Deploy Codex Extractor Gradio app

app.py

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+from __future__ import annotations
+
+import json
+from html import escape
+from pathlib import Path
+
+import gradio as gr
+import pandas as pd
+
+from src.totem_workbook import (
+    DEFAULT_WORKBOOK,
+    LOG_COLUMNS,
+    METRICS,
+    export_updated_workbook,
+    manuscript_tracker_table,
+    recalculate_log,
+    score_log,
+    score_single_row,
+    viability_table,
+    workbook_overview,
+    workbook_path,
+    workstack_table,
+)
+
+from src.codex_extractor import process_upload, format_fingerprint_report
+
+ORIGINAL_WORKBOOK_PATH = "data/order69_macmillan_totem_rebuilt.xlsx"
+
+CSS = """
+:root {
+  --studio-green: #0e4a1d;
+  --studio-green-2: #17642a;
+  --studio-gold: #e4aa1a;
+  --studio-cream: #fffaf0;
+  --studio-ink: #15351d;
+  --studio-muted: #6d725f;
+  --studio-line: #eadfbd;
+  --studio-red: #cf4b3f;
+}
+
+.gradio-container {
+  max-width: none !important;
+  padding: 0 !important;
+  background: #fffaf0 !important;
+  color: var(--studio-ink) !important;
+  font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif !important;
+}
+
+footer { display: none !important; }
+
+#hidden-export, #hidden-status {
+  max-width: 1180px;
+  margin: 0 auto 18px auto;
+}
+
+#studio-actions {
+  max-width: 1180px;
+  margin: -705px auto 16px auto;
+  padding-left: 18px;
+  position: relative;
+  z-index: 5;
+}
+
+#studio-actions .wrap {
+  max-width: 430px;
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 12px;
+}
+
+#studio-actions button {
+  border-radius: 8px !important;
+  min-height: 48px !important;
+  font-weight: 800 !important;
+}
+
+#path-panel {
+  max-width: 1180px;
+  margin: 18px auto;
+  padding: 0 18px;
+}
+
+#path-panel .wrap {
+  display: grid;
+  grid-template-columns: minmax(360px, 1fr) 210px;
+  gap: 12px;
+  max-width: 760px;
+}
+
+#path-panel textarea,
+#path-panel input {
+  border-radius: 8px !important;
+  border: 1px solid var(--studio-line) !important;
+  background: white !important;
+}
+
+#path-panel button {
+  border-radius: 8px !important;
+  min-height: 52px !important;
+  font-weight: 800 !important;
+}
+
+#score-panel {
+  max-width: 1180px;
+  margin: 18px auto 44px auto;
+  padding: 0 18px;
+}
+
+#score-panel .score-card {
+  border: 1px solid var(--studio-line);
+  background: #fffef8;
+  border-radius: 8px;
+  padding: 18px;
+}
+
+#score-panel h3 {
+  margin: 0 0 12px 0;
+  font-size: 18px;
+  color: var(--studio-green);
+}
+
+#score-panel button {
+  border-radius: 8px !important;
+  font-weight: 800 !important;
+}
+
+#score-panel .wrap {
+  gap: 12px;
+}
+
+#codex-panel {
+  max-width: 1180px;
+  margin: 18px auto 44px auto;
+  padding: 0 18px;
+}
+
+#codex-panel .codex-header {
+  background: linear-gradient(135deg, #0e4a1d, #17642a);
+  border-radius: 8px 8px 0 0;
+  padding: 20px 24px;
+  color: white;
+}
+
+#codex-panel .codex-header h3 {
+  margin: 0;
+  color: #f8e838;
+  font-size: 20px;
+  font-family: Georgia, serif;
+}
+
+#codex-panel .codex-header p {
+  margin: 6px 0 0;
+  color: #d9ead0;
+  font-size: 13px;
+}
+
+#codex-panel .codex-body {
+  border: 1px solid var(--studio-line);
+  border-top: none;
+  border-radius: 0 0 8px 8px;
+  padding: 24px;
+  background: #fffef8;
+}
+
+#codex-panel .confidence-high {
+  background: #e8f5e9;
+  border: 1px solid #a5d6a7;
+  border-radius: 6px;
+  padding: 10px 14px;
+  color: #1b5e20;
+  font-weight: 700;
+}
+
+#codex-panel .confidence-medium {
+  background: #fff8e1;
+  border: 1px solid #ffe082;
+  border-radius: 6px;
+  padding: 10px 14px;
+  color: #e65100;
+  font-weight: 700;
+}
+
+#codex-panel .confidence-low {
+  background: #ffebee;
+  border: 1px solid #ef9a9a;
+  border-radius: 6px;
+  padding: 10px 14px;
+  color: #b71c1c;
+  font-weight: 700;
+}
+
+.dataframe, .table-wrap, .sheet, .tabs, .tabitem {
+  border-radius: 8px !important;
+}
+
+@media (max-width: 900px) {
+  #studio-actions {
+    margin-top: 0;
+    padding: 14px;
+  }
+  #studio-actions .wrap,
+  #path-panel .wrap {
+    grid-template-columns: 1fr;
+  }
+}
+"""
+
+
+REVISION_ACTIONS = {
+    "Clarity": "Simplify the line and sharpen the subject/action.",
+    "Rhythm": "Rework beat pattern and remove drag.",
+    "Read-aloud Flow": "Run a speak-test pass and cut mouth knots.",
+    "Emotional Truth": "Anchor the feeling in the child-facing moment.",
+    "Visual Strength": "Sharpen the drawable page beat.",
+    "Commercial Publishability": "Tighten hook, age fit, and list-readiness.",
+}
+
+
+def _clean_path(uploaded_file) -> Path:
+    return workbook_path(uploaded_file)
+
+
+def _validate_workbook_path(path: Path) -> Path:
+    if not path.exists():
+        raise gr.Error(f"Workbook path does not exist: {path}")
+    if path.suffix.lower() not in {".xlsx", ".xlsm"}:
+        raise gr.Error("Upload or load an Excel workbook: .xlsx or .xlsm.")
