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Concept Agent
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"""
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Lahr Carpet Cleaning — Service card image generator.
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Generates 12 unique images for residential and commercial service cards.
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Saves to: assets/images/services/
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Run: python3 tools/gen-service-images.py
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"""
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import os
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import sys
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try:
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from google import genai
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from google.genai import types
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except ImportError:
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print("Installing google-genai...")
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os.system(f"{sys.executable} -m pip install google-genai --quiet")
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from google import genai
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from google.genai import types
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API_KEY = os.environ.get("GEMINI_API_KEY", "AIzaSyB_1p8KvaT_rdNJGPs8HKk8bKsvUlcL6Kg")
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OUT_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "images", "services")
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os.makedirs(OUT_DIR, exist_ok=True)
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client = genai.Client(api_key=API_KEY)
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IMAGES = [
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# ── Residential ──────────────────────────────────────────────────────────
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{
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"name": "carpet-cleaning",
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"prompt": (
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"Wide shot of a large industrial stand-up hot water extraction machine being pushed across "
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"a plush beige residential carpet. The machine is a heavy commercial-grade upright extractor "
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"on wheels — tall, wide cleaning head at the base, long upright handle. "
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"The carpet behind it transitions from dirty and matted to clean, bright, and fluffy. "
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"Completely dry machine exterior, no steam, no water spraying anywhere. "
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"Warm natural interior light. Ultra-realistic professional photography."
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),
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},
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{
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"name": "stairs-cleaning",
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"prompt": (
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"Wide cinematic shot of a carpeted residential staircase. Each step has clean, "
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"bright plush carpet that looks freshly cleaned with visible extraction lines. "
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"Modern upstate New York home interior, natural light from above, "
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"warm wood banisters, no people, no equipment visible. "
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"Professional interior photography, ultra-realistic."
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),
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},
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{
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"name": "upholstery-cleaning",
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"prompt": (
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"Close-up of a clean grey linen sofa cushion showing bright, lifted fabric texture "
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"after professional upholstery cleaning. Half the cushion shows the before "
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"(slightly soiled, flat fabric) and half shows the cleaned result (bright, fluffy, refreshed). "
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"Natural window light, residential living room in background, no people, no equipment. "
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"Ultra-realistic product photography."
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),
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},
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{
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"name": "floor-cleaning",
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"prompt": (
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"Wide shot of a gleaming hardwood floor in a modern residential home after professional "
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"cleaning. The floor reflects soft natural window light, showing deep grain detail. "
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"Contemporary furniture in background, no people, no cleaning equipment visible. "
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"Professional interior photography, ultra-realistic."
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),
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},
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{
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"name": "area-rug-cleaning",
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"prompt": (
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"Overhead flat-lay shot of a large vibrant Persian or oriental area rug "
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"with rich red, navy, and cream geometric patterns, looking freshly cleaned — "
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"colors vivid, fibers lifted and bright. Hardwood floor beneath. "
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"No people, no equipment, no water. Professional product photography, ultra-realistic."
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),
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},
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{
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"name": "add-ons",
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"prompt": (
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"Close-up macro shot of clean carpet fibers being lifted by a professional "
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"grooming brush after hot water extraction cleaning. The fibers are bright, "
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"fluffy, and standing upright. Warm light catches the texture. "
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"No steam, no water, no people. Ultra-realistic macro photography."
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),
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},
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# ── Commercial ───────────────────────────────────────────────────────────
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{
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"name": "vacation-rentals",
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"prompt": (
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"Bright, airy vacation rental living room in the Finger Lakes region of upstate New York. "
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"Spotlessly clean cream carpet, contemporary furniture, large windows with lake views, "
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"warm natural afternoon light. Inviting and fresh. No people, no equipment. "
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"Professional real estate photography, ultra-realistic."
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),
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},
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{
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"name": "office-spaces",
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"prompt": (
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"Wide shot of a clean modern corporate office with freshly cleaned dark charcoal carpet "
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"throughout. Open plan workspace, glass partitions, professional lighting. "
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"Carpet shows neat vacuum lines indicating recent professional cleaning. "
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"No people, no equipment. Professional architectural photography, ultra-realistic."
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),
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},
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{
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"name": "hotels-inns",
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"prompt": (
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"Elegant hotel corridor in a boutique upstate New York inn. Clean, plush patterned "
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"carpet runner down the hallway with fresh vacuum lines. Warm sconce lighting, "
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"wood paneling, framed art on walls. No people, no equipment. "
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"Professional hospitality photography, ultra-realistic."
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),
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},
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{
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"name": "retail-showrooms",
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"prompt": (
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"Wide shot of an upscale retail showroom or winery tasting room in the Finger Lakes. "
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"Clean, rich carpet throughout, warm lighting, product displays on shelves. "
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"Carpet looks freshly extracted — bright and spotless. "
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"No people, no equipment. Professional commercial interior photography, ultra-realistic."
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),
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},
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{
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"name": "property-management",
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"prompt": (
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"View across three clean apartment living rooms in sequence, each showing "
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"spotlessly clean beige carpet with fresh vacuum lines after professional cleaning. "
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"Bright, neutral interiors ready for new tenants. Natural light, no furniture, "
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"no people, no equipment. Professional real estate photography, ultra-realistic."
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),
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},
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{
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"name": "commercial-overview",
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"prompt": (
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"Professional carpet cleaning technician in a plain black shirt, shown from the side, "
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"pushing a large industrial stand-up hot water extraction machine through a bright commercial "
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"building lobby. The machine is a heavy commercial-grade upright extractor on wheels — "
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"tall, wide cleaning head, long handle. Clean carpet visible. No steam, no water spraying, "
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"no face visible. Professional editorial photography, ultra-realistic."
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),
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},
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]
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def generate():
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saved = []
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total = len(IMAGES)
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for i, item in enumerate(IMAGES, 1):
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out_path = os.path.join(OUT_DIR, f"{item['name']}.jpg")
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print(f"[{i}/{total}] Generating {item['name']}...")
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try:
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resp = client.models.generate_images(
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model="imagen-4.0-generate-001",
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prompt=item["prompt"],
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config=types.GenerateImagesConfig(
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number_of_images=1,
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aspect_ratio="4:3",
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output_mime_type="image/jpeg",
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safety_filter_level="block_low_and_above",
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),
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)
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if resp.generated_images:
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img_bytes = resp.generated_images[0].image.image_bytes
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with open(out_path, "wb") as f:
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f.write(img_bytes)
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print(f" Saved {out_path} ({len(img_bytes)//1024}KB)")
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saved.append(item["name"])
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else:
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print(f" No image returned for {item['name']}")
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except Exception as e:
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print(f" Error on {item['name']}: {e}")
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return saved
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if __name__ == "__main__":
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saved = generate()
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print(f"\nDone. {len(saved)}/{len(IMAGES)} images saved to assets/images/services/")
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if saved:
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print("Generated:", ", ".join(saved))
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