feat: Add evaluation report pipeline for prompt tuning feedback

Adds a full per-chapter evaluation logging system that captures every
score, critique, and quality decision made during writing, then renders
a self-contained HTML report shareable with critics or prompt engineers.

New file — story/eval_logger.py:
- append_eval_entry(folder, entry): writes per-chapter eval data to
  eval_log.json in the book folder (called from write_chapter() at
  every return point).
- generate_html_report(folder, bp): reads eval_log.json and produces a
  self-contained HTML file (no external deps) with:
    • Summary cards (avg score, auto-accepted, rewrites, below-threshold)
    • Score timeline bar chart (one bar per chapter, colour-coded)
    • Score distribution histogram
    • Chapter breakdown table with expand-on-click critique details
      (attempt number, score, decision badge, full critique text)
    • Critique pattern frequency table (keyword mining across all critiques)
    • Auto-generated prompt tuning observations (systemic issues, POV
      character weak spots, pacing type analysis, climax vs. early
      chapter comparison)

story/writer.py:
- Imports time and eval_logger.
- Initialises _eval_entry dict (chapter metadata + polish flags + thresholds)
  after all threshold variables are set.
- Records each evaluation attempt's score, critique (truncated to 700 chars),
  and decision (auto_accepted / full_rewrite / refinement / accepted /
  below_threshold / eval_error / refinement_failed) before every return.

web/routes/run.py:
- Imports story_eval_logger.
- New route GET /project/<run_id>/eval_report/<book_folder>: loads
  eval_log.json, calls generate_html_report(), returns the HTML as a
  downloadable attachment named eval_report_<title>.html.
  Returns a user-friendly "not yet available" page if no log exists.

templates/run_details.html:
- Adds "Eval Report" (btn-outline-info) button next to "Check Consistency"
  in each book's artifact section.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

README.md

@@ -104,6 +104,15 @@ Open `http://localhost:5000`.
 - **Admin Panel:** Manage all users, view spend, and perform factory resets at `/admin`.
 - **Per-User API Keys:** Each user can supply their own Gemini API key; costs are tracked per account.
 
+### Cost-Effective by Design
+
+This engine was built with the goal of producing high-quality fiction at the lowest possible cost. This is achieved through several architectural optimizations:
+
+*   **Tiered AI Models**: The system uses cheaper, faster models (like Gemini Pro) for structural and analytical tasks—planning the plot, scoring chapter quality, and ensuring consistency. The more powerful and expensive creative models are reserved for the actual writing process.
+*   **Intelligent Context Management**: To minimize the number of tokens sent to the AI, the system is very selective about the data it includes in each request. For example, when writing a chapter, it only injects data for the characters who are currently in the scene, rather than the entire cast.
+*   **Adaptive Workflows**: The engine avoids unnecessary work. If a user provides a detailed outline for a chapter, the system skips the AI step that would normally expand on a basic idea, saving both time and money. It also adjusts its quality standards based on the chapter's importance, spending more effort on a climactic scene than on a simple transition.
+*   **Caching**: The system caches the results of deterministic AI tasks. If it needs to perform the same analysis twice, it reuses the original result instead of making a new API call.
+
 ### CLI Wizard (`cli/`)
 - **Interactive Setup:** Menu-driven interface (via Rich) for creating projects, managing personas, and defining characters and plot beats.
 - **Smart Resume:** Detects in-progress runs via lock files and prompts to resume.
@@ -118,8 +127,13 @@ Open `http://localhost:5000`.
 - **Persona Cache:** The author persona (including writing sample files) is loaded once at the start of the writing phase and reused for every chapter, eliminating redundant file I/O. The cache is refreshed whenever the persona is refined.
 - **Outline Validation Gate (`planner.py`):** Before the writing phase begins, a Logic-model pass checks the chapter plan for missing required beats, character continuity issues, pacing imbalances, and POV logic errors. Issues are logged as warnings so the writer can review them before generation begins.
 - **Adaptive Scoring Thresholds (`writer.py`):** Quality passing thresholds scale with chapter position — setup chapters use a lower bar (6.5) to avoid over-spending refinement tokens on early exposition, while climax chapters use a stricter bar (7.5) to ensure the most important scenes receive maximum effort.
+- **Adaptive Refinement Attempts (`writer.py`):** Climax and resolution chapters (position ≥ 75% through the book) receive up to 3 refinement attempts; earlier chapters keep 2. This concentrates quality effort on the scenes readers remember most.
+- **Stricter Polish Pass (`writer.py`):** The filter-word threshold for skipping the two-pass polish has been tightened from 1-per-83-words to 1-per-125-words, so more borderline drafts are cleaned before evaluation.
 - **Smart Beat Expansion Skip (`writer.py`):** If a chapter's scene beats are already detailed (>100 words total), the Director's Treatment expansion step is skipped, saving ~5K tokens per chapter.
-- **Consistency Checker (`editor.py`):** Scores chapters on 13 rubrics (engagement, voice, sensory detail, scene execution, dialogue, pacing, staging, prose dynamics, clarity, etc.) and flags AI-isms ("tapestry", "palpable tension") and weak filter verbs ("felt", "realized"). Chapter evaluation now uses the Logic model (free Pro) rather than the Writer model, ensuring stricter and more accurate scoring.
+- **Consistency Checker (`editor.py`):** Scores chapters on 13 rubrics (engagement, voice, sensory detail, scene execution, dialogue, pacing, staging, prose dynamics, clarity, etc.) and flags AI-isms ("tapestry", "palpable tension") and weak filter verbs ("felt", "realized"). Chapter evaluation now uses head+tail sampling (`keep_head=True`) ensuring the evaluator sees the chapter opening (hooks, sensory anchoring) as well as the ending — long chapters no longer receive scores based only on their tail.
+- **Rewrite Model Upgrade (`editor.py`):** Manual chapter rewrites and user-triggered edits now use `model_writer` (the creative writing model) instead of `model_logic`, producing significantly better prose quality on rewritten content.
+- **Improved Consistency Sampling (`editor.py`):** The mid-generation consistency analysis now samples head + middle + tail of each chapter (instead of head + tail only), giving the continuity LLM a complete picture of each chapter's events for more accurate contradiction detection.
+- **Larger Persona Validation Sample (`style_persona.py`):** The persona validation test passage has been increased from 200 words to 400 words, giving the scorer enough material to reliably assess sentence rhythm, filter-word habits, and deep POV quality before accepting a persona.
 - **Dynamic Character Injection (`writer.py`):** Only injects characters explicitly named in the chapter's `scene_beats` plus the POV character into the writer prompt. Eliminates token waste from unused characters and reduces hallucinated appearances.
 - **Smart Context Tail (`writer.py`):** Extracts the final ~1,000 tokens of the previous chapter (the actual ending) rather than blindly truncating from the front. Ensures the hand-off point — where characters are standing and what was last said — is always preserved.
 - **Stateful Scene Tracking (`bible_tracker.py`):** After each chapter, the tracker records each character's `current_location`, `time_of_day`, and `held_items` in addition to appearance and events. This scene state is injected into subsequent chapter prompts so the writer knows exactly where characters are, what time it is, and what they're carrying.

