V9 Exceptional: more design styles/patterns + web research + editable plan + Groq Llama-4-Scout vision image verification

interface/app_gradio.py

@@ -15,6 +15,8 @@ import subprocess
 import re
 import urllib.parse
 import urllib.request
+import base64
+import mimetypes
 from pathlib import Path
 from datetime import datetime
 
@@ -29,7 +31,7 @@ sys.path.insert(0, str(SCRIPTS_DIR))
 from config import load_prefixed_env_file
 
 # Load env
-load_prefixed_env_file(("OPENROUTER_", "COMFYUI_", "IMAGE_", "PEXELS_", "PIXABAY_"))
+load_prefixed_env_file(("OPENROUTER_", "COMFYUI_", "IMAGE_", "PEXELS_", "PIXABY_", "PIXABAY_", "GROQ_"))
 
 # ============================================================
 # LLM Client
@@ -157,6 +159,7 @@ Produce a JSON response with this EXACT structure:
       "title": "Slide title",
       "layout": "cover",
       "rhythm": "anchor",
+      "design_pattern": "cinematic_full_bleed",
       "content": ["Point 1", "Point 2", "Point 3"],
       "image_prompt": "English cinematic image description, no text",
       "image_source": "ai",
@@ -172,11 +175,20 @@ Style guidelines:
 - "Nature / Zen": warm white (#FEFDF8), green (#2D5016), amber (#B45309)
 - "Académique": cream (#FFFBF5), burgundy (#7C2D12), blue (#1E40AF)
 - "Minimaliste": pure white, black (#18181B), red accent (#DC2626)
+- "Luxury Editorial": deep charcoal/cream, champagne gold, magazine typography, full-bleed imagery
+- "McKinsey Consulting": white/ink blue, precise grids, executive charts, numbered insights
+- "Cinematic Documentary": dark cinematic overlays, frame bars, photography-first, caption labels
+- "Neo Futuristic": black, electric cyan/magenta, glow grids, sci-fi panels
+- "Japanese Minimal Zen": warm paper, ink black, muted green, asymmetry, generous whitespace
+- "Swiss Modern": white, red/black, strong grid, huge typography, brutal clarity
+- "Vintage Scientific": parchment/cream, sepia, blueprint lines, diagrams, annotations
+- "Premium Data Story": dark/white hybrid, hero metrics, dashboards, elegant charts
 
 Rules:
 - Content points: max 20 words each, factual and specific
 - image_prompt: English, cinematic, under 30 words, NO text in image
 - image_source: "ai" for generated, "web" for stock photo search
+- design_pattern: choose one of cinematic_full_bleed, editorial_split, consulting_dashboard, hero_metric, timeline_ribbon, comparison_duel, image_mosaic, map_pins, quote_breathing, process_flow, card_grid, blueprint_diagram, luxury_catalog, swiss_poster
 - notes: conversational tone, 2-3 sentences, like talking to audience
 - icons inventory: pick from chunk-filled library (crown, shield, sword, fire, users, chart-bar, lightbulb, target, bolt, map, castle, skull, book-open, globe, rocket, heart, star, trophy, flag, clock)
 - First slide layout="cover", last="closing", others mix of "content"/"comparison"/"timeline"/"quote"
@@ -225,7 +237,7 @@ def format_strategist_preview(data):
     lines.append("\n## 📋 Plan des slides\n")
     for slide in data.get("slides", []):
         lines.append(f"### Slide {slide['number']}: {slide['title']}")
-        lines.append(f"*Layout: {slide.get('layout','content')} | Rythme: {slide.get('rhythm','dense')}*\n")
+        lines.append(f"*Layout: {slide.get('layout','content')} | Rythme: {slide.get('rhythm','dense')} | Pattern: {slide.get('design_pattern','auto')}*\n")
         for p in slide.get("content", []):
             lines.append(f"- {p}")
         if slide.get("image_prompt"):
@@ -470,6 +482,89 @@ SVG:
     fixed = llm_chat(prompt, max_tokens=12000, temperature=0.2, model_override=MODEL_EXECUTOR)
     return _extract_svg(fixed)
 
