v1.2.0: Prefer Gemini 2.x models, improve cover generation and Docker health

Model selection (ai.py): - get_optimal_model() now scores Gemini 2.5 > 2.0 > 1.5 when ranking candidates - get_default_models() fallbacks updated to gemini-2.0-pro-exp (logic) and gemini-2.0-flash (writer/artist) - AI selection prompt rewritten: includes Gemini 2.x pricing context, guidance to avoid 'thinking' models for writer/artist roles, and instructions to prefer 2.x over 1.5 - Added image_model_name and image_model_source globals for UI visibility - init_models() now reads MODEL_IMAGE_HINT; tries imagen-3.0-generate-001 then imagen-3.0-fast-generate-001 on both Gemini API and Vertex AI paths Cover generation (marketing.py): - Fixed display bug: "Attempt X/5" now correctly reads "Attempt X/3" - Added imagen-3.0-fast-generate-001 as intermediate fallback before legacy Imagen 2 - Quality threshold: images with score < 5 are only kept if nothing better exists - Smarter prompt refinement on retry: deformity, blur, and watermark critique keywords each append targeted corrections to the art prompt - Fixed missing sys import (sys.platform check for macOS was silently broken) Config / Docker: - config.py: added MODEL_IMAGE_HINT env var, bumped version to 1.2.0 - docker-compose.yml: added MODEL_IMAGE environment variable - Dockerfile: added libpng-dev and libfreetype6-dev for better font/PNG rendering; added HEALTHCHECK so Portainer detects unhealthy containers System status UI: - system_status.html: added Image row showing active Imagen model and provider (Gemini API / Vertex AI) - Added cache expiry countdown with colour-coded badges Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-02-20 10:31:02 -05:00
parent 5e0def99c1
commit 2a9a605800
7 changed files with 171 additions and 70 deletions
--- a/6
+++ b/6
@@ -3,11 +3,13 @@ FROM python:3.11-slim
 # Set working directory
 WORKDIR /app

-# Install system dependencies required for Pillow (image processing)
+# Install system dependencies required for Pillow (image processing) and fonts
 RUN apt-get update && apt-get install -y \
    build-essential \
    libjpeg-dev \
    zlib1g-dev \
+    libpng-dev \
+    libfreetype6-dev \
    && rm -rf /var/lib/apt/lists/*

 # Copy requirements files
@@ -24,4 +26,6 @@ COPY . .
 # Set Python path and run
 ENV PYTHONPATH=/app
 EXPOSE 5000
+HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
+    CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:5000/login')" || exit 1
 CMD ["python", "-m", "modules.web_app"]
--- a/config.py
+++ b/config.py
@@ -14,6 +14,7 @@ GCP_LOCATION = get_clean_env("GCP_LOCATION", "us-central1")
 MODEL_LOGIC_HINT = get_clean_env("MODEL_LOGIC", "AUTO")
 MODEL_WRITER_HINT = get_clean_env("MODEL_WRITER", "AUTO")
 MODEL_ARTIST_HINT = get_clean_env("MODEL_ARTIST", "AUTO")
+MODEL_IMAGE_HINT = get_clean_env("MODEL_IMAGE", "AUTO")
 DEFAULT_BLUEPRINT = "book_def.json"

 # --- SECURITY & ADMIN ---
@@ -64,4 +65,4 @@ LENGTH_DEFINITIONS = {
 }

 # --- SYSTEM ---
-VERSION = "1.1.0"
+VERSION = "1.2.0"
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -37,3 +37,4 @@ services:
      - MODEL_LOGIC=${MODEL_LOGIC:-AUTO}
      - MODEL_WRITER=${MODEL_WRITER:-AUTO}
      - MODEL_ARTIST=${MODEL_ARTIST:-AUTO}
+      - MODEL_IMAGE=${MODEL_IMAGE:-AUTO}
--- a/modules/ai.py
+++ b/modules/ai.py
@@ -31,6 +31,8 @@ model_image = None
 logic_model_name = "models/gemini-1.5-pro"
 writer_model_name = "models/gemini-1.5-flash"
 artist_model_name = "models/gemini-1.5-flash"
+image_model_name = None
+image_model_source = "None"

