src/types.ts

export type ProjectionMode = 'cartesian' | 'spherical'

export type CacheMode = "use" | "renew" | "ignore"

export interface RenderRequest {
  prompt: string

  // whether to use video segmentation
  // disabled (default)
  // firstframe: we only analyze the first frame
  // allframes: we analyze all the frames
  segmentation: 'disabled' | 'firstframe' | 'allframes'

  // segmentation will only be executed if we have a non-empty list of actionnables
  // actionnables are names of things like "chest", "key", "tree", "chair" etc
  actionnables: string[]

  // note: this is the number of frames for Zeroscope,
  // which is currently configured to only output 3 seconds, so:
  // nbFrames=8 -> 1 sec
  // nbFrames=16 -> 2 sec
  // nbFrames=24 -> 3 sec
  nbFrames: number // min: 1, max: 24

  nbSteps: number // min: 1, max: 50

  seed: number

  width: number // fixed at 1024 for now
  height: number // fixed at 512 for now

  // upscaling factor
  // 0: no upscaling
  // 1: no upscaling
  // 2: 2x larger
  // 3: 3x larger
  // 4x: 4x larger, up to 4096x4096 (warning: a PNG of this size can be 50 Mb!)
  upscalingFactor: number

  projection: ProjectionMode

  /**
   * Use turbo mode
   * 
   * At the time of writing this will use SSD-1B + LCM
   * https://huggingface.co/spaces/jbilcke-hf/fast-image-server
   */
  turbo: boolean

  cache: CacheMode

  wait: boolean // wait until the job is completed

  analyze: boolean // analyze the image to generate a caption (optional)
}

export interface ImageSegment {
  id: number
  box: number[]
  color: number[]
  label: string
  score: number 
}

export type RenderedSceneStatus =
  | "pending"
  | "completed"
  | "error"

export interface RenderedScene {
  renderId: string
  status: RenderedSceneStatus
  assetUrl: string 
  alt: string
  error: string
  maskUrl: string
  segments: ImageSegment[]
}

export interface ImageAnalysisRequest {
  image: string // in base64
  prompt: string
}

export interface ImageAnalysisResponse {
  result: string
  error?: string
}

export type LLMResponse = Array<{panel: number; instructions: string; caption: string }>

export type LLMEngine =
  | "INFERENCE_API"
  | "INFERENCE_ENDPOINT"
  | "OPENAI"
  | "REPLICATE"

  export type RenderingEngine =
  | "VIDEOCHAIN"
  | "OPENAI"
  | "REPLICATE"
  | "INFERENCE_API"
  | "INFERENCE_ENDPOINT"

  export type RenderingModelVendor =
  | "SERVER"
  | "OPENAI"
  | "REPLICATE"
  | "HUGGINGFACE"

export type PostVisibility =
  | "featured" // featured by admins
  | "trending" // top trending / received more than 10 upvotes
  | "normal" // default visibility

export type Post = {
  postId: string
  appId: string
  prompt: string
  previewUrl: string
  assetUrl: string
  createdAt: string
  visibility: PostVisibility
  upvotes: number
  downvotes: number
}

export type CreatePostResponse = {
  success?: boolean
  error?: string
  post: Post
}

export type GetAppPostsResponse = {
  success?: boolean
  error?: string
  posts: Post[]
}

export type GetAppPostResponse = {
  success?: boolean
  error?: string
  post: Post
}

export type LayoutProps = {
  page: number
  nbPanels: number
}

export type Settings = {
  renderingModelVendor: RenderingModelVendor
  renderingUseTurbo: boolean
  huggingfaceApiKey: string
  huggingfaceInferenceApiModel: string
  huggingfaceInferenceApiModelTrigger: string
  replicateApiKey: string
  replicateApiModel: string
  replicateApiModelVersion: string
  replicateApiModelTrigger: string
  openaiApiKey: string
  openaiApiModel: string
}