Update app/main.py

app/main.py

@@ -2,16 +2,18 @@
 import json
 import os
 import re
+import tempfile
 from statistics import mean
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional, Tuple
 
 import requests
 from fastapi import FastAPI, File, Form, UploadFile
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 
-app = FastAPI(title="Japanese AI Interview API", version="category-fix-1.0")
+APP_VERSION = "5.0.0"
 
+app = FastAPI(title="Japanese Role Interview API", version=APP_VERSION)
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -20,99 +22,186 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+# -----------------------------
+# Config
+# -----------------------------
 HF_TOKEN = os.getenv("HF_TOKEN", "").strip()
-ASR_MODEL = os.getenv("ASR_MODEL", "openai/whisper-large-v3")
+HF_ROUTER_URL = os.getenv("HF_ROUTER_URL", "https://router.huggingface.co/v1/chat/completions")
 HF_INFERENCE_BASE = os.getenv("HF_INFERENCE_BASE", "https://router.huggingface.co/hf-inference/models")
+ASR_MODEL = os.getenv("ASR_MODEL", "openai/whisper-large-v3")
+CHAT_MODEL = os.getenv("CHAT_MODEL", "Qwen/Qwen2.5-7B-Instruct-1M")
+USE_FASTER_WHISPER = os.getenv("USE_FASTER_WHISPER", "true").lower() in {"1", "true", "yes", "on"}
+FASTER_WHISPER_MODEL = os.getenv("FASTER_WHISPER_MODEL", "small")
+MAX_QUESTION_LIMIT = int(os.getenv("MAX_QUESTION_LIMIT", "20"))
+ASR_TIMEOUT_SECONDS = int(os.getenv("ASR_TIMEOUT_SECONDS", "180"))
+LLM_TIMEOUT_SECONDS = int(os.getenv("LLM_TIMEOUT_SECONDS", "90"))
+
+_REPEAT_PROMPTS = [
+    "声が小さいです。もう少し大きい声で、もう一度お願いします。",
+    "まだ音がはっきり聞こえません。マイクを確認して、もう一度お願いします。",
+    "音がうまく入っていません。マイクを近づけて、もう一度お願いします。",
+]
+
+_LOCAL_ASR_MODEL = None
 