cli/engine.py

@@ -219,7 +219,8 @@ def process_book(bp, folder, context="", resume=False, interactive=False):
 
             # Refine Persona to match the actual output (every 5 chapters)
             if (i == 0 or i % 5 == 0) and txt:
-                bp['book_metadata']['author_details'] = style_persona.refine_persona(bp, txt, folder)
+                pov_char = ch.get('pov_character')
+                bp['book_metadata']['author_details'] = style_persona.refine_persona(bp, txt, folder, pov_character=pov_char)
                 with open(bp_path, "w") as f: json.dump(bp, f, indent=2)
                 cached_persona = build_persona_info(bp)  # Rebuild cache with updated bio
 
@@ -275,6 +276,10 @@ def process_book(bp, folder, context="", resume=False, interactive=False):
                 tracking['lore'] = bible_tracker.update_lore_index(folder, txt, tracking.get('lore', {}))
                 with open(lore_track_path, "w") as f: json.dump(tracking['lore'], f, indent=2)
 
+            # Persist dynamic tracking changes back to the bible (Step 1: Bible-Tracking Merge)
+            bp = bible_tracker.merge_tracking_to_bible(bp, tracking)
+            with open(bp_path, "w") as f: json.dump(bp, f, indent=2)
+
             # Update Structured Story State (Item 9: Thread Tracking)
             current_story_state = story_state.update_story_state(txt, ch['chapter_number'], current_story_state, folder)
 

core/config.py

@@ -66,4 +66,4 @@ LENGTH_DEFINITIONS = {
 }
 
 # --- SYSTEM ---
-VERSION = "3.0"
+VERSION = "3.1"

story/bible_tracker.py

@@ -141,6 +141,30 @@ def update_lore_index(folder, chapter_text, current_lore):
         return current_lore
 
 
+def merge_tracking_to_bible(bible, tracking):
+    """Merge dynamic tracking state back into the bible dict.
+
+    Makes bible.json the single persistent source of truth by updating
+    character data and lore from the in-memory tracking object.
+    Returns the modified bible dict.
+    """
+    for name, data in tracking.get('characters', {}).items():
+        matched = False
+        for char in bible.get('characters', []):
+            if char.get('name') == name:
+                char.update(data)
+                matched = True
+                break
+        if not matched:
+            utils.log("TRACKER", f"  -> Character '{name}' in tracking not found in bible. Skipping.")
+
+    if 'lore' not in bible:
+        bible['lore'] = {}
+    bible['lore'].update(tracking.get('lore', {}))
+
+    return bible
+
+
 def harvest_metadata(bp, folder, full_manuscript):
     utils.log("HARVESTER", "Scanning for new characters...")
     full_text = "\n".join([c.get('content', '') for c in full_manuscript])[:500000]

story/editor.py

@@ -67,7 +67,7 @@ def evaluate_chapter_quality(text, chapter_title, genre, model, folder, series_c
     }}
     """
     try:
-        response = model.generate_content([prompt, utils.truncate_to_tokens(text, 7500)])
+        response = model.generate_content([prompt, utils.truncate_to_tokens(text, 7500, keep_head=True)])
         model_name = getattr(model, 'name', ai_models.logic_model_name)
         utils.log_usage(folder, model_name, response.usage_metadata)
         data = json.loads(utils.clean_json(response.text))
@@ -129,7 +129,13 @@ def analyze_consistency(bp, manuscript, folder):
     chapter_summaries = []
     for ch in manuscript:
         text = ch.get('content', '')
-        excerpt = text[:1000] + "\n...\n" + text[-1000:] if len(text) > 2000 else text
+        if len(text) > 3000:
+            mid = len(text) // 2
+            excerpt = text[:800] + "\n...\n" + text[mid - 200:mid + 200] + "\n...\n" + text[-800:]
+        elif len(text) > 1600:
+            excerpt = text[:800] + "\n...\n" + text[-800:]
+        else:
+            excerpt = text
         chapter_summaries.append(f"Ch {ch.get('num')}: {excerpt}")
 
     context = "\n".join(chapter_summaries)
@@ -236,8 +242,8 @@ def rewrite_chapter_content(bp, manuscript, chapter_num, instruction, folder):
     """
 
     try:
-        response = ai_models.model_logic.generate_content(prompt)
+        response = ai_models.model_writer.generate_content(prompt)
-        utils.log_usage(folder, ai_models.model_logic.name, response.usage_metadata)
+        utils.log_usage(folder, ai_models.model_writer.name, response.usage_metadata)
         try:
             data = json.loads(utils.clean_json(response.text))
             return data.get('content'), data.get('summary')

