Update app/frontend/app.py

app/frontend/app.py

@@ -1,838 +1,838 @@
-from __future__ import annotations
-
-import json
-from datetime import datetime
-from typing import Any, Dict, Optional
-
-import requests
-import streamlit as st
-
-API_URL = "http://localhost:8000/analyze"
-
-# =========================
-# Page config
-# =========================
-
-st.set_page_config(
-    page_title="Poster Design Analyzer",
-    page_icon="🎨",
-    layout="wide",
-)
-
-# =========================
-# Constants
-# =========================
-
-SCORE_ORDER = [
-    "composition",
-    "typography",
-    "color",
-    "message_clarity",
-    "quality",
-]
-
-SCORE_LABELS = {
-    "composition": "Композиция",
-    "typography": "Шрифты",
-    "color": "Цвета",
-    "message_clarity": "Ясность сообщения",
-    "quality": "Качество",
-}
-
-# =========================
-# Styles
-# =========================
-
-st.markdown(
-    """
-    <style>
-    .block-container {
-        padding-top: 1.5rem;
-        padding-bottom: 2rem;
-        max-width: 1200px;
-    }
-
-    .hero-box {
-        padding: 1.25rem 1.4rem;
-        border-radius: 18px;
-        background: linear-gradient(135deg, #121826 0%, #1f2a44 100%);
-        color: white;
-        margin-bottom: 1rem;
-        border: 1px solid rgba(255,255,255,0.08);
-        box-shadow: 0 12px 32px rgba(0,0,0,0.18);
-    }
-
-    .hero-title {
-        font-size: 1.75rem;
-        font-weight: 700;
-        margin-bottom: 0.35rem;
-    }
-
-    .hero-subtitle {
-        font-size: 1rem;
-        opacity: 0.88;
-        line-height: 1.5;
-    }
-
-    .section-card {
-        background: rgba(255,255,255,0.04);
-        border: 1px solid rgba(128,128,128,0.15);
-        border-radius: 18px;
-        padding: 1rem 1rem 0.9rem 1rem;
-        margin-bottom: 0.9rem;
-    }
-
-    .metric-card {
-        border-radius: 18px;
-        padding: 1rem;
-        background: linear-gradient(180deg, rgba(32,36,48,0.95), rgba(22,25,35,0.95));
-        border: 1px solid rgba(255,255,255,0.06);
-        box-shadow: 0 8px 24px rgba(0,0,0,0.12);
-        min-height: 118px;
-    }
-
-    .metric-card-title {
-        font-size: 0.95rem;
-        color: #b8bfd3;
-        margin-bottom: 0.35rem;
-    }
-
-    .metric-card-value {
-        font-size: 1.55rem;
-        font-weight: 700;
-        color: #ffffff;
-        margin-bottom: 0.25rem;
-    }
-
-    .metric-card-sub {
-        font-size: 0.92rem;
-        color: #d6dbeb;
-    }
-
-    .score-row {
-        padding: 0.55rem 0 0.2rem 0;
-    }
-
-    .score-label {
-        font-weight: 600;
-        margin-bottom: 0.2rem;
-    }
-
-    .small-note {
-        color: #98a2b3;
-        font-size: 0.9rem;
-    }
-
-    .ok-badge {
-        display: inline-block;
-        padding: 0.25rem 0.55rem;
-        border-radius: 999px;
-        background: rgba(16, 185, 129, 0.15);
-        color: #34d399;
-        font-weight: 600;
-        font-size: 0.85rem;
-        border: 1px solid rgba(52, 211, 153, 0.25);
-    }
-
-    .bad-badge {
-        display: inline-block;
-        padding: 0.25rem 0.55rem;
-        border-radius: 999px;
-        background: rgba(239, 68, 68, 0.14);
-        color: #f87171;
-        font-weight: 600;
-        font-size: 0.85rem;
-        border: 1px solid rgba(248, 113, 113, 0.22);
-    }
-
-    .mid-badge {
-        display: inline-block;
-        padding: 0.25rem 0.55rem;
-        border-radius: 999px;
-        background: rgba(245, 158, 11, 0.14);
-        color: #fbbf24;
-        font-weight: 600;
-        font-size: 0.85rem;
-        border: 1px solid rgba(251, 191, 36, 0.2);
-    }
-    </style>
-    """,
-    unsafe_allow_html=True,
-)
-
-# =========================
-# Helpers
-# =========================
-
-def render_ai_detection_block(title: str, ai_detection: Optional[Dict[str, Any]]) -> None:
-    st.markdown(f"### {title}")
-
-    if not ai_detection:
-        st.info("Нет данных.")
-        return
-
-    st.write(f"**Label:** {ai_detection.get('label', 'unknown')}")
-    st.write(f"**prob_ai:** {ai_detection.get('prob_ai', 'n/a')}")
-    st.write(f"**prob_human:** {ai_detection.get('prob_human', 'n/a')}")
-    st.write(f"**confidence:** {ai_detection.get('confidence', 'unknown')}")
-    st.write(f"**source:** `{ai_detection.get('source', 'unknown')}`")
-
-    comment = ai_detection.get("comment")
-    if comment:
-        st.info(comment)
-
-    votes = ai_detection.get("votes")
-    if votes:
-        with st.expander("Votes / ensemble details"):
-            st.json(votes)
-
-def render_generatedness_tab(
-    ai_detection: Optional[Dict[str, Any]],
-    ai_detection_final: Optional[Dict[str, Any]],
-    ai_detection_ml: Optional[Dict[str, Any]],
-    ai_detection_llm: Optional[Dict[str, Any]],
-    ai_detection_hf: Optional[Dict[str, Any]],
-) -> None:
-    st.markdown("### Анализ сгенерированности")
-
-    top1, top2, top3, top4 = st.columns(4, gap="medium")
-
-    def card_payload(ai_block: Optional[Dict[str, Any]]) -> tuple[str, str, str, Optional[str]]:
-        if not ai_block:
-            return "—", "Нет данных", "source: n/a", None
-
-        prob_ai = ai_block.get("prob_ai", None)
-        label = str(ai_block.get("label", "unknown"))
-        source = str(ai_block.get("source", "unknown"))
-
-        if prob_ai is None:
-            value = "n/a"
-        else:
-            try:
-                value = f"{float(prob_ai):.3f}"
-            except Exception:
-                value = "n/a"
-
-        return value, label, f"source: {source}", label_badge(label)
-
-    with top1:
-        value, subtitle, source_text, badge = card_payload(ai_detection_final)
-        render_metric_card(
-            title="AI / Final Ensemble",
-            value=value,
-            subtitle=f"{subtitle} • {source_text}",
-            badge_html=badge,
-        )
-
-    with top2:
-        value, subtitle, source_text, badge = card_payload(ai_detection_ml)
-        render_metric_card(
-            title="AI / ML",
-            value=value,
-            subtitle=f"{subtitle} • {source_text}",
-            badge_html=badge,
-        )
-
-    with top3:
-        value, subtitle, source_text, badge = card_payload(ai_detection_llm)
-        render_metric_card(
-            title="AI / LLM",
-            value=value,
-            subtitle=f"{subtitle} • {source_text}",
-            badge_html=badge,
-        )
-
-    with top4:
-        value, subtitle, source_text, badge = card_payload(ai_detection_hf)
-        render_metric_card(
-            title="AI / HF",
-            value=value,
-            subtitle=f"{subtitle} • {source_text}",
-            badge_html=badge,
-        )
-
-    st.markdown("---")
-
-    left, right = st.columns(2, gap="large")
-
-    with left:
-        render_ai_detection_block("Итоговый ансамбль", ai_detection_final)
-        render_ai_detection_block("ML detector", ai_detection_ml)
-
-    with right:
-        render_ai_detection_block("LLM / Ollama detector", ai_detection_llm)
-        render_ai_detection_block("HF detector", ai_detection_hf)
-
-def render_dual_scores(
-    official_scores: Dict[str, Any],
-    ollama_scores: Dict[str, Any],
-    ollama_score_reasons: Dict[str, Any],
-    vlm_critic_status: Dict[str, Any],
-    ollama_score_block_valid: bool,
-    ollama_invalid_reason: str,
-) -> None:
-    st.markdown("### 5 официальных оценок")
-
-    left, right = st.columns(2, gap="large")
-
-    with left:
-        st.markdown("#### ML /  scores")
-        for key in SCORE_ORDER:
-            value = to_float(official_scores.get(key, 0.0))
-            render_score_block(SCORE_LABELS[key], value)
-
-        cols = st.columns(len(SCORE_ORDER))
-        for i, key in enumerate(SCORE_ORDER):
-            with cols[i]:
-                st.metric(SCORE_LABELS[key], f"{to_float(official_scores.get(key, 0.0)):.2f}")
-
-    with right:
-        st.markdown("#### LLM / scores")
-
-        if not ollama_scores:
-            if not ollama_score_block_valid:
-                st.warning("Ollama score block был отброшен как недостоверный.")
