app.py

"""
AI面接システム - Streamlit Web UI
===================================
streamlit run app.py
"""

import io
import os
import sys
import tempfile
import wave
from datetime import datetime

import numpy as np
import plotly.graph_objects as go
import streamlit as st

# プロジェクトルートをパスに追加
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

from config import Config, load_config
from csv_loader import load_questions
from evaluator import ClaudeEvaluator, evaluate_answer
from models import Answer, InterviewReport, Question, QuestionResult
from reporter import ReportGenerator

# ─────────────────────────────────────
# HuggingFace Spaces 環境検出
# ─────────────────────────────────────
IS_HF_SPACE = os.getenv("SPACE_ID") is not None
OUTPUT_DIR = "/tmp/interview_output" if IS_HF_SPACE else "output"

# Whisper/WebRTC が利用可能か（HFではインストールされない場合あり）
HAS_WHISPER = False
try:
    import whisper
    HAS_WHISPER = True
except ImportError:
    pass

HAS_WEBRTC = False
try:
    from streamlit_webrtc import webrtc_streamer, WebRtcMode
    HAS_WEBRTC = True
except ImportError:
    pass

# ─────────────────────────────────────
# ページ設定
# ─────────────────────────────────────
st.set_page_config(
    page_title="AI 面接システム",
    page_icon="🎙️",
    layout="wide",
    initial_sidebar_state="expanded",
)

# ─────────────────────────────────────
# カスタムCSS
# ─────────────────────────────────────
st.markdown("""
<style>
    /* 全体 */
    .main .block-container { max-width: 1100px; padding-top: 2rem; }

    /* ヘッダー */
    .app-header {
        background: linear-gradient(135deg, #1e3a5f 0%, #2d6a9f 100%);
        color: white; padding: 1.5rem 2rem; border-radius: 12px;
        margin-bottom: 1.5rem;
    }
    .app-header h1 { margin: 0; font-size: 1.8rem; }
    .app-header p  { margin: 0.3rem 0 0 0; opacity: 0.85; font-size: 0.95rem; }

    /* 質問カード */
    .question-card {
        background: #f8fafc; border-left: 4px solid #2d6a9f;
        padding: 1.2rem 1.5rem; border-radius: 0 8px 8px 0;
        margin: 1rem 0; font-size: 1.1rem;
    }

    /* スコアバッジ */
    .score-badge {
        display: inline-block; padding: 0.3rem 0.8rem;
        border-radius: 20px; font-weight: 600; font-size: 0.9rem;
    }
    .score-high   { background: #dcfce7; color: #166534; }
    .score-mid    { background: #fef9c3; color: #854d0e; }
    .score-low    { background: #fee2e2; color: #991b1b; }

    /* 進捗バー外枠 */
    .progress-outer {
        background: #e2e8f0; border-radius: 999px; height: 8px;
        overflow: hidden; margin: 0.5rem 0;
    }
    .progress-inner {
        height: 100%; border-radius: 999px;
        background: linear-gradient(90deg, #2d6a9f, #38bdf8);
        transition: width 0.4s ease;
    }

    /* ステップインジケータ */
    .step-indicator {
        display: flex; justify-content: center; gap: 0.5rem;
        margin: 1rem 0;
    }
    .step-dot {
        width: 12px; height: 12px; border-radius: 50%;
        background: #cbd5e1;
    }
    .step-dot.active  { background: #2d6a9f; }
    .step-dot.done    { background: #22c55e; }

    /* ステータスカード */
    .status-card {
        border-radius: 12px; padding: 1.5rem; text-align: center;
        font-size: 1.1rem;
    }
    .status-pass { background: #dcfce7; border: 2px solid #22c55e; }
    .status-fail { background: #fee2e2; border: 2px solid #ef4444; }
</style>
""", unsafe_allow_html=True)