 ROLE_BANK: Dict[str, Dict[str, Any]] = {
     "construction": {
-        "label": "Construction / 建設",
-        "intro": "こんにちは。建設の仕事の面接練習を始めます。よろしくお願いします。",
+        "english_name": "Construction",
+        "japanese_name": "建設",
+        "intro_jp": "こんにちは。建設の仕事の面接練習を始めます。よろしくお願いします。",
+        "min_questions": 3,
+        "max_questions": 20,
+        "expected_keywords": ["安全", "ヘルメット", "現場", "工具", "体力", "チーム", "ルール"],
         "questions": [
-            "お名前を教えてください。",
-            "どこの国から来ましたか。",
-            "日本へ行きたい理由は何ですか。",
-            "建設の仕事をしたことがありますか。",
-            "危ない場所で働くとき、何に気をつけますか。",
-            "チームで働くことはできますか。",
-            "体力に自信はありますか。",
-            "最後に、建設の仕事でがんばりたいことを話してください。"
-        ]
+            {"id":"name","theme":"intro","stage":"screening","jp":"お名前を教えてください。","branch":"all"},
+            {"id":"country","theme":"intro","stage":"screening","jp":"どこの国から来ましたか。","branch":"all"},
+            {"id":"reason","theme":"motivation","stage":"screening","jp":"日本へ行きたい理由は何ですか。","branch":"all"},
+            {"id":"japanese","theme":"language","stage":"screening","jp":"日本語はどのくらい勉強しましたか。","branch":"all"},
+            {"id":"ready_check","theme":"intro","stage":"screening","jp":"面接の準備はできていますか。","branch":"all"},
+            {"id":"experience_gate","theme":"experience","stage":"role","jp":"建設の仕事をしたことがありますか。","branch":"all"},
+            {"id":"exp_yes_detail","theme":"experience","stage":"role","jp":"どんな建設の仕事をしましたか。","branch":"yes_exp"},
+            {"id":"exp_years","theme":"experience","stage":"role","jp":"その仕事は何年ぐらいしましたか。","branch":"yes_exp"},
+            {"id":"exp_no_motivation","theme":"motivation","stage":"role","jp":"経験がなくても、建設の仕事を勉強してがんばれますか。","branch":"no_exp"},
+            {"id":"physical","theme":"role_fit","stage":"role","jp":"体力が必要な仕事ですが、大丈夫ですか。","branch":"all"},
+            {"id":"safety","theme":"safety","stage":"role","jp":"危ない場所で働くとき、何に気をつけますか。","branch":"all"},
+            {"id":"teamwork","theme":"teamwork","stage":"role","jp":"チームで仕事をするとき、大切なことは何ですか。","branch":"all"},
+            {"id":"tools","theme":"role_fit","stage":"followup","jp":"工具を使う仕事に興味はありますか。","branch":"all"},
+            {"id":"morning","theme":"schedule","stage":"followup","jp":"朝早い仕事や外の仕事はできますか。","branch":"all"},
+            {"id":"report","theme":"teamwork","stage":"followup","jp":"分からないとき、先輩にすぐ相談できますか。","branch":"all"},
+            {"id":"mistake","theme":"reliability","stage":"followup","jp":"仕事でミスをしたら、どうしますか。","branch":"all"},
+            {"id":"strength","theme":"personality","stage":"followup","jp":"建設の仕事に向いている自分の長所を一つ話してください。","branch":"all"},
+            {"id":"closing","theme":"closing","stage":"closing","jp":"本日の建設の面接練習はここまでです。ご参加ありがとうございました。","branch":"all"},
+        ],
     },
     "restaurant_konbini": {
-        "label": "Restaurant / Konbini / 外食・コンビニ",
-        "intro": "こんにちは。外食・コンビニの仕事の面接練習を始めます。よろしくお願いします。",
+        "english_name": "Restaurant / Konbini",
+        "japanese_name": "外食・コンビニ",
+        "intro_jp": "こんにちは。外食・コンビニの仕事の面接練習を始めます。よろしくお願いします。",
+        "min_questions": 3,
+        "max_questions": 20,
+        "expected_keywords": ["接客", "レジ", "お客様", "笑顔", "ていねい", "品出し", "掃除"],
         "questions": [
-            "お名前を教えてください。",
-            "どこの国から来ましたか。",
-            "日本へ行きたい理由は何ですか。",
-            "接客の仕事をしたことがありますか。",
-            "お客様にはどのように話しますか。",
-            "忙しい時間でも落ち着いて働けますか。",
-            "笑顔で接客できますか。",
-            "最後に、この仕事に向いている自分の長所を話してください。"
-        ]
+            {"id":"name","theme":"intro","stage":"screening","jp":"お名前を教えてください。","branch":"all"},
+            {"id":"country","theme":"intro","stage":"screening","jp":"どこの国から来ましたか。","branch":"all"},
+            {"id":"reason","theme":"motivation","stage":"screening","jp":"日本へ行きたい理由は何ですか。","branch":"all"},
+            {"id":"japanese","theme":"language","stage":"screening","jp":"日本語はどのくらい勉強しましたか。","branch":"all"},
+            {"id":"ready_check","theme":"intro","stage":"screening","jp":"面接の準備はできていますか。","branch":"all"},
+            {"id":"experience_gate","theme":"experience","stage":"role","jp":"レストランやコンビニで働いた経験はありますか。","branch":"all"},
+            {"id":"exp_yes_detail","theme":"experience","stage":"role","jp":"どんな仕事をしましたか。レジ、接客、品出しなどを話してください。","branch":"yes_exp"},
+            {"id":"exp_no_motivation","theme":"motivation","stage":"role","jp":"経験がなくても、接客を勉強する気持ちはありますか。","branch":"no_exp"},
+            {"id":"customer","theme":"service","stage":"role","jp":"お客様には、どんな話し方をしたいですか。","branch":"all"},
+            {"id":"busy","theme":"role_fit","stage":"role","jp":"忙しい時間でも落ち着いて働けますか。","branch":"all"},
+            {"id":"cleanliness","theme":"service","stage":"role","jp":"お店の清潔さは大切ですか。なぜですか。","branch":"all"},
+            {"id":"shift","theme":"schedule","stage":"followup","jp":"立ち仕事やシフト勤務は大丈夫ですか。","branch":"all"},
+            {"id":"mistake","theme":"reliability","stage":"followup","jp":"注文やレジで間違えたら、どうしますか。","branch":"all"},
+            {"id":"teamwork","theme":"teamwork","stage":"followup","jp":"ほかのスタッフと協力できますか。","branch":"all"},
+            {"id":"strength","theme":"personality","stage":"followup","jp":"この仕事に向いている自分の長所を一つ話してください。","branch":"all"},
+            {"id":"closing","theme":"closing","stage":"closing","jp":"本日の外食・コンビニの面接練習はここまでです。ご参加ありがとうございました。","branch":"all"},
+        ],
     },
     "nursing_care": {
-        "label": "Nursing Care / 介護",
-        "intro": "こんにちは。介護の仕事の面接練習を始めます。よろしくお願いします。",
+        "english_name": "Nursing Care",
+        "japanese_name": "介護",
+        "intro_jp": "こんにちは。介護の仕事の面接練習を始めます。よろしくお願いします。",
+        "min_questions": 3,
+        "max_questions": 20,
+        "expected_keywords": ["介護", "やさしい", "利用者", "お年寄り", "清潔", "手伝う", "責任"],
         "questions": [
-            "お名前を教えてください。",
-            "どこの国から来ましたか。",
-            "日本へ行きたい理由は何ですか。",
-            "介護の仕事をしたことがありますか。",
-            "お年寄りや利用者さんにやさしく話せますか。",
-            "介護の仕事で清潔さは大切ですか。なぜですか。",
-            "利用者さんが困っていたら、どうしますか。",
-            "最後に、介護の仕事で大切だと思うことを話してください。"
-        ]
+            {"id":"name","theme":"intro","stage":"screening","jp":"お名前を教えてください。","branch":"all"},
+            {"id":"country","theme":"intro","stage":"screening","jp":"どこの国から来ましたか。","branch":"all"},
+            {"id":"reason","theme":"motivation","stage":"screening","jp":"日本へ行きたい理由は何ですか。","branch":"all"},
+            {"id":"japanese","theme":"language","stage":"screening","jp":"日本語はどのくらい勉強しましたか。","branch":"all"},
+            {"id":"ready_check","theme":"intro","stage":"screening","jp":"面接の準備はできていますか。","branch":"all"},
+            {"id":"experience_gate","theme":"experience","stage":"role","jp":"介護の仕事をしたことがありますか。","branch":"all"},
+            {"id":"exp_yes_detail","theme":"experience","stage":"role","jp":"どんな介護の仕事をしましたか。","branch":"yes_exp"},
+            {"id":"exp_no_motivation","theme":"motivation","stage":"role","jp":"経験がなくても、介護の勉強をしてがんばれますか。","branch":"no_exp"},
+            {"id":"kindness","theme":"service","stage":"role","jp":"お年寄りや利用者さんに、やさしく話せますか。","branch":"all"},
+            {"id":"cleanliness","theme":"safety","stage":"role","jp":"介護の仕事で清潔さは大切ですか。なぜですか。","branch":"all"},
+            {"id":"communication","theme":"service","stage":"role","jp":"利用者さんが困っていたら、まず何をしますか。","branch":"all"},
+            {"id":"hard_work","theme":"role_fit","stage":"followup","jp":"大変な仕事でも、落ち着いて続けられますか。","branch":"all"},
+            {"id":"teamwork","theme":"teamwork","stage":"followup","jp":"スタッフと協力できますか。","branch":"all"},
