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"use server"
import { v4 as uuidv4 } from "uuid"
import Replicate from "replicate"
import { RenderRequest, RenderedScene, RenderingEngine } from "@/types"
import { generateSeed } from "@/lib/generateSeed"
import { sleep } from "@/lib/sleep"
const renderingEngine = `${process.env.RENDERING_ENGINE || ""}` as RenderingEngine
// TODO: we should split Hugging Face and Replicate backends into separate files
const huggingFaceToken = `${process.env.AUTH_HF_API_TOKEN || ""}`
const huggingFaceInferenceEndpointUrl = `${process.env.RENDERING_HF_INFERENCE_ENDPOINT_URL || ""}`
const huggingFaceInferenceApiModel = `${process.env.RENDERING_HF_INFERENCE_API_MODEL || ""}`
const replicateToken = `${process.env.AUTH_REPLICATE_API_TOKEN || ""}`
const replicateModel = `${process.env.RENDERING_REPLICATE_API_MODEL || ""}`
const replicateModelVersion = `${process.env.RENDERING_REPLICATE_API_MODEL_VERSION || ""}`
const videochainToken = `${process.env.AUTH_VIDEOCHAIN_API_TOKEN || ""}`
const videochainApiUrl = `${process.env.RENDERING_VIDEOCHAIN_API_URL || ""}`
export async function newRender({
prompt,
// negativePrompt,
width,
height
}: {
prompt: string
// negativePrompt: string[]
width: number
height: number
}) {
if (!prompt) {
const error = `cannot call the rendering API without a prompt, aborting..`
console.error(error)
throw new Error(error)
}
let defaulResult: RenderedScene = {
renderId: "",
status: "error",
assetUrl: "",
alt: prompt || "",
maskUrl: "",
error: "failed to fetch the data",
segments: []
}
try {
if (renderingEngine === "REPLICATE") {
if (!replicateToken) {
throw new Error(`you need to configure your REPLICATE_API_TOKEN in order to use the REPLICATE rendering engine`)
}
if (!replicateModel) {
throw new Error(`you need to configure your REPLICATE_API_MODEL in order to use the REPLICATE rendering engine`)
}
if (!replicateModelVersion) {
throw new Error(`you need to configure your REPLICATE_API_MODEL_VERSION in order to use the REPLICATE rendering engine`)
}
const replicate = new Replicate({ auth: replicateToken })
// console.log("Calling replicate..")
const seed = generateSeed()
const prediction = await replicate.predictions.create({
version: replicateModelVersion,
input: {
prompt: [
"beautiful",
"intricate details",
prompt,
"award winning",
"high resolution"
].join(", "),
width,
height,
seed
}
})
// console.log("prediction:", prediction)
// no need to reply straight away as images take time to generate, this isn't instantaneous
// also our friends at Replicate won't like it if we spam them with requests
await sleep(4000)
return {
renderId: prediction.id,
status: "pending",
assetUrl: "",
alt: prompt,
error: prediction.error,
maskUrl: "",
segments: []
} as RenderedScene
} if (renderingEngine === "INFERENCE_ENDPOINT" || renderingEngine === "INFERENCE_API") {
if (!huggingFaceToken) {
throw new Error(`you need to configure your HF_API_TOKEN in order to use the ${renderingEngine} rendering engine`)
}
if (renderingEngine === "INFERENCE_ENDPOINT" && !huggingFaceInferenceEndpointUrl) {
throw new Error(`you need to configure your RENDERING_HF_INFERENCE_ENDPOINT_URL in order to use the INFERENCE_ENDPOINT rendering engine`)
}
if (renderingEngine === "INFERENCE_API" && !huggingFaceInferenceApiModel) {
throw new Error(`you need to configure your RENDERING_HF_INFERENCE_API_MODEL in order to use the INFERENCE_API rendering engine`)
}
const url = renderingEngine === "INFERENCE_ENDPOINT"
? huggingFaceInferenceEndpointUrl
: `https://api-inference.huggingface.co/models/${huggingFaceInferenceApiModel}`
/*
console.log(`calling ${url} with params: `, {
num_inference_steps: 25,
guidance_scale: 8,
width,
height,
})
*/
const res = await fetch(url, {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${huggingFaceToken}`,
},
body: JSON.stringify({
inputs: [
"beautiful",
"intricate details",
prompt,
"award winning",
"high resolution"
].join(", "),
parameters: {
num_inference_steps: 25,
guidance_scale: 8,
width,
height,
},
use_cache: false,
}),
cache: "no-store",
// we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache)
// next: { revalidate: 1 }
})
// Recommendation: handle errors
if (res.status !== 200) {
const content = await res.text()
console.error(content)
// This will activate the closest `error.js` Error Boundary
throw new Error('Failed to fetch data')
}
const blob = await res.arrayBuffer()
const contentType = res.headers.get('content-type')
const assetUrl = `data:${contentType};base64,${Buffer.from(blob).toString('base64')}`
return {
renderId: uuidv4(),
status: "completed",
assetUrl,
alt: prompt,
error: "",
maskUrl: "",
segments: []
} as RenderedScene
} else {
const res = await fetch(`${videochainApiUrl}/render`, {
method: "POST",
headers: {
Accept: "application/json",
"Content-Type": "application/json",
Authorization: `Bearer ${videochainToken}`,
},
body: JSON.stringify({
prompt,
// negativePrompt, unused for now
nbFrames: 1,
nbSteps: 25, // 20 = fast, 30 = better, 50 = best
actionnables: [], // ["text block"],
segmentation: "disabled", // "firstframe", // one day we will remove this param, to make it automatic
width,
height,
// no need to upscale right now as we generate tiny panels
// maybe later we can provide an "export" button to PDF
// unfortunately there are too many requests for upscaling,
// the server is always down
upscalingFactor: 1, // 2,
// analyzing doesn't work yet, it seems..
