chore(cleanup): 참조 0 dead code 제거 (Category C 축소 · demo-final)
당초 조사 대상 5개 중 3개 (DfuBleScanner · DfuNus · DfuNusConnection) 는 DfuManager 실제 참조 확인 (컴파일 실패로 발견 · Explore 조사 오탐) → 유지. 완전 제거 (2 항목): · speech/HfVolumeExtractor.kt (298 L · 옛 HuggingFace API 실험 · 참조 0 검증) · res/raw/bladdy_loading3.riv (asset · 코드 참조 0 · bladdy_final12 만 사용) okhttp3 dependency 는 LabdbClient (labdb 세션 업로드) 에서 사용 중이라 유지. 합계: 약 298 LOC + asset 1개 감소. 빌드: BUILD SUCCESSFUL.
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/*
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* Copyright 2026 Charles KWON (KWON Ohjun)
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* charleskwon@medithings.co.kr / charleskwonohjun@gmail.com
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* MEDiThings Inc.
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* All rights reserved.
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*/
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package com.medithings.vesiscan.speech
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import android.util.Log
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import kotlinx.coroutines.Dispatchers
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import kotlinx.coroutines.withContext
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import okhttp3.MediaType.Companion.toMediaType
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import okhttp3.OkHttpClient
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import okhttp3.Request
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import okhttp3.RequestBody.Companion.toRequestBody
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import org.json.JSONArray
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import org.json.JSONObject
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import java.util.concurrent.TimeUnit
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/**
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* Extracts a milliliter volume from speech-recognition text via the
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* Hugging Face Inference API (Qwen 2.5-72B model).
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*
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* Selects a language-specific system prompt to maximize extraction accuracy.
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* Falls back to [VolumeParser] when the API key is missing or the request fails.
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*
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* @param apiKey Hugging Face bearer token.
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*/
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class HfVolumeExtractor(private val apiKey: String) {
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/** Successful extraction result. */
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data class ExtractResult(
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val volumeMl: Int,
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val rawText: String,
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val explanation: String,
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)
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private val client = OkHttpClient.Builder()
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.connectTimeout(CONNECT_TIMEOUT_SECONDS, TimeUnit.SECONDS)
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.readTimeout(READ_TIMEOUT_SECONDS, TimeUnit.SECONDS)
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.build()
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/**
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* Send [speechText] to the AI model using a prompt tailored to [language],
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* and return the extracted volume, or `null` when extraction fails.
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*/
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suspend fun extract(
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speechText: String,
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language: SpeechLanguage = SpeechLanguage.KOREAN,
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): ExtractResult? = withContext(Dispatchers.IO) {
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Log.d(TAG, "Starting extraction | lang=${language.code} | input: $speechText")
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if (apiKey.isBlank()) {
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Log.w(TAG, "API key is blank; falling back to local parser")
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return@withContext fallbackParse(speechText)
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}
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try {
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val prompt = getSystemPrompt(language)
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val requestBody = buildRequestBody(speechText, prompt)
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val request = Request.Builder()
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.url(API_URL)
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.addHeader("Authorization", "Bearer $apiKey")
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.addHeader("Content-Type", JSON_MEDIA_TYPE)
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.post(requestBody.toRequestBody(JSON_MEDIA_TYPE.toMediaType()))
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.build()
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val response = client.newCall(request).execute()
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val body = response.body?.string() ?: return@withContext null
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Log.d(TAG, "Response code: ${response.code}")
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if (!response.isSuccessful) {
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Log.e(TAG, "API failed (${response.code}); falling back to local parser")
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return@withContext fallbackParse(speechText)
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}
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parseApiResponse(body, speechText)
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} catch (e: Exception) {
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Log.e(TAG, "API error: ${e.message}", e)
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fallbackParse(speechText)
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}
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}
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// ── Private helpers ─────────────────────────────────────────────────
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private fun buildRequestBody(speechText: String, systemPrompt: String): String =
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JSONObject().apply {
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put("model", MODEL_ID)
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put("max_tokens", MAX_TOKENS)
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put("stream", false)
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put("messages", JSONArray().apply {
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put(JSONObject().apply {
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put("role", "system")
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put("content", systemPrompt)
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})
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put(JSONObject().apply {
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put("role", "user")
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put("content", speechText)
