES_EAST_ASIAN_C=new Set(['Wei','Mei','Yi','Jin','Chen','Lin','Liu','Wang','Zhang','Yang','Wu','Zhao','Sun','Hiroshi','Yuki','Akira','Kenji','Sakura','Aiko','Haruto','Sora','Hyun','Eun','Yoon','Kai','Long','Hong','Xiu','Lan','Hua','Hao','Tao','Bao','Cheng','Feng','Jian','Dong','Bin','Min','Lei','Hui','Yu','Xin','Ying','Zhen','Yuan','Yan']);
|
||||
var NAMES_HISPANIC_C=new Set(['Carmen','Carlos','Maria','Diego','Hector','Jorge','Julio','Manuel','Miguel','Pedro','Raul','Ricardo','Roberto','Sergio','Antonio','Esperanza','Luz','Sofia','Lucia','Isabella','Camila','Valentina','Mariana','Elena','Rosa','Catalina','Esteban','Fernando','Eduardo','Javier','Alejandro','Andres','Mateo','Santiago','Sebastian','Emilio','Tomas','Cristina','Daniela','Gabriela','Ximena','Adriana','Beatriz','Pilar','Mercedes','Xavier','Marisol','Guadalupe','Lupita','Inez','Itzel','Yesenia','Joaquin','Ignacio','Rafael','Salvador','Cesar','Arturo','Armando','Hugo','Marco','Alejandra','Felipe','Gerardo','Jaime','Leonardo','Luis','Pablo','Ramon']);
|
||||
var NAMES_BLACK_C=new Set(['DeShawn','Jamal','Aisha','Latoya','Tyrone','Malik','Imani','Keisha','Tariq','Lakisha','Kenya','Tamika','Andre','Marcus','Demetrius','Jermaine','Reggie','Tyrese','Darius','Trevon','Kareem','Damon','Jalen','Jaylen','Dwayne','DaQuan','Aaliyah','Kiara','Janelle','Jasmine','Tanisha','Maurice','Tyrell','Kwame','Khalil','Terrell','Cedric','Nia','Zuri','Jada','Ebony','Dominique']);
|
||||
var NAMES_MIDDLE_EASTERN_C=new Set(['Layla','Omar','Khalid','Fatima','Yasmin','Hassan','Hussein','Ahmed','Mohamed','Mohammed','Ali','Karim','Yusuf','Yara','Nadia','Zainab','Rania','Samira','Mariam','Salma','Ibrahim','Mahmoud','Saif','Anwar','Bilal','Faisal','Hamza','Imran','Sami','Wael','Zaid','Amira','Iman','Lina','Mona','Noor','Rana','Soha','Zara']);
|
||||
function guessEthnicityFromFirstName(n){
|
||||
if(!n) return 'caucasian';
|
||||
var clean=n.replace(/[^A-Za-z]/g,''); if(!clean) return 'caucasian';
|
||||
var c=clean[0].toUpperCase()+clean.slice(1).toLowerCase();
|
||||
if(NAMES_MIDDLE_EASTERN_C.has(c)) return 'middle_eastern';
|
||||
if(NAMES_BLACK_C.has(c)) return 'black';
|
||||
if(NAMES_HISPANIC_C.has(c)) return 'hispanic';
|
||||
if(NAMES_SOUTH_ASIAN_C.has(c)) return 'south_asian';
|
||||
if(NAMES_EAST_ASIAN_C.has(c)) return 'east_asian';
|
||||
return 'caucasian';
|
||||
}
|
||||
|
||||
function workerRow(name, role, detail, opts){
|
||||
opts = opts || {};
|
||||
var band = roleBand(role||'');
|
||||
var w = el('div','worker');
|
||||
if(band.band) w.dataset.roleBand = band.band;
|
||||
var initials = (name||'?').split(' ').map(function(s){return (s[0]||'').toUpperCase()}).join('').substring(0,2);
|
||||
w.appendChild(el('div','av',initials));
|
||||
var av = el('div','av',initials);
|
||||
// Real synthetic headshot via /headshots/<key>; deterministic so
|
||||
// same worker always gets the same face. Falls back to monogram if
|
||||
// pool isn't fetched yet.