# ─────────────────────────────────────
# Session State 初期化
# ─────────────────────────────────────
def init_session_state():
    defaults = {
        "page": "setup",             # setup / interview / result / history
        "config": None,
        "questions": [],
        "current_q_idx": 0,
        "results": [],
        "candidate_name": "",
        "report": None,
        "input_mode": "text",        # text / audio
        "history": [],               # 過去の面接結果
    }
    for k, v in defaults.items():
        if k not in st.session_state:
            st.session_state[k] = v


init_session_state()


# ─────────────────────────────────────
# ユーティリティ
# ─────────────────────────────────────
def score_class(score: float, max_score: float) -> str:
    pct = score / max_score if max_score else 0
    if pct >= 0.7:
        return "score-high"
    elif pct >= 0.4:
        return "score-mid"
    return "score-low"


def transcribe_audio_bytes(audio_bytes: bytes, config: Config) -> str:
    """ブラウザから受け取ったWAV音声をWhisperで書き起こす"""
    if not HAS_WHISPER:
        raise RuntimeError("Whisper が利用できません。テキスト入力を使用してください。")

    model = whisper.load_model(config.whisper_model)

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
        tmp.write(audio_bytes)
        tmp_path = tmp.name

    try:
        result = model.transcribe(tmp_path, language="ja")
        return result["text"].strip()
    finally:
        os.unlink(tmp_path)


def build_radar_chart(results: list[QuestionResult]) -> go.Figure:
    """カテゴリ別レーダーチャート"""
    categories: dict[str, list[QuestionResult]] = {}
    for r in results:
        categories.setdefault(r.question.category, []).append(r)

    labels, values = [], []
    for cat, rs in categories.items():
        total = sum(r.total_score for r in rs)
        mx = sum(r.question.max_score for r in rs)
        labels.append(cat)
        values.append(round(total / mx * 100 if mx else 0, 1))

    # 閉じる
    labels_closed = labels + [labels[0]]
    values_closed = values + [values[0]]

    fig = go.Figure()
    fig.add_trace(go.Scatterpolar(
        r=values_closed, theta=labels_closed,
        fill='toself', fillcolor='rgba(45,106,159,0.15)',
        line=dict(color='#2d6a9f', width=2),
        marker=dict(size=6),
    ))
    fig.update_layout(
        polar=dict(
            radialaxis=dict(visible=True, range=[0, 100], ticksuffix="%"),
        ),
        showlegend=False,
        margin=dict(l=60, r=60, t=40, b=40),
        height=350,
    )
    return fig


def build_bar_chart(results: list[QuestionResult]) -> go.Figure:
    """質問別スコア棒グラフ"""
    labels = [f"Q{r.question.id}" for r in results]
    kw_scores = [r.keyword_score for r in results]
    ai_scores = [r.ai_content_score for r in results]
    imp_scores = [r.improvisation_score for r in results]
    max_scores = [r.question.max_score for r in results]

    fig = go.Figure()
    fig.add_trace(go.Bar(name="キーワード", x=labels, y=kw_scores,
                         marker_color="#38bdf8"))
    fig.add_trace(go.Bar(name="内容評価", x=labels, y=ai_scores,
                         marker_color="#2d6a9f"))
    fig.add_trace(go.Bar(name="即興力", x=labels, y=imp_scores,
                         marker_color="#7dd3fc"))
    fig.add_trace(go.Scatter(
        name="満点", x=labels, y=max_scores,
        mode="lines+markers", line=dict(color="#ef4444", dash="dash"),
    ))
    fig.update_layout(
        barmode="stack", height=350,
        margin=dict(l=40, r=40, t=40, b=40),
        legend=dict(orientation="h", yanchor="bottom", y=1.02),
        yaxis_title="スコア",
    )
    return fig