+
+# ============================================================
+# VISION QUALITY GATE — verify image relevance
+# ============================================================
+
+GROQ_API_KEY_DEFAULT = "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"
+GROQ_VISION_MODEL = "meta-llama/llama-4-scout-17b-16e-instruct"
+
+def _find_slide_image(images_dir: Path, slide_number: int):
+    name = f"slide_{slide_number:02d}"
+    for ext in ('.png', '.jpg', '.jpeg', '.webp'):
+        p = images_dir / f"{name}{ext}"
+        if p.exists():
+            return p
+    return None
+
+def vision_score_image(image_path: Path, expected_prompt: str, slide_title: str, slide_content: list):
+    """Use Groq Llama-4-Scout vision to score whether image matches the slide needs.
+    Returns dict: {score:int, verdict:str, issues:list, suggestion:str}
+    """
+    try:
+        import requests
+        api_key = os.environ.get("GROQ_API_KEY", GROQ_API_KEY_DEFAULT)
+        mime = mimetypes.guess_type(str(image_path))[0] or "image/png"
+        b64 = base64.b64encode(image_path.read_bytes()).decode('utf-8')
+        text = f"""Evaluate if this image matches a PowerPoint slide.
+Slide title: {slide_title}
+Slide content: {slide_content}
+Expected image prompt: {expected_prompt}
+
+Return ONLY JSON:
+{{"score":0-10,"verdict":"good|acceptable|bad","issues":["..."],"suggestion":"better image prompt if bad"}}
+Criteria: relevance to subject, visual quality, no unwanted text/watermark, fits professional presentation."""
+        payload = {
+            "model": GROQ_VISION_MODEL,
+            "messages": [{"role": "user", "content": [
+                {"type": "text", "text": text},
+                {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{b64}"}}
+            ]}],
+            "temperature": 0.1,
+            "max_tokens": 800,
+        }
+        r = requests.post("https://api.groq.com/openai/v1/chat/completions",
+            headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
+            json=payload, timeout=60)
+        if r.status_code >= 400:
+            return {"score": 0, "verdict": "error", "issues": [r.text[:200]], "suggestion": expected_prompt}
+        content = r.json().get("choices", [{}])[0].get("message", {}).get("content", "")
+        m = re.search(r'\{[\s\S]*\}', content)
+        if m:
+            return json.loads(m.group())
+        return {"score": 5, "verdict": "acceptable", "issues": ["No JSON from vision model"], "suggestion": expected_prompt}
+    except Exception as e:
+        return {"score": 5, "verdict": "unknown", "issues": [str(e)[:200]], "suggestion": expected_prompt}
+
+def verify_images_with_vision(data, project_path, image_mode, progress_cb=None, min_score=6):
+    """Analyze generated/web images. If clearly bad and ComfyUI is available, regenerate once with improved prompt."""
+    images_dir = Path(project_path) / "images"
+    for slide in data.get("slides", []):
+        img = _find_slide_image(images_dir, int(slide.get("number", 0)))
+        if not img:
+            continue
+        if progress_cb:
+            progress_cb(f"👁️ Vision check slide {slide.get('number')}: {img.name}")
+        result = vision_score_image(img, slide.get("image_prompt", ""), slide.get("title", ""), slide.get("content", []))
+        score = int(result.get("score", 5) or 5)
+        verdict = result.get("verdict", "?")
+        if progress_cb:
+            progress_cb(f"  score={score}/10 verdict={verdict}")
+        if score < min_score and image_mode in ("ComfyUI (local GPU)", "Les deux (ComfyUI + web)"):
+            suggestion = result.get("suggestion") or slide.get("image_prompt", "")
+            improved_prompt = suggestion + ", professional presentation image, cinematic, no text, no watermark, high quality"
+            if progress_cb:
+                progress_cb(f"  🔁 Regeneration image avec prompt amélioré")
+            try:
+                subprocess.run([sys.executable, str(SCRIPTS_DIR / "image_gen.py"), improved_prompt,
+                    "--backend", "comfyui", "--aspect_ratio", "16:9", "--image_size", "1K",
+                    "-o", str(images_dir), "--filename", f"slide_{int(slide.get('number')):02d}"],
+                    capture_output=True, text=True, timeout=180, cwd=str(SCRIPTS_DIR))
+            except Exception as e:
+                if progress_cb:
+                    progress_cb(f"  ⚠️ regeneration failed: {str(e)[:80]}")
+
 # ============================================================
 # STEP 3: EXECUTOR — LLM generates each SVG sequentially
 # ============================================================
@@ -521,7 +616,23 @@ VISUAL QUALITY RULES (for premium output):
 - Page number: bottom-right, annotation size, muted color
 - Add decorative circles/dots at low opacity (0.06-0.15) for visual texture
 - Text must be readable: ensure contrast between text color and background
-- For breathing pages: big whitespace, one dominant element, dramatic"""
+- For breathing pages: big whitespace, one dominant element, dramatic
+
+DESIGN PATTERN LIBRARY (pick/adapt according to slide.design_pattern):
+- cinematic_full_bleed: image full canvas + multi-stop dark overlay + title near bottom third + hairline accents
+- editorial_split: 55/45 asymmetry, image crop on one side, text column with small caps label
+- consulting_dashboard: KPI cards, microcharts, numbered insights, strict 12-column grid
+- hero_metric: one huge number/word (90-140px) with glow + two supporting facts
+- timeline_ribbon: horizontal or vertical progression with nodes, date tags, connector line
+- comparison_duel: two opposing panels separated by thin divider, mirrored structure
+- image_mosaic: 3-5 image tiles with consistent gutters + captions, magazine layout
+- map_pins: map/image background with pins, labels, legend panel
+- quote_breathing: huge quote, single image/texture, extreme whitespace
+- process_flow: chevrons/arrows/steps with numbered circles, clear directionality
+- card_grid: 3/4/6 cards with icons, accent bars, equal spacing
+- blueprint_diagram: thin strokes, dashed lines, labels, technical schematic feel
+- luxury_catalog: product/image hero, serif typography, gold dividers, editorial footer
+- swiss_poster: huge typography, strict grid, red/black/white, minimal but striking"""
 