+            {"id":"report","theme":"teamwork","stage":"followup","jp":"報告・連絡・相談はできますか。","branch":"all"},
+            {"id":"strength","theme":"personality","stage":"followup","jp":"介護の仕事に向いている自分の長所を一つ話してください。","branch":"all"},
+            {"id":"closing","theme":"closing","stage":"closing","jp":"本日の介護の面接練習はここまでです。ご参加ありがとうございました。","branch":"all"},
+        ],
     },
     "hotel_accommodation": {
-        "label": "Hotel / Accommodation / 宿泊",
-        "intro": "こんにちは。宿泊の仕事の面接練習を始めます。よろしくお願いします。",
+        "english_name": "Hotel / Accommodation",
+        "japanese_name": "宿泊",
+        "intro_jp": "こんにちは。宿泊・ホテルの仕事の面接練習を始めます。よろしくお願いします。",
+        "min_questions": 3,
+        "max_questions": 20,
+        "expected_keywords": ["ホテル", "お客様", "ていねい", "笑顔", "掃除", "フロント", "案内"],
         "questions": [
-            "お名前を教えてください。",
-            "どこの国から来ましたか。",
-            "日本へ行きたい理由は何ですか。",
-            "ホテルの仕事をしたことがありますか。",
-            "お客様にていねいに話せますか。",
-            "掃除やベッドメイクはできますか。",
-            "忙しい時間でも落ち着いて働けますか。",
-            "最後に、ホテルの仕事で自分の長所を話してください。"
-        ]
+            {"id":"name","theme":"intro","stage":"screening","jp":"お名前を教えてください。","branch":"all"},
+            {"id":"country","theme":"intro","stage":"screening","jp":"どこの国から来ましたか。","branch":"all"},
+            {"id":"reason","theme":"motivation","stage":"screening","jp":"日本へ行きたい理由は何ですか。","branch":"all"},
+            {"id":"japanese","theme":"language","stage":"screening","jp":"日本語はどのくらい勉強しましたか。","branch":"all"},
+            {"id":"ready_check","theme":"intro","stage":"screening","jp":"面接の準備はできていますか。","branch":"all"},
+            {"id":"experience_gate","theme":"experience","stage":"role","jp":"ホテルや宿泊の仕事をしたことがありますか。","branch":"all"},
+            {"id":"exp_yes_detail","theme":"experience","stage":"role","jp":"どんな仕事をしましたか。フロント、掃除、ベッドメイクなどを話してください。","branch":"yes_exp"},
+            {"id":"exp_no_motivation","theme":"motivation","stage":"role","jp":"経験がなくても、ホテルの仕事を勉強してがんばれますか。","branch":"no_exp"},
+            {"id":"customer","theme":"service","stage":"role","jp":"お客様に話すとき、どんなことを大切にしますか。","branch":"all"},
+            {"id":"cleanliness","theme":"service","stage":"role","jp":"部屋の掃除や整理整頓は好きですか。","branch":"all"},
+            {"id":"busy","theme":"role_fit","stage":"role","jp":"忙しい時間でも落ち着いて働けますか。","branch":"all"},
+            {"id":"teamwork","theme":"teamwork","stage":"followup","jp":"スタッフと協力して働くことはできますか。","branch":"all"},
+            {"id":"shift","theme":"schedule","stage":"followup","jp":"夜や朝のシフトは大丈夫ですか。","branch":"all"},
+            {"id":"strength","theme":"personality","stage":"followup","jp":"ホテルの仕事に向いている自分の長所を一つ話してください。","branch":"all"},
+            {"id":"closing","theme":"closing","stage":"closing","jp":"本日の宿泊の面接練習はここまでです。ご参加ありがとうございました。","branch":"all"},
+        ],
     },
     "agriculture": {
-        "label": "Agriculture / 農業",
-        "intro": "こんにちは。農業の仕事の面接練習を始めます。よろしくお願いします。",
+        "english_name": "Agriculture",
+        "japanese_name": "農業",
+        "intro_jp": "こんにちは。農業の仕事の面接練習を始めます。よろしくお願いします。",
+        "min_questions": 3,
+        "max_questions": 20,
+        "expected_keywords": ["農業", "畑", "体力", "朝", "収穫", "外", "時間"],
         "questions": [
-            "お名前を教えてください。",
-            "どこの国から来ましたか。",
-            "日本へ行きたい理由は何ですか。",
-            "農業の仕事をしたことがありますか。",
-            "外で働くことは大丈夫ですか。",
-            "朝早い仕事でも時間を守れますか。",
-            "体力に自信はありますか。",
-            "最後に、農業の仕事でがんばりたいことを話してください。"
-        ]
+            {"id":"name","theme":"intro","stage":"screening","jp":"お名前を教えてください。","branch":"all"},
+            {"id":"country","theme":"intro","stage":"screening","jp":"どこの国から来ましたか。","branch":"all"},
+            {"id":"reason","theme":"motivation","stage":"screening","jp":"日本へ行きたい理由は何ですか。","branch":"all"},
+            {"id":"japanese","theme":"language","stage":"screening","jp":"日本語はどのくらい勉強しましたか。","branch":"all"},
+            {"id":"ready_check","theme":"intro","stage":"screening","jp":"面接の準備はできていますか。","branch":"all"},
+            {"id":"experience_gate","theme":"experience","stage":"role","jp":"農業の仕事をしたことがありますか。","branch":"all"},
+            {"id":"exp_yes_detail","theme":"experience","stage":"role","jp":"どんな農業の仕事をしましたか。","branch":"yes_exp"},
+            {"id":"exp_no_motivation","theme":"motivation","stage":"role","jp":"経験がなくても、農業を勉強してがんばれますか。","branch":"no_exp"},
+            {"id":"outside_work","theme":"role_fit","stage":"role","jp":"外で長い時間働くことは大丈夫ですか。","branch":"all"},
+            {"id":"early_morning","theme":"schedule","stage":"role","jp":"朝早い仕事でも時間を守れますか。","branch":"all"},
+            {"id":"physical","theme":"role_fit","stage":"role","jp":"体力に自信はありますか。","branch":"all"},
+            {"id":"weather","theme":"role_fit","stage":"followup","jp":"暑い日や寒い日でも、まじめに働けますか。","branch":"all"},
+            {"id":"teamwork","theme":"teamwork","stage":"followup","jp":"ほかの人と一緒に働けますか。","branch":"all"},
+            {"id":"strength","theme":"personality","stage":"followup","jp":"農業の仕事に向いている自分の長所を一つ話してください。","branch":"all"},
+            {"id":"closing","theme":"closing","stage":"closing","jp":"本日の農業の面接練習はここまでです。ご参加ありがとうございました。","branch":"all"},
+        ],
     },
     "manufacturing": {
-        "label": "Manufacturing / 製造業",
-        "intro": "こんにちは。製造業の仕事の面接練習を始めます。よろしくお願いします。",
+        "english_name": "Manufacturing",
+        "japanese_name": "製造業",
+        "intro_jp": "こんにちは。製造業の仕事の面接練習を始めます。よろしくお願いします。",
+        "min_questions": 3,
+        "max_questions": 20,
+        "expected_keywords": ["工場", "安全", "正確", "確認", "時間", "ルール", "集中"],
         "questions": [
-            "お名前を教えてください。",
-            "どこの国から来ましたか。",
-            "日本へ行きたい理由は何ですか。",
-            "工場の仕事をしたことがありますか。",
-            "安全ルールを守ることはできますか。",
-            "同じ作業を正確に続けられますか。",
-            "時間を守ることはできますか。",
-            "最後に、製造業の仕事で自分の長所を話してください。"
-        ]
+            {"id":"name","theme":"intro","stage":"screening","jp":"お名前を教えてください。","branch":"all"},
+            {"id":"country","theme":"intro","stage":"screening","jp":"どこの国から来ましたか。","branch":"all"},
+            {"id":"reason","theme":"motivation","stage":"screening","jp":"日本へ行きたい理由は何ですか。","branch":"all"},
+            {"id":"japanese","theme":"language","stage":"screening","jp":"日本語はどのくらい勉強しましたか。","branch":"all"},
+            {"id":"ready_check","theme":"intro","stage":"screening","jp":"面接の準備はできていますか。","branch":"all"},
+            {"id":"experience_gate","theme":"experience","stage":"role","jp":"工場や製造の仕事をしたことがありますか。","branch":"all"},
+            {"id":"exp_yes_detail","theme":"experience","stage":"role","jp":"どんな製造の仕事をしましたか。","branch":"yes_exp"},
+            {"id":"exp_no_motivation","theme":"motivation","stage":"role","jp":"経験がなくても、製造の仕事を勉強してがんばれますか。","branch":"no_exp"},
+            {"id":"accuracy","theme":"role_fit","stage":"role","jp":"ミスを少なくするために、何を大切にしますか。","branch":"all"},
+            {"id":"safety","theme":"safety","stage":"role","jp":"機械を使うとき、安全のために何をしますか。","branch":"all"},
+            {"id":"time","theme":"schedule","stage":"role","jp":"時間を守って、同じ作業を続けることはできますか。","branch":"all"},
+            {"id":"quality","theme":"reliability","stage":"followup","jp":"品質を守ることは大切ですか。なぜですか。","branch":"all"},
+            {"id":"teamwork","theme":"teamwork","stage":"followup","jp":"チームで協力できますか。","branch":"all"},
+            {"id":"strength","theme":"personality","stage":"followup","jp":"製造業の仕事に向いている自分の長所を一つ話してください。","branch":"all"},
+            {"id":"closing","theme":"closing","stage":"closing","jp":"本日の製造業の面接練習はここまでです。ご参加ありがとうございました。","branch":"all"},
+        ],
     },
 }
 