+    return path
+
+
+def _score_summary(log_df: pd.DataFrame | None) -> str:
+    if log_df is None or log_df.empty:
+        return "No scored rows yet."
+    scored = log_df[log_df["Weighted Score"].astype(str) != ""].copy()
+    if scored.empty:
+        return "No scored rows yet."
+    scored["Weighted Score"] = pd.to_numeric(scored["Weighted Score"], errors="coerce")
+    average = round(float(scored["Weighted Score"].mean()), 1)
+    revisions = int((scored["Revision Flag"] == "Yes").sum())
+    return f"{len(scored)} scored rows. Average weighted score {average}/10. Revision flags {revisions}."
+
+
+def _metric_value(log_df: pd.DataFrame, metric: str, fallback: float) -> int:
+    if log_df is not None and not log_df.empty and metric in log_df:
+        values = pd.to_numeric(log_df[metric], errors="coerce").dropna()
+        if not values.empty:
+            return int(round(float(values.mean()) * 10))
+    return int(round(fallback * 10))
+
+
+def _small_spark(value: int, tone: str) -> str:
+    heights = [19, 23, 16, 18, 17, 20, 22, 30, 25, 29, 34, 27]
+    color = "#3f8f2f" if tone == "green" else "#dda10c" if tone == "gold" else "#d84f45"
+    bars = "".join(f"<i style='height:{h}px;background:{color}'></i>" for h in heights)
+    return f"<div class='spark' aria-label='score trend {value}'>{bars}</div>"
+
+
+def _kpi_card(title: str, value: int, icon: str, tone: str, delta: str) -> str:
+    return f"""
+    <article class="kpi-card">
+      <div class="kpi-top">
+        <span class="kpi-icon {tone}">{icon}</span>
+        <div><b>{escape(title)}</b><strong>{value}<em>/100</em></strong></div>
+      </div>
+      {_small_spark(value, tone)}
+      <p class="{tone}">{escape(delta)}</p>
+    </article>
+    """
+
+
+def _priority_badge(gate: str) -> tuple[str, str]:
+    if gate in {"HARD FAIL", "SOFT FAIL", "READ-ALOUD BLOCK"}:
+        return "High", "high"
+    if gate in {"COMMERCIAL CHECK", "REVISE"}:
+        return "Medium", "medium"
+    return "Low", "low"
+
+
+def _revision_rows(log_df: pd.DataFrame, tracker_df: pd.DataFrame) -> str:
+    rows = []
+    if log_df is not None and not log_df.empty:
+        working = log_df.copy()
+        working["Weighted Score"] = pd.to_numeric(working["Weighted Score"], errors="coerce")
+        working = working.sort_values(["Revision Flag", "Weighted Score"], ascending=[False, True])
+        for _, row in working.head(5).iterrows():
+            metric = str(row.get("Priority Fix") or "Read-aloud Flow")
+            gate = str(row.get("Gate") or "REVISE")
+            priority, cls = _priority_badge(gate)
+            block = str(row.get("Stanza ID") or row.get("Sequence") or "Live block")
+            action = REVISION_ACTIONS.get(metric, "Revise the weakest pressure point first.")
+            rows.append(
+                f"""
+                <div class="queue-row">
+                  <span>{escape(block)}</span>
+                  <span>{escape(metric)}</span>
+                  <span>{escape(gate.title())}</span>
+                  <b class="{cls}">{priority}</b>
+                  <span>{escape(action)}</span>
+                </div>
+                """
+            )
+
+    if len(rows) < 5 and tracker_df is not None and not tracker_df.empty:
+        for _, row in tracker_df.head(5 - len(rows)).iterrows():
+            block = str(row.get("Block", "Block"))
+            metric = str(row.get("TOTEM priority", "Read-aloud Flow"))
+            action = str(row.get("Next action", REVISION_ACTIONS.get(metric, "Continue next pass.")))