story/eval_logger.py

@@ -0,0 +1,473 @@
+"""eval_logger.py — Per-chapter evaluation log and HTML report generator.
+
+Writes a structured eval_log.json to the book folder during writing, then
+generates a self-contained HTML report that can be downloaded and shared with
+critics / prompt engineers to analyse quality patterns across a run.
+"""
+
+import json
+import os
+import time
+from core import utils
+
+
+# ---------------------------------------------------------------------------
+# Log writer
+# ---------------------------------------------------------------------------
+
+def append_eval_entry(folder, entry):
+    """Append one chapter's evaluation record to eval_log.json.
+
+    Called from story/writer.py at every return point in write_chapter().
+    Each entry captures the chapter metadata, polish decision, per-attempt
+    scores/critiques/decisions, and the final accepted score.
+    """
+    log_path = os.path.join(folder, "eval_log.json")
+    data = []
+    if os.path.exists(log_path):
+        try:
+            with open(log_path, 'r', encoding='utf-8') as f:
+                data = json.load(f)
+            if not isinstance(data, list):
+                data = []
+        except Exception:
+            data = []
+    data.append(entry)
+    try:
+        with open(log_path, 'w', encoding='utf-8') as f:
+            json.dump(data, f, indent=2)
+    except Exception as e:
+        utils.log("EVAL", f"Failed to write eval log: {e}")
+
+
+# ---------------------------------------------------------------------------
+# Report generation
+# ---------------------------------------------------------------------------
+
+def generate_html_report(folder, bp=None):
+    """Generate a self-contained HTML evaluation report from eval_log.json.
+
+    Returns the HTML string, or None if no log file exists / is empty.
+    """
+    log_path = os.path.join(folder, "eval_log.json")
+    if not os.path.exists(log_path):
+        return None
+    try:
+        with open(log_path, 'r', encoding='utf-8') as f:
+            chapters = json.load(f)
+    except Exception:
+        return None
+
+    if not isinstance(chapters, list) or not chapters:
+        return None
+
+    title, genre = "Unknown Book", "Fiction"
+    if bp:
+        meta = bp.get('book_metadata', {})
+        title = meta.get('title', title)
+        genre = meta.get('genre', genre)
+
+    # --- Summary stats ---
+    scores = [c.get('final_score', 0) for c in chapters if isinstance(c.get('final_score'), (int, float)) and c.get('final_score', 0) > 0]
+    avg_score = round(sum(scores) / len(scores), 2) if scores else 0
+    total = len(chapters)
+    auto_accepted   = sum(1 for c in chapters if c.get('final_decision') == 'auto_accepted')
+    multi_attempt   = sum(1 for c in chapters if len(c.get('attempts', [])) > 1)
+    full_rewrites   = sum(1 for c in chapters for a in c.get('attempts', []) if a.get('decision') == 'full_rewrite')
+    below_threshold = sum(1 for c in chapters if c.get('final_decision') == 'below_threshold')
+    polish_applied  = sum(1 for c in chapters if c.get('polish_applied'))
+
+    score_dist = {i: 0 for i in range(1, 11)}
+    for c in chapters:
+        s = c.get('final_score', 0)
+        if isinstance(s, int) and 1 <= s <= 10:
+            score_dist[s] += 1
+
+    patterns = _mine_critique_patterns(chapters, total)
+    report_date = time.strftime('%Y-%m-%d %H:%M')
+    return _build_html(title, genre, report_date, chapters, avg_score, total,
+                       auto_accepted, multi_attempt, full_rewrites, below_threshold,
+                       polish_applied, score_dist, patterns)
+
+
+# ---------------------------------------------------------------------------
+# Pattern mining
+# ---------------------------------------------------------------------------
+
+def _mine_critique_patterns(chapters, total):
+    pattern_keywords = {
+        "Filter words (felt/saw/noticed)":    ["filter word", "filter", "felt ", "noticed ", "realized ", "saw the", "heard the"],
+        "Summary mode / telling":             ["summary mode", "summariz", "telling", "show don't tell", "show, don't tell", "instead of dramatiz"],
+        "Emotion labeling":                   ["emotion label", "told the reader", "labeling", "labelling", "she felt", "he felt", "was nervous", "was angry", "was sad"],
+        "Deep POV issues":                    ["deep pov", "deep point of view", "distant narration", "remove the reader", "external narration"],
+        "Pacing problems":                    ["pacing", "rushing", "too fast", "too slow", "dragging", "sagging", "abrupt"],
+        "Dialogue too on-the-nose":           ["on-the-nose", "on the nose", "subtext", "exposition dump", "characters explain"],
+        "Weak chapter hook / ending":         ["hook", "cliffhanger", "cut off abruptly", "anticlimax", "ending falls flat", "no tension"],
+        "Passive voice / weak verbs":         ["passive voice", "was [v", "were [v", "weak verb", "adverb"],
+        "AI-isms / clichés":                  ["ai-ism", "cliché", "tapestry", "palpable", "testament", "azure", "cerulean", "bustling"],
+        "Voice / tone inconsistency":         ["voice", "tone inconsist", "persona", "shift in tone", "register"],
+        "Missing sensory / atmosphere":       ["sensory", "grounding", "atmosphere", "immersiv", "white room"],
+    }
+    counts = {}
+    for pattern, keywords in pattern_keywords.items():
+        matching = []
+        for c in chapters:
+            critique_blob = " ".join(
+                a.get('critique', '').lower()
+                for a in c.get('attempts', [])
+            )
+            if any(kw.lower() in critique_blob for kw in keywords):
+                matching.append(c.get('chapter_num', '?'))
+        counts[pattern] = {'count': len(matching), 'chapters': matching}
+    return dict(sorted(counts.items(), key=lambda x: x[1]['count'], reverse=True))
+
+
+# ---------------------------------------------------------------------------
+# HTML builder
+# ---------------------------------------------------------------------------
+
+def _score_color(s):
+    try:
+        s = float(s)
+    except (TypeError, ValueError):
+        return '#6c757d'
+    if s >= 8:  return '#28a745'
+    if s >= 7:  return '#20c997'
+    if s >= 6:  return '#ffc107'
+    return '#dc3545'
+
+
+def _decision_badge(d):
+    MAP = {
+        'auto_accepted':          ('⚡ Auto-Accept',    '#28a745'),
+        'accepted':               ('✓ Accepted',        '#17a2b8'),
+        'accepted_at_max':        ('✓ Accepted',        '#17a2b8'),
+        'below_threshold':        ('⚠ Below Threshold', '#dc3545'),
+        'below_threshold_accepted': ('⚠ Below Threshold', '#dc3545'),
+        'full_rewrite':           ('🔄 Full Rewrite',   '#6f42c1'),
+        'full_rewrite_failed':    ('🔄✗ Rewrite Failed','#6f42c1'),
+        'refinement':             ('✏ Refined',         '#fd7e14'),
+        'refinement_failed':      ('✏✗ Refine Failed',  '#fd7e14'),
+        'eval_error':             ('⚠ Eval Error',      '#6c757d'),
+    }
+    label, color = MAP.get(d, (d or '?', '#6c757d'))
+    return f'<span style="background:{color};color:white;padding:2px 8px;border-radius:4px;font-size:0.78em">{label}</span>'
+
+
+def _safe_int_fmt(v):
+    try:
+        return f"{int(v):,}"
+    except (TypeError, ValueError):
+        return str(v) if v else '?'
+
+
+def _build_html(title, genre, report_date, chapters, avg_score, total,
+                auto_accepted, multi_attempt, full_rewrites, below_threshold,
+                polish_applied, score_dist, patterns):
+
+    avg_color = _score_color(avg_score)
+
+    # --- Score timeline ---
+    MAX_BAR = 260
+    timeline_rows = ''
+    for c in chapters:
+        s = c.get('final_score', 0)
+        color = _score_color(s)
+        width = max(2, int((s / 10) * MAX_BAR)) if s else 2
+        ch_num = c.get('chapter_num', '?')
+        ch_title = str(c.get('title', ''))[:35]
+        timeline_rows += (
+            f'<div style="display:flex;align-items:center;margin-bottom:4px;font-size:0.8em">'