-REPEAT_PROMPT = "声が小さいです。もう少し大きい声で、もう一度お願いします。"
-
 
 class StartRequest(BaseModel):
     session_uuid: str
@@ -121,18 +210,27 @@ class StartRequest(BaseModel):
 
 @app.get("/")
 def root() -> Dict[str, Any]:
-    return {"ok": True, "service": "jp-interview", "version": "category-fix-1.0"}
+    return {
+        "ok": True,
+        "service": "jp-role-interview",
+        "version": APP_VERSION,
+        "routes": ["/health", "/roles", "/start", "/answer"],
+    }
 
 
 @app.get("/health")
 def health() -> Dict[str, Any]:
+    asr_backend = "hf_api"
+    if USE_FASTER_WHISPER:
+        asr_backend = "faster_whisper_then_hf_api"
     return {
         "ok": True,
-        "service": "jp-interview",
-        "version": "category-fix-1.0",
+        "service": "jp-role-interview",
+        "version": APP_VERSION,
         "hf_token_set": bool(HF_TOKEN),
-        "asr_model": ASR_MODEL,
-        "roles": list(ROLE_BANK.keys()),
+        "asr_backend": asr_backend,
+        "chat_model": CHAT_MODEL,
+        "role_count": len(ROLE_BANK),
     }
 
 
@@ -140,31 +238,55 @@ def health() -> Dict[str, Any]:
 def roles() -> Dict[str, Any]:
     return {
         "ok": True,
-        "roles": [{"key": key, "label": cfg["label"]} for key, cfg in ROLE_BANK.items()]
+        "roles": [
+            {
+                "key": key,
+                "english_name": cfg["english_name"],
+                "japanese_name": cfg["japanese_name"],
+                "min_questions": cfg["min_questions"],
+                "max_questions": cfg["max_questions"],
+            }
+            for key, cfg in ROLE_BANK.items()
+        ],
     }
 
