File size: 3,035 Bytes
f4dea7d
2f7798c
 
 
f4dea7d
 
 
c32ec0d
 
f4dea7d
 
 
 
5dd2af5
f4dea7d
 
 
5dd2af5
c32ec0d
c65c1bb
855183b
048071f
 
f4dea7d
 
 
 
 
cea3188
f309083
81eb27e
f4dea7d
f309083
f4dea7d
 
 
 
 
 
974ed41
f4dea7d
 
 
2f7798c
f4dea7d
5dd2af5
879455c
f4dea7d
 
 
 
 
5dd2af5
879455c
f4dea7d
 
 
 
 
 
 
 
f5d8038
c32ec0d
2f7798c
c32ec0d
f4dea7d
 
c32ec0d
f4dea7d
 
f5d8038
11443d4
2f7798c
 
 
c32ec0d
 
 
 
 
 
 
 
 
f4dea7d
2f7798c
c32ec0d
f4dea7d
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
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 = "",
  nbTotalPanels = 4,
}: {
  preset: Preset;
  prompt: string;
  nbTotalPanels: number;
}): Promise<LLMResponse> => {
  // throw new Error("Planned maintenance")
  
  // 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 writer specialized in ${preset.llmPrompt}`,
        `Please write detailed drawing instructions and a short (1 or 2 sentences long) speech caption 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. 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}`,
    }
  ]) + "```json\n["


  let result = ""

  try {
    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))
}