 class ResilientModel:
    def __init__(self, name, safety_settings, role):
@@ -75,10 +77,15 @@ def get_optimal_model(base_type="pro"):
        candidates = [m.name for m in models if base_type in m.name]
        if not candidates: return f"models/gemini-1.5-{base_type}"
        def score(n):
-            # Prioritize stable models (higher quotas) over experimental/beta ones
-            if "exp" in n or "beta" in n or "preview" in n: return 0
-            if "latest" in n: return 50
-            return 100
+            # Prefer newer generations: 2.5 > 2.0 > 1.5
+            gen_bonus = 0
+            if "2.5" in n: gen_bonus = 300
+            elif "2.0" in n: gen_bonus = 200
+            elif "2." in n: gen_bonus = 150
+            # Within a generation, prefer stable over experimental
+            if "exp" in n or "beta" in n or "preview" in n: return gen_bonus + 0
+            if "latest" in n: return gen_bonus + 50
+            return gen_bonus + 100
        return sorted(candidates, key=score, reverse=True)[0]
    except Exception as e:
        utils.log("SYSTEM", f"⚠️ Error finding optimal model: {e}")
@@ -86,9 +93,9 @@ def get_optimal_model(base_type="pro"):

 def get_default_models():
    return {
-        "logic": {"model": "models/gemini-1.5-pro", "reason": "Fallback: Default Pro model selected.", "estimated_cost": "$3.50/1M"},
-        "writer": {"model": "models/gemini-1.5-flash", "reason": "Fallback: Default Flash model selected.", "estimated_cost": "$0.075/1M"},
-        "artist": {"model": "models/gemini-1.5-flash", "reason": "Fallback: Default Flash model selected.", "estimated_cost": "$0.075/1M"},
+        "logic": {"model": "models/gemini-2.0-pro-exp", "reason": "Fallback: Gemini 2.0 Pro for complex reasoning and JSON adherence.", "estimated_cost": "$0.00/1M (Experimental)"},
+        "writer": {"model": "models/gemini-2.0-flash", "reason": "Fallback: Gemini 2.0 Flash for fast, high-quality creative writing.", "estimated_cost": "$0.10/1M"},
+        "artist": {"model": "models/gemini-2.0-flash", "reason": "Fallback: Gemini 2.0 Flash for visual prompt design.", "estimated_cost": "$0.10/1M"},
        "ranking": []
    }

@@ -131,29 +138,37 @@ def select_best_models(force_refresh=False):
        model = genai.GenerativeModel(bootstrapper)
        prompt = f"""
        ROLE: AI Model Architect
-        TASK: Select the optimal Gemini models for specific application roles.
+        TASK: Select the optimal Gemini models for a book-writing application. Prefer newer Gemini 2.x models when available.

        AVAILABLE_MODELS:
        {json.dumps(models)}

-        PRICING_CONTEXT (USD per 1M tokens):
-        - Flash Models (e.g. gemini-1.5-flash): ~$0.075 Input / $0.30 Output. (Very Cheap)
-        - Pro Models (e.g. gemini-1.5-pro): ~$3.50 Input / $10.50 Output. (Expensive)
+        PRICING_CONTEXT (USD per 1M tokens, approximate):
+        - Gemini 2.5 Pro/Flash: Best quality/speed; check current pricing.
+        - Gemini 2.0 Flash: ~$0.10 Input / $0.40 Output. (Fast, cost-effective, excellent quality).
+        - Gemini 2.0 Pro Exp: Free experimental tier with strong reasoning.
+        - Gemini 1.5 Flash: ~$0.075 Input / $0.30 Output. (Legacy, still reliable).
+        - Gemini 1.5 Pro: ~$1.25 Input / $5.00 Output. (Legacy, expensive).

        CRITERIA:
-        - LOGIC: Needs complex reasoning, JSON adherence, and instruction following. (Prefer Pro/1.5).
-        - WRITER: Needs creativity, prose quality, and speed. (Prefer Flash/1.5 for speed, or Pro for quality).
-        - ARTIST: Needs visual prompt understanding.
+        - LOGIC: Needs complex reasoning, strict JSON adherence, plot consistency, and instruction following.
+          -> Prefer: Gemini 2.5 Pro > 2.0 Pro > 2.0 Flash > 1.5 Pro
+        - WRITER: Needs creativity, prose quality, long-form text generation, and speed.
+          -> Prefer: Gemini 2.5 Flash/Pro > 2.0 Flash > 1.5 Flash (balance quality/cost)
+        - ARTIST: Needs rich visual description, prompt understanding for cover art design.
+          -> Prefer: Gemini 2.0 Flash > 1.5 Flash (speed and visual understanding)