# ─────────────────────────────────────
# サイドバー
# ─────────────────────────────────────
def render_sidebar():
    with st.sidebar:
        st.markdown("### 🎙️ AI 面接システム")
        st.divider()

        page = st.session_state.page
        labels = {
            "setup": "⚙️ 設定",
            "interview": "🎤 面接中",
            "result": "📊 結果",
            "history": "📁 履歴",
        }
        for key, label in labels.items():
            disabled = (key == "interview" and page == "setup")
            if key == "result" and st.session_state.report is None:
                disabled = True
            if st.button(label, key=f"nav_{key}", use_container_width=True,
                         disabled=disabled):
                st.session_state.page = key
                st.rerun()

        st.divider()

        # 面接中の進捗表示
        if page == "interview" and st.session_state.questions:
            total = len(st.session_state.questions)
            current = st.session_state.current_q_idx
            done = len(st.session_state.results)
            st.markdown(f"**進捗: {done}/{total}**")
            pct = done / total * 100 if total else 0
            st.markdown(f"""
            <div class="progress-outer">
                <div class="progress-inner" style="width:{pct}%"></div>
            </div>
            """, unsafe_allow_html=True)

            st.markdown(f"**候補者:** {st.session_state.candidate_name}")

        st.divider()
        st.caption("v1.0 — Streamlit UI")


# ═══════════════════════════════════════
# ページ: 設定
# ═══════════════════════════════════════
def page_setup():
    st.markdown("## 🎙️ AI 面接システム")
    st.caption("音声 or テキストで候補者にインタビュー → AI が自動評価・スコアリング")

    col1, col2 = st.columns([3, 2])

    with col1:
        st.subheader("👤 候補者情報")
        candidate_name = st.text_input(
            "候補者名", value=st.session_state.candidate_name,
            placeholder="例: 山田太郎"
        )

        st.subheader("📋 質問ファイル")
        upload_tab, default_tab = st.tabs(["CSVアップロード", "デフォルト使用"])

        with upload_tab:
            uploaded = st.file_uploader(
                "質問CSV をアップロード", type=["csv"],
                help="id, category, question_text, expected_keywords, keyword_weight, ai_weight, improv_weight, max_score, scoring_criteria, follow_up"
            )

        with default_tab:
            st.info("📂 `data/questions.csv` を使用します（5問）")

    with col2:
        st.subheader("⚙️ 設定")

        if HAS_WEBRTC and HAS_WHISPER:
            input_mode = st.radio(
                "入力方式",
                ["テキスト入力", "音声入力（マイク）"],
                help="テキスト入力はブラウザだけで動作します"
            )
        else:
            input_mode = "テキスト入力"
            st.info("📝 テキスト入力モード" + ("（HuggingFace Spaces 環境）" if IS_HF_SPACE else ""))

        pass_threshold = st.slider(
            "合格ライン（%）", 30, 100, 60, step=5
        )

        if HAS_WHISPER:
            whisper_model = st.selectbox(
                "Whisperモデル",
                ["tiny", "base", "small", "medium"],
                index=1,
                help="大きいモデルほど精度が高いが遅い"
            )
        else:
            whisper_model = "base"

        claude_model = st.selectbox(
            "Claude モデル",
            ["claude-sonnet-4-20250514", "claude-haiku-4-20250414"],
            index=0,
        )

    st.divider()

    # 質問プレビュー
    try:
        if uploaded:
            tmp_dir = tempfile.mkdtemp()
            tmp_path = os.path.join(tmp_dir, "uploaded.csv")
            with open(tmp_path, "wb") as f:
                f.write(uploaded.getvalue())
            questions = load_questions(tmp_path)
        else:
            questions = load_questions("data/questions.csv")

        st.subheader(f"📝 質問一覧（{len(questions)}問）")
        for q in questions:
            with st.expander(f"Q{q.id}. [{q.category}] {q.question_text[:50]}..."):
                st.markdown(f"**質問:** {q.question_text}")
                st.markdown(f"**キーワード:** {', '.join(q.expected_keywords)}")
                st.markdown(f"**配点:** {q.max_score}点 "
                            f"(KW:{q.keyword_weight} / AI:{q.ai_weight} / 即興:{q.improv_weight})")
                st.markdown(f"**評価基準:** {q.scoring_criteria}")
                if q.follow_up:
                    st.markdown(f"**追加質問:** {q.follow_up}")

    except Exception as e:
        st.error(f"質問ファイルの読み込みエラー: {e}")
        questions = []

    st.divider()