-                if ollama_invalid_reason:
-                    st.info(ollama_invalid_reason)
-            else:
-                st.warning("Ollama score block не вернулся.")
-
-            if vlm_critic_status:
-                st.json(vlm_critic_status)
-            else:
-                st.info("Нет данных о причине сбоя Ollama.")
-        else:
-            for key in SCORE_ORDER:
-                value = to_float(ollama_scores.get(key, 0.0))
-                render_score_block(SCORE_LABELS[key], value)
-
-            cols = st.columns(len(SCORE_ORDER))
-            for i, key in enumerate(SCORE_ORDER):
-                with cols[i]:
-                    st.metric(SCORE_LABELS[key], f"{to_float(ollama_scores.get(key, 0.0)):.2f}")
-
-            with st.expander("Причины оценок от Ollama"):
-                for key in SCORE_ORDER:
-                    reason = str(ollama_score_reasons.get(key, "") or "")
-                    st.write(f"**{SCORE_LABELS[key]}:** {reason if reason else '—'}")
-
-def safe_get(d: Any, *keys: str, default=None):
-    cur = d
-    for key in keys:
-        if not isinstance(cur, dict) or key not in cur:
-            return default
-        cur = cur[key]
-    return cur
-
-
-def to_float(value: Any, default: float = 0.0) -> float:
-    try:
-        if value is None:
-            return default
-        return float(value)
-    except Exception:
-        return default
-
-
-def normalize_score_5(value: float) -> float:
-    return max(0.0, min(1.0, value / 5.0))
-
-
-def label_badge(label: str) -> str:
-    label = str(label).lower()
-
-    if label in {"good", "human_like"}:
-        cls = "ok-badge"
-    elif label in {"bad", "ai_generated"}:
-        cls = "bad-badge"
-    else:
-        cls = "mid-badge"
-
-    return f'<span class="{cls}">{label}</span>'
-
-
-def build_export_payload(data: Dict[str, Any]) -> bytes:
-    return json.dumps(data, ensure_ascii=False, indent=2).encode("utf-8")
-
-
-def render_metric_card(title: str, value: str, subtitle: str, badge_html: Optional[str] = None) -> None:
-    extra = f'<div style="margin-top:0.5rem;">{badge_html}</div>' if badge_html else ""
-    st.markdown(
-        f"""
-        <div class="metric-card">
-            <div class="metric-card-title">{title}</div>
-            <div class="metric-card-value">{value}</div>
-            <div class="metric-card-sub">{subtitle}</div>
-            {extra}
-        </div>
-        """,
-        unsafe_allow_html=True,
-    )
-
-
-def render_score_block(label: str, value: float) -> None:
-    st.markdown(
-        f'<div class="score-row"><div class="score-label">{label}: {value:.2f} / 5</div></div>',
-        unsafe_allow_html=True,
-    )
-    st.progress(normalize_score_5(value))
-
-
-def render_official_scores(official_scores: Dict[str, Any]) -> None:
-    st.markdown("### 5 официальных оценок")
-
-    for key in SCORE_ORDER:
-        value = to_float(official_scores.get(key, 0.0))
-        render_score_block(SCORE_LABELS[key], value)
-
-    cols = st.columns(len(SCORE_ORDER))
-    for i, key in enumerate(SCORE_ORDER):
-        with cols[i]:
-            st.metric(SCORE_LABELS[key], f"{to_float(official_scores.get(key, 0.0)):.2f}")
-
-
-def render_model_comparison(prediction: Dict[str, Any], filename: str) -> None:
-    st.markdown("### Сравнение разных подходов анализа")
-    st.markdown(
-        """
-        Здесь показаны результаты разных моделей:
-        CLIP image-only, hybrid LogisticRegression, hybrid RandomForest и итоговый ансамбль.
-        """
-    )
-
-    models_to_render = [
-        (
-            "Ансамбль",
-            safe_get(prediction, "ensemble_label", default="unknown"),
-            to_float(safe_get(prediction, "ensemble_prob_good", default=0.0)),
-        ),
-        (
-            "CLIP image-only",
-            safe_get(prediction, "clip_image_only", "label", default="unknown"),
-            to_float(safe_get(prediction, "clip_image_only", "prob_good", default=0.0)),
-        ),
-        (
-            "Hybrid LogisticRegression",
-            safe_get(prediction, "clean_hybrid_logreg", "label", default="unknown"),
-            to_float(safe_get(prediction, "clean_hybrid_logreg", "prob_good", default=0.0)),
-        ),
-        (
-            "Hybrid RandomForest",
-            safe_get(prediction, "clean_hybrid_random_forest", "label", default="unknown"),
-            to_float(safe_get(prediction, "clean_hybrid_random_forest", "prob_good", default=0.0)),
-        ),
-    ]
-
-    cols = st.columns(4, gap="medium")
-    for idx, (title, label, prob) in enumerate(models_to_render):
-        with cols[idx]:
-            render_metric_card(
-                title=title,
-                value=f"{prob:.3f}",
-                subtitle="prob_good",
-                badge_html=label_badge(label),
-            )
-
-    st.markdown("### Интерпретация")
-    st.write(
-        f"""
-        - Если ансамбль согласуется с CLIP и Hybrid LogisticRegression, результат обычно стабильнее.