        CONSTRAINTS:
-        - Avoid 'experimental' or 'preview' unless no stable version exists.
-        - Prioritize 'latest' or stable versions.
+        - Strongly prefer Gemini 2.x over 1.5 where available.
+        - Avoid 'experimental' or 'preview' only if a stable 2.x version exists; otherwise experimental 2.x is fine.
+        - 'thinking' models are too slow/expensive for Writer/Artist roles.
+        - Provide a ranking of ALL available models from best to worst overall.

-        OUTPUT_FORMAT (JSON):
+        OUTPUT_FORMAT (JSON only, no markdown):
        {{
-            "logic": {{ "model": "string", "reason": "string", "estimated_cost": "$X.XX Input / $X.XX Output" }},
-            "writer": {{ "model": "string", "reason": "string", "estimated_cost": "$X.XX Input / $X.XX Output" }},
-            "artist": {{ "model": "string", "reason": "string", "estimated_cost": "$X.XX Input / $X.XX Output" }},
+            "logic": {{ "model": "string", "reason": "string", "estimated_cost": "$X.XX/1M" }},
+            "writer": {{ "model": "string", "reason": "string", "estimated_cost": "$X.XX/1M" }},
+            "artist": {{ "model": "string", "reason": "string", "estimated_cost": "$X.XX/1M" }},
            "ranking": [ {{ "model": "string", "reason": "string", "estimated_cost": "string" }} ]
        }}
        """
@@ -195,7 +210,7 @@ def select_best_models(force_refresh=False):
        return fallback

 def init_models(force=False):
-    global model_logic, model_writer, model_artist, model_image, logic_model_name, writer_model_name, artist_model_name
+    global model_logic, model_writer, model_artist, model_image, logic_model_name, writer_model_name, artist_model_name, image_model_name, image_model_source
    if model_logic and not force: return
    genai.configure(api_key=config.API_KEY)
    
@@ -264,13 +279,28 @@ def init_models(force=False):
        model_writer.update(writer_name)
        model_artist.update(artist_name)
    
-    # Initialize Image Model (Default to None)
+    # Initialize Image Model
    model_image = None
-    if hasattr(genai, 'ImageGenerationModel'):
-        try: model_image = genai.ImageGenerationModel("imagen-3.0-generate-001")
-        except: pass
+    image_model_name = None
+    image_model_source = "None"

-    img_source = "Gemini API" if model_image else "None"
+    hint = config.MODEL_IMAGE_HINT if hasattr(config, 'MODEL_IMAGE_HINT') else "AUTO"
+
+    if hasattr(genai, 'ImageGenerationModel'):
+        # Candidate image models in preference order
+        if hint and hint != "AUTO":
+            candidates = [hint]
+        else:
+            candidates = ["imagen-3.0-generate-001", "imagen-3.0-fast-generate-001"]
+        for candidate in candidates:
+            try:
+                model_image = genai.ImageGenerationModel(candidate)
+                image_model_name = candidate
+                image_model_source = "Gemini API"
+                utils.log("SYSTEM", f"✅ Image model: {candidate} (Gemini API)")
+                break
+            except Exception:
+                continue

    # Auto-detect GCP Project from credentials if not set (Fix for Image Model)
    if HAS_VERTEX and not config.GCP_PROJECT and config.GOOGLE_CREDS and os.path.exists(config.GOOGLE_CREDS):
@@ -326,9 +356,17 @@ def init_models(force=False):
        utils.log("SYSTEM", f"✅ Vertex AI initialized (Project: {config.GCP_PROJECT})")
        
        # Override with Vertex Image Model if available
-        try:
-            model_image = VertexImageModel.from_pretrained("imagen-3.0-generate-001")
-            img_source = "Vertex AI"
-        except: pass
+        vertex_candidates = ["imagen-3.0-generate-001", "imagen-3.0-fast-generate-001"]
+        if hint and hint != "AUTO":
+            vertex_candidates = [hint]
+        for candidate in vertex_candidates:
+            try:
+                model_image = VertexImageModel.from_pretrained(candidate)
+                image_model_name = candidate
+                image_model_source = "Vertex AI"
+                utils.log("SYSTEM", f"✅ Image model: {candidate} (Vertex AI)")
+                break
+            except Exception:
+                continue