    # 面接開始ボタン
    col_start, _ = st.columns([1, 3])
    with col_start:
        can_start = bool(candidate_name.strip() and questions)
        if st.button("🚀 面接を開始", type="primary", use_container_width=True,
                      disabled=not can_start):
            # APIキー取得: .env → 環境変数 → st.secrets の順で検索
            api_key = ""
            try:
                config = load_config()
                api_key = config.anthropic_api_key
            except ValueError:
                api_key = os.getenv("ANTHROPIC_API_KEY", "")
                if not api_key:
                    try:
                        api_key = st.secrets["ANTHROPIC_API_KEY"]
                    except (KeyError, FileNotFoundError):
                        pass

            if not api_key:
                st.error(
                    "ANTHROPIC_API_KEY が設定されていません。\n\n"
                    "- **ローカル:** `.env` ファイルに設定\n"
                    "- **HuggingFace:** Settings → Secrets で設定"
                )
                return

            config = Config(anthropic_api_key=api_key)
            config.pass_threshold = pass_threshold / 100
            config.whisper_model = whisper_model
            config.claude_model = claude_model
            config.output_dir = OUTPUT_DIR
            config.dry_run = True  # Web UIではdry_run扱い

            st.session_state.config = config
            st.session_state.questions = questions
            st.session_state.candidate_name = candidate_name.strip()
            st.session_state.current_q_idx = 0
            st.session_state.results = []
            st.session_state.report = None
            st.session_state.input_mode = "text" if "テキスト" in input_mode else "audio"
            st.session_state.page = "interview"
            st.rerun()


# ═══════════════════════════════════════
# ページ: 面接中
# ═══════════════════════════════════════
def page_interview():
    config = st.session_state.config
    questions = st.session_state.questions
    idx = st.session_state.current_q_idx
    total = len(questions)

    if idx >= total:
        _finalize_interview()
        return

    question = questions[idx]

    # ── ヘッダー ──
    st.markdown(f"## 🎤 面接中 — {st.session_state.candidate_name}")
    st.caption(f"質問 {idx + 1} / {total}")

    # ── 進捗バー ──
    st.progress((idx) / total, text=f"{idx}/{total} 完了")

    st.divider()

    # ── 質問表示 ──
    st.markdown(f"### 📋 質問 {idx + 1}　`{question.category}`")
    st.info(f"**{question.question_text}**")

    # キーワードは面接官用（デフォルト非表示）
    with st.expander("🔑 評価キーワード（面接官用）", expanded=False):
        st.write("、".join(question.expected_keywords))
        st.caption(f"配点: {question.max_score}点 (KW:{question.keyword_weight} / AI:{question.ai_weight} / 即興:{question.improv_weight})")

    st.divider()

    # ── 回答入力 ──
    st.markdown("### 💬 回答")

    if st.session_state.input_mode == "audio":
        _audio_input_section(config, question, idx, total)
    else:
        _text_input_section(config, question, idx, total)


def _text_input_section(config: Config, question: Question, idx: int, total: int):
    """テキスト入力モード"""
    answer_key = f"answer_text_{idx}"
    answer_text = st.text_area(
        "回答を入力してください",
        height=150,
        key=answer_key,
        placeholder="ここに回答を入力...",
    )

    col_submit, col_skip, col_abort = st.columns([2, 1, 1])