+            rows.append(
+                f"""
+                <div class="queue-row">
+                  <span>{escape(block)}</span>
+                  <span>{escape(metric)}</span>
+                  <span>Development Gate</span>
+                  <b class="medium">Medium</b>
+                  <span>{escape(action)}</span>
+                </div>
+                """
+            )
+
+    return "\n".join(rows) or "<p class='empty'>No revision rows found.</p>"
+
+
+def _risk_cards(log_df: pd.DataFrame) -> str:
+    if log_df is None or log_df.empty:
+        risks = [("Workbook Intake", "No scored rows detected yet.", "Medium Risk", "medium", "▁▂▃▂▁▂")]
+    else:
+        counts: dict[str, int] = {}
+        for metric in METRICS:
+            values = pd.to_numeric(log_df[metric], errors="coerce").dropna()
+            weak = int((values < 7).sum())
+            if weak:
+                counts[metric] = weak
+        if not counts:
+            counts = {"Commercial Publishability": 1}
+        ordered = sorted(counts.items(), key=lambda item: item[1], reverse=True)[:3]
+        risks = []
+        for metric, count in ordered:
+            tone = "high" if count >= 2 else "medium"
+            label = "High Risk" if tone == "high" else "Medium Risk"
+            risks.append((metric, f"Detected under target in {count} scored block(s).", label, tone, "▂▅▃▇▁▆▂▅"))
+
+    cards = []
+    icons = {
+        "Read-aloud Flow": "≋", "Rhythm": "≋", "Visual Strength": "◉",
+        "Emotional Truth": "♡", "Commercial Publishability": "↗",
+    }
+    for metric, detail, label, tone, bars in risks:
+        cards.append(
+            f"""
+            <div class="risk-row">
+              <span class="risk-icon {tone}">{icons.get(metric, "!")}</span>
+              <div><b>{escape(metric)}</b><small>{escape(detail)}</small></div>
+              <em class="{tone}">{escape(label)}</em>
+              <code>{escape(bars)}</code>
+            </div>
+            """
+        )
+    return "\n".join(cards)
+
+
+def _recent_workbooks(path: Path, overall: int) -> str:
+    name = path.stem.replace("_", " ")
+    return f"""
+    <div class="book-card">
+      <div class="book-cover">TOTEM</div>
+      <div>
+        <b>{escape(name[:38])}</b>
+        <small>Current workbook · Loaded now</small>
+        <div class="progress"><i style="width:{max(8, min(overall, 100))}%"></i></div>
+      </div>
+      <strong>{overall}%</strong>
+    </div>
+    """
+
+
+def dashboard_html(path: Path, notice: str = "") -> str:
+    path = _validate_workbook_path(path)
+    overview = workbook_overview(path)
+    log_df = score_log(path)
+    viability_df, viability_summary = viability_table(path)
+    tracker_df = manuscript_tracker_table(path)
+    workstack_df = workstack_table(path)
+
+    avg_viability = float(viability_df["Score"].mean()) if viability_df is not None and not viability_df.empty else 0
+    overall = int(round(avg_viability * 10)) if avg_viability else 0
+    read_flow = _metric_value(log_df, "Read-aloud Flow", 6.4)
+    emotional = _metric_value(log_df, "Emotional Truth", 7.8)
+    visual = _metric_value(log_df, "Visual Strength", 6.9)
+    commercial = _metric_value(log_df, "Commercial Publishability", avg_viability or 7.1)
+    weakest = "Read-aloud Flow"
+    strongest = "Emotional Truth"
+    if log_df is not None and not log_df.empty:
+        metric_means = {
+            metric: pd.to_numeric(log_df[metric], errors="coerce").dropna().mean()
+            for metric in METRICS
+        }
+        metric_means = {metric: value for metric, value in metric_means.items() if pd.notna(value)}
+        if metric_means:
+            weakest = min(metric_means, key=metric_means.get)
+            strongest = max(metric_means, key=metric_means.get)
+
+    next_item = ""
+    if workstack_df is not None and not workstack_df.empty and "Status" in workstack_df:
+        active = workstack_df[workstack_df["Status"].isin(["Active", "Queued"])]
+        if not active.empty:
+            next_item = str(active.iloc[0].get("Next item", "Run next pass"))
+    next_item = next_item or "Run the next TOTEM pass"
+
+    notice_block = f"<div class='notice'>{escape(notice)}</div>" if notice else ""
+
+    return f"""
+    <style>
+      .studio-shell {{
+        min-height: 900px;
+        display: grid;
+        grid-template-columns: 250px 1fr;
+        background: var(--studio-cream);
+      }}
+      .studio-sidebar {{
+        background: linear-gradient(180deg, #145b23 0%, #063f18 100%);
+        color: white;
+        padding: 22px 13px;
+      }}
+      .brand {{
+        padding: 4px 10px 22px 10px;
+      }}
+      .brand h1 {{
+        margin: 0;
+        color: #f8e838;
+        font-size: 28px;
