import { predict } from "./predict"
import { Preset } from "../engine/presets"
import { GeneratedPanel } from "@/types"
import { cleanJson } from "@/lib/cleanJson"
import { createZephyrPrompt } from "@/lib/createZephyrPrompt"
import { dirtyGeneratedPanelCleaner } from "@/lib/dirtyGeneratedPanelCleaner"
import { dirtyGeneratedPanelsParser } from "@/lib/dirtyGeneratedPanelsParser"
import { sleep } from "@/lib/sleep"

export const predictNextPanels = async ({
  preset,
  prompt = "",
  nbPanelsToGenerate = 2,
  nbTotalPanels = 4,
  existingPanels = [],
}: {
  preset: Preset;
  prompt: string;
  nbPanelsToGenerate?: number;
  nbTotalPanels?: number;
  existingPanels: GeneratedPanel[];
}): Promise<GeneratedPanel[]> => {
  // console.log("predictNextPanels: ", { prompt, nbPanelsToGenerate })
  // throw new Error("Planned maintenance")
  
  // In case you need to quickly debug the RENDERING engine you can uncomment this:
  // return mockGeneratedPanels

  const existingPanelsTemplate = existingPanels.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(existingPanels, null, 2)}`
    : ''


  const firstNextOrLast =
    existingPanels.length === 0
      ? "first"
      : (nbTotalPanels - existingPanels.length) === nbTotalPanels
      ? "last"
      : "next"

  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 ${firstNextOrLast} ${nbPanelsToGenerate} panels (out of ${nbTotalPanels} in total) of a new story, but keep it open-ended (it will be continued and expanded later). Please make sure each of those ${nbPanelsToGenerate} panels include info about character gender, age, origin, clothes, colors, location, lights, etc. Only generate those ${nbPanelsToGenerate} panels, but take into account the fact the panels are part of a longer story (${nbTotalPanels} panels long).`,
        `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 the instructions and narrative captions of those ${nbPanelsToGenerate} panels, 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!! Important: Write valid JSON!`
      ].filter(item => item).join("\n")
    },
    {
      role: "user",
      content: `The story is about: ${prompt}.${existingPanelsTemplate}`,
    }
  ]) + "\n[{"


  let result = ""

  // we don't require a lot of token for our task
  // but to be safe, let's count ~130 tokens per panel
  const nbTokensPerPanel = 130

  const nbMaxNewTokens = nbPanelsToGenerate * nbTokensPerPanel

  try {
    // console.log(`calling predict(${query}, ${nbTotalPanels})`)
    result = `${await predict(query, nbMaxNewTokens)}`.trim()
    console.log("LLM result (1st trial):", result)
    if (!result.length) {
      throw new Error("empty result on 1st trial!")
    }
  } catch (err) {
    // console.log(`prediction of the story failed, trying again..`)
    // this should help throttle things on a bit on the LLM API side
    await sleep(2000)

    try {
      result = `${await predict(query + " \n ", nbMaxNewTokens)}`.trim()
      console.log("LLM result (2nd trial):", result)
      if (!result.length) {
        throw new Error("empty result on 2nd trial!")
      }
    } catch (err) {
      console.error(`prediction of the story failed twice 💩`)
      throw new Error(`failed to generate the story twice 💩 ${err}`)
    }
  }

  // console.log("Raw response from LLM:", result)
  const tmp = cleanJson(result)
  
  let generatedPanels: GeneratedPanel[] = []

  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))
}