-        - Если RandomForest заметно расходится с остальными, это индикатор спорного кейса.
-        - Для файла **{filename}** итоговая метка ансамбля: **{safe_get(prediction, "ensemble_label", default="unknown")}**.
-        """
-    )
-
-
-def render_ai_detection(ai_detection: Optional[Dict[str, Any]]) -> None:
-    st.markdown("### AI detection")
-
-    if not ai_detection:
-        st.info("AI detector не подключен или не вернул результат.")
-        return
-
-    prob_ai = ai_detection.get("prob_ai", None)
-    prob_human = ai_detection.get("prob_human", None)
-
-    st.write(f"**Label:** {ai_detection.get('label', 'unknown')}")
-    st.write(f"**prob_ai:** {prob_ai if prob_ai is not None else 'n/a'}")
-    st.write(f"**prob_human:** {prob_human if prob_human is not None else 'n/a'}")
-    st.write(f"**confidence:** {ai_detection.get('confidence', 'unknown')}")
-    st.write(f"**source:** `{ai_detection.get('source', 'unknown')}`")
-
-    comment = ai_detection.get("comment")
-    if comment:
-        st.info(comment)
-
-
-def render_vlm_critic(vlm_critic: Optional[Dict[str, Any]]) -> None:
-    st.markdown("### VLM critic")
-    if vlm_critic:
-        st.json(vlm_critic)
-    else:
-        st.info("Локальный vision-LLM critic не подключен.")
-
-
-def render_diagnostics(
-    diagnostic_metrics: Dict[str, Any],
-    metric_sources: Dict[str, Any],
-    image_features: Dict[str, Any],
-    prediction: Dict[str, Any],
-    ai_detection: Optional[Dict[str, Any]],
-    ai_detection_ml: Optional[Dict[str, Any]],
-    ai_detection_llm: Optional[Dict[str, Any]],
-    ai_detection_hf: Optional[Dict[str, Any]],
-    ai_detection_final: Optional[Dict[str, Any]],
-    vlm_critic: Optional[Dict[str, Any]],
-    show_debug: bool,
-    show_image_features: bool,
-) -> None:
-    left, right = st.columns(2, gap="large")
-
-    with left:
-        st.markdown("### Диагностические метрики")
-        if diagnostic_metrics:
-            for k, v in diagnostic_metrics.items():
-                src = metric_sources.get(k, "unknown")
-                st.write(f"**{k}**: {v}  \nИсточник: `{src}`")
-        else:
-            st.info("Диагностические метрики отсутствуют.")
-
-        render_ai_detection_block("AI detection / final", ai_detection_final or ai_detection)
-        render_ai_detection_block("AI detection / ML", ai_detection_ml)
-        render_ai_detection_block("AI detection / LLM", ai_detection_llm)
-        render_ai_detection_block("AI detection / HF", ai_detection_hf)
-        render_vlm_critic(vlm_critic)
-
-    with right:
-        if show_image_features:
-            st.markdown("### Image features")
-            if image_features:
-                st.json(image_features)
-            else:
-                st.info("Image features отсутствуют.")
-
-        if show_debug:
-            st.markdown("### Technical / prediction block")
-            if prediction:
-                st.json(prediction)
-            else:
-                st.info("Prediction block отсутствует.")
-
-
-def call_backend(api_url: str, uploaded_file) -> Dict[str, Any]:
-    files = {
-        "file": (
-            uploaded_file.name,
-            uploaded_file.getvalue(),
-            uploaded_file.type or "application/octet-stream",
-        )
-    }
-
-    response = requests.post(api_url, files=files, timeout=180)
-
-    if response.status_code != 200:
-        try:
-            payload = response.json()
-        except Exception:
-            payload = {"raw_text": response.text}
-
-        raise RuntimeError(
-            json.dumps(
-                {
-                    "status_code": response.status_code,
-                    "response": payload,
-                },
-                ensure_ascii=False,
-                indent=2,
-            )
-        )
-
-    return response.json()
-
-def render_chip_row(title: str, items: list[str]) -> None:
-    st.markdown(f"### {title}")
-    if not items:
-        st.info("Нет данных.")
-        return
-
-    html = []
-    for item in items:
-        html.append(
-            f"""
-            <span style="
-                display:inline-block;
-                padding:0.28rem 0.6rem;
-                margin:0.12rem 0.25rem 0.12rem 0;
-                border-radius:999px;
-                background:rgba(59,130,246,0.12);
-                border:1px solid rgba(59,130,246,0.22);
-                color:#dbeafe;
-                font-size:0.88rem;
-                font-weight:600;
-            ">{item}</span>
-            """
-        )
-    st.markdown("".join(html), unsafe_allow_html=True)
-
-def render_findings_block(title: str, items: list[str]) -> None:
-    st.markdown(f"### {title}")
-    if not items:
-        st.info("Нет данных.")
-        return
-    for item in items:
-        st.write(f"- {item}")
-
-def render_verdict_block(verdict: dict[str, Any]) -> None:
-    st.markdown("### Вердикт")
-    if not verdict:
-        st.info("Нет данных.")
-        return
-
-    level = verdict.get("level", "unknown")
-    summary = verdict.get("summary", "")
-    takeaway = verdict.get("takeaway", "")
-
-    st.write(f"**Уровень:** {level}")
-    if summary:
-        st.write(f"**Summary:** {summary}")
-    if takeaway:
-        st.write(f"**Takeaway:** {takeaway}")
-
-
-
-# =========================
-# Header
-# =========================
-
-st.markdown(
-    """
-    <div class="hero-box">
-        <div class="hero-title">🎨 Анализ дизайна постера</div>
-        <div class="hero-subtitle">
-            Загрузите изображение, и система выполнит анализ по 5 ключевым осям:
-            композиция, шрифты, цвета, ясность сообщения и качество.
-            Отдельно показываются результаты разных моделей и AI/VLM diagnostic block.
-        </div>
-    </div>
-    """,
-    unsafe_allow_html=True,
-)
-
-# =========================
-# Sidebar
-# =========================
-
-with st.sidebar:
-    st.header("Настройки")
-    api_url = st.text_input("URL backend API", value=API_URL)
-
-    st.markdown(
-        """
-        <div class="small-note">
-        Backend должен поддерживать POST <code>/analyze</code> и возвращать JSON анализа.
-        </div>
-        """,
-        unsafe_allow_html=True,
-    )
-
-    show_debug = st.checkbox("Показывать technical/debug blocks", value=True)
-    show_image_features = st.checkbox("Показывать image features", value=True)
-    show_raw_json = st.checkbox("Показывать raw JSON", value=False)
-
-# =========================
-# Upload block
-# =========================
-
-left_col, right_col = st.columns([1.1, 1.0], gap="large")
-
-with left_col:
-    st.markdown('<div class="section-card">', unsafe_allow_html=True)
-    uploaded_file = st.file_uploader(
-        "Перетащи файл сюда или выбери изображение",
-        type=["jpg", "jpeg", "png", "webp", "bmp"],
-    )
-    st.markdown("</div>", unsafe_allow_html=True)
-
-    analyze_clicked = st.button("Анализировать", type="primary", use_container_width=True)
-
-with right_col:
-    st.markdown('<div class="section-card">', unsafe_allow_html=True)
-    if uploaded_file is not None:
-        st.image(uploaded_file, caption="Загруженное изображение", use_container_width=True)
-    else:
-        st.info("Здесь будет превью изображения.")