|
||||
var faceKey = (opts.face_key) || name || '';
|
||||
var firstName = (name||'').split(/\s+/)[0]||'';
|
||||
var gHint = genderFor(firstName);
|
||||
var eHint = guessEthnicityFromFirstName(firstName);
|
||||
if(faceKey){
|
||||
var img=document.createElement('img');
|
||||
img.alt='';
|
||||
img.loading='lazy';
|
||||
img.src = P + '/headshots/' + encodeURIComponent(faceKey) + '?g='+gHint+'&e='+eHint;
|
||||
img.onerror=function(){ this.remove(); };
|
||||
av.appendChild(img);
|
||||
}
|
||||
w.appendChild(av);
|
||||
var info = el('div','info');
|
||||
var nm = el('div','nm', name||'?');
|
||||
if(opts.endorsed){
|
||||
|
||||
@ -1225,6 +1225,78 @@ async function main() {
|
||||
// OSHA national, Chicago history, ticker chart, parent link,
|
||||
// federal contracts, debarment, unions, training. Click any
|
||||
// contractor name in a permit Entity Brief to land here.
|
||||
// Headshot pool — synthetic StyleGAN faces from
|
||||
// thispersondoesnotexist.com fetched offline by
|
||||
// scripts/staffing/fetch_face_pool.py. Deterministic mapping:
|
||||
// hash(worker key) → pool index → image bytes. Same key always
|
||||
// gets the same face; different keys spread evenly.
|
||||
//
|
||||
// Optional gender hint: ?g=man|woman narrows the pool to
|
||||
// matching tagged faces (set by deepface during fetch). Falls
|
||||
// back to whole pool if no matches.
|
||||
if (url.pathname.startsWith("/headshots/") && req.method === "GET") {
|
||||
const key = decodeURIComponent(url.pathname.slice("/headshots/".length));
|
||||
const wantGender = url.searchParams.get("g") || "";
|
||||
if (!key) return new Response("missing key", { status: 400 });
|
||||
// Manifest is loaded lazily on first request and cached.
|
||||
// Re-runs of the fetch script overwrite the manifest; the
|
||||
// mcp-server can be poked to reload by hitting
|
||||
// /headshots/__reload — the hash-key path will never have
|
||||
// exactly two underscores so the collision risk is zero.
|
||||
const HEADSHOT_DIR = "/home/profit/lakehouse/data/headshots";
|
||||
if (key === "__reload" || !(globalThis as any)._faces) {
|
||||
try {
|
||||
const raw = await Bun.file(`${HEADSHOT_DIR}/manifest.jsonl`).text();
|
||||
const lines = raw.trim().split("\n").filter(Boolean);
|
||||
const all = lines.map((l) => JSON.parse(l));
|
||||
(globalThis as any)._faces = {
|
||||
all,
|
||||
man: all.filter((r: any) => r.gender === "man"),
|
||||
woman: all.filter((r: any) => r.gender === "woman"),
|
||||
untagged: all.filter((r: any) => !r.gender || (r.gender !== "man" && r.gender !== "woman")),
|
||||
loaded_at: Date.now(),
|
||||
};
|
||||
if (key === "__reload") {
|
||||
return Response.json({
|
||||
reloaded: true,
|
||||
total: all.length,
|
||||
man: (globalThis as any)._faces.man.length,
|
||||
woman: (globalThis as any)._faces.woman.length,
|
||||
untagged: (globalThis as any)._faces.untagged.length,
|
||||
});
|
||||
}
|
||||
} catch (e: any) {
|
||||
return new Response(`face pool not available: ${e.message}. Run scripts/staffing/fetch_face_pool.py first.`, { status: 503 });
|
||||
}
|
||||
}
|
||||
const F = (globalThis as any)._faces as { all: any[]; man: any[]; woman: any[]; untagged: any[] };
|
||||
if (!F || !F.all.length) {
|
||||
return new Response("face pool empty", { status: 503 });
|
||||
}
|
||||
// Pool selection: gender hint > full pool. If no gender match,
|
||||
// fall back to the full pool so the worker still gets a face.
|
||||
let pool = F.all;
|
||||
if (wantGender === "man" && F.man.length) pool = F.man;
|
||||
else if (wantGender === "woman" && F.woman.length) pool = F.woman;
|
||||
// Hash key → pool index. djb2-ish, fits any string.
|
||||
let h = 5381;
|
||||
for (let i = 0; i < key.length; i++) h = ((h << 5) + h + key.charCodeAt(i)) | 0;
|
||||
const idx = Math.abs(h) % pool.length;
|
||||
const pick = pool[idx];
|
||||
const file = Bun.file(`${HEADSHOT_DIR}/${pick.file}`);
|
||||
if (!(await file.exists())) {
|
||||
return new Response("face missing on disk", { status: 404 });
|
||||
}
|
||||
return new Response(file, {
|
||||
headers: {
|
||||
"Content-Type": "image/jpeg",
|
||||
"Cache-Control": "public, max-age=86400, immutable",
|
||||
"X-Face-Pool-Idx": String(pick.id),
|
||||
"X-Face-Pool-Gender": pick.gender || "untagged",
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
// Profiler index — directory page of everyone who's filed a
|
||||
// Chicago permit (clickable directory of contractors).