analyze: false, // analyze: true,
cache: "ignore"
} as Partial<RenderRequest>),
cache: 'no-store',
// we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache)
// next: { revalidate: 1 }
})
if (res.status !== 200) {
throw new Error('Failed to fetch data')
}
const response = (await res.json()) as RenderedScene
return response
}
} catch (err) {
console.error(err)
return defaulResult
}
}
export async function getRender(renderId: string) {
if (!renderId) {
const error = `cannot call the rendering API without a renderId, aborting..`
console.error(error)
throw new Error(error)
}
let defaulResult: RenderedScene = {
renderId: "",
status: "pending",
assetUrl: "",
alt: "",
maskUrl: "",
error: "failed to fetch the data",
segments: []
}
try {
if (renderingEngine === "REPLICATE") {
if (!replicateToken) {
throw new Error(`you need to configure your AUTH_REPLICATE_API_TOKEN in order to use the REPLICATE rendering engine`)
}
if (!replicateModel) {
throw new Error(`you need to configure your RENDERING_REPLICATE_API_MODEL in order to use the REPLICATE rendering engine`)
}
const res = await fetch(`https://api.replicate.com/v1/predictions/${renderId}`, {
method: "GET",
headers: {
Authorization: `Token ${replicateToken}`,
},
cache: 'no-store',
// we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache)
// next: { revalidate: 1 }
})
// Recommendation: handle errors
if (res.status !== 200) {
// This will activate the closest `error.js` Error Boundary
throw new Error('Failed to fetch data')
}
const response = (await res.json()) as any
return {
renderId,
status: response?.error ? "error" : response?.status === "succeeded" ? "completed" : "pending",
assetUrl: `${response?.output || ""}`,
alt: `${response?.input?.prompt || ""}`,
error: `${response?.error || ""}`,
maskUrl: "",
segments: []
} as RenderedScene
} else {
// console.log(`calling GET ${apiUrl}/render with renderId: ${renderId}`)
const res = await fetch(`${videochainApiUrl}/render/${renderId}`, {
method: "GET",
headers: {
Accept: "application/json",
"Content-Type": "application/json",
Authorization: `Bearer ${videochainToken}`,
},
cache: 'no-store',
// we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache)
// next: { revalidate: 1 }
})
if (res.status !== 200) {
throw new Error('Failed to fetch data')
}
const response = (await res.json()) as RenderedScene
return response
}
} catch (err) {
console.error(err)
defaulResult.status = "error"
defaulResult.error = `${err}`
return defaulResult
}
}
export async function upscaleImage(image: string): Promise<{
assetUrl: string
error: string
}> {
if (!image) {
const error = `cannot call the rendering API without an image, aborting..`
console.error(error)
throw new Error(error)
}
let defaulResult = {
assetUrl: "",
error: "failed to fetch the data",
}
try {
// console.log(`calling GET ${apiUrl}/render with renderId: ${renderId}`)
const res = await fetch(`${videochainApiUrl}/upscale`, {
method: "POST",
headers: {
Accept: "application/json",
"Content-Type": "application/json",
Authorization: `Bearer ${videochainToken}`,
},
cache: 'no-store',
body: JSON.stringify({ image, factor: 3 })
// we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache)
// next: { revalidate: 1 }
})
if (res.status !== 200) {
throw new Error('Failed to fetch data')
}
const response = (await res.json()) as {
assetUrl: string
error: string
}
return response
} catch (err) {
console.error(err)
return defaulResult
}
}
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