+        line-height: .82;
+        text-shadow: 0 2px 0 #2b8c23;
+        letter-spacing: 0;
+      }}
+      .brand small {{
+        display: block;
+        margin-top: 13px;
+        letter-spacing: 4px;
+        font-size: 11px;
+      }}
+      .nav-item {{
+        display: flex;
+        gap: 12px;
+        align-items: center;
+        padding: 14px 13px;
+        margin: 6px 0;
+        border-radius: 8px;
+        color: #eef6e8;
+        font-weight: 700;
+      }}
+      .nav-item.active {{
+        background: linear-gradient(90deg, #f5c93c, #e5a721);
+        color: white;
+      }}
+      .sidebar-foot {{
+        margin-top: 170px;
+        border-top: 1px solid rgba(255,255,255,.12);
+        padding: 24px 10px;
+        color: #d9ead0;
+        font-size: 13px;
+      }}
+      .studio-main {{
+        background: linear-gradient(180deg, #fffdf7 0%, #fff7de 42%, #fffaf0 100%);
+      }}
+      .topbar {{
+        height: 72px;
+        display: flex;
+        align-items: center;
+        justify-content: space-between;
+        gap: 24px;
+        padding: 0 28px;
+        border-bottom: 1px solid var(--studio-line);
+      }}
+      .search {{
+        flex: 1;
+        max-width: 640px;
+        border: 1px solid var(--studio-line);
+        border-radius: 999px;
+        padding: 13px 18px;
+        background: rgba(255,255,255,.78);
+        color: #8d8d78;
+      }}
+      .profile {{
+        display: flex;
+        align-items: center;
+        gap: 14px;
+        font-weight: 800;
+      }}
+      .avatar {{
+        width: 46px;
+        height: 46px;
+        border-radius: 50%;
+        background: linear-gradient(135deg, #c8915e, #f3d3ad);
+        display: grid;
+        place-items: center;
+        color: #5b2b13;
+      }}
+      .hero {{
+        position: relative;
+        padding: 34px 36px 28px;
+        min-height: 205px;
+        overflow: hidden;
+      }}
+      .hero::before {{
+        content: "";
+        position: absolute;
+        inset: 44px 0 0 0;
+        background: radial-gradient(circle at 72% 32%, rgba(246,207,80,.28), transparent 18%),
+                    radial-gradient(circle at 86% 65%, rgba(89,145,49,.12), transparent 20%),
+                    linear-gradient(160deg, transparent 0 35%, rgba(238,192,66,.18) 36% 52%, transparent 53%);
+      }}
+      .hero h2 {{
+        position: relative;
+        margin: 0;
+        font-family: Georgia, serif;
+        font-size: 44px;
+        color: #0e4a1d;
+        letter-spacing: 0;
+      }}
+      .hero p {{
+        position: relative;
+        margin: 8px 0 22px;
+        font-size: 20px;
+        color: #355b35;
+      }}
+      .mascot {{
+        position: absolute;
+        right: 88px;
+        top: 10px;
+        width: 156px;
+        height: 156px;
+        border-radius: 50%;
+        background: radial-gradient(circle at 48% 43%, #fff7db 0 18%, #e7b96c 19% 48%, #3f8f2f 49% 61%, transparent 62%),
+                    radial-gradient(circle at 32% 11%, #f6d08f 0 13%, transparent 14%),
+                    radial-gradient(circle at 70% 8%, #f6d08f 0 13%, transparent 14%);
+        box-shadow: 0 15px 35px rgba(120,88,24,.18);
+      }}
+      .content {{
+        padding: 0 28px 34px;
+      }}
+      .kpi-grid {{
+        display: grid;
+        grid-template-columns: repeat(5, minmax(150px, 1fr));
+        gap: 14px;
+      }}
+      .kpi-card, .panel {{
+        background: rgba(255,255,255,.88);
+        border: 1px solid var(--studio-line);
+        border-radius: 8px;
+        box-shadow: 0 10px 28px rgba(58,44,14,.06);
+      }}
+      .kpi-card {{
+        padding: 18px;
+      }}
+      .kpi-top {{
+        display: flex;
+        align-items: center;
+        gap: 14px;
+      }}
+      .kpi-icon {{
+        width: 48px;
+        height: 48px;
+        border-radius: 50%;
+        display: grid;
+        place-items: center;
+        color: white;
+        font-weight: 900;
+        font-size: 24px;
+      }}
+      .green {{ color: #348426; }}
+      .gold {{ color: #d69200; }}
+      .red {{ color: #d84f45; }}
+      .kpi-icon.green {{ background: #4e982f; color: white; }}
+      .kpi-icon.gold {{ background: #e6a500; color: white; }}
+      .kpi-icon.red {{ background: #d84f45; color: white; }}
+      .kpi-card b {{
+        display: block;
+        font-size: 13px;
+        color: var(--studio-ink);
+      }}
+      .kpi-card strong {{
+        display: block;
+        color: var(--studio-ink);
+        font-size: 34px;
+        line-height: 1;
+      }}
+      .kpi-card em {{
+        font-size: 13px;
+        font-style: normal;
+        color: var(--studio-muted);
+      }}
+      .spark {{
+        height: 42px;
+        display: flex;
+        align-items: end;