 
 @app.post("/start")
 def start_interview(payload: StartRequest) -> Dict[str, Any]:
-    role_key = payload.job_role if payload.job_role in ROLE_BANK else "construction"
-    cfg = ROLE_BANK[role_key]
+    role_key = normalize_role_key(payload.job_role)
+    role_cfg = ROLE_BANK[role_key]
+    first_q = get_question(role_cfg, "name")
+    opening = f"{role_cfg['intro_jp']} {first_q['jp']}"
+
     memory = {
         "job_role": role_key,
-        "job_role_label": cfg["label"],
+        "job_role_en": role_cfg["english_name"],
+        "job_role_jp": role_cfg["japanese_name"],
         "candidate_name": None,
+        "country_name": None,
+        "age": None,
+        "reason_for_japan": None,
+        "occupation": None,
+        "japanese_level": None,
+        "experience_state": "unknown",
         "answers_so_far": [],
-        "low_score_count": 0,
+        "asked_question_ids": [first_q["id"]],
+        "asked_themes": [first_q["theme"]],
+        "low_score_streak": 0,
         "no_sound_count": 0,
-        "question_count_mode": "auto",
+        "min_questions": role_cfg["min_questions"],
+        "max_questions": min(role_cfg["max_questions"], MAX_QUESTION_LIMIT),
+        "auto_question_mode": True,
     }
-    opening = f"{cfg['intro']} {cfg['questions'][0]}"
+
     return {
         "ok": True,
         "session_uuid": payload.session_uuid,
         "job_role": role_key,
-        "job_role_label": cfg["label"],
+        "job_role_label": f"{role_cfg['english_name']} / {role_cfg['japanese_name']}",
         "question_no": 1,
-        "question_jp": cfg["questions"][0],
+        "question_id": first_q["id"],
+        "question_jp": first_q["jp"],
         "speech_text_jp": opening,
         "memory": memory,
         "is_finished": False,
@@ -176,194 +298,490 @@ def start_interview(payload: StartRequest) -> Dict[str, Any]:
 async def answer_interview(
     session_uuid: str = Form(...),
     question_no: int = Form(...),
+    question_id: str = Form(...),
     question_jp: str = Form(...),
     memory_json: str = Form("{}"),
     audio: UploadFile = File(...),
 ) -> Dict[str, Any]:
     memory = safe_json_loads(memory_json)
-    role_key = memory.get("job_role") if memory.get("job_role") in ROLE_BANK else "construction"
-    cfg = ROLE_BANK[role_key]
+    role_key = normalize_role_key(memory.get("job_role"))
+    role_cfg = ROLE_BANK[role_key]
 
-    audio_bytes = await audio.read()
-    transcript = ""
-    if audio_bytes and HF_TOKEN:
-        try:
-            transcript = transcribe_audio_with_hf(audio_bytes, audio.filename or "audio.webm")
-        except Exception:
-            transcript = ""
+    transcript, asr_backend, asr_error = await transcribe_upload(audio)
 
     if not transcript.strip():
         memory["no_sound_count"] = int(memory.get("no_sound_count", 0)) + 1
-        speech_text = REPEAT_PROMPT
-        if memory.get("candidate_name"):
-            speech_text = f"{memory['candidate_name']}さん、{REPEAT_PROMPT}"
+        name = memory.get("candidate_name")
+        spoken = build_repeat_prompt(name, memory["no_sound_count"])
+        should_finish = memory["no_sound_count"] >= 2 and question_no >= role_cfg["min_questions"]
+        if should_finish:
+            result = build_final_result(role_cfg, memory, force_fail=True, summary_jp="音声が聞こえないため、面接を終了しました。")
+            return {
+                "ok": True,
+                "is_finished": True,
+                "session_uuid": session_uuid,
+                "job_role": role_key,
+                "transcript_jp": "",
+                "answer_score": 0,
+                "feedback_jp": spoken,
+                "speech_text_jp": spoken,
+                "memory": memory,
+                "asr_backend": asr_backend,
+                "asr_error": asr_error,
+                "result": result,
+            }
         return {
             "ok": True,
             "is_finished": False,
             "needs_repeat": True,
             "session_uuid": session_uuid,
+            "job_role": role_key,
             "question_no": question_no,
+            "question_id": question_id,
             "question_jp": question_jp,
-            "speech_text_jp": speech_text,
+            "speech_text_jp": spoken,
             "transcript_jp": "",
             "answer_score": 0,
-            "feedback_jp": REPEAT_PROMPT,
+            "feedback_jp": spoken,
             "memory": memory,
             "next_question_no": question_no,
+            "next_question_id": question_id,
             "next_question_jp": question_jp,
+            "asr_backend": asr_backend,
+            "asr_error": asr_error,
             "speak_now": True,
         }
 
-    if question_no == 1 and not memory.get("candidate_name"):
-        name = extract_name(transcript)
-        if name:
-            memory["candidate_name"] = name
+    memory["no_sound_count"] = 0
+    profile_update = maybe_extract_basic_profile(memory, transcript, question_id)
+    memory = merge_memory(memory, profile_update)
 
-    score = heuristic_score(transcript)
-    if score <= 3:
-        memory["low_score_count"] = int(memory.get("low_score_count", 0)) + 1
-    else:
-        memory["low_score_count"] = 0
+    score = score_answer(role_cfg, question_id, transcript)
+    feedback = build_feedback(score)
 