|
||||
if (url.pathname === "/profiler" || url.pathname === "/contractors") {
|
||||
|
||||
@ -48,7 +48,8 @@ body{font-family:'Inter',-apple-system,system-ui,'Segoe UI',sans-serif;backgroun
|
||||
/* Workers */
|
||||
.iworker{display:flex;align-items:center;gap:12px;padding:10px 12px;background:#161b22;border-radius:8px;margin-bottom:4px;transition:background 0.15s}
|
||||
.iworker:hover{background:#1c2333}
|
||||
.iworker .av{width:40px;height:40px;border-radius:50%;display:flex;align-items:center;justify-content:center;font-weight:600;font-size:13px;color:#c9d1d9;flex-shrink:0;background:#161b22;border:1px solid #21262d;letter-spacing:0.5px;font-family:'Inter',-apple-system,sans-serif}
|
||||
.iworker .av{width:40px;height:40px;border-radius:50%;display:flex;align-items:center;justify-content:center;font-weight:600;font-size:13px;color:#c9d1d9;flex-shrink:0;background:#161b22;border:1px solid #21262d;letter-spacing:0.5px;font-family:'Inter',-apple-system,sans-serif;overflow:hidden;position:relative}
|
||||
.iworker .av img{position:absolute;inset:0;width:100%;height:100%;object-fit:cover;display:block}
|
||||
.iworker .role-pill{display:inline-block;font-size:10px;padding:2px 8px;border-radius:3px;background:#0d1117;color:#8b949e;margin-right:8px;font-weight:600;letter-spacing:0.4px;text-transform:uppercase;border-left:2px solid #30363d}
|
||||
.iworker[data-role-band="warehouse"]{border-left:3px solid #58a6ff}
|
||||
.iworker[data-role-band="production"]{border-left:3px solid #d29922}
|
||||
@ -2260,6 +2261,86 @@ function addBigMeter(parent,label,val,desc){
|
||||
d.appendChild(lb);d.appendChild(row);d.appendChild(ds);parent.appendChild(d);
|
||||
}
|
||||
|
||||
// First-name → gender hint for face-pool selection. Built from the
|
||||
// most common 200 US given names. Lookup is best-effort — undefined
|
||||
// returns means "no hint, draw from full pool." Don't surface this
|
||||
// anywhere user-visible; it's purely a face-pool selector.
|
||||
var FEMALE_NAMES = new Set(['Mary','Patricia','Jennifer','Linda','Elizabeth','Barbara','Susan','Jessica','Sarah','Karen','Lisa','Nancy','Betty','Sandra','Margaret','Ashley','Kimberly','Emily','Donna','Michelle','Carol','Amanda','Melissa','Deborah','Stephanie','Dorothy','Rebecca','Sharon','Laura','Cynthia','Amy','Kathleen','Angela','Shirley','Brenda','Emma','Anna','Pamela','Nicole','Samantha','Katherine','Christine','Helen','Debra','Rachel','Carolyn','Janet','Maria','Catherine','Heather','Diane','Olivia','Julie','Joyce','Victoria','Ruth','Virginia','Lauren','Kelly','Christina','Joan','Evelyn','Judith','Andrea','Hannah','Megan','Cheryl','Jacqueline','Martha','Madison','Teresa','Gloria','Sara','Janice','Ann','Kathryn','Abigail','Sophia','Frances','Jean','Alice','Judy','Isabella','Julia','Grace','Amber','Denise','Danielle','Marilyn','Beverly','Charlotte','Natalie','Theresa','Diana','Brittany','Doris','Kayla','Alexis','Lori','Marie','Carmen','Aisha','Rosa','Kim','Mia','Audrey','Erin','Tina','Vanessa','Tara','Wendy','Tanya','Maya','Crystal','Yvonne','Kara','Shannon','Brianna','Faith','Caroline','Carla','Tracey','Tracy','Rita','Dawn','Tiffany','Stacy','Stacey','Gina','Bonnie','Tammy','Joanne','Jamie','Tonya','Alyssa','Ariana','Elena','Ellie','Erica','Erika','Felicia','Holly','Jenna','Jenny','Krista','Kristen','Kristin','Krystal','Lana','Leah','Lucy','Mallory','Melinda','Meredith','Misty','Monica','Mya','Naomi','Paige','Patrice','Paula','Renee','Rhonda','Robin','Roxanne','Sadie','Selena','Shari','Shauna','Sierra','Skylar','Sonia','Stella','Tamara','Taryn','Trina','Veronica','Vivian','Whitney','Yolanda','Zoe','Lakeisha','Latoya','Tasha',
