src/app/queries/getStoryContinuation.ts

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"
import { createZephyrPrompt } from "@/lib/createZephyrPrompt"

export const getStoryContinuation = async ({
  preset,
  prompt = "",
  nbTotalPanels = 2,
  previousCaptions = [],
}: {
  preset: Preset;
  prompt: string;
  nbTotalPanels: number;
  previousCaptions: string[];
}): Promise<LLMResponse> => {
  // throw new Error("Planned maintenance")
  
  // In case you need to quickly debug the RENDERING engine you can uncomment this:
  // return mockLLMResponse

  const previousCaptionsTemplate = previousCaptions.length
    ? `To help you, here are the previous panels and their captions (note: if you see an anomaly here eg. no caption or the same description repeated multiple times, do not hesitate to fix the story): ${JSON.stringify(previousCaptions, null, 2)}`
    : ''

  const query = createZephyrPrompt([
    {
      role: "system",
      content: [
        `You are a writer specialized in ${preset.llmPrompt}`,
        `Please write detailed drawing instructions and a short (2-3 sentences long) speech caption for the next ${nbTotalPanels} panels of a new story, but keep it open-ended (it will be continued and expanded later). Please make sure each of those ${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 captions 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 about: ${prompt}.${previousCaptionsTemplate}`,
    }
  ]) + "\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 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))
}