-    history = list(memory.get("answers_so_far", []))
-    history.append({
+    answers = list(memory.get("answers_so_far", []))
+    answers.append({
         "question_no": question_no,
+        "question_id": question_id,
         "question_jp": question_jp,
         "answer_text_jp": transcript,
         "answer_score": score,
+        "feedback_jp": feedback,
     })
-    memory["answers_so_far"] = history
-
-    # automatic interview length
-    min_questions = 3
-    max_questions = len(cfg["questions"])
-    avg_score = mean([x.get("answer_score", 0) for x in history])
-
-    finish_now = False
-    if question_no >= min_questions and memory["low_score_count"] >= 2:
-        finish_now = True
-    elif question_no >= min_questions and avg_score < 3.5:
-        finish_now = True
-    elif question_no >= max_questions:
-        finish_now = True
-    elif question_no >= 5 and avg_score < 5:
-        finish_now = True
-
-    if finish_now:
-        closing = f"本日の{cfg['label'].split('/')[1].strip() if '/' in cfg['label'] else cfg['label']}の面接練習はここまでです。ご参加ありがとうございました。"
-        result = {
-            "candidate_name": memory.get("candidate_name"),
-            "job_role": role_key,
-            "job_role_label": cfg["label"],
-            "total_questions": len(history),
-            "overall_score": round(avg_score * 10),
-            "pass_fail": "PASS" if avg_score >= 6 else "FAIL",
-            "closing_message_jp": closing,
-            "answers": history,
-        }
+    memory["answers_so_far"] = answers
+
+    if score <= 3:
+        memory["low_score_streak"] = int(memory.get("low_score_streak", 0)) + 1
+    else:
+        memory["low_score_streak"] = 0
+
+    should_finish = decide_finish(role_cfg, memory, question_no, score)
+    if should_finish:
+        result = build_final_result(role_cfg, memory)
         return {
             "ok": True,
             "is_finished": True,
             "session_uuid": session_uuid,
+            "job_role": role_key,
             "question_no": question_no,
             "transcript_jp": transcript,
             "answer_score": score,
-            "feedback_jp": default_feedback(score),
-            "speech_text_jp": closing,
+            "feedback_jp": feedback,
+            "speech_text_jp": result["closing_message_jp"],
             "memory": memory,
+            "asr_backend": asr_backend,
+            "asr_error": asr_error,
             "result": result,
         }
 
-    next_question_no = question_no + 1
-    next_question = cfg["questions"][next_question_no - 1]
-    speech_text = next_question
-    if question_no == 1 and memory.get("candidate_name"):
-        speech_text = f"{memory['candidate_name']}さん、ありがとうございます。面接の準備はできていますか。では、次の質問です。{next_question}"
+    next_q = select_next_question(role_cfg, memory)
+    next_no = question_no + 1
+    if next_q["id"] not in memory["asked_question_ids"]:
+        memory["asked_question_ids"].append(next_q["id"])
+    if next_q["theme"] not in memory["asked_themes"]:
+        memory["asked_themes"].append(next_q["theme"])
+
+    spoken_next = next_q["jp"]
+    if next_q["id"] == "ready_check" and memory.get("candidate_name"):
+        spoken_next = f"{memory['candidate_name']}さん、ありがとうございます。{next_q['jp']}"
 
     return {
         "ok": True,
         "is_finished": False,
         "session_uuid": session_uuid,
+        "job_role": role_key,
         "question_no": question_no,
         "transcript_jp": transcript,
         "answer_score": score,
-        "feedback_jp": default_feedback(score),
-        "speech_text_jp": speech_text,
+        "feedback_jp": feedback,
+        "speech_text_jp": spoken_next,
         "memory": memory,
-        "next_question_no": next_question_no,
-        "next_question_jp": next_question,
+        "asr_backend": asr_backend,
+        "asr_error": asr_error,
+        "next_question_no": next_no,
+        "next_question_id": next_q["id"],
+        "next_question_jp": next_q["jp"],
         "speak_now": True,
     }
 
 
-def transcribe_audio_with_hf(audio_bytes: bytes, filename: str) -> str:
+def normalize_role_key(value: Any) -> str:
+    key = str(value or "construction").strip().lower()
+    aliases = {
+        "restaurant": "restaurant_konbini",
+        "konbini": "restaurant_konbini",
+        "nursing": "nursing_care",
+        "care": "nursing_care",
+        "hotel": "hotel_accommodation",
+        "accommodation": "hotel_accommodation",
+    }
+    key = aliases.get(key, key)
+    return key if key in ROLE_BANK else "construction"
+
+
+def get_question(role_cfg: Dict[str, Any], qid: str) -> Dict[str, Any]:
+    for q in role_cfg["questions"]:
+        if q["id"] == qid:
+            return q
+    return role_cfg["questions"][0]
+
+
+def build_repeat_prompt(name: Optional[str], count: int) -> str:
+    idx = max(0, min(count - 1, len(_REPEAT_PROMPTS) - 1))
+    base = _REPEAT_PROMPTS[idx]
+    if name:
+        return f"{name}さん、{base}"
+    return base
+
+
+async def transcribe_upload(audio: UploadFile) -> Tuple[str, str, Optional[str]]:
+    content = await audio.read()
+    filename = audio.filename or "answer.webm"
+
+    if USE_FASTER_WHISPER:
+        try:
+            text = transcribe_with_faster_whisper(content, filename)
+            return normalize_text(text), "faster_whisper", None
+        except Exception as exc:
+            if HF_TOKEN:
+                try:
+                    text = transcribe_with_hf_api(content, filename)
+                    return normalize_text(text), "hf_api_fallback", str(exc)
+                except Exception as exc2:
+                    return "", "hf_api_fallback_failed", f"{exc} | {exc2}"
+            return "", "faster_whisper_failed", str(exc)
+
+    if HF_TOKEN:
+        try:
+            text = transcribe_with_hf_api(content, filename)
+            return normalize_text(text), "hf_api", None
+        except Exception as exc:
+            return "", "hf_api_failed", str(exc)
+
+    return "", "no_asr_backend", "Neither faster-whisper nor HF API is available."
+
+
+def transcribe_with_faster_whisper(content: bytes, filename: str) -> str:
+    global _LOCAL_ASR_MODEL
+    from faster_whisper import WhisperModel  # lazy import
+
+    suffix = os.path.splitext(filename)[1] or ".webm"
+    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+        tmp.write(content)
+        temp_path = tmp.name
+
+    try:
+        if _LOCAL_ASR_MODEL is None:
+            _LOCAL_ASR_MODEL = WhisperModel(FASTER_WHISPER_MODEL, device="cpu", compute_type="int8")
+        segments, _info = _LOCAL_ASR_MODEL.transcribe(temp_path, language="ja", vad_filter=True)
+        return " ".join(seg.text.strip() for seg in segments).strip()
+    finally:
+        try:
+            os.remove(temp_path)
+        except OSError:
+            pass
+
+
+def transcribe_with_hf_api(content: bytes, filename: str) -> str:
     url = f"{HF_INFERENCE_BASE}/{ASR_MODEL}"
     headers = {
         "Authorization": f"Bearer {HF_TOKEN}",
         "Content-Type": guess_mime_type(filename),
     }
-    response = requests.post(url, headers=headers, data=audio_bytes, timeout=180)
+    response = requests.post(url, headers=headers, data=content, timeout=ASR_TIMEOUT_SECONDS)
     response.raise_for_status()
     data = response.json()
     if isinstance(data, dict):
-        return normalize_text(data.get("text") or data.get("generated_text") or "")
+        return str(data.get("text") or data.get("generated_text") or "")
     if isinstance(data, list) and data and isinstance(data[0], dict):
-        return normalize_text(data[0].get("text", ""))
+        return str(data[0].get("text") or "")
     return ""
 