-    st.markdown("</div>", unsafe_allow_html=True)
-
-# =========================
-# Main flow
-# =========================
-
-if uploaded_file is not None and analyze_clicked:
-    with st.spinner("Выполняется анализ..."):
-        try:
-            data = call_backend(api_url, uploaded_file)
-
-            final_design = safe_get(data, "final_design", default={}) or {}
-            official_scores = safe_get(data, "official_scores", default={}) or {}
-            prediction = safe_get(data, "prediction", default={}) or {}
-            diagnostic_metrics = safe_get(data, "diagnostic_metrics", default={}) or {}
-            metric_sources = safe_get(data, "metric_sources", default={}) or {}
-            image_features = safe_get(data, "image_features", default={}) or {}
-            ai_detection_ml = safe_get(data, "ai_detection_ml", default=None)
-            ai_detection_llm = safe_get(data, "ai_detection_llm", default=None)
-            ai_detection_hf = safe_get(data, "ai_detection_hf", default=None)
-            ai_detection_final = safe_get(data, "ai_detection_final", default=None)
-            ai_detection = ai_detection_final or ai_detection_ml or ai_detection_llm or ai_detection_hf
-            ollama_actions = data.get("ollama_actions", {}) or {}
-            vlm_critic = safe_get(data, "vlm_critic", default=None)
-            comment = data.get("comment", "")
-            ollama_invalid_reason = safe_get(data, "vlm_critic", "invalid_reason", default="")
-            ollama_score_block_valid = safe_get(data, "vlm_critic", "score_block_valid", default=True)
-            filename = data.get("filename", uploaded_file.name)
-            ollama_scores = data.get("ollama_scores", {}) or {}
-            ollama_score_reasons = data.get("ollama_score_reasons", {}) or {}
-            vlm_critic_status = data.get("vlm_critic_status", {}) or {}
-            tags = data.get("tags", []) or []
-            pins = data.get("pins", []) or []
-            strengths = data.get("strengths", []) or []
-            weaknesses = data.get("weaknesses", []) or []
-            recommendations = data.get("recommendations", []) or []
-            verdict = data.get("verdict", {}) or {}
-
-
-            st.markdown("---")
-            st.subheader("Результат анализа")
-
-            card1, card2, card3, card4 = st.columns(4, gap="medium")
-
-            with card1:
-                render_metric_card(
-                    title="Итоговая метка",
-                    value=str(safe_get(final_design, "label", default="unknown")),
-                    subtitle="Финальное решение бинарного ансамбля",
-                )
-
-            with card2:
-                render_metric_card(
-                    title="Итоговый score",
-                    value=f"{to_float(safe_get(final_design, 'score', default=0.0)):.3f}",
-                    subtitle="Диагностический дизайн-скор по 5 осям",
-                )
-
-            with card3:
-                render_metric_card(
-                    title="Уверенность ансамбля",
-                    value=f"{to_float(safe_get(prediction, 'ensemble_prob_good', default=0.0)):.3f}",
-                    subtitle="Вероятность good по ансамблю",
-                )
-
-            with card4:
-                if ai_detection:
-                    ai_prob = ai_detection.get("prob_ai", None)
-                    ai_prob_value = to_float(ai_prob, 0.0) if ai_prob is not None else 0.0
-                    render_metric_card(
-                        title="AI check",
-                        value=f"{ai_prob_value:.3f}" if ai_prob is not None else "n/a",
-                        subtitle="Вероятность AI-generated",
-                        badge_html=label_badge(ai_detection.get("label", "unknown")),
-                    )
-                else:
-                    render_metric_card(
-                        title="AI check",
-                        value="—",
-                        subtitle="Локальная модель не подключена",
-                    )
-
-            tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(
-                ["5 оценок", "Сравнение подходов", "Диагностика", "Отчёт", "Экспорт", "AI / Generatedness"]
-            )
-
-            with tab1:
-                render_dual_scores(
-                    official_scores=official_scores,
-                    ollama_scores=ollama_scores,
-                    ollama_score_reasons=ollama_score_reasons,
-                    vlm_critic_status=vlm_critic_status,
-                    ollama_score_block_valid=ollama_score_block_valid,
-                    ollama_invalid_reason=ollama_invalid_reason,
-                )
-
-                if comment:
-                    st.markdown("### Краткий комментарий")
-                    st.info(comment)
-
-            with tab2:
-                render_model_comparison(prediction, filename)
-
-            with tab3:
-                render_diagnostics(
-                    diagnostic_metrics=diagnostic_metrics,
-                    metric_sources=metric_sources,
-                    image_features=image_features,
-                    prediction=prediction,
-                    ai_detection=ai_detection,
-                    ai_detection_ml=ai_detection_ml,
-                    ai_detection_llm=ai_detection_llm,
-                    ai_detection_hf=ai_detection_hf,
-                    ai_detection_final=ai_detection_final,
-                    vlm_critic=vlm_critic,
-                    show_debug=show_debug,
-                    show_image_features=show_image_features,
-                )
-
-            with tab4:
-                left_rep, right_rep = st.columns(2, gap="large")
-
-                with left_rep:
-                    render_chip_row("Tags", tags)
-                    render_chip_row("Pins", pins)
-
-                with right_rep:
-                    render_verdict_block(verdict)
-
-                st.markdown("---")
-                f1, f2, f3 = st.columns(3, gap="large")
-
-                with f1:
-                    render_findings_block("Strengths", strengths)
-                with f2:
-                    render_findings_block("Weaknesses", weaknesses)
-                with f3:
-                    render_findings_block("Recommendations", recommendations)
-
-            with tab5:
-                st.markdown("### Экспорт отчёта")
-                st.write("Можно скачать полный JSON-отчёт со всеми анализами.")
-
-                timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-                export_name = f"poster_report_{timestamp}.json"
-
-                st.download_button(
-                    label="Скачать отчёт JSON",
-                    data=build_export_payload(data),
-                    file_name=export_name,
-                    mime="application/json",
-                    use_container_width=True,
-                )
-
-                if show_raw_json:
-                    st.markdown("### Raw JSON")
-                    st.json(data)
-            with tab6:
-                render_generatedness_tab(
-                    ai_detection_final=ai_detection_final,
-                    ai_detection_ml=ai_detection_ml,
-                    ai_detection_llm=ai_detection_llm,
-                    ai_detection_hf=ai_detection_hf,
-                    ai_detection=ai_detection_final or ai_detection_ml or ai_detection_llm or ai_detection_hf
-                )
-
-        except requests.exceptions.ConnectionError:
-            st.error("Не удалось подключиться к backend. Убедись, что FastAPI запущен на localhost:8000.")
-        except requests.exceptions.Timeout:
-            st.error("Таймаут запроса к backend.")