+            f'<div style="width:45px;text-align:right;margin-right:8px;color:#888;flex-shrink:0">Ch {ch_num}</div>'
+            f'<div style="background:{color};height:16px;width:{width}px;border-radius:2px;flex-shrink:0"></div>'
+            f'<div style="margin-left:8px;color:#555">{s}/10 &mdash; {ch_title}</div>'
+            f'</div>'
+        )
+
+    # --- Score distribution ---
+    max_dist = max(score_dist.values()) if any(score_dist.values()) else 1
+    dist_rows = ''
+    for sv in range(10, 0, -1):
+        count = score_dist.get(sv, 0)
+        w = max(2, int((count / max_dist) * 200)) if count else 0
+        color = _score_color(sv)
+        dist_rows += (
+            f'<div style="display:flex;align-items:center;margin-bottom:4px;font-size:0.85em">'
+            f'<div style="width:28px;text-align:right;margin-right:8px;font-weight:bold;color:{color}">{sv}</div>'
+            f'<div style="background:{color};height:15px;width:{w}px;border-radius:2px;opacity:0.85"></div>'
+            f'<div style="margin-left:8px;color:#666">{count} ch{"apters" if count != 1 else "apter"}</div>'
+            f'</div>'
+        )
+
+    # --- Chapter rows ---
+    chapter_rows = ''
+    for c in chapters:
+        cid = c.get('chapter_num', 0)
+        ch_title  = str(c.get('title', '')).replace('<', '&lt;').replace('>', '&gt;')
+        pov       = str(c.get('pov_character') or '—')
+        pace      = str(c.get('pacing') or '—')
+        target_w  = _safe_int_fmt(c.get('target_words'))
+        actual_w  = _safe_int_fmt(c.get('actual_words'))
+        pos       = c.get('chapter_position')
+        pos_pct   = f"{int(pos * 100)}%" if pos is not None else '—'
+        threshold = c.get('score_threshold', '?')
+        fw_dens   = c.get('filter_word_density', 0)
+        polish    = '✓' if c.get('polish_applied') else '✗'
+        polish_c  = '#28a745' if c.get('polish_applied') else '#aaa'
+        fs        = c.get('final_score', 0)
+        fd        = c.get('final_decision', '')
+        attempts  = c.get('attempts', [])
+        n_att     = len(attempts)
+        fs_color  = _score_color(fs)
+        fd_badge  = _decision_badge(fd)
+
+        # Attempt detail sub-rows
+        att_rows = ''
+        for att in attempts:
+            an    = att.get('n', '?')
+            ascr  = att.get('score', '?')
+            adec  = att.get('decision', '')
+            acrit = str(att.get('critique', 'No critique.')).replace('&', '&amp;').replace('<', '&lt;').replace('>', '&gt;')
+            ac    = _score_color(ascr)
+            abadge = _decision_badge(adec)
+            att_rows += (
+                f'<tr style="background:#f6f8fa">'
+                f'<td colspan="11" style="padding:12px 16px 12px 56px;border-bottom:1px solid #e8eaed">'
+                f'<div style="margin-bottom:6px"><strong>Attempt {an}:</strong>'
+                f'<span style="font-size:1.1em;font-weight:bold;color:{ac};margin:0 8px">{ascr}/10</span>'
+                f'{abadge}</div>'
+                f'<div style="font-size:0.83em;color:#444;line-height:1.55;white-space:pre-wrap;'
+                f'background:#fff;padding:10px 12px;border-left:3px solid {ac};border-radius:2px;'
+                f'max-height:300px;overflow-y:auto">{acrit}</div>'
+                f'</td></tr>'
+            )
+
+        chapter_rows += (
+            f'<tr class="chrow" onclick="toggle({cid})" style="cursor:pointer">'
+            f'<td style="font-weight:700;text-align:center">{cid}</td>'
+            f'<td>{ch_title}</td>'
+            f'<td style="color:#666;font-size:0.85em">{pov}</td>'
+            f'<td style="color:#666;font-size:0.85em">{pace}</td>'
+            f'<td style="text-align:right">{actual_w} <span style="color:#aaa">/{target_w}</span></td>'
+            f'<td style="text-align:center;color:#888">{pos_pct}</td>'
+            f'<td style="text-align:center">{threshold}</td>'
+            f'<td style="text-align:center;color:{polish_c}">{polish} <span style="color:#aaa;font-size:0.8em">{fw_dens:.3f}</span></td>'
+            f'<td style="text-align:center;font-weight:700;font-size:1.1em;color:{fs_color}">{fs}</td>'
+            f'<td style="text-align:center;color:#888">{n_att}&times;</td>'
+            f'<td>{fd_badge}</td>'
+            f'</tr>'
+            f'<tr id="d{cid}" class="detrow">{att_rows}</tr>'
+        )
+
+    # --- Critique patterns ---
+    pat_rows = ''
+    for pattern, data in patterns.items():
+        count = data['count']
+        if count == 0:
+            continue
+        pct = int(count / total * 100) if total else 0
+        sev_color = '#dc3545' if pct >= 50 else '#fd7e14' if pct >= 30 else '#17a2b8'
+        chlist = ', '.join(f'Ch {x}' for x in data['chapters'][:10])
+        if len(data['chapters']) > 10:
+            chlist += f' (+{len(data["chapters"]) - 10} more)'
+        pat_rows += (
+            f'<tr>'
+            f'<td><strong>{pattern}</strong></td>'
+            f'<td style="text-align:center;color:{sev_color};font-weight:700">{count}/{total} ({pct}%)</td>'
+            f'<td style="color:#666;font-size:0.83em">{chlist}</td>'
+            f'</tr>'
+        )
+    if not pat_rows:
+        pat_rows = '<tr><td colspan="3" style="color:#666;text-align:center;padding:12px">No significant patterns detected.</td></tr>'
+
+    # --- Prompt tuning notes ---
+    notes = _generate_prompt_notes(chapters, avg_score, total, full_rewrites, below_threshold, patterns)
+    notes_html = ''.join(f'<li style="margin-bottom:8px;line-height:1.55">{n}</li>' for n in notes)
+
+    return f'''<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>Eval Report &mdash; {title}</title>
+<style>
+*{{box-sizing:border-box;margin:0;padding:0}}
+body{{font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,sans-serif;background:#f0f2f5;color:#333;padding:20px}}
+.wrap{{max-width:1280px;margin:0 auto}}
+header{{background:#1a1d23;color:#fff;padding:22px 28px;border-radius:10px;margin-bottom:22px}}
+header h1{{font-size:0.9em;color:#8b92a1;margin-bottom:4px;font-weight:500}}
+header h2{{font-size:1.9em;font-weight:700;margin-bottom:6px}}
+header p{{color:#8b92a1;font-size:0.88em}}
+.cards{{display:grid;grid-template-columns:repeat(auto-fit,minmax(130px,1fr));gap:12px;margin-bottom:20px}}
+.card{{background:#fff;border-radius:8px;padding:16px;text-align:center;box-shadow:0 1px 3px rgba(0,0,0,.08)}}
+.card .val{{font-size:2em;font-weight:700}}
+.card .lbl{{font-size:0.75em;color:#888;margin-top:4px;line-height:1.3}}
+.two-col{{display:grid;grid-template-columns:1fr 1fr;gap:16px;margin-bottom:16px}}
+section{{background:#fff;border-radius:8px;padding:20px;margin-bottom:16px;box-shadow:0 1px 3px rgba(0,0,0,.08)}}
+section h3{{font-size:1em;font-weight:700;border-bottom:2px solid #f0f0f0;padding-bottom:8px;margin-bottom:14px}}
+table{{width:100%;border-collapse:collapse;font-size:0.86em}}
+th{{background:#f7f8fa;padding:8px 10px;text-align:left;font-weight:600;color:#555;border-bottom:2px solid #e0e4ea;white-space:nowrap}}
+td{{padding:8px 10px;border-bottom:1px solid #f0f0f0;vertical-align:middle}}
+.chrow:hover{{background:#f7f8fa}}
+.detrow{{display:none}}
+.legend{{display:flex;gap:14px;flex-wrap:wrap;font-size:0.78em;color:#777;margin-bottom:10px}}
+.dot{{display:inline-block;width:11px;height:11px;border-radius:50%;vertical-align:middle;margin-right:3px}}
+ul.notes{{padding-left:20px}}
+@media(max-width:768px){{.two-col{{grid-template-columns:1fr}}}}
+</style>
+</head>
+<body>
+<div class="wrap">
+
+<header>
+  <h1>BookApp &mdash; Evaluation Report</h1>
+  <h2>{title}</h2>
+  <p>Genre: {genre}&nbsp;&nbsp;|&nbsp;&nbsp;Generated: {report_date}&nbsp;&nbsp;|&nbsp;&nbsp;{total} chapter{"s" if total != 1 else ""}</p>
+</header>
+
+<div class="cards">
+  <div class="card"><div class="val" style="color:{avg_color}">{avg_score}</div><div class="lbl">Avg Score /10</div></div>
+  <div class="card"><div class="val" style="color:#28a745">{auto_accepted}</div><div class="lbl">Auto-Accepted (8+)</div></div>