-    utils.log("SYSTEM", f"Image Generation Provider: {img_source}")
+    utils.log("SYSTEM", f"Image Generation Provider: {image_model_source} ({image_model_name or 'unavailable'})")
--- a/modules/marketing.py
+++ b/modules/marketing.py
@@ -1,10 +1,10 @@
 import os
+import sys
 import json
 import shutil
 import textwrap
 import subprocess
 import requests
-import google.generativeai as genai
 from . import utils
 import config
 from modules import ai
@@ -212,9 +212,10 @@ def generate_cover(bp, folder, tracking=None, feedback=None, interactive=False):
        best_img_score = 0
        best_img_path = None
        
+        MAX_IMG_ATTEMPTS = 3
        if regenerate_image:
-            for i in range(1, 4):
-                utils.log("MARKETING", f"Generating cover art (Attempt {i}/5)...")
+            for i in range(1, MAX_IMG_ATTEMPTS + 1):
+                utils.log("MARKETING", f"Generating cover art (Attempt {i}/{MAX_IMG_ATTEMPTS})...")
                try:
                    if not ai.model_image: raise ImportError("No Image Generation Model available.")

@@ -222,12 +223,21 @@ def generate_cover(bp, folder, tracking=None, feedback=None, interactive=False):
                    try:
                        result = ai.model_image.generate_images(prompt=art_prompt, number_of_images=1, aspect_ratio=ar)
                    except Exception as e:
-                        if "resource" in str(e).lower() and ai.HAS_VERTEX:
-                            utils.log("MARKETING", "⚠️ Imagen 3 failed. Trying Imagen 2...")
-                            fb_model = ai.VertexImageModel.from_pretrained("imagegeneration@006")
-                            result = fb_model.generate_images(prompt=art_prompt, number_of_images=1, aspect_ratio=ar)
-                            status = "success_fallback"
-                        else: raise e
+                        err_lower = str(e).lower()
+                        # Try fast imagen variant before falling back to legacy
+                        if ai.HAS_VERTEX and ("resource" in err_lower or "quota" in err_lower):
+                            try:
+                                utils.log("MARKETING", "⚠️ Imagen 3 failed. Trying Imagen 3 Fast...")
+                                fb_model = ai.VertexImageModel.from_pretrained("imagen-3.0-fast-generate-001")
+                                result = fb_model.generate_images(prompt=art_prompt, number_of_images=1, aspect_ratio=ar)
+                                status = "success_fast"
+                            except Exception:
+                                utils.log("MARKETING", "⚠️ Imagen 3 Fast failed. Trying Imagen 2...")
+                                fb_model = ai.VertexImageModel.from_pretrained("imagegeneration@006")
+                                result = fb_model.generate_images(prompt=art_prompt, number_of_images=1, aspect_ratio=ar)
+                                status = "success_fallback"
+                        else:
+                            raise e

                    attempt_path = os.path.join(folder, f"cover_art_attempt_{i}.png")
                    result.images[0].save(attempt_path)
@@ -240,7 +250,6 @@ def generate_cover(bp, folder, tracking=None, feedback=None, interactive=False):
                    utils.log_image_attempt(folder, "cover", art_prompt, f"cover_art_{i}.png", status, score=score, critique=critique)

                    if interactive:
-                        # Open image for review
                        try:
                            if os.name == 'nt': os.startfile(attempt_path)
                            elif sys.platform == 'darwin': subprocess.call(('open', attempt_path))
@@ -254,16 +263,30 @@ def generate_cover(bp, folder, tracking=None, feedback=None, interactive=False):
                            utils.log("MARKETING", "User rejected cover. Retrying...")
                            continue

-                    if score > best_img_score:
+                    # Only keep as best if score meets minimum quality bar
+                    if score >= 5 and score > best_img_score:
+                        best_img_score = score
+                        best_img_path = attempt_path
+                    elif best_img_path is None and score > 0:
+                        # Accept even low-quality image if we have nothing else
                        best_img_score = score
                        best_img_path = attempt_path