    with col_submit:
        if st.button("✅ 回答を送信", type="primary", use_container_width=True,
                      disabled=not answer_text.strip()):
            _process_answer(config, question, answer_text.strip(), idx, total)

    with col_skip:
        if st.button("⏭️ スキップ", use_container_width=True):
            _process_answer(config, question, "（回答なし）", idx, total)

    with col_abort:
        if st.button("🛑 中断", use_container_width=True):
            st.session_state.page = "setup"
            st.rerun()


def _audio_input_section(config: Config, question: Question, idx: int, total: int):
    """音声入力モード（streamlit-webrtc）"""
    if not HAS_WEBRTC:
        st.warning("streamlit-webrtc が利用できません。テキスト入力に切り替えます。")
        st.session_state.input_mode = "text"
        st.rerun()
        return

    st.info("🎤 「START」を押して話してください → 「STOP」で終了 → 「回答を送信」")

    # WebRTC 録音
    webrtc_ctx = webrtc_streamer(
        key=f"recorder_{idx}",
        mode=WebRtcMode.SENDONLY,
        audio_receiver_size=4096,
        media_stream_constraints={"video": False, "audio": True},
        rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]},
    )

    # 録音データ収集
    audio_buffer_key = f"audio_buffer_{idx}"
    if audio_buffer_key not in st.session_state:
        st.session_state[audio_buffer_key] = []

    if webrtc_ctx.audio_receiver:
        try:
            audio_frames = webrtc_ctx.audio_receiver.get_frames(timeout=1)
            for frame in audio_frames:
                sound = frame.to_ndarray()
                st.session_state[audio_buffer_key].append(sound)
        except Exception:
            pass

    # テキストフォールバック
    st.divider()
    st.markdown("**または、テキストで入力:**")
    answer_text = st.text_area("回答テキスト", height=100, key=f"audio_fallback_{idx}")

    col_submit, col_skip, col_abort = st.columns([2, 1, 1])

    with col_submit:
        if st.button("✅ 回答を送信", type="primary", use_container_width=True):
            text = answer_text.strip()

            # 音声バッファがあればWhisperで書き起こし
            if st.session_state[audio_buffer_key] and not text:
                with st.spinner("🔄 音声を書き起こし中..."):
                    try:
                        all_audio = np.concatenate(st.session_state[audio_buffer_key])
                        # WAV化
                        buf = io.BytesIO()
                        with wave.open(buf, "wb") as wf:
                            wf.setnchannels(1)
                            wf.setsampwidth(2)
                            wf.setframerate(48000)
                            audio_int16 = (all_audio * 32767).astype(np.int16)
                            wf.writeframes(audio_int16.tobytes())
                        text = transcribe_audio_bytes(buf.getvalue(), config)
                        st.success(f"📝 書き起こし: {text}")
                    except Exception as e:
                        st.error(f"書き起こしエラー: {e}")
                        text = ""

            if text:
                st.session_state[audio_buffer_key] = []
                _process_answer(config, question, text, idx, total)
            else:
                st.warning("回答が空です。テキスト入力するか、音声を録音してください。")

    with col_skip:
        if st.button("⏭️ スキップ", use_container_width=True, key=f"skip_audio_{idx}"):
            st.session_state[audio_buffer_key] = []
            _process_answer(config, question, "（回答なし）", idx, total)

    with col_abort:
        if st.button("🛑 中断", use_container_width=True, key=f"abort_audio_{idx}"):
            st.session_state.page = "setup"
            st.rerun()


def _process_answer(config: Config, question: Question, text: str, idx: int, total: int):
    """回答を評価して次へ進む"""
    answer = Answer(transcribed_text=text, audio_duration_sec=0.0)

    claude = ClaudeEvaluator(
        api_key=config.anthropic_api_key,
        model=config.claude_model,
    )

    with st.spinner("🤖 AI が評価中..."):
        result = evaluate_answer(question, answer, claude)

    st.session_state.results.append(result)