 
 def guess_mime_type(filename: str) -> str:
-    name = (filename or "").lower()
-    if name.endswith(".wav"):
+    lower = (filename or "").lower()
+    if lower.endswith(".wav"):
         return "audio/wav"
-    if name.endswith(".mp3"):
+    if lower.endswith(".mp3"):
         return "audio/mpeg"
-    if name.endswith(".m4a"):
+    if lower.endswith(".m4a"):
         return "audio/mp4"
-    if name.endswith(".ogg"):
+    if lower.endswith(".ogg"):
         return "audio/ogg"
     return "audio/webm"
 
 
-def normalize_text(text: str) -> str:
-    return re.sub(r"\s+", " ", (text or "")).strip()
-
-
-def safe_json_loads(value: str) -> Dict[str, Any]:
-    try:
-        parsed = json.loads(value or "{}")
-        return parsed if isinstance(parsed, dict) else {}
-    except Exception:
-        return {}
-
-
-def extract_name(text: str) -> str:
-    value = text.replace("私は", "").replace("わたしは", "").replace("ぼくは", "")
-    value = value.replace("です", "").replace("と申します", "").replace("といいます", "").strip(" 。")
-    return value[:30] if value else ""
-
+def maybe_extract_basic_profile(memory: Dict[str, Any], transcript: str, question_id: str) -> Dict[str, Any]:
+    text = normalize_text(transcript)
+    update: Dict[str, Any] = {}
 
-def heuristic_score(text: str) -> int:
-    text = (text or "").strip()
+    if question_id == "name" and not memory.get("candidate_name"):
+        name = extract_name(text)
+        if name:
+            update["candidate_name"] = name
+
+    if question_id == "country" and not memory.get("country_name"):
+        country = extract_country(text)
+        if country:
+            update["country_name"] = country
+
+    if question_id == "reason" and not memory.get("reason_for_japan") and len(text) >= 4:
+        update["reason_for_japan"] = text[:120]
+
+    if question_id == "japanese" and not memory.get("japanese_level") and len(text) >= 4:
+        update["japanese_level"] = text[:120]
+
+    if question_id == "experience_gate":
+        update["experience_state"] = detect_experience_state(text, memory.get("experience_state", "unknown"))
+
+    age = extract_age(text)
+    if age and not memory.get("age"):
+        update["age"] = age
+
+    return update
+
+
+def detect_experience_state(text: str, current: str) -> str:
+    yes_markers = ["あります", "しました", "経験があります", "働いたことがあります"]
+    no_markers = ["ありません", "ないです", "経験がありません", "したことがありません"]
+    if any(m in text for m in yes_markers):
+        return "yes"
+    if any(m in text for m in no_markers):
+        return "no"
+    return current if current in {"yes", "no"} else "unknown"
+
+
+def select_next_question(role_cfg: Dict[str, Any], memory: Dict[str, Any]) -> Dict[str, Any]:
+    asked_ids = set(memory.get("asked_question_ids", []))
+    experience_state = memory.get("experience_state", "unknown")
+    answers = memory.get("answers_so_far", [])
+    avg = mean([a.get("answer_score", 0) for a in answers]) if answers else 0
+
+    # fixed screening order
+    for qid in ["country", "reason", "japanese", "ready_check", "experience_gate"]:
+        q = get_question(role_cfg, qid)
+        if q["id"] not in asked_ids:
+            return q
+
+    # branch by experience
+    branch_order = []
+    if experience_state == "yes":
+        branch_order = ["exp_yes_detail", "exp_years"]
+    elif experience_state == "no":
+        branch_order = ["exp_no_motivation"]
+
+    for qid in branch_order:
+        q = get_question(role_cfg, qid)
+        if q["id"] not in asked_ids:
+            return q
+
+    # weaker user gets simpler role questions first
+    if avg < 4.5:
+        simple_ids = ["physical", "busy", "kindness", "cleanliness", "outside_work", "time", "customer", "safety", "teamwork"]
+        for qid in simple_ids:
+            try:
+                q = get_question(role_cfg, qid)
+                if q["id"] not in asked_ids:
+                    return q
+            except Exception:
+                pass
+
+    # normal role/followup path
+    for q in role_cfg["questions"]:
+        if q["id"] in asked_ids:
+            continue
+        if q["stage"] == "closing":
+            continue
+        if q["branch"] == "yes_exp" and experience_state != "yes":
+            continue
+        if q["branch"] == "no_exp" and experience_state != "no":
+            continue
+        return q
+
+    return get_question(role_cfg, "closing")
+
+
+def score_answer(role_cfg: Dict[str, Any], question_id: str, transcript: str) -> int:
+    text = normalize_text(transcript)
     if not text:
         return 0
-    score = 4
-    if len(text) >= 6:
+    score = 3
+    if len(text) >= 4:
         score += 1
-    if len(text) >= 12:
+    if len(text) >= 10:
+        score += 1
+    if len(text) >= 20:
         score += 1
     if "です" in text or "ます" in text:
         score += 1
-    if len(text) >= 20:
+    role_hits = sum(1 for kw in role_cfg["expected_keywords"] if kw in text)
+    score += min(2, role_hits)
+    if question_id == "name" and extract_name(text):
         score += 1
-    return min(score, 10)
+    if question_id == "country" and extract_country(text):
+        score += 1
+    if question_id == "experience_gate" and detect_experience_state(text, "unknown") != "unknown":
+        score += 1
+    return max(0, min(score, 10))
 
