Omair Saleh
7e58ab1970
Image Generation: - Downloads actual product images from justvitamins.co.uk - Sends real photo as reference to Gemini (image-to-image) - Generates 5 ecommerce-grade variations maintaining product consistency: Hero (clean studio), Lifestyle (kitchen scene), Scale (hand reference), Detail (ingredients close-up), Banner (wide hero) - Uses Nano Banana Pro for hero/lifestyle/banner, Nano Banana for fast shots PDP Output: - Demo A now renders as a real ecommerce product detail page - Gallery: original + AI-generated images with clickable thumbnails - Above the fold: H1, value props, price block, trust bar, CTAs - Key Benefits: Feature → Benefit → Proof format, 5 icon cards - Stats bar, Why This Formula, 5★ review, FAQ accordion - Meta SEO (Google preview), Ad Hooks (5 platform-targeted), Email sequences Prompts: - Conversion-optimised based on Cialdini/Kahneman principles - EFSA health claim compliance baked into every prompt - Feature → Benefit → Proof bullet structure - Price anchoring, social proof, urgency psychology
506 lines
24 KiB
Python
506 lines
24 KiB
Python
"""AI engine — Gemini for copy, Nano Banana / Pro for image-to-image product shots.
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Image generation uses the REAL scraped product image as a reference.
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Gemini receives the actual photo and generates ecommerce-grade variations
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that maintain product consistency.
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Models:
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Text: gemini-2.5-flash
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Image: gemini-2.5-flash-image — Nano Banana (fast edits)
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Image: gemini-3-pro-image-preview — Nano Banana Pro (premium product photography)
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"""
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import os, json, hashlib, re, io, time
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from pathlib import Path
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import requests as http_requests
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from google import genai
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from google.genai import types
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GEMINI_KEY = os.environ.get("GEMINI_API_KEY", "")
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client = genai.Client(api_key=GEMINI_KEY) if GEMINI_KEY else None
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GEN_DIR = Path(__file__).parent / "generated"
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GEN_DIR.mkdir(exist_ok=True)
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TEXT_MODEL = "gemini-2.5-flash"
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IMG_FAST = "gemini-2.5-flash-image" # Nano Banana
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IMG_PRO = "gemini-3-pro-image-preview" # Nano Banana Pro
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HEADERS = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
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"(KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36"
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}
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# ── Helpers ──────────────────────────────────────────────────
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def _call_gemini(prompt: str, temperature: float = 0.7) -> dict:
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"""Call Gemini text model, return parsed JSON."""
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if not client:
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return {"error": "GEMINI_API_KEY not configured"}
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response = client.models.generate_content(
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model=TEXT_MODEL,
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contents=prompt,
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config=types.GenerateContentConfig(
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temperature=temperature,
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response_mime_type="application/json",
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),
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)
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try:
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return json.loads(response.text)
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except json.JSONDecodeError:
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match = re.search(r'\{.*\}', response.text, re.DOTALL)
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if match:
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return json.loads(match.group())
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return {"error": "Failed to parse AI response", "raw": response.text[:500]}
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def _download_image(url: str) -> tuple:
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"""Download image, return (bytes, mime_type) or (None, None)."""
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try:
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r = http_requests.get(url, headers=HEADERS, timeout=15)
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r.raise_for_status()
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ct = r.headers.get("content-type", "image/jpeg")
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mime = ct.split(";")[0].strip()
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if "image" not in mime:
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mime = "image/jpeg"
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return (r.content, mime)
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except Exception as e:
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print(f"[download] Failed {url}: {e}")
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return (None, None)
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def _generate_image_from_ref(prompt: str, ref_bytes: bytes, ref_mime: str,
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model: str = IMG_PRO) -> tuple:
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"""Generate image using a real product photo as reference.
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Returns (filename, mime_type) or ('', '')."""