+        gap: 8px;
+        margin: 12px 0 5px;
+      }}
+      .spark i {{
+        width: 4px;
+        border-radius: 4px;
+      }}
+      .kpi-card p {{
+        margin: 0;
+        font-size: 12px;
+        background: transparent !important;
+      }}
+      .dashboard-grid {{
+        display: grid;
+        grid-template-columns: 1.55fr 1fr;
+        gap: 14px;
+        margin-top: 16px;
+      }}
+      .panel h3 {{
+        margin: 0;
+        padding: 16px 18px;
+        border-bottom: 1px solid var(--studio-line);
+        color: var(--studio-ink);
+      }}
+      .queue-row {{
+        display: grid;
+        grid-template-columns: 1.15fr .9fr .85fr .55fr 1.35fr;
+        gap: 10px;
+        align-items: center;
+        padding: 12px 18px;
+        border-bottom: 1px solid #f0e7cf;
+        font-size: 13px;
+      }}
+      .queue-row b, .risk-row em {{
+        text-align: center;
+        border-radius: 999px;
+        padding: 5px 9px;
+        font-style: normal;
+      }}
+      .high {{ background: #ffe3da; color: var(--studio-red); }}
+      .medium {{ background: #fff0c5; color: #c28600; }}
+      .low {{ background: #e9f5dc; color: #4b8e2e; }}
+      .risk-row {{
+        display: grid;
+        grid-template-columns: 54px 1fr 96px 86px;
+        gap: 12px;
+        align-items: center;
+        padding: 14px 18px;
+        border-bottom: 1px solid #f0e7cf;
+      }}
+      .risk-icon {{
+        width: 46px;
+        height: 46px;
+        display: grid;
+        place-items: center;
+        border-radius: 50%;
+        color: white;
+        font-weight: 900;
+        background: #e6a500;
+      }}
+      .risk-icon.high {{ background: #d84f45; color: white; }}
+      .risk-icon.medium {{ background: #e6a500; color: white; }}
+      .risk-row small {{
+        display: block;
+        color: var(--studio-muted);
+        margin-top: 4px;
+      }}
+      .risk-row code {{
+        color: #d84f45;
+        font-size: 24px;
+        letter-spacing: 1px;
+      }}
+      .bottom-grid {{
+        display: grid;
+        grid-template-columns: 1fr 1fr;
+        gap: 14px;
+        margin-top: 16px;
+      }}
+      .book-card {{
+        display: grid;
+        grid-template-columns: 58px 1fr 48px;
+        align-items: center;
+        gap: 14px;
+        padding: 14px 18px;
+      }}
+      .book-cover {{
+        width: 54px;
+        height: 76px;
+        display: grid;
+        place-items: center;
+        border-radius: 6px;
+        background: linear-gradient(160deg, #072d18, #135b25);
+        color: #f2d444;
+        font-weight: 900;
+        font-size: 11px;
+      }}
+      .book-card small {{
+        display: block;
+        color: var(--studio-muted);
+        margin: 5px 0 12px;
+      }}
+      .progress {{
+        height: 7px;
+        border-radius: 999px;
+        background: #eadfbd;
+      }}
+      .progress i {{
+        display: block;
+        height: 7px;
+        border-radius: 999px;
+        background: #4e982f;
+      }}
+      .snapshot {{
+        display: grid;
+        grid-template-columns: repeat(3, 1fr);
+        gap: 8px;
+        padding: 18px;
+      }}
+      .snapshot div {{
+        border-right: 1px solid var(--studio-line);
+        min-height: 82px;
+      }}
+      .snapshot div:last-child {{
+        border-right: none;
+      }}
+      .snapshot small {{
+        color: var(--studio-muted);
+        display: block;
+      }}
+      .snapshot b {{
+        display: block;
+        margin-top: 9px;
+      }}
+      .snapshot strong {{
+        font-size: 28px;
+      }}
+      .notice {{
+        margin: 0 28px 16px;
+        padding: 12px 14px;
+        background: #e9f5dc;
+        border: 1px solid #c5ddb5;
+        border-radius: 8px;
+        font-weight: 700;
+      }}
+      @media (max-width: 1100px) {{
+        .studio-shell {{ grid-template-columns: 1fr; }}
+        .studio-sidebar {{ display: none; }}
+        .kpi-grid, .dashboard-grid, .bottom-grid {{ grid-template-columns: 1fr; }}
+        .mascot {{ display: none; }}
+        .queue-row {{ grid-template-columns: 1fr; }}
+      }}
+    </style>
+    <div class="studio-shell">
+      <aside class="studio-sidebar">
+        <div class="brand">
+          <h1>TOTEM<br>Studio</h1>
+          <small>PUBLISHING INTERFACE</small>
+        </div>
+        <div class="nav-item active">⌂ Home</div>
+        <div class="nav-item">▣ Projects</div>
+        <div class="nav-item">▤ Workbook Upload</div>
+        <div class="nav-item">▥ TOTEM Analytics</div>
+        <div class="nav-item">☷ Revision Queue</div>
+        <div class="nav-item">⬡ Risk Clusters</div>
+        <div class="nav-item">◈ Codex Extractor</div>
+        <div class="nav-item">⇩ Export</div>