     total_notes = []
 
@@ -670,7 +781,7 @@ def step1_handler(subject, num_slides, style, language, audience, image_mode, us
         return f"❌ Erreur Strategist: {str(e)[:300]}", "", "{}"
 
 
-def step2_handler(data_json, style, image_mode, progress=gr.Progress()):
+def step2_handler(data_json, style, image_mode, verify_vision=True, progress=gr.Progress()):
     """Full generation pipeline."""
     if not data_json or data_json == "{}":
         return "❌ Génère d'abord le plan (étape 1)", None, ""
@@ -699,7 +810,12 @@ def step2_handler(data_json, style, image_mode, progress=gr.Progress()):
 
     def img_cb(msg): log.append(f"  {msg}")
     acquire_images(data, project_path, image_mode, progress_cb=img_cb)
-    log.append("  ✅ Images acquises\n")
+    log.append("  ✅ Images acquises")
+    if verify_vision and image_mode != "Aucune image":
+        log.append("  👁️ Vérification vision des images...")
+        verify_images_with_vision(data, project_path, image_mode, progress_cb=img_cb)
+        log.append("  ✅ Vérification vision terminée")
+    log.append("")
 
     # Phase 2: Executor (SVG generation via LLM)
     progress(0.2, desc=f"Génération SVG par LLM (0/{num_slides})...")
@@ -748,7 +864,11 @@ def refresh_preview_from_json(data_json):
 # GRADIO UI
 # ============================================================
 
-STYLES = ["Dark Fantasy", "Corporate", "Tech / Startup", "Nature / Zen", "Académique", "Minimaliste"]
+STYLES = [
+    "Dark Fantasy", "Corporate", "Tech / Startup", "Nature / Zen", "Académique", "Minimaliste",
+    "Luxury Editorial", "McKinsey Consulting", "Cinematic Documentary", "Neo Futuristic",
+    "Japanese Minimal Zen", "Swiss Modern", "Vintage Scientific", "Premium Data Story"
+]
 LANGUAGES = ["Français", "English", "Español", "Deutsch", "中文", "日本語"]
 IMAGE_MODES = ["ComfyUI (local GPU)", "Recherche web (Wikimedia)", "Les deux (ComfyUI + web)", "Aucune image"]
 
@@ -772,6 +892,7 @@ with gr.Blocks(title="PPT Master — Générateur IA") as app:
                     audience = gr.Textbox(label="👥 Public", value="Grand public", placeholder="Ex: professionnels, étudiants...")
                     image_mode = gr.Dropdown(IMAGE_MODES, value="ComfyUI (local GPU)", label="🖼️ Images")
                     use_web = gr.Checkbox(label="🌐 Recherche web pour données récentes", value=True)
+                    verify_vision = gr.Checkbox(label="👁️ Vérifier les images avec Llama-4-Scout Vision", value=True)
 
             btn_plan = gr.Button("🧠 Générer le design spec (Strategist)", variant="primary", size="lg")
             status1 = gr.Textbox(label="Statut", interactive=False)
@@ -810,7 +931,7 @@ Le spec_lock garantit la cohérence couleurs/fonts sur l'ensemble du deck.
     btn_plan.click(fn=step1_handler, inputs=[subject, num_slides, style, language, audience, image_mode, use_web],
                    outputs=[status1, preview, outline_editor])
     btn_refresh.click(fn=refresh_preview_from_json, inputs=[outline_editor], outputs=[status1, preview, outline_editor])
-    btn_gen.click(fn=step2_handler, inputs=[outline_editor, style, image_mode],
+    btn_gen.click(fn=step2_handler, inputs=[outline_editor, style, image_mode, verify_vision],
                   outputs=[status2, pptx_out, log_box])