|
||||
// Hispanic female
|
||||
'Esperanza','Luz','Lucia','Camila','Valentina','Mariana','Catalina','Cristina','Daniela','Gabriela','Ximena','Adriana','Beatriz','Pilar','Consuelo','Dolores','Mercedes','Marisol','Guadalupe','Lupita','Inez','Itzel','Yesenia','Monserrat','Renata','Alejandra','Alma','Belen','Blanca','Esmeralda','Imelda','Lourdes','Magdalena','Olga','Refugio','Rocio','Susana','Anita','Fatima',
|
||||
// South Asian female
|
||||
'Priya','Anjali','Neha','Kavya','Pooja','Divya','Meera','Lakshmi','Rani','Asha','Saanvi','Aanya','Aaradhya','Shreya','Riya','Tanvi','Ishita','Shivani','Padma','Sita','Geeta','Rekha','Amira',
|
||||
// East Asian female
|
||||
'Mei','Sakura','Aiko','Sora','Chiyo','Hana','Eun','Xiu','Lan','Hua','Min','Xin','Ying','Zhen','Yan',
|
||||
// Black female (additional)
|
||||
'Imani','Keisha','Lakisha','Kenya','Tamika','Latanya','Latrice','Aaliyah','Kiara','Janelle','Jasmine','Tanisha','Maliyah','Imari','Nia','Zuri','Talia','Jada','Ebony','Dominique',
|
||||
// Middle Eastern female
|
||||
'Layla','Yasmin','Yara','Nadia','Zainab','Rania','Samira','Mariam','Salma','Dunia','Iman','Lina','Mona','Noor','Rana','Sabrina','Soha','Zara'
|
||||
]);
|
||||
var MALE_NAMES = new Set(['James','Robert','John','Michael','David','William','Richard','Joseph','Thomas','Charles','Christopher','Daniel','Matthew','Anthony','Mark','Donald','Steven','Paul','Andrew','Joshua','Kenneth','Kevin','Brian','George','Edward','Ronald','Timothy','Jason','Jeffrey','Ryan','Jacob','Gary','Nicholas','Eric','Jonathan','Stephen','Larry','Justin','Scott','Brandon','Benjamin','Samuel','Gregory','Frank','Alexander','Raymond','Patrick','Jack','Dennis','Jerry','Tyler','Aaron','Jose','Adam','Henry','Nathan','Douglas','Zachary','Peter','Kyle','Walter','Ethan','Jeremy','Harold','Keith','Christian','Roger','Noah','Gerald','Carl','Terry','Sean','Austin','Arthur','Lawrence','Jesse','Dylan','Bryan','Joe','Jordan','Billy','Bruce','Albert','Willie','Gabriel','Logan','Alan','Juan','Wayne','Roy','Ralph','Randy','Eugene','Vincent','Russell','Elijah','Louis','Bobby','Philip','Johnny','Marcus','Antonio','Carlos','Diego','Hector','Jorge','Julio','Manuel','Miguel','Pedro','Raul','Ricardo','Roberto','Sergio','Victor','Jamal','Xavier','DeShawn','Dwayne','Jermaine','Malik','Tyrone','Devon','Andre','Anwar','Brent','Calvin','Casey','Cody','Cole','Cory','Curt','Dale','Damon','Darius','Darrell','Dean','Derek','Donnie','Drew','Earl','Eddie','Floyd','Glenn','Greg','Howard','Ivan','Jared','Jay','Jeff','Joel','Johnnie','Lance','Lee','Leonard','Lloyd','Mario','Martin','Mason','Maurice','Max','Mitchell','Morgan','Nick','Norman','Oliver','Owen','Pete','Quincy','Rafael','Reggie','Rex','Ricky','Rod','Russ','Salvatore','Shane','Shaun','Stanley','Steve','Theodore','Todd','Travis','Trevor','Troy','Wade','Warren','Wesley',
|
||||
// Hispanic male
|
||||
'Alejandro','Andres','Mateo','Santiago','Sebastian','Emilio','Tomas','Joaquin','Ignacio','Salvador','Cesar','Arturo','Armando','Hugo','Marco','Felipe','Gerardo','Jaime','Leonardo','Luis','Pablo','Ramon','Reynaldo','Vincente','Javier','Esteban','Eduardo','Fernando','Humberto','Ernestino','Cristian','Hernan',
|
||||
// South Asian male
|
||||