+        <div class="sidebar-foot">Knowledge. Structure.<br>Story. Performance.</div>
+      </aside>
+      <main class="studio-main">
+        <div class="topbar">
+          <div class="search">Search projects, workbooks, blocks...</div>
+          <div class="profile"><span>Editorial Workspace</span><span>🔔</span><span class="avatar">T</span><span>TOTEM<br><small>Studio</small></span></div>
+        </div>
+        <section class="hero">
+          <h2>TOTEM Studio.</h2>
+          <p>Data-driven insight for stronger stories.</p>
+          <div class="mascot" aria-label="TOTEM Studio mascot"></div>
+        </section>
+        {notice_block}
+        <section class="content">
+          <div class="kpi-grid">
+            {_kpi_card("Overall Publishability", overall, "✦", "green", "Source: viability lens")}
+            {_kpi_card("Read-Aloud Flow", read_flow, "≋", "red" if read_flow < 65 else "gold", "Weakest live pressure")}
+            {_kpi_card("Emotional Truth", emotional, "♡", "green", "Strongest story signal")}
+            {_kpi_card("Visual Strength", visual, "◉", "gold" if visual < 75 else "green", "Drawable page value")}
+            {_kpi_card("Commercial Viability", commercial, "↗", "green", "Publisher-facing lens")}
+          </div>
+          <div class="dashboard-grid">
+            <section class="panel">
+              <h3>Revision Priority Queue <small>{len(log_df) if log_df is not None else 0} live item(s)</small></h3>
+              <div class="queue-row header"><b>Block</b><b>Weakest Dimension</b><b>Gate</b><b>Priority</b><b>Recommended Action</b></div>
+              {_revision_rows(log_df, tracker_df)}
+            </section>
+            <section class="panel">
+              <h3>Risk Clusters</h3>
+              {_risk_cards(log_df)}
+            </section>
+          </div>
+          <div class="bottom-grid">
+            <section class="panel">
+              <h3>Recent Workbook</h3>
+              {_recent_workbooks(path, overall)}
+            </section>
+            <section class="panel">
+              <h3>TOTEM Snapshot <small>Based on latest run</small></h3>
+              <div class="snapshot">
+                <div><small>Weakest Dimension</small><b>{escape(weakest)}</b><strong>{read_flow}</strong><small>/100</small></div>
+                <div><small>Strongest Dimension</small><b>{escape(strongest)}</b><strong>{max(emotional, visual)}</strong><small>/100</small></div>
+                <div><small>Next Work</small><b>{escape(next_item[:42])}</b><small>{escape(viability_summary)}</small></div>
+              </div>
+            </section>
+          </div>
+          <p style="color:#6d725f;font-size:12px;margin:18px 0 0;">Loaded {escape(path.name)} · {overview['sheet_count']} sheets read privately · matrix hidden from the product surface.</p>
+        </section>
+      </main>
+    </div>
+    """
+
+
+# ── CODEX EXTRACTOR FUNCTIONS ─────────────────────────────────────────────────
+
+def run_codex_extraction(
+    file_obj,
+    author_name: str,
+    author_id: str,
+    works_sampled: str,
+) -> tuple[str, str]:
+    """
+    Gradio handler for the Codex Extraction tab.
+    Returns (report_text, json_output) tuple.
+    """
+    if file_obj is None:
+        return "No file uploaded. Please upload a .txt or .pdf file.", ""
+
+    if not author_name.strip():
+        return "Please enter the author's full name before extracting.", ""
+
+    try:
+        report, fp_dict = process_upload(
+            file_path=file_obj,
+            author_name=author_name.strip(),
+            author_id=author_id.strip() or "CA-XXX",
+            works_sampled=works_sampled.strip(),
+        )
+
+        if not fp_dict:
+            return report, ""
+
+        # Format JSON output for workbook entry
+        json_out = json.dumps(fp_dict, indent=2)
+        return report, json_out
+
+    except Exception as e:
+        return f"Extraction error: {type(e).__name__}: {str(e)}", ""
+
+
+def clear_codex_form() -> tuple[None, str, str, str, str, str]:
+    """Reset the Codex extraction form."""
+    return None, "", "CA-XXX", "", "", ""
+
+
+# ── WORKBOOK FUNCTIONS ────────────────────────────────────────────────────────
+
+def load_workbook(uploaded_file=None, notice: str = ""):
+    path = _validate_workbook_path(_clean_path(uploaded_file))
+    log_df = score_log(path)
+    return str(path), dashboard_html(path, notice), log_df, _score_summary(log_df)
+
+
+def load_default():
+    return load_workbook(None, "Bundled workbook reloaded.")
+
+
+def load_uploaded(uploaded_file):
+    if uploaded_file is None:
+        raise gr.Error("Choose an .xlsx or .xlsm workbook first.")
+    return load_workbook(uploaded_file, "Workbook uploaded and analysed.")
