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import { predict } from "./predict" | |
import { Preset } from "../engine/presets" | |
import { GeneratedPanels } from "@/types" | |
import { cleanJson } from "@/lib/cleanJson" | |
import { createZephyrPrompt } from "@/lib/createZephyrPrompt" | |
import { dirtyGeneratedPanelCleaner } from "@/lib/dirtyGeneratedPanelCleaner" | |
import { dirtyGeneratedPanelsParser } from "@/lib/dirtyGeneratedPanelsParser" | |
export const getStory = async ({ | |
preset, | |
prompt = "", | |
nbTotalPanels = 4, | |
}: { | |
preset: Preset; | |
prompt: string; | |
nbTotalPanels: number; | |
}): Promise<GeneratedPanels> => { | |
throw new Error("legacy, deprecated") | |
// In case you need to quickly debug the RENDERING engine you can uncomment this: | |
// return mockGeneratedPanels | |
const query = createZephyrPrompt([ | |
{ | |
role: "system", | |
content: [ | |
`You are a writer specialized in ${preset.llmPrompt}`, | |
`Please write detailed drawing instructions and short (2-3 sentences long) speech captions for the ${nbTotalPanels} panels of a new story. Please make sure each of the ${nbTotalPanels} panels include info about character gender, age, origin, clothes, colors, location, lights, etc.`, | |
`Give your response as a VALID JSON array like this: \`Array<{ panel: number; instructions: string; caption: string; }>\`.`, | |
// `Give your response as Markdown bullet points.`, | |
`Be brief in your ${nbTotalPanels} instructions and narrative captions, don't add your own comments. The whole story must be captivating, smart, entertaining. Be straight to the point, and never reply things like "Sure, I can.." etc. Reply using valid JSON.` | |
].filter(item => item).join("\n") | |
}, | |
{ | |
role: "user", | |
content: `The story is: ${prompt}`, | |
} | |
]) + "\n```[{" | |
let result = "" | |
try { | |
// console.log(`calling predict(${query}, ${nbTotalPanels})`) | |
result = `${await predict(query, nbTotalPanels) || ""}`.trim() | |
if (!result.length) { | |
throw new Error("empty result!") | |
} | |
} catch (err) { | |
// console.log(`prediction of the story failed, trying again..`) | |
try { | |
result = `${await predict(query+".", nbTotalPanels) || ""}`.trim() | |
if (!result.length) { | |
throw new Error("empty result!") | |
} | |
} catch (err) { | |
console.error(`prediction of the story failed again 💩`) | |
throw new Error(`failed to generate the story ${err}`) | |
} | |
} | |
// console.log("Raw response from LLM:", result) | |
const tmp = cleanJson(result) | |
let GeneratedPanels: GeneratedPanels = [] | |
try { | |
GeneratedPanels = dirtyGeneratedPanelsParser(tmp) | |
} catch (err) { | |
// console.log(`failed to read LLM response: ${err}`) | |
// console.log(`original response was:`, result) | |
// in case of failure here, it might be because the LLM hallucinated a completely different response, | |
// such as markdown. There is no real solution.. but we can try a fallback: | |
GeneratedPanels = ( | |
tmp.split("*") | |
.map(item => item.trim()) | |
.map((cap, i) => ({ | |
panel: i, | |
caption: cap, | |
instructions: cap, | |
})) | |
) | |
} | |
return GeneratedPanels.map(res => dirtyGeneratedPanelCleaner(res)) | |
} |