-        except RuntimeError as e:
-            st.error("Ошибка API / backend")
-            st.code(str(e), language="json")
-        except Exception as e:
+from __future__ import annotations
+
+import json
+from datetime import datetime
+from typing import Any, Dict, Optional
+
+import requests
+import streamlit as st
+
+API_URL = "http://127.0.0.1:8000/analyze"
+
+# =========================
+# Page config
+# =========================
+
+st.set_page_config(
+    page_title="Poster Design Analyzer",
+    page_icon="🎨",
+    layout="wide",
+)
+
+# =========================
+# Constants
+# =========================
+
+SCORE_ORDER = [
+    "composition",
+    "typography",
+    "color",
+    "message_clarity",
+    "quality",
+]
+
+SCORE_LABELS = {
+    "composition": "Композиция",
+    "typography": "Шрифты",
+    "color": "Цвета",
+    "message_clarity": "Ясность сообщения",
+    "quality": "Качество",
+}
+
+# =========================
+# Styles
+# =========================
+
+st.markdown(
+    """
+    <style>
+    .block-container {
+        padding-top: 1.5rem;
+        padding-bottom: 2rem;
+        max-width: 1200px;
+    }
+
+    .hero-box {
+        padding: 1.25rem 1.4rem;
+        border-radius: 18px;
+        background: linear-gradient(135deg, #121826 0%, #1f2a44 100%);
+        color: white;
+        margin-bottom: 1rem;
+        border: 1px solid rgba(255,255,255,0.08);
+        box-shadow: 0 12px 32px rgba(0,0,0,0.18);
+    }
+
+    .hero-title {
+        font-size: 1.75rem;
+        font-weight: 700;
+        margin-bottom: 0.35rem;
+    }
+
+    .hero-subtitle {
+        font-size: 1rem;
+        opacity: 0.88;
+        line-height: 1.5;
+    }
+
+    .section-card {
+        background: rgba(255,255,255,0.04);
+        border: 1px solid rgba(128,128,128,0.15);
+        border-radius: 18px;
+        padding: 1rem 1rem 0.9rem 1rem;
+        margin-bottom: 0.9rem;
+    }
+
+    .metric-card {
+        border-radius: 18px;
+        padding: 1rem;
+        background: linear-gradient(180deg, rgba(32,36,48,0.95), rgba(22,25,35,0.95));
+        border: 1px solid rgba(255,255,255,0.06);
+        box-shadow: 0 8px 24px rgba(0,0,0,0.12);
+        min-height: 118px;
+    }
+
+    .metric-card-title {
+        font-size: 0.95rem;
+        color: #b8bfd3;
+        margin-bottom: 0.35rem;
+    }
+
+    .metric-card-value {
+        font-size: 1.55rem;
+        font-weight: 700;
+        color: #ffffff;
+        margin-bottom: 0.25rem;
+    }
+
+    .metric-card-sub {
+        font-size: 0.92rem;
+        color: #d6dbeb;
+    }
+
+    .score-row {
+        padding: 0.55rem 0 0.2rem 0;
+    }
+
+    .score-label {
+        font-weight: 600;
+        margin-bottom: 0.2rem;
+    }
+
+    .small-note {
+        color: #98a2b3;
+        font-size: 0.9rem;
+    }
+
+    .ok-badge {
+        display: inline-block;
+        padding: 0.25rem 0.55rem;
+        border-radius: 999px;
+        background: rgba(16, 185, 129, 0.15);
+        color: #34d399;
+        font-weight: 600;
+        font-size: 0.85rem;
+        border: 1px solid rgba(52, 211, 153, 0.25);
+    }
+
+    .bad-badge {
+        display: inline-block;
+        padding: 0.25rem 0.55rem;
+        border-radius: 999px;
+        background: rgba(239, 68, 68, 0.14);
+        color: #f87171;
+        font-weight: 600;
+        font-size: 0.85rem;
+        border: 1px solid rgba(248, 113, 113, 0.22);
+    }
+
+    .mid-badge {
+        display: inline-block;
+        padding: 0.25rem 0.55rem;
+        border-radius: 999px;
+        background: rgba(245, 158, 11, 0.14);
+        color: #fbbf24;
+        font-weight: 600;
+        font-size: 0.85rem;
+        border: 1px solid rgba(251, 191, 36, 0.2);
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# =========================
+# Helpers
+# =========================
+
+def render_ai_detection_block(title: str, ai_detection: Optional[Dict[str, Any]]) -> None:
+    st.markdown(f"### {title}")
+
+    if not ai_detection:
+        st.info("Нет данных.")
+        return
+
+    st.write(f"**Label:** {ai_detection.get('label', 'unknown')}")
+    st.write(f"**prob_ai:** {ai_detection.get('prob_ai', 'n/a')}")
+    st.write(f"**prob_human:** {ai_detection.get('prob_human', 'n/a')}")
+    st.write(f"**confidence:** {ai_detection.get('confidence', 'unknown')}")
+    st.write(f"**source:** `{ai_detection.get('source', 'unknown')}`")
+
+    comment = ai_detection.get("comment")
+    if comment:
+        st.info(comment)
+
+    votes = ai_detection.get("votes")
+    if votes:
+        with st.expander("Votes / ensemble details"):
+            st.json(votes)
+
+def render_generatedness_tab(
+    ai_detection: Optional[Dict[str, Any]],
+    ai_detection_final: Optional[Dict[str, Any]],
+    ai_detection_ml: Optional[Dict[str, Any]],
+    ai_detection_llm: Optional[Dict[str, Any]],
+    ai_detection_hf: Optional[Dict[str, Any]],
+) -> None:
+    st.markdown("### Анализ сгенерированности")
+
+    top1, top2, top3, top4 = st.columns(4, gap="medium")
+
+    def card_payload(ai_block: Optional[Dict[str, Any]]) -> tuple[str, str, str, Optional[str]]:
+        if not ai_block:
+            return "—", "Нет данных", "source: n/a", None
+
+        prob_ai = ai_block.get("prob_ai", None)
+        label = str(ai_block.get("label", "unknown"))
+        source = str(ai_block.get("source", "unknown"))
+
+        if prob_ai is None:
+            value = "n/a"
+        else:
+            try:
+                value = f"{float(prob_ai):.3f}"
+            except Exception:
+                value = "n/a"
+
+        return value, label, f"source: {source}", label_badge(label)
+
+    with top1:
+        value, subtitle, source_text, badge = card_payload(ai_detection_final)
+        render_metric_card(
+            title="AI / Final Ensemble",
+            value=value,
+            subtitle=f"{subtitle} • {source_text}",
+            badge_html=badge,
+        )
+
+    with top2:
+        value, subtitle, source_text, badge = card_payload(ai_detection_ml)
+        render_metric_card(
+            title="AI / ML",
+            value=value,
+            subtitle=f"{subtitle} • {source_text}",
+            badge_html=badge,
+        )
+
+    with top3:
+        value, subtitle, source_text, badge = card_payload(ai_detection_llm)
+        render_metric_card(
+            title="AI / LLM",
+            value=value,
+            subtitle=f"{subtitle} • {source_text}",
+            badge_html=badge,
+        )
+
+    with top4:
+        value, subtitle, source_text, badge = card_payload(ai_detection_hf)
+        render_metric_card(
+            title="AI / HF",
+            value=value,
+            subtitle=f"{subtitle} • {source_text}",
+            badge_html=badge,
+        )
+
+    st.markdown("---")
+
+    left, right = st.columns(2, gap="large")
+
+    with left:
+        render_ai_detection_block("Итоговый ансамбль", ai_detection_final)
+        render_ai_detection_block("ML detector", ai_detection_ml)
+
+    with right:
+        render_ai_detection_block("LLM / Ollama detector", ai_detection_llm)
+        render_ai_detection_block("HF detector", ai_detection_hf)
+
+def render_dual_scores(
+    official_scores: Dict[str, Any],
+    ollama_scores: Dict[str, Any],
+    ollama_score_reasons: Dict[str, Any],
+    vlm_critic_status: Dict[str, Any],
+    ollama_score_block_valid: bool,
+    ollama_invalid_reason: str,
+) -> None:
+    st.markdown("### 5 официальных оценок")
+
+    left, right = st.columns(2, gap="large")
+
+    with left:
+        st.markdown("#### ML /  scores")
+        for key in SCORE_ORDER:
+            value = to_float(official_scores.get(key, 0.0))
+            render_score_block(SCORE_LABELS[key], value)
+
+        cols = st.columns(len(SCORE_ORDER))
+        for i, key in enumerate(SCORE_ORDER):
+            with cols[i]:
+                st.metric(SCORE_LABELS[key], f"{to_float(official_scores.get(key, 0.0)):.2f}")
+
+    with right:
+        st.markdown("#### LLM / scores")
+
+        if not ollama_scores:
+            if not ollama_score_block_valid:
+                st.warning("Ollama score block был отброшен как недостоверный.")
