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.

app/src/main/java/com/medithings/vesiscan/speech/HfVolumeExtractor.kt

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