+  <div class="card"><div class="val" style="color:#17a2b8">{multi_attempt}</div><div class="lbl">Multi-Attempt</div></div>
+  <div class="card"><div class="val" style="color:#6f42c1">{full_rewrites}</div><div class="lbl">Full Rewrites</div></div>
+  <div class="card"><div class="val" style="color:#dc3545">{below_threshold}</div><div class="lbl">Below Threshold</div></div>
+  <div class="card"><div class="val" style="color:#fd7e14">{polish_applied}</div><div class="lbl">Polish Passes</div></div>
+</div>
+
+<div class="two-col">
+<section>
+  <h3>&#128202; Score Timeline</h3>
+  <div class="legend">
+    <span><span class="dot" style="background:#28a745"></span>8&ndash;10 Great</span>
+    <span><span class="dot" style="background:#20c997"></span>7&ndash;7.9 Good</span>
+    <span><span class="dot" style="background:#ffc107"></span>6&ndash;6.9 Passable</span>
+    <span><span class="dot" style="background:#dc3545"></span>&lt;6 Fail</span>
+  </div>
+  <div style="overflow-y:auto;max-height:420px;padding-right:4px">{timeline_rows}</div>
+</section>
+<section>
+  <h3>&#128200; Score Distribution</h3>
+  <div style="margin-top:8px">{dist_rows}</div>
+</section>
+</div>
+
+<section>
+  <h3>&#128203; Chapter Breakdown &nbsp;<small style="font-weight:400;color:#888">(click any row to expand critiques)</small></h3>
+  <div style="overflow-x:auto">
+  <table>
+    <thead><tr>
+      <th>#</th><th>Title</th><th>POV</th><th>Pacing</th>
+      <th style="text-align:right">Words</th>
+      <th style="text-align:center">Pos%</th>
+      <th style="text-align:center">Threshold</th>
+      <th style="text-align:center">Polish&nbsp;/&nbsp;FW</th>
+      <th style="text-align:center">Score</th>
+      <th style="text-align:center">Att.</th>
+      <th>Decision</th>
+    </tr></thead>
+    <tbody>{chapter_rows}</tbody>
+  </table>
+  </div>
+</section>
+
+<section>
+  <h3>&#128269; Critique Patterns &nbsp;<small style="font-weight:400;color:#888">Keyword frequency across all evaluation critiques &mdash; high % = prompt gap</small></h3>
+  <table>
+    <thead><tr><th>Issue Pattern</th><th style="text-align:center">Frequency</th><th>Affected Chapters</th></tr></thead>
+    <tbody>{pat_rows}</tbody>
+  </table>
+</section>
+
+<section>
+  <h3>&#128161; Prompt Tuning Observations</h3>
+  <ul class="notes">{notes_html}</ul>
+</section>
+
+</div>
+<script>
+function toggle(id){{
+  var r=document.getElementById('d'+id);
+  if(r) r.style.display=(r.style.display==='none'||r.style.display==='')?'table-row':'none';
+}}
+document.querySelectorAll('.detrow').forEach(function(r){{r.style.display='none';}});
+</script>
+</body>
+</html>'''
+
+
+# ---------------------------------------------------------------------------
+# Auto-observations for prompt tuning
+# ---------------------------------------------------------------------------
+
+def _generate_prompt_notes(chapters, avg_score, total, full_rewrites, below_threshold, patterns):
+    notes = []
+
+    # Overall score
+    if avg_score >= 8:
+        notes.append(f"&#9989; <strong>High average score ({avg_score}/10).</strong> The generation pipeline is performing well. Focus on the few outlier chapters below the threshold.")
+    elif avg_score >= 7:
+        notes.append(f"&#10003; <strong>Solid average score ({avg_score}/10).</strong> Minor prompt reinforcement should push this above 8. Focus on the most common critique pattern.")
+    elif avg_score >= 6:
+        notes.append(f"&#9888; <strong>Average score of {avg_score}/10 is below target.</strong> Strengthen the draft prompt's Deep POV mandate and filter-word removal rules.")
+    else:
+        notes.append(f"&#128680; <strong>Low average score ({avg_score}/10).</strong> The core writing prompt needs significant work &mdash; review the Deep POV mandate, genre mandates, and consider adding concrete negative examples.")
+
+    # Full rewrite rate
+    if total > 0:
+        rw_pct = int(full_rewrites / total * 100)
+        if rw_pct > 30:
+            notes.append(f"&#128260; <strong>High full-rewrite rate ({rw_pct}%, {full_rewrites} triggers).</strong> The initial draft prompt produces too many sub-6 drafts. Add stronger examples or tighten the DEEP_POV_MANDATE and PROSE_RULES sections.")
+        elif rw_pct > 15:
+            notes.append(f"&#8617; <strong>Moderate full-rewrite rate ({rw_pct}%, {full_rewrites} triggers).</strong> The draft quality could be improved. Check the genre mandates for the types of chapters that rewrite most often.")
+
+        # Below threshold
+        if below_threshold > 0:
+            bt_pct = int(below_threshold / total * 100)
+            notes.append(f"&#9888; <strong>{below_threshold} chapter{'s' if below_threshold != 1 else ''} ({bt_pct}%) finished below the quality threshold.</strong> Inspect the individual critiques to see if these cluster by POV, pacing, or story position.")
+
+    # Top critique patterns
+    for pattern, data in list(patterns.items())[:5]:
+        pct = int(data['count'] / total * 100) if total else 0
+        if pct >= 50:
+            notes.append(f"&#128308; <strong>'{pattern}' appears in {pct}% of critiques.</strong> This is systemic &mdash; the current prompt does not prevent it. Add an explicit enforcement instruction with a concrete example of the wrong pattern and the correct alternative.")
+        elif pct >= 30:
+            notes.append(f"&#128993; <strong>'{pattern}' mentioned in {pct}% of critiques.</strong> Consider reinforcing the relevant prompt instruction with a stronger negative example.")
+
+    # Climax vs. early chapter comparison
+    high_scores = [c.get('final_score', 0) for c in chapters if isinstance(c.get('chapter_position'), float) and c['chapter_position'] >= 0.75]
+    low_scores  = [c.get('final_score', 0) for c in chapters if isinstance(c.get('chapter_position'), float) and c['chapter_position'] < 0.25]
+    if high_scores and low_scores:
+        avg_climax = round(sum(high_scores) / len(high_scores), 1)
+        avg_early  = round(sum(low_scores)  / len(low_scores),  1)
+        if avg_climax < avg_early - 0.5:
+            notes.append(f"&#128197; <strong>Climax chapters average {avg_climax}/10 vs early chapters {avg_early}/10.</strong> The high-stakes scenes underperform. Strengthen the genre mandates for climax pacing and consider adding specific instructions for emotional payoff.")
+        elif avg_climax > avg_early + 0.5:
+            notes.append(f"&#128197; <strong>Climax chapters outperform early chapters ({avg_climax} vs {avg_early}).</strong> Good &mdash; the adaptive threshold and extra attempts are concentrating quality where it matters.")
+
+    # POV character analysis
+    pov_scores = {}
+    for c in chapters:
+        pov = c.get('pov_character') or 'Unknown'
+        s = c.get('final_score', 0)
+        if s > 0:
+            pov_scores.setdefault(pov, []).append(s)
+    for pov, sc in sorted(pov_scores.items(), key=lambda x: sum(x[1]) / len(x[1])):
+        if len(sc) >= 2 and sum(sc) / len(sc) < 6.5:
+            avg_pov = round(sum(sc) / len(sc), 1)
+            notes.append(f"&#128100; <strong>POV '{pov}' averages {avg_pov}/10.</strong> Consider adding or strengthening a character voice profile for this character, or refining the persona bio to match how this POV character should speak and think.")
+
+    # Pacing analysis
+    pace_scores = {}
+    for c in chapters:
+        pace = c.get('pacing', 'Standard')
+        s = c.get('final_score', 0)
+        if s > 0:
+            pace_scores.setdefault(pace, []).append(s)
+    for pace, sc in pace_scores.items():
+        if len(sc) >= 3 and sum(sc) / len(sc) < 6.5:
+            avg_p = round(sum(sc) / len(sc), 1)
+            notes.append(f"&#9193; <strong>'{pace}' pacing chapters average {avg_p}/10.</strong> The writing model struggles with this rhythm. Revisit the PACING_GUIDE instructions for '{pace}' chapters &mdash; they may need more concrete direction.")
+
+    if not notes:
+        notes.append("No significant patterns detected. Review the individual chapter critiques for targeted improvements.")
+    return notes