'Raj','Anil','Rohan','Vikram','Arjun','Sanjay','Ravi','Krishna','Pradeep','Sunil','Amit','Deepak','Ashok','Manoj','Rahul','Vijay','Suresh','Naveen','Anand','Nikhil','Aditya','Karan','Rajesh','Ramesh','Kishore','Mohan','Ajay','Aarav','Ishaan',
|
||||
// East Asian male
|
||||
'Wei','Yi','Jin','Hiroshi','Akira','Kenji','Haruto','Hyun','Yoon','Kai','Long','Hong','Hao','Tao','Bao','Cheng','Feng','Qiang','Jian','Dong','Bin','Lei','Hui','Yu','Yuan',
|
||||
// Black male (additional)
|
||||
'Demetrius','Tyrese','Trevon','Kareem','DaQuan','Tyrell','Kwame','Khalil','Rashid','Terrell','Chauncey','Cedric','Imari','Jalen','Jaylen',
|
||||
// Middle Eastern male
|
||||
'Omar','Khalid','Hassan','Hussein','Ahmed','Mohamed','Mohammed','Ali','Karim','Yusuf','Ibrahim','Mahmoud','Saif','Bilal','Faisal','Hamza','Imran','Sami','Wael','Yasin','Zaid'
|
||||
]);
|
||||
function guessGenderFromFirstName(name){
|
||||
if(!name) return null;
|
||||
var clean = name.replace(/[^A-Za-z]/g,'');
|
||||
if(!clean) return null;
|
||||
// Title-case
|
||||
var c = clean[0].toUpperCase() + clean.slice(1).toLowerCase();
|
||||
if(FEMALE_NAMES.has(c)) return 'woman';
|
||||
if(MALE_NAMES.has(c)) return 'man';
|
||||
return null;
|
||||
}
|
||||
|
||||
// First-name → ethnicity hint. The synthetic-data generator built
|
||||
// workers_500k from a multi-cultural name pool (Raj, DeShawn, Jamal,
|
||||
// Mei, Wei, Carmen, Esperanza, Aisha, etc.). The hint is used ONLY
|
||||
// to bias face-pool selection toward visually-aligned StyleGAN faces;
|
||||
// it never surfaces in any worker-facing label or report. Buckets
|
||||
// match common deepface "race" categories so the pool tags align
|
||||
// when deepface is later run over the headshot pool.
|
||||
var NAMES_SOUTH_ASIAN = new Set(['Raj','Anil','Rohan','Vikram','Arjun','Sanjay','Ravi','Krishna','Pradeep','Sunil','Amit','Deepak','Ashok','Manoj','Rahul','Vijay','Suresh','Naveen','Anand','Nikhil','Aditya','Karan','Rajesh','Ramesh','Kishore','Mohan','Ajay','Priya','Anjali','Neha','Kavya','Pooja','Divya','Meera','Lakshmi','Rani','Asha','Saanvi','Aanya','Aaradhya','Shreya','Riya','Tanvi','Ishita','Aarav','Ishaan','Shivani','Padma','Sita','Geeta','Rekha']);
|
||||
var NAMES_EAST_ASIAN = new Set(['Wei','Mei','Yi','Jin','Chen','Lin','Liu','Wang','Zhang','Yang','Wu','Zhao','Sun','Hiroshi','Yuki','Akira','Kenji','Sakura','Aiko','Haruto','Sora','Chiyo','Hana','Hyun','Eun','Yoon','Kai','Long','Hong','Xiu','Lan','Hua','Hao','Tao','Bao','Cheng','Feng','Qiang','Jian','Dong','Bin','Min','Lei','Hui','Yu','Xin','Ying','Zhen','Yuan','Yan']);
|
||||
var NAMES_HISPANIC = new Set(['Carmen','Carlos','Maria','Diego','Hector','Jorge','Julio','Manuel','Miguel','Pedro','Raul','Ricardo','Roberto','Sergio','Antonio','Esperanza','Luz','Sofia','Lucia','Isabella','Camila','Valentina','Mariana','Elena','Rosa','Catalina','Esteban','Fernando','Eduardo','Javier','Alejandro','Andres','Mateo','Santiago','Sebastian','Emilio','Tomas','Cristina','Daniela','Gabriela','Ximena','Adriana','Beatriz','Pilar','Consuelo','Dolores','Mercedes','Xavier','Marisol','Guadalupe','Lupita','Inez','Itzel','Yolanda','Yesenia','Monserrat','Renata','Ximena','Joaquin','Ignacio','Rafael','Salvador','Cesar','Arturo','Armando','Hugo','Marco','Alejandra','Alma','Belen','Blanca','Esmeralda','Fatima','Gloria','Imelda','Lourdes','Magdalena','Olga','Paula','Refugio','Rocio','Susana','Teresa','Veronica','Anita','Ernestino','Felipe','Gerardo','Humberto','Jaime','Leonardo','Luis','Pablo','Ramon','Reynaldo','Vincente']);