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})
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})
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}.toString()
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private fun parseApiResponse(body: String, speechText: String): ExtractResult? {
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val content = JSONObject(body)
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.getJSONArray("choices")
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.getJSONObject(0)
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.getJSONObject("message")
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.getString("content")
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Log.d(TAG, "Model response: $content")
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val jsonStr = extractJsonObject(content)
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val resultJson = JSONObject(jsonStr)
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val ml = resultJson.getInt("ml")
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val explanation = resultJson.optString("explanation", "")
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Log.d(TAG, "Parsed result: ${ml}ml - $explanation")
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return if (ml in VALID_RANGE) {
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ExtractResult(ml, speechText, explanation)
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} else {
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Log.w(TAG, "Volume out of range: ${ml}ml")
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null
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}
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}
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private fun extractJsonObject(text: String): String {
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val start = text.indexOf('{')
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val end = text.lastIndexOf('}')
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return if (start >= 0 && end > start) text.substring(start, end + 1) else text
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}
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private fun fallbackParse(text: String): ExtractResult? {
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val result = VolumeParser.parse(text) ?: return null
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Log.d(TAG, "Local parse result: ${result.volumeMl}ml")
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return ExtractResult(result.volumeMl, result.rawText, "Local parse")
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}
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/** Return a system prompt optimized for the given [language]. */
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private fun getSystemPrompt(language: SpeechLanguage): String = when (language) {
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SpeechLanguage.KOREAN -> PROMPT_KOREAN
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SpeechLanguage.ENGLISH -> PROMPT_ENGLISH
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SpeechLanguage.CHINESE -> PROMPT_CHINESE
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SpeechLanguage.JAPANESE -> PROMPT_JAPANESE
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}
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companion object {
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private const val TAG = "HfVolumeExtractor"
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private const val API_URL = "https://router.huggingface.co/v1/chat/completions"
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private const val MODEL_ID = "Qwen/Qwen2.5-72B-Instruct"
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private const val JSON_MEDIA_TYPE = "application/json"
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private const val MAX_TOKENS = 100
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private const val CONNECT_TIMEOUT_SECONDS = 15L
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private const val READ_TIMEOUT_SECONDS = 30L
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private val VALID_RANGE = 1..1000
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// ── Language-specific prompts ───────────────────────────────────
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private val PROMPT_BASE = """
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## Your Mission
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You are specialized in extracting MILLILITER (ml) volumes from voice input.
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The text you receive is raw speech-to-text output from a medical/lab device.
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Your ONLY job: find the ml number the user intended to say.
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## Rules
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- Extract a single integer in ml, range 1~1000
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- mm and ml are the SAME in this context (user ALWAYS means ml, never millimeters)
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- cc and ml are the SAME (1cc = 1ml)
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- If ambiguous, prefer the most likely medical volume
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- Look at ALL candidates (separated by " / ") to find consensus on the number
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- If no valid volume found, return -1
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- Respond ONLY in this JSON format:
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""".trimIndent()
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private val PROMPT_KOREAN = """
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You are a milliliter (ml) volume extraction AI.
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## Context
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This is a KOREAN speech-to-text result from a medical/lab voice input device.
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The user spoke in KOREAN to input a liquid volume in milliliters (ml).
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Your task: understand Korean speech and extract the ml value accurately.
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## Input Format
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Multiple STT candidates separated by " / " (e.g. "95mm / 95ml / 95 mm").
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STT often confuses mm/ml/미리/밀리 — the user ALWAYS means milliliters (ml).
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## Korean-Specific Patterns
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- Pure Korean numerals: "삼백 밀리", "백오십 미리"
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- Numbers with units: "300ml", "150 미리"
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- Korean-English mix: "삼백 ml", "원헌드레드 미리"
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- Misheard Korean that is actually English: "쓰리헌드레드", "투헌드레드 피프티"
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- Phonetic Korean of English: "파이브 헌드레드" = 500, "투 피프티" = 250
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- Casual/slang: "반 리터" = 500, "한 컵" = 250, "반" = 500, "쿼터" = 250
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- Unit variations: 밀리리터, 밀리미터, 미리, 밀리, ml, cc, 시시
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## Critical: Korean Phonetic Analysis
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Korean STT may transcribe English words as Korean phonetics.