story/style_persona.py

@@ -121,7 +121,7 @@ def validate_persona(bp, persona_details, folder):
 
     sample_prompt = f"""
     ROLE: Fiction Writer
-    TASK: Write a 200-word opening scene that perfectly demonstrates this author's voice.
+    TASK: Write a 400-word opening scene that perfectly demonstrates this author's voice.
 
     AUTHOR_PERSONA:
     Name: {name}
@@ -131,7 +131,7 @@ def validate_persona(bp, persona_details, folder):
     TONE: {tone}
 
     RULES:
-    - Exactly ~200 words of prose (no chapter header, no commentary)
+    - Exactly ~400 words of prose (no chapter header, no commentary)
     - Must reflect the persona's stated sentence structure, vocabulary, and voice
     - Show, don't tell — no filter words (felt, saw, heard, realized, noticed)
     - Deep POV: immerse the reader in a character's immediate experience
@@ -184,11 +184,42 @@ def validate_persona(bp, persona_details, folder):
         return True, 7
 
 
-def refine_persona(bp, text, folder):
+def refine_persona(bp, text, folder, pov_character=None):
     utils.log("SYSTEM", "Refining Author Persona based on recent chapters...")
     ad = bp.get('book_metadata', {}).get('author_details', {})
-    current_bio = ad.get('bio', 'Standard style.')
 