+                if ollama_invalid_reason:
+                    st.info(ollama_invalid_reason)
+            else:
+                st.warning("Ollama score block не вернулся.")
+
+            if vlm_critic_status:
+                st.json(vlm_critic_status)
+            else:
+                st.info("Нет данных о причине сбоя Ollama.")
+        else:
+            for key in SCORE_ORDER:
+                value = to_float(ollama_scores.get(key, 0.0))
+                render_score_block(SCORE_LABELS[key], value)
+
+            cols = st.columns(len(SCORE_ORDER))
+            for i, key in enumerate(SCORE_ORDER):
+                with cols[i]:
+                    st.metric(SCORE_LABELS[key], f"{to_float(ollama_scores.get(key, 0.0)):.2f}")
+
+            with st.expander("Причины оценок от Ollama"):
+                for key in SCORE_ORDER:
+                    reason = str(ollama_score_reasons.get(key, "") or "")
+                    st.write(f"**{SCORE_LABELS[key]}:** {reason if reason else '—'}")
+
+def safe_get(d: Any, *keys: str, default=None):
+    cur = d
+    for key in keys:
+        if not isinstance(cur, dict) or key not in cur:
+            return default
+        cur = cur[key]
+    return cur
+
+
+def to_float(value: Any, default: float = 0.0) -> float:
+    try:
+        if value is None:
+            return default
+        return float(value)
+    except Exception:
+        return default
+
+
+def normalize_score_5(value: float) -> float:
+    return max(0.0, min(1.0, value / 5.0))
+
+
+def label_badge(label: str) -> str:
+    label = str(label).lower()
+
+    if label in {"good", "human_like"}:
+        cls = "ok-badge"
+    elif label in {"bad", "ai_generated"}:
+        cls = "bad-badge"
+    else:
+        cls = "mid-badge"
+
+    return f'<span class="{cls}">{label}</span>'
+
+
+def build_export_payload(data: Dict[str, Any]) -> bytes:
+    return json.dumps(data, ensure_ascii=False, indent=2).encode("utf-8")
+
+
+def render_metric_card(title: str, value: str, subtitle: str, badge_html: Optional[str] = None) -> None:
+    extra = f'<div style="margin-top:0.5rem;">{badge_html}</div>' if badge_html else ""
+    st.markdown(
+        f"""
+        <div class="metric-card">
+            <div class="metric-card-title">{title}</div>
+            <div class="metric-card-value">{value}</div>
+            <div class="metric-card-sub">{subtitle}</div>
+            {extra}
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+
+
+def render_score_block(label: str, value: float) -> None:
+    st.markdown(
+        f'<div class="score-row"><div class="score-label">{label}: {value:.2f} / 5</div></div>',
+        unsafe_allow_html=True,
+    )
+    st.progress(normalize_score_5(value))
+
+
+def render_official_scores(official_scores: Dict[str, Any]) -> None:
+    st.markdown("### 5 официальных оценок")
+
+    for key in SCORE_ORDER:
+        value = to_float(official_scores.get(key, 0.0))
+        render_score_block(SCORE_LABELS[key], value)
+
+    cols = st.columns(len(SCORE_ORDER))
+    for i, key in enumerate(SCORE_ORDER):
+        with cols[i]:
+            st.metric(SCORE_LABELS[key], f"{to_float(official_scores.get(key, 0.0)):.2f}")
+
+
+def render_model_comparison(prediction: Dict[str, Any], filename: str) -> None:
+    st.markdown("### Сравнение разных подходов анализа")
+    st.markdown(
+        """
+        Здесь показаны результаты разных моделей:
+        CLIP image-only, hybrid LogisticRegression, hybrid RandomForest и итоговый ансамбль.
+        """
+    )
+
+    models_to_render = [
+        (
+            "Ансамбль",
+            safe_get(prediction, "ensemble_label", default="unknown"),
+            to_float(safe_get(prediction, "ensemble_prob_good", default=0.0)),
+        ),
+        (
+            "CLIP image-only",
+            safe_get(prediction, "clip_image_only", "label", default="unknown"),
+            to_float(safe_get(prediction, "clip_image_only", "prob_good", default=0.0)),
+        ),
+        (
+            "Hybrid LogisticRegression",
+            safe_get(prediction, "clean_hybrid_logreg", "label", default="unknown"),
+            to_float(safe_get(prediction, "clean_hybrid_logreg", "prob_good", default=0.0)),
+        ),
+        (
+            "Hybrid RandomForest",
+            safe_get(prediction, "clean_hybrid_random_forest", "label", default="unknown"),
+            to_float(safe_get(prediction, "clean_hybrid_random_forest", "prob_good", default=0.0)),
+        ),
+    ]
+
+    cols = st.columns(4, gap="medium")
+    for idx, (title, label, prob) in enumerate(models_to_render):
+        with cols[idx]:
+            render_metric_card(
+                title=title,
+                value=f"{prob:.3f}",
+                subtitle="prob_good",
+                badge_html=label_badge(label),
+            )
+
+    st.markdown("### Интерпретация")
+    st.write(
+        f"""
+        - Если ансамбль согласуется с CLIP и Hybrid LogisticRegression, результат обычно стабильнее.