+
+
+def load_local_path(path_text: str):
+    path = _validate_workbook_path(Path(path_text or "").expanduser())
+    log_df = score_log(path)
+    return str(path), dashboard_html(path, "Local workbook loaded."), log_df, _score_summary(log_df)
+
+
+def run_analysis(active_path: str):
+    path = _validate_workbook_path(Path(active_path) if active_path else DEFAULT_WORKBOOK)
+    log_df = score_log(path)
+    return dashboard_html(path, "TOTEM analysis refreshed."), log_df, _score_summary(log_df)
+
+
+def recalc_log(log_df, active_path: str):
+    path = _validate_workbook_path(Path(active_path) if active_path else DEFAULT_WORKBOOK)
+    recalculated = recalculate_log(log_df, path)
+    return dashboard_html(path, "Gates recalculated."), recalculated, _score_summary(recalculated)
+
+
+def export_log(log_df, active_path: str):
+    path = _validate_workbook_path(Path(active_path) if active_path else DEFAULT_WORKBOOK)
+    return export_updated_workbook(log_df, path)
+
+
+def single_score(active_path, sequence, stanza_id, draft_pass, clarity, rhythm, flow,
+                 emotional_truth, visual_strength, commercial, notes):
+    path = _validate_workbook_path(Path(active_path) if active_path else DEFAULT_WORKBOOK)
+    df = score_single_row(path, sequence, stanza_id, draft_pass, clarity, rhythm,
+                          flow, emotional_truth, visual_strength, commercial, notes)
+    return df, _score_summary(df)
+
+
+# ── GRADIO INTERFACE ──────────────────────────────────────────────────────────
+
+with gr.Blocks(title="TOTEM Studio", css=CSS) as demo:
+    active_path = gr.State(str(DEFAULT_WORKBOOK))
+    log_state = gr.State(pd.DataFrame(columns=LOG_COLUMNS))
+
+    with gr.Tabs():
+
+        # ── TAB 1: DASHBOARD ─────────────────────────────────────────────────
+        with gr.TabItem("Dashboard"):
+            dashboard = gr.HTML()
+
+            with gr.Row(elem_id="studio-actions"):
+                with gr.Column(elem_classes=["wrap"]):
+                    workbook_upload = gr.UploadButton(
+                        "Upload Workbook",
+                        file_types=[".xlsx", ".xlsm"],
+                        type="filepath",
+                        variant="primary",
+                        scale=1,
+                    )
+                    run_button = gr.Button("Run TOTEM Analysis", variant="secondary", scale=1)
+
+            with gr.Row(elem_id="path-panel"):
+                with gr.Column(elem_classes=["wrap"]):
+                    path_input = gr.Textbox(label="Local workbook path", value=ORIGINAL_WORKBOOK_PATH)
+                    path_button = gr.Button("Load Local Path", variant="primary")
+
+            with gr.Accordion("Private scoring controls", open=False, elem_id="score-panel"):
+                gr.HTML("<div class='score-card'><h3>Live Score A Block</h3></div>")
+                with gr.Row():
+                    sequence = gr.Textbox(label="Sequence", value="Live pass")
+                    stanza_id = gr.Textbox(label="Stanza ID", value="New block")
+                    draft_pass = gr.Textbox(label="Draft / Pass", value="First score")
+                with gr.Row():
+                    clarity = gr.Slider(1, 10, value=7, step=0.5, label="Clarity")
+                    rhythm = gr.Slider(1, 10, value=7, step=0.5, label="Rhythm")
+                    flow = gr.Slider(1, 10, value=7, step=0.5, label="Read-aloud Flow")
+                with gr.Row():
+                    emotional_truth = gr.Slider(1, 10, value=7, step=0.5, label="Emotional Truth")
+                    visual_strength = gr.Slider(1, 10, value=7, step=0.5, label="Visual Strength")
+                    commercial = gr.Slider(1, 10, value=7, step=0.5, label="Commercial Publishability")
+                notes = gr.Textbox(label="Notes", lines=2)
+                with gr.Row():
+                    single_button = gr.Button("Score Block", variant="primary")
+                    recalc_button = gr.Button("Recalculate Gates")
+                    export_button = gr.Button("Download Updated Workbook")
+                single_df = gr.Dataframe(label="Latest scorecard", interactive=False, visible=False)
+                score_status = gr.Markdown(elem_id="hidden-status")
+                exported_file = gr.File(label="Export appears here", elem_id="hidden-export")
+
+        # ── TAB 2: CODEX EXTRACTOR ───────────────────────────────────────────
+        with gr.TabItem("◈ Codex Extractor"):
+            gr.HTML("""
+            <div style="
+                background: linear-gradient(135deg, #0e4a1d, #17642a);
+                border-radius: 8px;
+                padding: 20px 24px;
+                margin-bottom: 16px;
+            ">
+                <h3 style="margin:0; color:#f8e838; font-family:Georgia,serif; font-size:20px;">
+                    Codex Fingerprint Extractor
+                </h3>
+                <p style="margin:8px 0 0; color:#d9ead0; font-size:13px;">
+                    Upload an author's text or PDF. The extractor computes 17 Tier 1 voice metrics
+                    (VM-001 to VM-013, VM-024 to VM-028) mathematically from the text.
