AI

Base URL https://api.flam.fashion. Send Authorization: Bearer flam_sk_… on every call; a handful of routes are session-only and say so. How keys and roles work.

POST /api/toolkit/ai/depth

Depth map for the Lens Blur tool (1 token)

Depth Anything V2 on fal → a grayscale depth PNG url. The map is an INTERNAL input to the client-side depth-of-field; it is NOT captured as a library asset. Reserve → settle on 200 / release on any failure, so a failed call charges nothing.

Request body — multipart/form-data (required)

Responses

200 returns:

{
  "url": "string",
  "spent": 0
}

Call it

curl -X POST "https://api.flam.fashion/api/toolkit/ai/depth" \
  -H "Authorization: Bearer $FLAM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"imageUrl":"string"}'

POST /api/toolkit/ai/describe-item

Classify one piece — auto-name + wardrobe bucket (1 token)

The smart-upload classifier as a standalone lane. The same read runs in-process for /api/toolkit/assets/upload?classify=1. nameEn is capped at 40 characters and an unknown kind is clamped to "clothes" — the model can drift, the contract cannot.

Request body — multipart/form-data (required)

Responses

200 returns:

{
  "ok": true,
  "nameEn": "string",
  "kind": "clothes"
}

Call it

curl -X POST "https://api.flam.fashion/api/toolkit/ai/describe-item" \
  -H "Authorization: Bearer $FLAM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"imageUrl":"string"}'

POST /api/toolkit/ai/detect-items

Find every garment and accessory in a frame

Step 1 of 2 (detect then segment). Exhaustively detects each distinct garment, accessory and editable hair/face feature so a UI can draw tap targets. A rail of hanging clothes, a flat lay or a shop shelf is a normal input. Boxes are [ymin, xmin, ymax, xmax] normalized 0-1000 and are APPROXIMATE tap targets, not cutouts. ORIENTATION: boxes are in the RAW stored pixel matrix; this lane does not apply a JPEG's EXIF Orientation flag and fal (behind /segment) does. Pass the returned label to /segment, or bake orientation before both calls. Flat 1 token, refunded on any failure.

Request body — multipart/form-data (required)

Responses

200 returns:

{
  "ok": true,
  "items": [
    {
      "label": "string",
      "box": [
        0
      ]
    }
  ]
}

Call it

curl -X POST "https://api.flam.fashion/api/toolkit/ai/detect-items" \
  -H "Authorization: Bearer $FLAM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"imageUrl":"string"}'

POST /api/toolkit/ai/extract-character

Read a person out of a reference image (or a four-view collage)

Returns the five character-studio fields — age, skin, eyes, hair, extras — so a director can review WHO the house just cast before any frame is developed. A FLAM house model is a collage of several views of one person; the engine reads across all the panels and answers once. No-face guard: a landscape, an object or a garment shot returns faceDetected=false with every field empty. It never invents a person, and extras stays empty unless a real identifying mark is visible. Flat 1 token, refunded on any failure.

Request body — multipart/form-data (required)

Responses

200 returns:

{
  "ok": true,
  "faceDetected": true,
  "fields": {
    "age": "string",
    "skin": "string",
    "eyes": "string",
    "hair": "string",
    "extras": "string"
  }
}

Call it

curl -X POST "https://api.flam.fashion/api/toolkit/ai/extract-character" \
  -H "Authorization: Bearer $FLAM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"imageUrl":"string"}'

POST /api/toolkit/ai/segment

A clean cutout of the ONE item that was tapped

Step 2 of 2 (detect then segment). SAM 3 on fal, prompted by the detect-items label (orientation-immune — prefer it), a box [x0,y0,x1,y1] in PIXELS of the EXIF-APPLIED frame, or a point [x,y]. At least one target is required. What comes back is an RGBA CUTOUT, not a white-on-black binary mask. It is an internal compositing input and is never captured as a library asset. Flat 1 token, refunded on failure.

Request body — application/json (required)

{
  "imageUrl": "string",
  "label": "string",
  "box": [
    0
  ],
  "point": [
    0
  ]
}

Responses

200 returns:

{
  "url": "string",
  "bbox": [
    0
  ],
  "spent": 0
}

Call it

curl -X POST "https://api.flam.fashion/api/toolkit/ai/segment" \
  -H "Authorization: Bearer $FLAM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"imageUrl":"string","label":"string","box":[0],"point":[0]}'