    # 結果をインライン表示
    pct = result.total_score / question.max_score * 100 if question.max_score else 0
    if pct >= 70:
        st.success(f"**{result.total_score:.1f} / {question.max_score} 点 ({pct:.0f}%)**")
    elif pct >= 40:
        st.warning(f"**{result.total_score:.1f} / {question.max_score} 点 ({pct:.0f}%)**")
    else:
        st.error(f"**{result.total_score:.1f} / {question.max_score} 点 ({pct:.0f}%)**")
    st.info(f"💬 {result.ai_feedback}")

    # 次の質問へ
    st.session_state.current_q_idx = idx + 1

    if idx + 1 >= total:
        _finalize_interview()
    else:
        st.rerun()


def _finalize_interview():
    """面接結果をまとめてレポートを生成"""
    config = st.session_state.config
    results = st.session_state.results

    total_score = sum(r.total_score for r in results)
    max_possible = sum(r.question.max_score for r in results)
    percentage = total_score / max_possible if max_possible > 0 else 0
    is_passed = percentage >= config.pass_threshold

    report = InterviewReport(
        candidate_name=st.session_state.candidate_name,
        interview_date=datetime.now(),
        results=results,
        total_score=total_score,
        max_possible_score=max_possible,
        pass_threshold=config.pass_threshold,
        is_passed=is_passed,
    )

    # ファイル保存
    reporter = ReportGenerator(output_dir=config.output_dir)
    reporter.generate(report)

    st.session_state.report = report
    st.session_state.history.append(report)
    st.session_state.page = "result"
    st.rerun()


# ═══════════════════════════════════════
# ページ: 結果
# ═══════════════════════════════════════
def page_result():
    report: InterviewReport = st.session_state.report
    if report is None:
        st.warning("面接結果がありません。")
        return

    pct = report.total_score / report.max_possible_score * 100 if report.max_possible_score else 0

    # ── ヘッダー ──
    st.markdown("## 📊 面接結果レポート")
    st.caption(f"{report.candidate_name} — {report.interview_date.strftime('%Y年%m月%d日 %H:%M')}")

    # ── 合否判定カード ──
    if report.is_passed:
        st.success(
            f"### ✅ 合格\n\n"
            f"**{report.total_score:.1f} / {report.max_possible_score:.0f} 点 ({pct:.1f}%)**"
            f" — 合格ライン {report.pass_threshold*100:.0f}%"
        )
    else:
        st.error(
            f"### ❌ 不合格\n\n"
            f"**{report.total_score:.1f} / {report.max_possible_score:.0f} 点 ({pct:.1f}%)**"
            f" — 合格ライン {report.pass_threshold*100:.0f}%"
        )

    st.markdown("")

    # ── チャート ──
    chart_col1, chart_col2 = st.columns(2)
    with chart_col1:
        st.markdown("#### カテゴリ別レーダー")
        st.plotly_chart(build_radar_chart(report.results), use_container_width=True)
    with chart_col2:
        st.markdown("#### 質問別スコア内訳")
        st.plotly_chart(build_bar_chart(report.results), use_container_width=True)

    st.divider()

    # ── 質問ごとの詳細 ──
    st.subheader("📝 質問別 詳細評価")

    for i, result in enumerate(report.results, start=1):
        q = result.question
        pct_q = result.total_score / q.max_score * 100 if q.max_score else 0
        cls = score_class(result.total_score, q.max_score)

        with st.expander(
            f"Q{q.id}. [{q.category}] {q.question_text[:40]}... — "
            f"{result.total_score:.1f}/{q.max_score}点",
            expanded=(i <= 2),
        ):
            st.markdown(f"**質問:** {q.question_text}")
            st.markdown(f"**回答:** {result.answer.transcribed_text}")
            st.divider()