|
||||
var NAMES_BLACK = new Set(['DeShawn','Jamal','Aisha','Latoya','Tyrone','Malik','Imani','Keisha','Tariq','Lakisha','Kenya','Tamika','Shaquille','Andre','Marcus','Demetrius','Jermaine','Reggie','Tyrese','Darius','Trevon','Kareem','Damon','Jalen','Jaylen','Dwayne','DaQuan','Latanya','Latrice','Aaliyah','Kiara','Janelle','Jasmine','Tanisha','Yolanda','Maurice','Tyrell','Kwame','Khalil','Rashid','Terrell','Chauncey','Cedric','Maliyah','Imari','Nia','Zuri','Talia','Jada','Ebony','Dominique']);
|
||||
var NAMES_MIDDLE_EASTERN = new Set(['Layla','Omar','Khalid','Fatima','Yasmin','Hassan','Hussein','Ahmed','Mohamed','Mohammed','Ali','Karim','Yusuf','Yara','Nadia','Zainab','Rania','Samira','Mariam','Salma','Ibrahim','Mahmoud','Saif','Anwar','Bilal','Faisal','Hamza','Imran','Jamal','Rashid','Sami','Tariq','Wael','Yasin','Zaid','Amira','Dunia','Iman','Lina','Mona','Noor','Rana','Sabrina','Soha','Yara','Zara']);
|
||||
function guessEthnicityFromFirstName(name){
|
||||
if(!name) return 'caucasian';
|
||||
var clean = name.replace(/[^A-Za-z]/g,'');
|
||||
if(!clean) return 'caucasian';
|
||||
var c = clean[0].toUpperCase() + clean.slice(1).toLowerCase();
|
||||
// Order matters where names overlap. We're CREATING this profile so
|
||||
// the assumptions are first-pass confident — fallback is caucasian
|
||||
// (the largest US Census bucket), so every worker resolves to a
|
||||
// category the face pool can be biased toward.
|
||||
if(NAMES_MIDDLE_EASTERN.has(c)) return 'middle_eastern';
|
||||
if(NAMES_BLACK.has(c)) return 'black';
|
||||
if(NAMES_HISPANIC.has(c)) return 'hispanic';
|
||||
if(NAMES_SOUTH_ASIAN.has(c)) return 'south_asian';
|
||||
if(NAMES_EAST_ASIAN.has(c)) return 'east_asian';
|
||||
return 'caucasian';
|
||||
}
|
||||
// Forced-confident gender resolver — defaults to a deterministic guess
|
||||
// when the name table doesn't match, rather than leaving "unknown."
|
||||
// We're authoring the synthetic data; we own the confidence call.
|
||||
function genderFor(name){
|
||||
var g = guessGenderFromFirstName(name);
|
||||
if(g) return g;
|
||||
if(!name) return 'man';
|
||||
// hash-based fallback so unknown names still spread roughly 50/50
|
||||
var s = String(name);
|
||||
var h = 0; for (var i=0;i<s.length;i++) h = (h*31 + s.charCodeAt(i))|0;
|
||||
return (Math.abs(h) & 1) ? 'man' : 'woman';
|
||||
}
|
||||
|
||||
// Role classification — sober, no illustrations. Maps a role string
|
||||
// to a short uppercase label and a "band" used as a left-edge color
|
||||
// on the card. Five bands cover the warehouse/manufacturing surface;
|
||||
@ -2295,14 +2376,33 @@ function addWorkerInsight(parent,name,detail,why,idx,highlight){
|
||||
var workerDataRef=arguments[6]||null; // passed as 7th arg
|
||||
var boostInfo=arguments[7]||null; // {boost, citations} — Phase 19
|
||||
w.onclick=function(){if(workerDataRef)showProfile(workerDataRef)};
|
||||
// Sober avatar: monogram initials, dark muted background, subtle
|
||||
// ring. Role classification adds a colored left-edge band on the
|
||||
// card and on the role pill; no emoji, no illustrations.
|
||||
// Avatar: monogram initials underneath, real synthetic headshot on
|
||||
// top via /headshots/<key>. Same key → same face by deterministic
|
||||
// hash. If the image fails to load (face pool not yet fetched, CDN
|
||||
// blocked, etc.), the monogram remains visible.
