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[]

  nbFrames: number
  nbFPS: number

  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)

  identityImage: string // reference image for the main entity
}

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 GeneratedPanel = {
  panel: number
  instructions: string
  caption: string
}

export type GeneratedPanels = GeneratedPanel[]

// LLMVendor = what the user configure in the UI (eg. a dropdown item called default server)
// LLMEngine = the actual engine to use (eg. hugging face)
export type LLMEngine =
  | "INFERENCE_API"
  | "INFERENCE_ENDPOINT"
  | "OPENAI"
  | "REPLICATE"
  | "GROQ"
  | "ANTHROPIC"

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

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

// LLMVendor = what the user configure in the UI (eg. a dropdown item called default server)
// LLMEngine = the actual engine to use (eg. hugging face)
export type LLMVendor =
  | "SERVER"
  | "OPENAI"
  | "GROQ"
  | "ANTHROPIC"

export type LLMVendorConfig = {
  vendor: LLMVendor
  apiKey: string
  modelId: string
}

export type LLMPredictionFunctionParams = {
  systemPrompt: string
  userPrompt: string
  nbMaxNewTokens: number
  llmVendorConfig: LLMVendorConfig
}

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
}

// TODO: rename the *Model fields to better indicate if this is a LLM or RENDER mdoel
export type Settings = {
  renderingModelVendor: RenderingModelVendor
  renderingUseTurbo: boolean
  llmVendor: LLMVendor
  huggingFaceOAuth: string
  huggingfaceApiKey: string
  huggingfaceInferenceApiModel: string
  huggingfaceInferenceApiModelTrigger: string
  huggingfaceInferenceApiFileType: string
  replicateApiKey: string
  replicateApiModel: string
  replicateApiModelVersion: string
  replicateApiModelTrigger: string
  openaiApiKey: string
  openaiApiModel: string
  openaiApiLanguageModel: string
  groqApiKey: string
  groqApiLanguageModel: string
  anthropicApiKey: string
  anthropicApiLanguageModel: string
  hasGeneratedAtLeastOnce: boolean
  userDefinedMaxNumberOfPages: number
}

export type DynamicConfig = {
  maxNbPages: number
  nbPanelsPerPage: number
  nbTotalPanelsToGenerate: number
  oauthClientId: string
  oauthRedirectUrl: string
  oauthScopes: string
  enableHuggingFaceOAuth: boolean
  enableHuggingFaceOAuthWall: boolean
}