+    # If a POV character is given and has a voice_profile, refine that instead
+    if pov_character:
+        for char in bp.get('characters', []):
+            if char.get('name') == pov_character and char.get('voice_profile'):
+                vp = char['voice_profile']
+                current_bio = vp.get('bio', 'Standard style.')
+                prompt = f"""
+    ROLE: Literary Stylist
+    TASK: Refine a POV character's voice profile based on the text sample.
+
+    INPUT_DATA:
+    - TEXT_SAMPLE: {text[:3000]}
+    - CHARACTER: {pov_character}
+    - CURRENT_VOICE_BIO: {current_bio}
+
+    GOAL: Ensure future chapters for this POV character sound exactly like the sample. Highlight quirks, patterns, vocabulary specific to this character's perspective.
+
+    OUTPUT_FORMAT (JSON): {{ "bio": "Updated voice bio..." }}
+    """
+                try:
+                    response = ai_models.model_logic.generate_content(prompt)
+                    utils.log_usage(folder, ai_models.model_logic.name, response.usage_metadata)
+                    new_bio = json.loads(utils.clean_json(response.text)).get('bio')
+                    if new_bio:
+                        char['voice_profile']['bio'] = new_bio
+                        utils.log("SYSTEM", f"  -> Voice profile bio updated for '{pov_character}'.")
+                except Exception as e:
+                    utils.log("SYSTEM", f"  -> Voice profile refinement failed for '{pov_character}': {e}")
+                return ad  # Return author_details unchanged
+
+    # Default: refine the main author persona bio
+    current_bio = ad.get('bio', 'Standard style.')
     prompt = f"""
     ROLE: Literary Stylist
     TASK: Refine Author Bio based on text sample.

story/writer.py

@@ -1,9 +1,11 @@
 import json
 import os
+import time
 from core import config, utils
 from ai import models as ai_models
 from story.style_persona import get_style_guidelines
 from story.editor import evaluate_chapter_quality
+from story import eval_logger
 
 
 def get_genre_instructions(genre):
@@ -168,8 +170,19 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
 
     pov_char = chap.get('pov_character', '')
 
-    # Use pre-loaded persona if provided (avoids re-reading sample files every chapter)
+    # Check for character-specific voice profile (Step 2: Character Voice Profiles)
-    if prebuilt_persona is not None:
+    character_voice = None
+    if pov_char:
+        for char in bp.get('characters', []):
+            if char.get('name') == pov_char and char.get('voice_profile'):
+                vp = char['voice_profile']
+                character_voice = f"Style/Bio: {vp.get('bio', '')}\nKeywords: {', '.join(vp.get('keywords', []))}"
+                utils.log("WRITER", f"  -> Using voice profile for POV character: {pov_char}")
+                break
+
+    if character_voice:
+        persona_info = character_voice
+    elif prebuilt_persona is not None:
         persona_info = prebuilt_persona
     else:
         persona_info = build_persona_info(bp) or "Standard, balanced writing style."
@@ -369,7 +382,7 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
     _draft_word_list = current_text.lower().split() if current_text else []
     _fw_hit_count = sum(1 for w in _draft_word_list if w in _fw_set)
     _fw_density = _fw_hit_count / max(len(_draft_word_list), 1)
-    _skip_polish = _fw_density < 0.012  # < ~1 filter word per 83 words → draft already clean
+    _skip_polish = _fw_density < 0.008  # < ~1 filter word per 125 words → draft already clean
 
     if current_text and not _skip_polish:
         utils.log("WRITER", f"  -> Two-pass polish (Pro model, FW density {_fw_density:.3f})...")
@@ -416,7 +429,11 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
     elif current_text:
         utils.log("WRITER", f"  -> Draft clean (FW density {_fw_density:.3f}). Skipping polish pass.")
 
-    # Reduced from 3 → 2 attempts since polish pass already refines prose before evaluation
+    # Adaptive attempts: climax/resolution chapters (position >= 0.75) get 3 passes;
+    # earlier chapters keep 2 (polish pass already refines prose before evaluation).
+    if chapter_position is not None and chapter_position >= 0.75:
+        max_attempts = 3
+    else:
         max_attempts = 2
     SCORE_AUTO_ACCEPT = 8
     # Adaptive passing threshold: lenient for early setup chapters, strict for climax/resolution.
@@ -428,6 +445,25 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
         SCORE_PASSING = 7
     SCORE_REWRITE_THRESHOLD = 6
 
+    # Evaluation log entry — written to eval_log.json for the HTML report.
+    _eval_entry = {
+        "ts":                  time.strftime('%Y-%m-%d %H:%M:%S'),
+        "chapter_num":         chap['chapter_number'],
+        "title":               chap.get('title', ''),
+        "pov_character":       chap.get('pov_character', ''),
+        "pacing":              pacing,
+        "target_words":        est_words,
+        "actual_words":        draft_words,
+        "chapter_position":    chapter_position,
+        "score_threshold":     SCORE_PASSING,
+        "score_auto_accept":   SCORE_AUTO_ACCEPT,
+        "polish_applied":      bool(current_text and not _skip_polish),
+        "filter_word_density": round(_fw_density, 4),
+        "attempts":            [],
+        "final_score":         0,
+        "final_decision":      "unknown",
+    }
+
     best_score = 0
     best_text = current_text
     past_critiques = []
@@ -437,16 +473,27 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
         score, critique = evaluate_chapter_quality(current_text, chap['title'], meta.get('genre', 'Fiction'), ai_models.model_logic, folder, series_context=series_block.strip())
 
         past_critiques.append(f"Attempt {attempt}: {critique}")
+        _att = {"n": attempt, "score": score, "critique": critique[:700], "decision": None}
 
         if "Evaluation error" in critique:
             utils.log("WRITER", f"     ⚠️ {critique}. Keeping current draft.")
             if best_score == 0: best_text = current_text
+            _att["decision"] = "eval_error"
+            _eval_entry["attempts"].append(_att)
+            _eval_entry["final_score"] = best_score
+            _eval_entry["final_decision"] = "eval_error"
+            eval_logger.append_eval_entry(folder, _eval_entry)
             break
 
         utils.log("WRITER", f"     Score: {score}/10. Critique: {critique}")
 