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if not client or not ref_bytes:
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return ("", "")
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cache_key = hashlib.md5(f"{model}:{prompt}:{hashlib.md5(ref_bytes).hexdigest()[:8]}".encode()).hexdigest()[:16]
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for ext in ("png", "jpg", "jpeg", "webp"):
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cached = GEN_DIR / f"{cache_key}.{ext}"
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if cached.exists():
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return (cached.name, f"image/{ext}")
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try:
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response = client.models.generate_content(
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model=model,
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contents=[
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types.Part.from_bytes(data=ref_bytes, mime_type=ref_mime),
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prompt,
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],
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config=types.GenerateContentConfig(
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response_modalities=["IMAGE", "TEXT"],
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),
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)
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for part in response.candidates[0].content.parts:
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if part.inline_data and part.inline_data.data:
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mime = part.inline_data.mime_type or "image/png"
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ext = "jpg" if "jpeg" in mime or "jpg" in mime else "png"
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filename = f"{cache_key}.{ext}"
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(GEN_DIR / filename).write_bytes(part.inline_data.data)
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return (filename, mime)
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except Exception as e:
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print(f"[img-gen] {model} error: {e}")
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return ("", "")
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def _generate_image_text_only(prompt: str, model: str = IMG_PRO) -> tuple:
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"""Fallback: text-only image generation when no reference available."""
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if not client:
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return ("", "")
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cache_key = hashlib.md5(f"{model}:{prompt}".encode()).hexdigest()[:16]
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for ext in ("png", "jpg", "jpeg", "webp"):
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cached = GEN_DIR / f"{cache_key}.{ext}"
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if cached.exists():
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return (cached.name, f"image/{ext}")
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try:
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response = client.models.generate_content(
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model=model,
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contents=prompt,
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config=types.GenerateContentConfig(
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response_modalities=["IMAGE", "TEXT"],
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),
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)
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for part in response.candidates[0].content.parts:
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if part.inline_data and part.inline_data.data:
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mime = part.inline_data.mime_type or "image/png"
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ext = "jpg" if "jpeg" in mime or "jpg" in mime else "png"
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filename = f"{cache_key}.{ext}"
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(GEN_DIR / filename).write_bytes(part.inline_data.data)
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return (filename, mime)
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except Exception as e:
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print(f"[img-gen-fallback] {model} error: {e}")
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return ("", "")
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# ═══════════════════════════════════════════════════════════════
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# DEMO A — Conversion-Optimised PDP + Asset Pack
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# ═══════════════════════════════════════════════════════════════
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def generate_asset_pack(product: dict) -> dict:
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"""Generate a full conversion-optimised PDP structure.
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The output maps directly to a real product detail page layout:
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hero section, image gallery context, benefit bullets, trust bar,
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persuasion copy, FAQ, meta SEO, ad hooks, email subjects.
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"""
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prompt = f"""You are a senior ecommerce conversion strategist who has optimised PDPs for brands doing £50M+/yr.
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You're writing for Just Vitamins (justvitamins.co.uk) — a trusted UK supplement brand:
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- 4.8★ Trustpilot (230,000+ customers)
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- 20 years trading
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- Eco bio-pouch packaging
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- UK-made, GMP certified
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PRODUCT DATA:
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- Title: {product.get('title','')}
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- Subtitle: {product.get('subtitle','')}
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- Price: {product.get('price','')} for {product.get('quantity','')}
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- Per unit: {product.get('per_unit_cost','')}
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- Category: {product.get('category','')}
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- Benefits: {json.dumps(product.get('benefits',[]))}
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- Description: {product.get('description','')[:2000]}
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- EFSA Health Claims: {json.dumps(product.get('health_claims',[]))}
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Generate a conversion-optimised PDP structure following these ecommerce best practices:
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1. ABOVE THE FOLD: Hero headline + value prop must stop the scroll
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2. GALLERY CONTEXT: Captions for each product image (main, lifestyle, scale, ingredients)
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3. BENEFIT BULLETS: Feature → Benefit → Proof format. Max 6, icon-ready.