+        - Если RandomForest заметно расходится с остальными, это индикатор спорного кейса.
+        - Для файла **{filename}** итоговая метка ансамбля: **{safe_get(prediction, "ensemble_label", default="unknown")}**.
+        """
+    )
+
+
+def render_ai_detection(ai_detection: Optional[Dict[str, Any]]) -> None:
+    st.markdown("### AI detection")
+
+    if not ai_detection:
+        st.info("AI detector не подключен или не вернул результат.")
+        return
+
+    prob_ai = ai_detection.get("prob_ai", None)
+    prob_human = ai_detection.get("prob_human", None)
+
+    st.write(f"**Label:** {ai_detection.get('label', 'unknown')}")
+    st.write(f"**prob_ai:** {prob_ai if prob_ai is not None else 'n/a'}")
+    st.write(f"**prob_human:** {prob_human if prob_human is not None else 'n/a'}")
+    st.write(f"**confidence:** {ai_detection.get('confidence', 'unknown')}")
+    st.write(f"**source:** `{ai_detection.get('source', 'unknown')}`")
+
+    comment = ai_detection.get("comment")
+    if comment:
+        st.info(comment)
+
+
+def render_vlm_critic(vlm_critic: Optional[Dict[str, Any]]) -> None:
+    st.markdown("### VLM critic")
+    if vlm_critic:
+        st.json(vlm_critic)
+    else:
+        st.info("Локальный vision-LLM critic не подключен.")
+
+
+def render_diagnostics(
+    diagnostic_metrics: Dict[str, Any],
+    metric_sources: Dict[str, Any],
+    image_features: Dict[str, Any],
+    prediction: Dict[str, Any],
+    ai_detection: Optional[Dict[str, Any]],
+    ai_detection_ml: Optional[Dict[str, Any]],
+    ai_detection_llm: Optional[Dict[str, Any]],
+    ai_detection_hf: Optional[Dict[str, Any]],
+    ai_detection_final: Optional[Dict[str, Any]],
+    vlm_critic: Optional[Dict[str, Any]],
+    show_debug: bool,
+    show_image_features: bool,
+) -> None:
+    left, right = st.columns(2, gap="large")
+
+    with left:
+        st.markdown("### Диагностические метрики")
+        if diagnostic_metrics:
+            for k, v in diagnostic_metrics.items():
+                src = metric_sources.get(k, "unknown")
+                st.write(f"**{k}**: {v}  \nИсточник: `{src}`")
+        else:
+            st.info("Диагностические метрики отсутствуют.")
+
+        render_ai_detection_block("AI detection / final", ai_detection_final or ai_detection)
+        render_ai_detection_block("AI detection / ML", ai_detection_ml)
+        render_ai_detection_block("AI detection / LLM", ai_detection_llm)
+        render_ai_detection_block("AI detection / HF", ai_detection_hf)
+        render_vlm_critic(vlm_critic)
+
+    with right:
+        if show_image_features:
+            st.markdown("### Image features")
+            if image_features:
+                st.json(image_features)
+            else:
+                st.info("Image features отсутствуют.")
+
+        if show_debug:
+            st.markdown("### Technical / prediction block")
+            if prediction:
+                st.json(prediction)
+            else:
+                st.info("Prediction block отсутствует.")
+
+
+def call_backend(api_url: str, uploaded_file) -> Dict[str, Any]:
+    files = {
+        "file": (
+            uploaded_file.name,
+            uploaded_file.getvalue(),
+            uploaded_file.type or "application/octet-stream",
+        )
+    }
+
+    response = requests.post(api_url, files=files, timeout=600)
+
+    if response.status_code != 200:
+        try:
+            payload = response.json()
+        except Exception:
+            payload = {"raw_text": response.text}
+
+        raise RuntimeError(
+            json.dumps(
+                {
+                    "status_code": response.status_code,
+                    "response": payload,
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        )
+
+    return response.json()
+
+def render_chip_row(title: str, items: list[str]) -> None:
+    st.markdown(f"### {title}")
+    if not items:
+        st.info("Нет данных.")
+        return
+
+    html = []
+    for item in items:
+        html.append(
+            f"""
+            <span style="
+                display:inline-block;
+                padding:0.28rem 0.6rem;
+                margin:0.12rem 0.25rem 0.12rem 0;
+                border-radius:999px;
+                background:rgba(59,130,246,0.12);
+                border:1px solid rgba(59,130,246,0.22);
+                color:#dbeafe;
+                font-size:0.88rem;
+                font-weight:600;
+            ">{item}</span>
+            """
+        )
+    st.markdown("".join(html), unsafe_allow_html=True)
+
+def render_findings_block(title: str, items: list[str]) -> None:
+    st.markdown(f"### {title}")
+    if not items:
+        st.info("Нет данных.")
+        return
+    for item in items:
+        st.write(f"- {item}")
+
+def render_verdict_block(verdict: dict[str, Any]) -> None:
+    st.markdown("### Вердикт")
+    if not verdict:
+        st.info("Нет данных.")
+        return
+
+    level = verdict.get("level", "unknown")
+    summary = verdict.get("summary", "")
+    takeaway = verdict.get("takeaway", "")
+
+    st.write(f"**Уровень:** {level}")
+    if summary:
+        st.write(f"**Summary:** {summary}")
+    if takeaway:
+        st.write(f"**Takeaway:** {takeaway}")
+
+
+
+# =========================
+# Header
+# =========================
+
+st.markdown(
+    """
+    <div class="hero-box">
+        <div class="hero-title">🎨 Анализ дизайна постера</div>
+        <div class="hero-subtitle">
+            Загрузите изображение, и система выполнит анализ по 5 ключевым осям:
+            композиция, шрифты, цвета, ясность сообщения и качество.
+            Отдельно показываются результаты разных моделей и AI/VLM diagnostic block.
+        </div>
+    </div>
+    """,
+    unsafe_allow_html=True,
+)
+
+# =========================
+# Sidebar
+# =========================
+
+with st.sidebar:
+    st.header("Настройки")
+    api_url = st.text_input("URL backend API", value=API_URL)
+
+    st.markdown(
+        """
+        <div class="small-note">
+        Backend должен поддерживать POST <code>/analyze</code> и возвращать JSON анализа.
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    show_debug = st.checkbox("Показывать technical/debug blocks", value=True)
+    show_image_features = st.checkbox("Показывать image features", value=True)
+    show_raw_json = st.checkbox("Показывать raw JSON", value=False)
+
+# =========================
+# Upload block
+# =========================
+
+left_col, right_col = st.columns([1.1, 1.0], gap="large")
+
+with left_col:
+    st.markdown('<div class="section-card">', unsafe_allow_html=True)
+    uploaded_file = st.file_uploader(
+        "Перетащи файл сюда или выбери изображение",
+        type=["jpg", "jpeg", "png", "webp", "bmp"],
+    )
+    st.markdown("</div>", unsafe_allow_html=True)
+
+    analyze_clicked = st.button("Анализировать", type="primary", use_container_width=True)
+
+with right_col:
+    st.markdown('<div class="section-card">', unsafe_allow_html=True)
+    if uploaded_file is not None:
+        st.image(uploaded_file, caption="Загруженное изображение", use_container_width=True)
+    else:
+        st.info("Здесь будет превью изображения.")