+                    Copy the output into CODEX_03_FINGERPRINTS in the workbook.
+                    Use Codex Build Prompt 2 (ChatGPT/Gemini) for the 10 Tier 2 qualitative metrics.
+                </p>
+            </div>
+            """)
+
+            with gr.Row():
+                with gr.Column(scale=1):
+                    gr.Markdown("### Author Details")
+                    codex_author_name = gr.Textbox(
+                        label="Author Full Name",
+                        placeholder="e.g. Julia Donaldson",
+                    )
+                    codex_author_id = gr.Textbox(
+                        label="Codex Author ID",
+                        placeholder="e.g. CA-001",
+                        value="CA-XXX",
+                    )
+                    codex_works = gr.Textbox(
+                        label="Works Sampled (comma-separated)",
+                        placeholder="e.g. The Gruffalo, Room on the Broom, Zog",
+                        lines=2,
+                    )
+                    codex_file = gr.File(
+                        label="Upload Text or PDF",
+                        file_types=[".txt", ".pdf"],
+                        type="filepath",
+                    )
+                    with gr.Row():
+                        codex_extract_btn = gr.Button(
+                            "Extract Fingerprint",
+                            variant="primary",
+                            scale=2,
+                        )
+                        codex_clear_btn = gr.Button(
+                            "Clear",
+                            variant="secondary",
+                            scale=1,
+                        )
+
+                    gr.HTML("""
+                    <div style="
+                        background: #fff8e1;
+                        border: 1px solid #ffe082;
+                        border-radius: 6px;
+                        padding: 12px 14px;
+                        margin-top: 12px;
+                        font-size: 13px;
+                        color: #5d4037;
+                    ">
+                        <b>File requirements:</b><br>
+                        • PDF must contain selectable text (not scanned images)<br>
+                        • Minimum 1,000 words for HIGH confidence fingerprint<br>
+                        • Combine multiple works in one file to increase sample size<br>
+                        • Visual-primary books (Van Allsburg, Jeffers) will flag LOW confidence
+                    </div>
+                    """)
+
+                with gr.Column(scale=2):
+                    gr.Markdown("### Extraction Report — Tier 1 Metrics")
+                    codex_report = gr.Textbox(
+                        label="",
+                        lines=32,
+                        interactive=False,
+                        placeholder="Upload a file and click Extract Fingerprint to see results here...",
+                        elem_id="codex-report",
+                    )
+
+            gr.Markdown("### Raw Output — Copy into CODEX_03_FINGERPRINTS")
+            codex_json = gr.Textbox(
+                label="",
+                lines=20,
+                interactive=False,
+                placeholder="JSON values appear here after extraction. Copy individual metric values into the workbook row.",
+                elem_id="codex-json",
+            )
+
+            gr.HTML("""
+            <div style="
+                background: #e8f5e9;
+                border: 1px solid #a5d6a7;
+                border-radius: 6px;
+                padding: 14px 18px;
+                margin-top: 8px;
+                font-size: 13px;
+            ">
+                <b style="color:#1b5e20;">Tier 2 reminder:</b>
+                <span style="color:#2e7d32;">
+                VM-014 (Narrative person) through VM-023 (Animal/nature imagery ratio) require
+                qualitative judgment. Use Codex Build Prompt 2 from the Codex Build Prompts document
+                with the same text in ChatGPT or Gemini to complete the remaining 10 metrics.
+                </span>
+            </div>
+            """)
+
+    # ── EVENT WIRING ─────────────────────────────────────────────────────────
+
+    # Dashboard tab
+    demo.load(load_workbook, outputs=[active_path, dashboard, log_state, score_status])
+    workbook_upload.upload(load_uploaded, inputs=[workbook_upload],
+                           outputs=[active_path, dashboard, log_state, score_status])
+    run_button.click(run_analysis, inputs=[active_path],
+                     outputs=[dashboard, log_state, score_status])
+    path_button.click(load_local_path, inputs=[path_input],
+                      outputs=[active_path, dashboard, log_state, score_status])
+    recalc_button.click(recalc_log, inputs=[log_state, active_path],
+                        outputs=[dashboard, log_state, score_status])
+    export_button.click(export_log, inputs=[log_state, active_path], outputs=[exported_file])
+    single_button.click(
+        single_score,
+        inputs=[active_path, sequence, stanza_id, draft_pass, clarity, rhythm,
+                flow, emotional_truth, visual_strength, commercial, notes],
+        outputs=[single_df, score_status],
+    )
+
+    # Codex Extractor tab
+    codex_extract_btn.click(
+        run_codex_extraction,
+        inputs=[codex_file, codex_author_name, codex_author_id, codex_works],
+        outputs=[codex_report, codex_json],
+    )
+    codex_clear_btn.click(
+        clear_codex_form,
+        outputs=[codex_file, codex_author_name, codex_author_id, codex_works,
+                 codex_report, codex_json],
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()