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SOUND THEM OUT to decode:
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- 쓰리 = three, 투 = two, 원 = one, 포 = four, 파이브 = five
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- 식스 = six, 세븐 = seven, 에잇 = eight, 나인 = nine, 텐 = ten
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- 헌드레드 = hundred, 피프티 = fifty, 트웬티 = twenty, 서티 = thirty
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- 지로/제로 = zero, 오 = five OR "o" (context dependent)
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- 더블 = double, 트리플 = triple
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$PROMPT_BASE
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{"ml": <integer>, "explanation": "<한국어로 간단한 설명>"}
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""".trimIndent()
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private val PROMPT_ENGLISH = """
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You are a milliliter (ml) volume extraction AI.
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## Context
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This is an ENGLISH speech-to-text result from a medical/lab voice input device.
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The user spoke in ENGLISH to input a liquid volume in milliliters (ml).
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Your task: understand English speech and extract the ml value accurately.
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## Input Format
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Multiple STT candidates separated by " / " (e.g. "95mm / 95ml / 95 mm").
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STT often confuses mm/ml — the user ALWAYS means milliliters (ml).
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## English-Specific Patterns
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- Standard: "three hundred milliliters", "one fifty ml"
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- Digit-by-digit: "three zero zero" = 300, "one five o" = 150, "two o five" = 205
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- Casual: "half a liter" = 500, "a quarter liter" = 250, "half" = 500
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- Compound: "one hundred and fifty", "two fifty"
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- With modifiers: "double zero" = 00, "triple zero" = 000
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- Unit variations: ml, milliliters, millimeters, cc
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$PROMPT_BASE
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{"ml": <integer>, "explanation": "<brief reason in English>"}
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""".trimIndent()
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private val PROMPT_CHINESE = """
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你是一个毫升（ml）容量提取AI。
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## 背景
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这是来自医疗/实验室语音输入设备的中文语音识别结果。
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用户用中文说出了一个液体容量（毫升/ml）。
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你的任务：准确理解中文语音并提取毫升值。
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## 输入格式
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多个语音识别候选项用 " / " 分隔（例如 "95毫升 / 95ml / 九十五"）。
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语音识别经常混淆毫米/毫升——用户始终指的是毫升（ml）。
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## 中文特有模式
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- 中文数字: "三百毫升", "一百五十", "二百五"
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- 阿拉伯数字+中文单位: "300毫升", "150ml"
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- 口语: "半升" = 500, "一杯" = 250, "半" = 500
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- 口语简称: "二百五" = 250（此处不是骂人）, "三百" = 300
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- 单位变体: 毫升, 毫米, ml, cc, 西西
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## 关键：中文数字规则
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- 一百五 = 150（一百五十的简称）
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- 二百五 = 250（二百五十的简称）
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- 两百 = 200 (两 = 2 in spoken Chinese)
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- 半升 = 500ml, 四分之一升 = 250ml
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- 一千 = 1000
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$PROMPT_BASE
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{"ml": <integer>, "explanation": "<用中文简要说明>"}
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""".trimIndent()
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private val PROMPT_JAPANESE = """
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あなたはミリリットル（ml）容量抽出AIです。
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## 背景
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これは医療・実験室の音声入力デバイスからの日本語音声認識結果です。
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ユーザーは日本語で液体の容量（ミリリットル/ml）を発話しました。
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あなたのタスク：日本語の音声を正確に理解し、ml値を抽出すること。
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## 入力形式
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複数の音声認識候補が " / " で区切られています（例："95ミリ / 95ml / きゅうじゅうご"）。
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音声認識はミリメートル/ミリリットルを混同することが多い — ユーザーは常にミリリットル（ml）を意味します。
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## 日本語特有のパターン
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- 日本語数字: "さんびゃくミリ", "ひゃくごじゅう"
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- 漢数字: "三百ミリリットル", "百五十"
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- 数字+単位: "300ml", "150ミリ"
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- カジュアル: "半リットル" = 500, "コップ一杯" = 250
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- 省略形: "にひゃくごじゅう" = 250, "さんびゃく" = 300
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- 単位バリエーション: ミリリットル, ミリ, ml, cc, シーシー
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## 重要：日本語数字ルール
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- ひゃくごじゅう = 150
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- にひゃく = 200
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- にひゃくご = 250（にひゃくごじゅうの略）
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- さんびゃく = 300
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- ごひゃく = 500
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- せん = 1000
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- 半分 = half (context: 半リットル = 500ml)
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$PROMPT_BASE
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{"ml": <integer>, "explanation": "<日本語で簡単な説明>"}
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""".trimIndent()
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}
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}
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