chore: migrate with new structure

app/composables/queues.ts

@@ -0,0 +1,236 @@
+import type { Emotion } from '../constants/emotions'
+import { ref } from 'vue'
+
+import { llmInferenceEndToken } from '../constants'
+import { EMOTION_VALUES } from '../constants/emotions'
+import { useQueue } from './queue'
+
+export function useEmotionsMessageQueue(emotionsQueue: ReturnType<typeof useQueue<Emotion>>, messageContentQueue: ReturnType<typeof useQueue<string>>) {
+  function splitEmotion(content: string) {
+    for (const emotion of EMOTION_VALUES) {
+      // doesn't include the emotion, continue
+      if (!content.includes(emotion))
+        continue
+
+      // find the emotion and push the content before the emotion to the queue
+      const emotionIndex = content.indexOf(emotion)
+      const beforeEmotion = content.slice(0, emotionIndex)
+      const afterEmotion = content.slice(emotionIndex + emotion.length)
+
+      return {
+        ok: true,
+        emotion: emotion as Emotion,
+        before: beforeEmotion,
+        after: afterEmotion,
+      }
+    }
+
+    return {
+      ok: false,
+      emotion: '' as Emotion,
+      before: content,
+      after: '',
+    }
+  }
+
+  const processed = ref<string>('')
+
+  return useQueue<string>({
+    handlers: [
+      async (ctx) => {
+        // inference ended, push the last content to the message queue
+        if (ctx.data.includes(llmInferenceEndToken)) {
+          const content = processed.value.trim()
+          if (content)
+            await messageContentQueue.add(content)
+
+          processed.value = ''
+
+          return
+        }
+        // if the message is an emotion, push the last content to the message queue
+        if (EMOTION_VALUES.includes(ctx.data as Emotion)) {
+          const content = processed.value.trim()
+          if (content)
+            await messageContentQueue.add(content)
+
+          processed.value = ''
+          ctx.emit('emotion', ctx.data as Emotion)
+          await emotionsQueue.add(ctx.data as Emotion)
+
+          return
+        }
+
+        // otherwise we should process the message to find the emotions
+
+        {
+        // iterate through the message to find the emotions
+          const { ok, before, emotion, after } = splitEmotion(ctx.data)
+          if (ok) {
+            await messageContentQueue.add(before)
+            ctx.emit('emotion', emotion)
+            await emotionsQueue.add(emotion)
+            await messageContentQueue.add(after)
+            processed.value = ''
+
+            return
+          }
+          else {
+            // if none of the emotions are found, push the content to the temp queue
+            processed.value += ctx.data
+          }
+        }
+
+        // iterate through the message to find the emotions
+        {
+          const { ok, before, emotion, after } = splitEmotion(processed.value)
+          if (ok) {
+            await messageContentQueue.add(before)
+            ctx.emit('emotion', emotion)
+            await emotionsQueue.add(emotion)
+            await messageContentQueue.add(after)
+            processed.value = ''
+          }
+        }
+      },
+    ],
+  })
+}
+
+export function useDelayMessageQueue(useEmotionsMessageQueue: ReturnType<typeof useQueue<string>>) {
+  function splitDelays(content: string) {
+    // doesn't include the emotion, continue
+    if (!(/<\|DELAY:\d+\|>/i.test(content))) {
+      return {
+        ok: false,
+        delay: 0,
+        before: content,
+        after: '',
+      }
+    }
+
+    const delayExecArray = /<\|DELAY:(\d+)\|>/i.exec(content)
+
+    const delay = delayExecArray?.[1]
+    if (!delay) {
+      return {
+        ok: false,
+        delay: 0,
+        before: content,
+        after: '',
+      }
+    }
+
+    const delaySeconds = Number.parseFloat(delay)
+    const before = content.split(delayExecArray[0])[0]
+    const after = content.split(delayExecArray[0])[1]
+
+    if (delaySeconds <= 0 || Number.isNaN(delaySeconds)) {
+      return {
+        ok: true,
+        delay: 0,
+        before,
+        after,
+      }
+    }
+
+    return {
+      ok: true,
+      delay: delaySeconds,
+      before,
+      after,
+    }
+  }
+
+  function sleep(ms: number) {
+    return new Promise(resolve => setTimeout(resolve, ms))
+  }
+
+  const delaysQueueProcessedTemp = ref<string>('')
+  return useQueue<string>({
+    handlers: [
+      async (ctx) => {
+        // inference ended, push the last content to the message queue
+        if (ctx.data.includes(llmInferenceEndToken)) {
+          const content = delaysQueueProcessedTemp.value.trim()
+          if (content)
+            await useEmotionsMessageQueue.add(content)
+
+          delaysQueueProcessedTemp.value = ''
+          return
+        }
+
+        {
+          // iterate through the message to find the emotions
+          const { ok, before, delay, after } = splitDelays(ctx.data)
+          if (ok && before) {
+            await useEmotionsMessageQueue.add(before)
+
+            if (delay) {
+              ctx.emit('delay', delay)
+              await sleep(delay * 1000)
+            }
+
+            if (after)
+              await useEmotionsMessageQueue.add(after)
+          }
+          else {
+            // if none of the emotions are found, push the content to the temp queue
+            delaysQueueProcessedTemp.value += ctx.data
+          }
+        }
+
+        // iterate through the message to find the emotions
+        {
+          const { ok, before, delay, after } = splitDelays(delaysQueueProcessedTemp.value)
+          if (ok && before) {
+            await useEmotionsMessageQueue.add(before)
+
+            if (delay) {
+              ctx.emit('delay', delay)
+              await sleep(delay * 1000)
+            }
+
+            if (after)
+              await useEmotionsMessageQueue.add(after)
+            delaysQueueProcessedTemp.value = ''
+          }
+        }
+      },
+    ],
+  })
+}
+
+export function useMessageContentQueue(ttsQueue: ReturnType<typeof useQueue<string>>) {
+  const processed = ref<string>('')
+
+  return useQueue<string>({
+    handlers: [
+      async (ctx) => {
+        if (ctx.data === llmInferenceEndToken) {
+          const content = processed.value.trim()
+          if (content)
+            await ttsQueue.add(content)
+
+          processed.value = ''
+          return
+        }
+
+        const endMarker = /[.?!]/
+        processed.value += ctx.data
+
+        while (processed.value) {
+          const endMarkerExecArray = endMarker.exec(processed.value)
+          if (!endMarkerExecArray || typeof endMarkerExecArray.index === 'undefined')
+            break
+
+          const before = processed.value.slice(0, endMarkerExecArray.index + 1)
+          const after = processed.value.slice(endMarkerExecArray.index + 1)
+
+          await ttsQueue.add(before)
+          processed.value = after
+        }
+      },
+    ],
+  })
+}