import { createLlamaPrompt } from "@/lib/createLlamaPrompt" import { dirtyLLMResponseCleaner } from "@/lib/dirtyLLMResponseCleaner" import { dirtyLLMJsonParser } from "@/lib/dirtyLLMJsonParser" import { dirtyCaptionCleaner } from "@/lib/dirtyCaptionCleaner" import { predict } from "./predict" import { Preset } from "../engine/presets" import { LLMResponse } from "@/types" import { cleanJson } from "@/lib/cleanJson" export const getStory = async ({ preset, prompt = "", }: { preset: Preset; prompt: string; }): Promise => { // In case you need to quickly debug the RENDERING engine you can uncomment this: // return mockLLMResponse const query = createLlamaPrompt([ { role: "system", content: [ `You are a comic book author specialized in ${preset.llmPrompt}`, `Please write detailed drawing instructions and a one-sentence short caption for the 4 panels of a new silent comic book page.`, `Give your response as a JSON array like this: \`Array<{ panel: number; instructions: string; caption: string}>\`.`, // `Give your response as Markdown bullet points.`, `Be brief in your 4 instructions and captions, don't add your own comments. Be straight to the point, and never reply things like "Sure, I can.." etc.` ].filter(item => item).join("\n") }, { role: "user", content: `The story is: ${prompt}`, } ]) + "```json\n[" let result = "" try { result = `${await predict(query) || ""}`.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+".") || ""}`.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 llmResponse: LLMResponse = [] try { llmResponse = dirtyLLMJsonParser(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: llmResponse = ( tmp.split("*") .map(item => item.trim()) .map((cap, i) => ({ panel: i, caption: cap, instructions: cap, })) ) } return llmResponse.map(res => dirtyCaptionCleaner(res)) }