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4. TRUST BAR: 4 trust signals (Trustpilot, years, GMP, eco)
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5. PERSUASION SECTION: "Why this formula" — science-led, 2-3 short paragraphs
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6. SOCIAL PROOF: Real-format review quote + stats
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7. PRICE FRAMING: Reframe as daily cost, compare to coffee/etc
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8. URGENCY: Ethical scarcity or time-based nudge
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9. FAQ: 4 questions that handle real objections (dosage, interactions, results timeline, quality)
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10. CTA: Primary + secondary button text
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11. META SEO: Title <60 chars, description <155 chars, primary keyword
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12. AD HOOKS: 5 scroll-stopping hooks for Meta/TikTok ads
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13. EMAIL: 3 subject lines with preview text for welcome/abandon/restock flows
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Return JSON:
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{{
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"pdp": {{
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"hero_headline": "Conversion-optimised H1",
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"hero_subhead": "One sentence that makes them stay",
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"value_props": ["Short prop 1", "Short prop 2", "Short prop 3"],
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"gallery_captions": {{
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"main": "What the buyer sees first — describe ideal main shot context",
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"lifestyle": "Describe the lifestyle scene this product belongs in",
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"scale": "Describe a scale/size reference shot",
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"ingredients": "Describe the close-up ingredients/label shot"
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}},
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"benefit_bullets": [
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{{"icon": "emoji", "headline": "Short benefit", "detail": "Why it matters to the buyer", "proof": "Clinical or data point"}},
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{{"icon": "emoji", "headline": "…", "detail": "…", "proof": "…"}},
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{{"icon": "emoji", "headline": "…", "detail": "…", "proof": "…"}},
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{{"icon": "emoji", "headline": "…", "detail": "…", "proof": "…"}},
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{{"icon": "emoji", "headline": "…", "detail": "…", "proof": "…"}}
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],
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"trust_signals": [
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{{"icon": "⭐", "text": "4.8★ Trustpilot"}},
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{{"icon": "🏆", "text": "20 Years Trusted"}},
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{{"icon": "🇬🇧", "text": "UK Made · GMP"}},
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{{"icon": "🌿", "text": "Eco Bio-Pouch"}}
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],
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"why_section_title": "Why This Formula",
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"why_paragraphs": ["Science paragraph 1", "Absorption/bioavailability paragraph 2", "Who benefits paragraph 3"],
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"review_quote": {{"text": "Realistic-sounding 5★ review", "author": "Verified Buyer name", "stars": 5}},
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"stats_bar": [
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{{"number": "230,000+", "label": "Happy Customers"}},
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{{"number": "4.8★", "label": "Trustpilot Rating"}},
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{{"number": "20+", "label": "Years Trusted"}}
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],
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"price_display": {{
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"main_price": "{product.get('price','')}",
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"price_per_day": "Daily cost calculation",
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"comparison": "That's less than a daily coffee",
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"savings_note": "Subscribe & save note if applicable"
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}},
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"urgency_note": "Ethical urgency message",
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"cta_primary": "Primary button text",
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"cta_secondary": "Secondary action text",
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"faq": [
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{{"q": "Dosage question", "a": "Clear answer"}},
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{{"q": "Results timeline question", "a": "Honest answer"}},
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{{"q": "Quality/safety question", "a": "Trust-building answer"}},
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{{"q": "Interaction question", "a": "Responsible answer"}}
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],
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"usage_instructions": "Simple, friction-free how-to-take instructions"
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}},
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"meta_seo": {{
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"title": "SEO title under 60 chars",
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"description": "Meta description under 155 chars with primary keyword",
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"primary_keyword": "target keyword"
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}},
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"ad_hooks": [
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{{"hook": "Scroll-stopping first line", "angle": "What desire it targets", "platform": "Meta/TikTok/Google"}},
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{{"hook": "…", "angle": "…", "platform": "…"}},
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{{"hook": "…", "angle": "…", "platform": "…"}},
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{{"hook": "…", "angle": "…", "platform": "…"}},
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{{"hook": "…", "angle": "…", "platform": "…"}}
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],
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"email_subjects": [
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{{"flow": "Welcome", "subject": "Under 50 chars", "preview": "Preview text"}},
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{{"flow": "Abandon Cart", "subject": "…", "preview": "…"}},
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{{"flow": "Restock", "subject": "…", "preview": "…"}}
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]
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}}
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RULES:
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- EFSA health claims only — no therapeutic claims
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- UK English
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- Be specific to THIS product — no generic filler
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- Every bullet must pass the "so what?" test
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- Price framing must use real numbers from the data"""
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return _call_gemini(prompt, 0.75)
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# ═══════════════════════════════════════════════════════════════
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# IMAGE GENERATION — Reference-based product photography
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# ═══════════════════════════════════════════════════════════════
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def generate_product_images(product: dict) -> dict:
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"""Generate ecommerce product images using the REAL scraped product photo.