            sc1, sc2, sc3, sc4 = st.columns(4)
            sc1.metric("キーワード", f"{result.keyword_score:.1f}")
            sc2.metric("内容評価", f"{result.ai_content_score:.1f}")
            sc3.metric("即興力", f"{result.improvisation_score:.1f}")
            sc4.metric("合計", f"{result.total_score:.1f} / {q.max_score}")

            kw_hit = ", ".join(result.keyword_hits) if result.keyword_hits else "なし"
            total_kw = len(q.expected_keywords)
            hit_kw = len(result.keyword_hits)
            st.markdown(f"**キーワード一致:** {hit_kw}/{total_kw} ({kw_hit})")
            st.info(f"💬 {result.ai_feedback}")

    st.divider()

    # ── レポートダウンロード ──
    reporter = ReportGenerator(output_dir=st.session_state.config.output_dir)
    report_text = reporter._build_report(report)

    col_dl, col_new, _ = st.columns([1, 1, 2])
    with col_dl:
        st.download_button(
            "📥 レポートをダウンロード",
            data=report_text,
            file_name=f"interview_{report.candidate_name}_{report.interview_date.strftime('%Y%m%d_%H%M%S')}.txt",
            mime="text/plain",
            use_container_width=True,
        )
    with col_new:
        if st.button("🔄 新しい面接を開始", use_container_width=True):
            st.session_state.page = "setup"
            st.session_state.current_q_idx = 0
            st.session_state.results = []
            st.session_state.report = None
            st.rerun()


# ═══════════════════════════════════════
# ページ: 履歴
# ═══════════════════════════════════════
def page_history():
    st.markdown("## 📁 面接履歴")
    st.caption("このセッション中に実施した面接の一覧")

    history = st.session_state.history

    if not history:
        st.info("まだ面接を実施していません。")
        if st.button("⚙️ 設定画面へ"):
            st.session_state.page = "setup"
            st.rerun()
        return

    # 一覧テーブル
    for i, report in enumerate(reversed(history)):
        pct = report.total_score / report.max_possible_score * 100 if report.max_possible_score else 0
        status = "✅ 合格" if report.is_passed else "❌ 不合格"
        date_str = report.interview_date.strftime('%Y/%m/%d %H:%M')

        col_info, col_score, col_btn = st.columns([3, 2, 1])
        with col_info:
            st.markdown(f"**{report.candidate_name}**")
            st.caption(date_str)
        with col_score:
            st.markdown(f"{report.total_score:.1f}/{report.max_possible_score:.0f}点 ({pct:.0f}%) {status}")
        with col_btn:
            if st.button("📊 詳細", key=f"hist_{i}"):
                st.session_state.report = report
                st.session_state.page = "result"
                st.rerun()

        st.divider()

    # 比較チャート（2件以上）
    if len(history) >= 2:
        st.subheader("📈 候補者比較")

        names = [r.candidate_name for r in history]
        scores = [r.total_score / r.max_possible_score * 100 if r.max_possible_score else 0
                  for r in history]

        fig = go.Figure()
        colors = ["#22c55e" if r.is_passed else "#ef4444" for r in history]
        fig.add_trace(go.Bar(x=names, y=scores, marker_color=colors))
        fig.add_hline(
            y=history[0].pass_threshold * 100,
            line_dash="dash", line_color="orange",
            annotation_text="合格ライン",
        )
        fig.update_layout(
            yaxis_title="スコア (%)", yaxis_range=[0, 100],
            height=350, margin=dict(l=40, r=40, t=40, b=40),
        )
        st.plotly_chart(fig, use_container_width=True)


# ─────────────────────────────────────
# メインルーティング
# ─────────────────────────────────────
def main():
    render_sidebar()

    page = st.session_state.page
    if page == "setup":
        page_setup()
    elif page == "interview":
        page_interview()
    elif page == "result":
        page_result()
    elif page == "history":
        page_history()
    else:
        st.session_state.page = "setup"
        st.rerun()


if __name__ == "__main__":
    main()