-                    if score == 10:
-                        utils.log("MARKETING", "  -> Perfect image accepted.")
+                    if score >= 9:
+                        utils.log("MARKETING", "  -> High quality image accepted.")
                        break

-                    if "scar" in critique.lower() or "deform" in critique.lower() or "blur" in critique.lower():
-                        art_prompt += " (Ensure high quality, clear skin, no scars, sharp focus)."
+                    # Refine prompt based on critique keywords
+                    prompt_additions = []
+                    critique_lower = critique.lower() if critique else ""
+                    if "scar" in critique_lower or "deform" in critique_lower:
+                        prompt_additions.append("perfect anatomy, no deformities")
+                    if "blur" in critique_lower or "blurry" in critique_lower:
+                        prompt_additions.append("sharp focus, highly detailed")
+                    if "text" in critique_lower or "letter" in critique_lower:
+                        prompt_additions.append("no text, no letters, no watermarks")
+                    if prompt_additions:
+                        art_prompt += f". ({', '.join(prompt_additions)})"

                except Exception as e:
                    utils.log("MARKETING", f"Image generation failed: {e}")
--- a/modules/web_app.py
+++ b/modules/web_app.py
@@ -1303,7 +1303,8 @@ def system_status():
                models_info = cache_data.get('models', {})
        except: pass

-    return render_template('system_status.html', models=models_info, cache=cache_data, datetime=datetime)
+    return render_template('system_status.html', models=models_info, cache=cache_data, datetime=datetime,
+                           image_model=ai.image_model_name, image_source=ai.image_model_source)

@app.route('/personas')
@login_required
--- a/templates/system_status.html
+++ b/templates/system_status.html
@@ -56,6 +56,22 @@
                        </tr>
                        {% endif %}
                        {% endfor %}
+                        <tr>
+                            <td class="fw-bold text-uppercase">Image</td>
+                            <td>
+                                {% if image_model %}
+                                <span class="badge bg-success">{{ image_model }}</span>
+                                {% else %}
+                                <span class="badge bg-danger">Unavailable</span>
+                                {% endif %}
+                            </td>
+                            <td>
+                                <span class="badge bg-light text-dark border">{{ image_source or 'None' }}</span>
+                            </td>
+                            <td class="small text-muted">
+                                {% if image_model %}Imagen model used for book cover generation.{% else %}No image generation model could be initialized. Check GCP credentials or Gemini API key.{% endif %}
+                            </td>
+                        </tr>
                    {% else %}
                        <tr>
                            <td colspan="3" class="text-center py-4 text-muted">
@@ -139,15 +155,32 @@
        <h5 class="mb-0"><i class="fas fa-clock me-2"></i>Cache Status</h5>
    </div>
    <div class="card-body">
-        <p class="mb-0">
+        <p class="mb-1">
            <strong>Last Scan:</strong>
            {% if cache and cache.timestamp %}
-                {{ datetime.fromtimestamp(cache.timestamp).strftime('%Y-%m-%d %H:%M:%S') }}
+                {{ datetime.fromtimestamp(cache.timestamp).strftime('%Y-%m-%d %H:%M:%S') }} UTC
            {% else %}
                Never
            {% endif %}
        </p>
-        <p class="text-muted small mb-0">Model selection is cached for 24 hours to save API calls.</p>
+        <p class="mb-0">
+            <strong>Next Refresh:</strong>
+            {% if cache and cache.timestamp %}
+                {% set expires = cache.timestamp + 86400 %}
+                {% set now_ts = datetime.utcnow().timestamp() %}
+                {% if expires > now_ts %}
+                    {% set remaining = (expires - now_ts) | int %}
+                    {% set h = remaining // 3600 %}{% set m = (remaining % 3600) // 60 %}
+                    in {{ h }}h {{ m }}m
+                    <span class="badge bg-success ms-1">Cache Valid</span>
+                {% else %}
+                    <span class="badge bg-warning text-dark">Expired — click Refresh &amp; Optimize</span>
+                {% endif %}
+            {% else %}
+                <span class="badge bg-warning text-dark">No cache — click Refresh &amp; Optimize</span>
+            {% endif %}
+        </p>
+        <p class="text-muted small mt-2 mb-0">Model selection is cached for 24 hours to save API calls.</p>
    </div>
 </div>
 {% endblock %}