|
||||
var av=document.createElement('div');av.className='av';
|
||||
var role = (workerDataRef && workerDataRef.role) || (detail||'').split(' · ')[0] || '';
|
||||
var band = roleBand(role);
|
||||
if(band.band) w.dataset.roleBand = band.band;
|
||||
av.textContent=(name||'?').split(' ').map(function(n){return(n[0]||'').toUpperCase()}).join('').substring(0,2);
|
||||
// Layer the headshot on top. We're CREATING this synthetic profile,
|
||||
// so the gender + ethnicity guesses are confident — the face-pool
|
||||
// selector uses both. Once deepface tags the pool the server will
|
||||
// narrow accordingly; until then it falls back to the full pool but
|
||||
// the URL shape is forward-compatible.
|
||||
var faceKey = (workerDataRef && (workerDataRef.candidate_id || workerDataRef.doc_id)) || name || '';
|
||||
var firstName = (name||'').split(/\s+/)[0]||'';
|
||||
var gHint = genderFor(firstName);
|
||||
var eHint = guessEthnicityFromFirstName(firstName);
|
||||
if(faceKey){
|
||||
var img=document.createElement('img');
|
||||
img.alt='';
|
||||
img.loading='lazy';
|
||||
var qs = '?g=' + gHint + '&e=' + eHint;
|
||||
img.src = P + '/headshots/' + encodeURIComponent(faceKey) + qs;
|
||||
img.onerror=function(){ this.remove(); };
|
||||
av.appendChild(img);
|
||||
}
|
||||
w.appendChild(av);
|
||||
var info=document.createElement('div');info.className='info';
|
||||
var nm=document.createElement('div');nm.className='nm';nm.textContent=name;
|
||||
|
||||
161
scripts/staffing/fetch_face_pool.py
Normal file
161
scripts/staffing/fetch_face_pool.py
Normal file
@ -0,0 +1,161 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
fetch_face_pool.py — pull N synthetic headshots from
|
||||
https://thispersondoesnotexist.com/, write to data/headshots/face_NNNN.jpg,
|
||||
optionally tag each with gender via deepface, emit a JSONL manifest.
|
||||
|
||||
Each fetch is a fresh StyleGAN face — no real people. Deterministic per
|
||||
worker mapping happens at serve time (mcp-server hashes the worker key
|
||||
into the pool); this script just builds the pool.
|
||||
|
||||
Usage:
|
||||
python3 scripts/staffing/fetch_face_pool.py --count 300 --concurrency 3
|
||||
python3 scripts/staffing/fetch_face_pool.py --count 50 --no-gender
|
||||
|
||||
Re-running is idempotent: existing face_NNNN.jpg files are skipped, and
|
||||
the manifest is rewritten from disk state.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
import argparse
|
||||
import hashlib
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
|
||||
import urllib.request
|
||||
import urllib.error
|
||||
|
||||
URL = "https://thispersondoesnotexist.com/"
|
||||
UA = "Lakehouse/1.0 (face-pool fetch · synthetic-only · no real-person tracking)"
|
||||
|
||||
|
||||
def fetch_one(idx: int, out_dir: str) -> tuple[int, str, bool, str | None]:
|
||||
"""Returns (idx, basename, cached, error)."""
|
||||
fname = f"face_{idx:04d}.jpg"
|
||||
full = os.path.join(out_dir, fname)
|
||||
if os.path.exists(full) and os.path.getsize(full) > 1024:
|
||||
return idx, fname, True, None
|
||||
try:
|
||||
req = urllib.request.Request(URL, headers={"User-Agent": UA})
|
||||
with urllib.request.urlopen(req, timeout=20) as resp:
|
||||
blob = resp.read()
|
||||
if len(blob) < 1024:
|
||||
return idx, fname, False, f"response too small ({len(blob)} bytes)"
|
||||
with open(full, "wb") as f:
|
||||
f.write(blob)
|
||||
return idx, fname, False, None
|
||||
except urllib.error.URLError as e:
|
||||
return idx, fname, False, f"urlerror: {e}"
|
||||
except Exception as e:
|
||||
return idx, fname, False, f"{type(e).__name__}: {e}"
|
||||
|
||||
|
||||
def maybe_tag_gender(records: list[dict], out_dir: str) -> dict[str, int]:
|
||||
"""If deepface is installed, label each record with gender. Returns
|
||||
a count summary; mutates records in place. On import error, returns
|
||||
None and tags every record as unknown."""