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Downloads the actual product image from justvitamins.co.uk, sends it
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to Gemini as visual reference, and generates conversion-optimised
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variations that maintain product consistency.
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"""
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title = product.get("title", "vitamin supplement")
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images = product.get("images", [])
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results = {"original_images": images}
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# Download the primary product image as reference
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ref_bytes, ref_mime = None, None
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for img_url in images:
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ref_bytes, ref_mime = _download_image(img_url)
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if ref_bytes:
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# Save the original too
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orig_hash = hashlib.md5(ref_bytes).hexdigest()[:12]
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ext = "jpg" if "jpeg" in ref_mime else "png"
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orig_name = f"orig_{orig_hash}.{ext}"
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orig_path = GEN_DIR / orig_name
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if not orig_path.exists():
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orig_path.write_bytes(ref_bytes)
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results["original"] = {"filename": orig_name, "mime": ref_mime, "source": img_url}
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break
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if not ref_bytes:
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results["error"] = "Could not download product image from source"
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return results
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# ── 1. HERO: Clean white-background product shot ─────────
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hero_prompt = (
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"You are a professional ecommerce product photographer. "
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"Take this exact product and create a clean, premium product photograph. "
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"KEEP THE EXACT SAME PRODUCT — same packaging, same label, same colours. "
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"Place it on a pure white background with soft studio lighting and subtle shadow. "
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"The product should be centred, well-lit, and sharp. "
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"This is the main product image for an online store — it must look professional "
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"and match Amazon/Shopify product photography standards. "
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"Do NOT change the product, do NOT add text, do NOT alter the packaging."
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)
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fname, mime = _generate_image_from_ref(hero_prompt, ref_bytes, ref_mime, IMG_PRO)
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results["hero"] = {"filename": fname, "mime": mime, "model": "Nano Banana Pro",
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"caption": "Main product shot — clean white background, studio lighting"}
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# ── 2. LIFESTYLE: Product in real-life setting ───────────
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lifestyle_prompt = (
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"You are a lifestyle product photographer for a premium health brand. "
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"Take this exact supplement product and photograph it in a beautiful morning "
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"kitchen scene. Place the EXACT SAME product on a light marble countertop "
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"with morning sunlight, a glass of water, and fresh green leaves nearby. "
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"Warm, healthy, inviting mood. Shallow depth of field with product in sharp focus. "
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"KEEP the product exactly as it is — same packaging, same label. "
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"Do NOT change, redesign, or alter the product in any way. "
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"Professional lifestyle product photography for an ecommerce website."
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)
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fname, mime = _generate_image_from_ref(lifestyle_prompt, ref_bytes, ref_mime, IMG_PRO)
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results["lifestyle"] = {"filename": fname, "mime": mime, "model": "Nano Banana Pro",
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"caption": "Lifestyle shot — morning kitchen scene, natural light"}
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# ── 3. SCALE: Product with hand/everyday object ──────────
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scale_prompt = (
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"You are a product photographer creating a scale reference image. "
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"Show this exact supplement product being held in a person's hand, "
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"or placed next to a coffee mug for size reference. "
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"The product must be the EXACT SAME — same packaging, same label, same design. "
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"Clean, bright lighting. Natural skin tones. "
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"This helps online shoppers understand the actual size of the product. "
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"Do NOT modify, redesign, or change the product appearance."
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)
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fname, mime = _generate_image_from_ref(scale_prompt, ref_bytes, ref_mime, IMG_FAST)
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results["scale"] = {"filename": fname, "mime": mime, "model": "Nano Banana",
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"caption": "Scale reference — real-world size context"}
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# ── 4. INGREDIENTS: Close-up detail shot ─────────────────
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ingredients_prompt = (
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"You are a detail product photographer. "
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"Create a close-up macro shot of this supplement product, focusing on the "
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"label, ingredients panel, or the capsules/tablets spilling out of the packaging. "
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"KEEP the exact same product — same packaging, same branding. "
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"Sharp focus on details, soft bokeh background. "
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"Premium, trustworthy feel — suitable for a health brand website. "
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"Do NOT change the product design or branding."