 
-def default_feedback(score: int) -> str:
+def build_feedback(score: int) -> str:
     if score >= 8:
         return "とても良いです。自然に答えられています。"
     if score >= 6:
         return "良いです。もう少し長く、ていねいに話すともっと良くなります。"
     if score >= 4:
-        return "意味は伝わりますが、少し短いです。完全な文で話してみましょう。"
+        return "意味は伝わりますが、短いです。完全な文で答えてみましょう。"
     return "短すぎるか、内容が分かりにくいです。もう少し詳しく話してください。"
+
+
+def decide_finish(role_cfg: Dict[str, Any], memory: Dict[str, Any], question_no: int, score: int) -> bool:
+    answers = memory.get("answers_so_far", [])
+    avg = mean([a.get("answer_score", 0) for a in answers]) if answers else 0
+    min_q = int(memory.get("min_questions", role_cfg["min_questions"]))
+    max_q = int(memory.get("max_questions", role_cfg["max_questions"]))
+
+    if question_no >= max_q:
+        return True
+    if question_no >= min_q and memory.get("low_score_streak", 0) >= 2:
+        return True
+    if question_no >= min_q and len(answers) >= 3 and avg < 3.5:
+        return True
+    if question_no >= 10 and avg >= 6:
+        # good candidate can continue; otherwise finish around middle
+        return False
+    if question_no >= 8 and avg < 5.5:
+        return True
+    return False
+
+
+def build_final_result(role_cfg: Dict[str, Any], memory: Dict[str, Any], force_fail: bool = False, summary_jp: str = "") -> Dict[str, Any]:
+    answers = list(memory.get("answers_so_far", []))
+    scores = [int(a.get("answer_score", 0)) for a in answers] or [0]
+    avg = mean(scores)
+    overall_score = max(0, min(100, int(round(avg * 10))))
+    if force_fail:
+        overall_score = min(overall_score, 39)
+    pass_fail = "PASS" if overall_score >= 60 and not force_fail else "FAIL"
+
+    strengths: List[str] = []
+    weaknesses: List[str] = []
+    tips: List[str] = []
+
+    if memory.get("candidate_name"):
+        strengths.append("Self introduction was understood.")
+    else:
+        weaknesses.append("Name was not clearly understood.")
+
+    if memory.get("experience_state") == "yes":
+        strengths.append("Role experience was communicated.")
+    elif memory.get("experience_state") == "no":
+        weaknesses.append("No direct role experience was explained clearly.")
+
+    if overall_score >= 70:
+        strengths.append("Answers were mostly clear and relevant.")
+    else:
+        weaknesses.append("Several answers were too short or unclear.")
+
+    tips.extend([
+        "Use one or two extra sentences in each answer.",
+        "Use polite endings like です and ます.",
+        "Speak a little louder and more clearly.",
+    ])
+
+    closing = get_question(role_cfg, "closing")["jp"]
+    return {
+        "candidate_name": memory.get("candidate_name"),
+        "country_name": memory.get("country_name"),
+        "age": memory.get("age"),
+        "job_role": memory.get("job_role"),
+        "job_role_en": role_cfg["english_name"],
+        "job_role_jp": role_cfg["japanese_name"],
+        "summary_jp": summary_jp or f"{role_cfg['japanese_name']}の面接練習が完了しました。",
+        "closing_message_jp": closing,
+        "total_questions": len(answers),
+        "overall_score": overall_score,
+        "scores": {
+            "fluency": clamp_int(round(avg), 1, 10),
+            "grammar": clamp_int(round(avg - 1), 1, 10),
+            "confidence": clamp_int(round(avg), 1, 10),
+            "relevance": clamp_int(round(avg + 1), 1, 10),
+            "role_fit": clamp_int(round(avg), 1, 10),
+        },
+        "pass_fail": pass_fail,
+        "strengths": strengths[:4],
+        "weaknesses": weaknesses[:4],
+        "tips": tips[:5],
+        "answers": answers,
+    }
+
+
+def merge_memory(memory: Dict[str, Any], update: Dict[str, Any]) -> Dict[str, Any]:
+    merged = dict(memory or {})
+    for k, v in (update or {}).items():
+        if v not in (None, "", [], {}):
+            merged[k] = v
+    return merged
+
+
+def normalize_text(text: str) -> str:
+    return re.sub(r"\s+", " ", (text or "")).strip()
+
+
+def safe_json_loads(value: str) -> Dict[str, Any]:
+    try:
+        obj = json.loads(value or "{}")
+        return obj if isinstance(obj, dict) else {}
+    except Exception:
+        return {}
+
+
+def extract_name(text: str) -> Optional[str]:
+    value = text.replace("私は", "").replace("わたしは", "").replace("ぼくは", "")
+    value = value.replace("です", "").replace("と申します", "").replace("といいます", "").strip(" 。")
+    if not value or len(value) > 30:
+        return None
+    return value
+
+
+def extract_country(text: str) -> Optional[str]:
+    known = ["ネパール", "日本", "インド", "バングラデシュ", "スリランカ", "ベトナム", "中国", "ミャンマー", "フィリピン", "インドネシア"]
+    for k in known:
+        if k in text:
+            return k
+    m = re.search(r"(.+?)から来ました", text)
+    if m:
+        return m.group(1).strip(" 。")
+    return None
+
+
+def extract_age(text: str) -> Optional[int]:
+    m = re.search(r"(\d{1,2})", text)
+    return int(m.group(1)) if m else None
+
+
+def clamp_int(value: Any, low: int, high: int) -> int:
+    try:
+        return max(low, min(high, int(round(float(value)))))
+    except Exception:
+        return low