         if score >= SCORE_AUTO_ACCEPT:
             utils.log("WRITER", "     🌟 Auto-Accept threshold met.")
+            _att["decision"] = "auto_accepted"
+            _eval_entry["attempts"].append(_att)
+            _eval_entry["final_score"] = score
+            _eval_entry["final_decision"] = "auto_accepted"
+            eval_logger.append_eval_entry(folder, _eval_entry)
             return current_text
 
         if score > best_score:
@@ -456,9 +503,19 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
         if attempt == max_attempts:
             if best_score >= SCORE_PASSING:
                 utils.log("WRITER", f"     ✅ Max attempts reached. Accepting best score ({best_score}).")
+                _att["decision"] = "accepted"
+                _eval_entry["attempts"].append(_att)
+                _eval_entry["final_score"] = best_score
+                _eval_entry["final_decision"] = "accepted"
+                eval_logger.append_eval_entry(folder, _eval_entry)
                 return best_text
             else:
                 utils.log("WRITER", f"     ⚠️ Quality low ({best_score}/{SCORE_PASSING}) but max attempts reached. Proceeding.")
+                _att["decision"] = "below_threshold"
+                _eval_entry["attempts"].append(_att)
+                _eval_entry["final_score"] = best_score
+                _eval_entry["final_decision"] = "below_threshold"
+                eval_logger.append_eval_entry(folder, _eval_entry)
                 return best_text
 
         if score < SCORE_REWRITE_THRESHOLD:
@@ -480,10 +537,17 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
                 utils.log_usage(folder, ai_models.model_logic.name, resp_rewrite.usage_metadata)
                 current_text = resp_rewrite.text
                 ai_models.model_logic.update(ai_models.logic_model_name)
+                _att["decision"] = "full_rewrite"
+                _eval_entry["attempts"].append(_att)
                 continue
             except Exception as e:
                 ai_models.model_logic.update(ai_models.logic_model_name)
                 utils.log("WRITER", f"Full rewrite failed: {e}. Falling back to refinement.")
+                _att["decision"] = "full_rewrite_failed"
+                # fall through to refinement; decision will be overwritten below
+
+        else:
+            _att["decision"] = "refinement"
 
         utils.log("WRITER", f"  -> Refining Ch {chap['chapter_number']} based on feedback...")
 
@@ -538,8 +602,21 @@ def write_chapter(chap, bp, folder, prev_sum, tracking=None, prev_content=None,
             resp_refine = ai_models.model_writer.generate_content(refine_prompt)
             utils.log_usage(folder, ai_models.model_writer.name, resp_refine.usage_metadata)
             current_text = resp_refine.text
+            if _att["decision"] == "full_rewrite_failed":
+                _att["decision"] = "refinement"  # rewrite failed, fell back to refinement
+            _eval_entry["attempts"].append(_att)
         except Exception as e:
             utils.log("WRITER", f"Refinement failed: {e}")
+            _att["decision"] = "refinement_failed"
+            _eval_entry["attempts"].append(_att)
+            _eval_entry["final_score"] = best_score
+            _eval_entry["final_decision"] = "refinement_failed"
+            eval_logger.append_eval_entry(folder, _eval_entry)
             return best_text
 
+    # Reached only if eval_error break occurred; write log before returning.
+    if _eval_entry["final_decision"] == "unknown":
+        _eval_entry["final_score"] = best_score
+        _eval_entry["final_decision"] = "best_available"
+        eval_logger.append_eval_entry(folder, _eval_entry)
     return best_text

templates/run_details.html

@@ -208,6 +208,9 @@
                         <a href="{{ url_for('run.check_consistency', run_id=run.id, book_folder=book.folder) }}" class="btn btn-outline-warning ms-2">
                             <i class="fas fa-search me-2"></i>Check Consistency
                         </a>
+                        <a href="{{ url_for('run.eval_report', run_id=run.id, book_folder=book.folder) }}" class="btn btn-outline-info ms-2" title="Download evaluation report (scores, critiques, prompt tuning notes)">
+                            <i class="fas fa-chart-bar me-2"></i>Eval Report
+                        </a>
                         <button class="btn btn-warning ms-2" data-bs-toggle="modal" data-bs-target="#reviseBookModal{{ loop.index }}" title="Regenerate this book with changes, keeping others.">
                             <i class="fas fa-pencil-alt me-2"></i>Revise
                         </button>

web/routes/run.py

@@ -10,7 +10,7 @@ from core import utils
 from ai import models as ai_models
 from ai import setup as ai_setup
 from story import editor as story_editor
-from story import bible_tracker, style_persona
+from story import bible_tracker, style_persona, eval_logger as story_eval_logger
 from export import exporter
 from web.tasks import huey, regenerate_artifacts_task, rewrite_chapter_task
 
@@ -434,6 +434,45 @@ def delete_run(id):
     return redirect(url_for('project.view_project', id=project_id))
 
 
+@run_bp.route('/project/<int:run_id>/eval_report/<string:book_folder>')
+@login_required
+def eval_report(run_id, book_folder):
+    """Generate and download the self-contained HTML evaluation report."""
+    run = db.session.get(Run, run_id) or Run.query.get_or_404(run_id)
+    if run.project.user_id != current_user.id:
+        return "Unauthorized", 403
+
+    if not book_folder or "/" in book_folder or "\\" in book_folder or ".." in book_folder:
+        return "Invalid book folder", 400
+
+    run_dir  = os.path.join(run.project.folder_path, "runs", f"run_{run.id}")
+    book_path = os.path.join(run_dir, book_folder)
+
+    bp = utils.load_json(os.path.join(book_path, "final_blueprint.json")) or \
+         utils.load_json(os.path.join(book_path, "blueprint_initial.json"))
+
+    html = story_eval_logger.generate_html_report(book_path, bp)
+    if not html:
+        return (
+            "<html><body style='font-family:sans-serif;padding:40px'>"
+            "<h2>No evaluation data yet.</h2>"
+            "<p>The evaluation report is generated during the writing phase. "
+            "Start a generation run and the report will be available once chapters have been evaluated.</p>"
+            "</body></html>"
+        ), 200
+
+    from flask import Response
+    safe_title = utils.sanitize_filename(
+        (bp or {}).get('book_metadata', {}).get('title', book_folder) or book_folder
+    )[:40]
+    filename = f"eval_report_{safe_title}.html"
+    return Response(
+        html,
+        mimetype='text/html',
+        headers={'Content-Disposition': f'attachment; filename="{filename}"'}
+    )
+
+
 @run_bp.route('/run/<int:id>/download_bible')
 @login_required
 def download_bible(id):