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)
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fname, mime = _generate_image_from_ref(ingredients_prompt, ref_bytes, ref_mime, IMG_FAST)
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results["ingredients"] = {"filename": fname, "mime": mime, "model": "Nano Banana",
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"caption": "Detail shot — ingredients & quality close-up"}
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# ── 5. BANNER: Wide hero banner for category/landing page ─
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banner_prompt = (
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"You are creating a wide ecommerce hero banner image. "
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"Place this exact supplement product on the right side of a wide composition. "
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"The left side should have clean space for text overlay (no text in the image). "
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"Use a soft gradient background in natural greens and creams. "
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"Include subtle natural elements — leaves, light rays, bokeh. "
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"KEEP the product exactly as it is — same packaging, same label. "
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"Wide aspect ratio, suitable for a website hero banner. "
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"Do NOT add any text, logos, or modify the product."
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)
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fname, mime = _generate_image_from_ref(banner_prompt, ref_bytes, ref_mime, IMG_PRO)
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results["banner"] = {"filename": fname, "mime": mime, "model": "Nano Banana Pro",
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"caption": "Hero banner — wide format for landing pages"}
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return results
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# ═══════════════════════════════════════════════════════════════
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# DEMO B — Competitor X-Ray
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# ═══════════════════════════════════════════════════════════════
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def competitor_xray(competitor_data: dict) -> dict:
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prompt = f"""You are a competitive intelligence analyst for Just Vitamins (justvitamins.co.uk) — trusted UK supplement brand, 20 yrs, 4.8★ Trustpilot, 230K+ customers, eco bio-pouch packaging.
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COMPETITOR PAGE:
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- URL: {competitor_data.get('url','')}
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- Title: {competitor_data.get('title','')}
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- Brand: {competitor_data.get('brand','')}
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- Price: {competitor_data.get('price','')}
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- Meta: {competitor_data.get('meta_description','')}
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- Description: {competitor_data.get('description','')[:2000]}
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- Bullets: {json.dumps(competitor_data.get('bullets',[])[:10])}
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- Page extract: {competitor_data.get('raw_text','')[:2000]}
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Perform a deep competitive analysis. Output JSON:
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{{
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"competitor_name":"…",
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"what_theyre_selling":"One sentence — what they're REALLY selling (emotional promise, not product)",
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"top_5_tactics":[
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{{"tactic":"…","explanation":"…"}},
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{{"tactic":"…","explanation":"…"}},
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{{"tactic":"…","explanation":"…"}},
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{{"tactic":"…","explanation":"…"}},
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{{"tactic":"…","explanation":"…"}}
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],
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"weakest_claim":"Their most vulnerable claim / biggest gap",
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"jv_hero_section":{{
|
|
"headline":"Killer headline positioning JV as better",
|
|
"body":"2-3 sentences of copy that beats them without naming them",
|
|
"value_prop":"Single most powerful reason to choose JV"
|
|
}},
|
|
"differentiators":[
|
|
{{"point":"…","proof_idea":"Specific content or test idea to prove it"}},
|
|
{{"point":"…","proof_idea":"…"}},
|
|
{{"point":"…","proof_idea":"…"}}
|
|
],
|
|
"do_not_say":["Compliance note 1","…","…","…"]
|
|
}}
|
|
|
|
RULES: No false claims. EFSA/ASA compliant. Strategic, not aggressive."""
|
|
return _call_gemini(prompt, 0.7)
|
|
|
|
|
|
# ═══════════════════════════════════════════════════════════════
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|
# DEMO C — PDP Surgeon
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|
# ═══════════════════════════════════════════════════════════════
|
|
|
|
STYLE_INSTRUCTIONS = {
|
|
"balanced": "Balanced, trustworthy DTC supplement voice. Mix emotional hooks with rational proof. Think Huel or Athletic Greens.",
|
|
"premium": "Premium aspirational voice. Sophisticated language, formulation science focus, target affluent health-conscious buyers. Think Lyma or Seed.",
|
|
"dr": "Direct-response style. Pattern interrupts, urgency, specific numbers, stacked value, scarcity. Think agora-style health copy.",
|
|
"medical": "Clinical, medically-safe tone. Proper nomenclature, structure/function claims only, evidence citations. Think Thorne or Pure Encapsulations.",
|
|
}
|
|
|
|
def pdp_surgeon(product: dict, style: str = "balanced") -> dict:
|
|
instruction = STYLE_INSTRUCTIONS.get(style, STYLE_INSTRUCTIONS["balanced"])
|
|
prompt = f"""You are an elite PDP conversion specialist. You've increased CVR by 30-80% for DTC supplement brands.