|
||||
try:
|
||||
from deepface import DeepFace # type: ignore
|
||||
except Exception as e:
|
||||
print(f" (deepface unavailable: {e}) — pool will mix naturally")
|
||||
for r in records:
|
||||
r["gender"] = None
|
||||
return {"unknown": len(records)}
|
||||
|
||||
print(" tagging gender via deepface (CPU; ~0.5-1s per face)…")
|
||||
counts: dict[str, int] = {}
|
||||
for i, r in enumerate(records):
|
||||
full = os.path.join(out_dir, r["file"])
|
||||
try:
|
||||
ana = DeepFace.analyze(
|
||||
img_path=full,
|
||||
actions=["gender"],
|
||||
enforce_detection=False,
|
||||
silent=True,
|
||||
)
|
||||
if isinstance(ana, list):
|
||||
ana = ana[0] if ana else {}
|
||||
g_raw = (ana.get("dominant_gender") or "").lower().strip()
|
||||
r["gender"] = (
|
||||
"man" if g_raw.startswith("man") else
|
||||
"woman" if g_raw.startswith("woman") else
|
||||
None
|
||||
)
|
||||
except Exception as e:
|
||||
r["gender"] = None
|
||||
r["gender_error"] = f"{type(e).__name__}: {e}"
|
||||
counts[r["gender"] or "unknown"] = counts.get(r["gender"] or "unknown", 0) + 1
|
||||
if (i + 1) % 25 == 0:
|
||||
print(f" [{i+1}/{len(records)}] {counts}")
|
||||
return counts
|
||||
|
||||
|
||||
def main():
|
||||
p = argparse.ArgumentParser()
|
||||
p.add_argument("--count", type=int, default=300, help="how many faces to maintain in pool")
|
||||
p.add_argument(
|
||||
"--out",
|
||||
default=os.path.join(os.path.dirname(__file__), "..", "..", "data", "headshots"),
|
||||
)
|
||||
p.add_argument("--concurrency", type=int, default=3, help="parallel fetches (be polite)")
|
||||
p.add_argument("--no-gender", action="store_true", help="skip deepface gender tagging")
|
||||
args = p.parse_args()
|
||||
|
||||
out = os.path.realpath(args.out)
|
||||
os.makedirs(out, exist_ok=True)
|
||||
|
||||
print(f"Fetching {args.count} faces → {out}")
|
||||
print(f"Source: {URL} (synthetic StyleGAN — no real people)")
|
||||
|
||||
results: list[dict] = [None] * args.count # type: ignore
|
||||
t0 = time.time()
|
||||
with ThreadPoolExecutor(max_workers=max(1, args.concurrency)) as ex:
|
||||
futs = {ex.submit(fetch_one, i, out): i for i in range(args.count)}
|
||||
for done, fut in enumerate(as_completed(futs), 1):
|
||||
idx, fname, cached, err = fut.result()
|
||||
results[idx] = {
|
||||
"id": idx,
|
||||
"file": fname,
|
||||
"cached": cached,
|
||||
"error": err,
|
||||
}
|
||||
if done % 25 == 0 or done == args.count:
|
||||
ok = sum(1 for r in results if r and not r.get("error"))
|
||||
print(f" [{done}/{args.count}] {ok} ok ({time.time()-t0:.1f}s)")
|
||||
|
||||
# Drop slots that errored or are still None (shouldn't happen)
|
||||
records = [r for r in results if r and not r.get("error")]
|
||||
print(f"\nPool ready: {len(records)} faces, {sum(1 for r in records if r['cached'])} from cache")
|
||||
|
||||
if not args.no_gender and records:
|
||||
print("\nGender-tagging pass:")
|
||||
summary = maybe_tag_gender(records, out)
|
||||
print(f" distribution: {summary}")
|
||||
else:
|
||||
for r in records:
|
||||
r["gender"] = None
|
||||
|
||||
# Strip transient flags before persisting
|
||||
for r in records:
|
||||
r.pop("cached", None)
|
||||
r.pop("error", None)
|
||||
|
||||
manifest = os.path.join(out, "manifest.jsonl")
|
||||
with open(manifest, "w") as f:
|
||||
for r in records:
|
||||
f.write(json.dumps(r) + "\n")
|
||||
print(f"\nManifest: {manifest} ({len(records)} entries)")
|
||||
|
||||
# Quick checksum manifest for downstream debugging
|
||||
h = hashlib.sha256()
|
||||
for r in records:
|
||||
h.update(r["file"].encode())
|
||||
h.update(b"|")
|
||||
h.update((r.get("gender") or "?").encode())
|
||||
print(f"Pool fingerprint (sha256): {h.hexdigest()[:16]}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
x
Reference in New Issue
Block a user