+    st.markdown("</div>", unsafe_allow_html=True)
+
+# =========================
+# Main flow
+# =========================
+
+if uploaded_file is not None and analyze_clicked:
+    with st.spinner("Выполняется анализ..."):
+        try:
+            data = call_backend(api_url, uploaded_file)
+
+            final_design = safe_get(data, "final_design", default={}) or {}
+            official_scores = safe_get(data, "official_scores", default={}) or {}
+            prediction = safe_get(data, "prediction", default={}) or {}
+            diagnostic_metrics = safe_get(data, "diagnostic_metrics", default={}) or {}
+            metric_sources = safe_get(data, "metric_sources", default={}) or {}
+            image_features = safe_get(data, "image_features", default={}) or {}
+            ai_detection_ml = safe_get(data, "ai_detection_ml", default=None)
+            ai_detection_llm = safe_get(data, "ai_detection_llm", default=None)
+            ai_detection_hf = safe_get(data, "ai_detection_hf", default=None)
+            ai_detection_final = safe_get(data, "ai_detection_final", default=None)
+            ai_detection = ai_detection_final or ai_detection_ml or ai_detection_llm or ai_detection_hf
+            ollama_actions = data.get("ollama_actions", {}) or {}
+            vlm_critic = safe_get(data, "vlm_critic", default=None)
+            comment = data.get("comment", "")
+            ollama_invalid_reason = safe_get(data, "vlm_critic", "invalid_reason", default="")
+            ollama_score_block_valid = safe_get(data, "vlm_critic", "score_block_valid", default=True)
+            filename = data.get("filename", uploaded_file.name)
+            ollama_scores = data.get("ollama_scores", {}) or {}
+            ollama_score_reasons = data.get("ollama_score_reasons", {}) or {}
+            vlm_critic_status = data.get("vlm_critic_status", {}) or {}
+            tags = data.get("tags", []) or []
+            pins = data.get("pins", []) or []
+            strengths = data.get("strengths", []) or []
+            weaknesses = data.get("weaknesses", []) or []
+            recommendations = data.get("recommendations", []) or []
+            verdict = data.get("verdict", {}) or {}
+
+
+            st.markdown("---")
+            st.subheader("Результат анализа")
+
+            card1, card2, card3, card4 = st.columns(4, gap="medium")
+
+            with card1:
+                render_metric_card(
+                    title="Итоговая метка",
+                    value=str(safe_get(final_design, "label", default="unknown")),
+                    subtitle="Финальное решение бинарного ансамбля",
+                )
+
+            with card2:
+                render_metric_card(
+                    title="Итоговый score",
+                    value=f"{to_float(safe_get(final_design, 'score', default=0.0)):.3f}",
+                    subtitle="Диагностический дизайн-скор по 5 осям",
+                )
+
+            with card3:
+                render_metric_card(
+                    title="Уверенность ансамбля",
+                    value=f"{to_float(safe_get(prediction, 'ensemble_prob_good', default=0.0)):.3f}",
+                    subtitle="Вероятность good по ансамблю",
+                )
+
+            with card4:
+                if ai_detection:
+                    ai_prob = ai_detection.get("prob_ai", None)
+                    ai_prob_value = to_float(ai_prob, 0.0) if ai_prob is not None else 0.0
+                    render_metric_card(
+                        title="AI check",
+                        value=f"{ai_prob_value:.3f}" if ai_prob is not None else "n/a",
+                        subtitle="Вероятность AI-generated",
+                        badge_html=label_badge(ai_detection.get("label", "unknown")),
+                    )
+                else:
+                    render_metric_card(
+                        title="AI check",
+                        value="—",
+                        subtitle="Локальная модель не подключена",
+                    )
+
+            tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(
+                ["5 оценок", "Сравнение подходов", "Диагностика", "Отчёт", "Экспорт", "AI / Generatedness"]
+            )
+
+            with tab1:
+                render_dual_scores(
+                    official_scores=official_scores,
+                    ollama_scores=ollama_scores,
+                    ollama_score_reasons=ollama_score_reasons,
+                    vlm_critic_status=vlm_critic_status,
+                    ollama_score_block_valid=ollama_score_block_valid,
+                    ollama_invalid_reason=ollama_invalid_reason,
+                )
+
+                if comment:
+                    st.markdown("### Краткий комментарий")
+                    st.info(comment)
+
+            with tab2:
+                render_model_comparison(prediction, filename)
+
+            with tab3:
+                render_diagnostics(
+                    diagnostic_metrics=diagnostic_metrics,
+                    metric_sources=metric_sources,
+                    image_features=image_features,
+                    prediction=prediction,
+                    ai_detection=ai_detection,
+                    ai_detection_ml=ai_detection_ml,
+                    ai_detection_llm=ai_detection_llm,
+                    ai_detection_hf=ai_detection_hf,
+                    ai_detection_final=ai_detection_final,
+                    vlm_critic=vlm_critic,
+                    show_debug=show_debug,
+                    show_image_features=show_image_features,
+                )
+
+            with tab4:
+                left_rep, right_rep = st.columns(2, gap="large")
+
+                with left_rep:
+                    render_chip_row("Tags", tags)
+                    render_chip_row("Pins", pins)
+
+                with right_rep:
+                    render_verdict_block(verdict)
+
+                st.markdown("---")
+                f1, f2, f3 = st.columns(3, gap="large")
+
+                with f1:
+                    render_findings_block("Strengths", strengths)
+                with f2:
+                    render_findings_block("Weaknesses", weaknesses)
+                with f3:
+                    render_findings_block("Recommendations", recommendations)
+
+            with tab5:
+                st.markdown("### Экспорт отчёта")
+                st.write("Можно скачать полный JSON-отчёт со всеми анализами.")
+
+                timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+                export_name = f"poster_report_{timestamp}.json"
+
+                st.download_button(
+                    label="Скачать отчёт JSON",
+                    data=build_export_payload(data),
+                    file_name=export_name,
+                    mime="application/json",
+                    use_container_width=True,
+                )
+
+                if show_raw_json:
+                    st.markdown("### Raw JSON")
+                    st.json(data)
+            with tab6:
+                render_generatedness_tab(
+                    ai_detection_final=ai_detection_final,
+                    ai_detection_ml=ai_detection_ml,
+                    ai_detection_llm=ai_detection_llm,
+                    ai_detection_hf=ai_detection_hf,
+                    ai_detection=ai_detection_final or ai_detection_ml or ai_detection_llm or ai_detection_hf
+                )
+
+        except requests.exceptions.ConnectionError:
+            st.error("Не удалось подключиться к backend. Убедись, что FastAPI запущен на localhost:8000.")
+        except requests.exceptions.Timeout:
+            st.error("Таймаут запроса к backend.")
+        except RuntimeError as e:
+            st.error("Ошибка API / backend")
+            st.code(str(e), language="json")
+        except Exception as e:
             st.error(f"Непредвиденная ошибка: {e}")