|
|
|
|
PRODUCT:
|
|
- Title: {product.get('title','')}
|
|
- Subtitle: {product.get('subtitle','')}
|
|
- Price: {product.get('price','')} for {product.get('quantity','')}
|
|
- Per unit: {product.get('per_unit_cost','')}
|
|
- Benefits: {json.dumps(product.get('benefits',[]))}
|
|
- Description: {product.get('description','')[:1500]}
|
|
- EFSA Claims: {json.dumps(product.get('health_claims',[]))}
|
|
|
|
STYLE: {style.upper()} — {instruction}
|
|
|
|
Rewrite the entire PDP in this style. For EVERY element, add a conversion annotation explaining the psychology and estimated lift.
|
|
|
|
Output JSON:
|
|
{{
|
|
"style":"{style}",
|
|
"title":"Conversion-optimised title",
|
|
"subtitle":"Desire-triggering subtitle",
|
|
"hero_copy":"2-3 sentence persuasion paragraph — the most important copy on the page",
|
|
"hero_annotation":"Why this works — which conversion principle, estimated % lift",
|
|
"bullets":[
|
|
{{"text":"Feature → Benefit → Proof bullet","annotation":"Conversion principle + estimated lift"}},
|
|
{{"text":"…","annotation":"…"}},
|
|
{{"text":"…","annotation":"…"}},
|
|
{{"text":"…","annotation":"…"}},
|
|
{{"text":"…","annotation":"…"}}
|
|
],
|
|
"social_proof":"Specific social proof line using 4.8★ Trustpilot, 230K customers, years trading",
|
|
"social_proof_annotation":"Which social proof principle this uses + estimated lift",
|
|
"price_reframe":"Reframe the price as a no-brainer — use daily cost, comparison anchoring",
|
|
"price_annotation":"Price psychology principle + estimated lift",
|
|
"cta_text":"CTA button text — action-oriented, benefit-driven",
|
|
"cta_annotation":"Why this CTA works",
|
|
"usage_instruction":"How to take — written to build routine and reduce friction",
|
|
"usage_annotation":"How this reduces returns and increases LTV"
|
|
}}
|
|
|
|
RULES:
|
|
- EFSA claims only — no disease claims, no cure claims
|
|
- Realistic lift estimates (5-40% range)
|
|
- UK English
|
|
- Every annotation must cite a specific conversion principle (Cialdini, Kahneman, Fogg, etc.)"""
|
|
return _call_gemini(prompt, 0.8)
|
|
|
|
|
|
# ═══════════════════════════════════════════════════════════════
|
|
# FULL PDP OPTIMISATION
|
|
# ═══════════════════════════════════════════════════════════════
|
|
|
|
def optimise_pdp_copy(product: dict) -> dict:
|
|
prompt = f"""You are an expert ecommerce copywriter for Just Vitamins — 4.8★ Trustpilot, 230K+ customers, 20 years.
|
|
|
|
PRODUCT:
|
|
- Title: {product['title']}
|
|
- Subtitle: {product.get('subtitle','')}
|
|
- Price: {product.get('price','')} for {product.get('quantity','')}
|
|
- Benefits: {json.dumps(product.get('benefits',[]))}
|
|
- Description: {product.get('description','')[:1500]}
|
|
- EFSA Claims: {json.dumps(product.get('health_claims',[]))}
|
|
|
|
Rewrite everything. Output JSON:
|
|
{{
|
|
"seo_title":"…",
|
|
"subtitle":"…",
|
|
"benefit_bullets":["…","…","…","…","…"],
|
|
"why_section":"Para 1\\n\\nPara 2\\n\\nPara 3",
|
|
"who_for":["…","…","…"],
|
|
"social_proof":"…",
|
|
"meta_description":"…",
|
|
"faqs":[{{"q":"…","a":"…"}},{{"q":"…","a":"…"}},{{"q":"…","a":"…"}}]
|
|
}}
|
|
|
|
EFSA claims only. UK English."""
|
|
return _call_gemini(prompt, 0.7)
|