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Concept Agent
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"""Regenerate carpet-cleaning and commercial-overview service images with industrial extractor prompts."""
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import os, sys, time, subprocess
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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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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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BASE_DIR = os.path.dirname(os.path.dirname(__file__))
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IMG_DIR = os.path.join(BASE_DIR, "assets", "images", "services")
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VID_DIR = os.path.join(BASE_DIR, "assets", "videos", "hero", "clips")
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REEL_OUT = os.path.join(BASE_DIR, "assets", "videos", "hero", "hero-reel.mp4")
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client = genai.Client(api_key=API_KEY)
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# ── Service images ────────────────────────────────────────────────────────────
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IMAGES = [
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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": "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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for item in IMAGES:
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out_path = os.path.join(IMG_DIR, f"{item['name']}.jpg")
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print(f"[IMG] 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, 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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b = resp.generated_images[0].image.image_bytes
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with open(out_path, "wb") as f:
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f.write(b)
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print(f" Saved {out_path} ({len(b)//1024}KB)")
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else:
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print(f" No image returned")
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except Exception as e:
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print(f" Error: {e}")
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# ── Video shots ───────────────────────────────────────────────────────────────
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SHOTS = [
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{
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"name": "shot-04-extraction-carpet",
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"prompt": (
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"Cinematic slow-motion wide shot: a large industrial stand-up hot water extraction machine "
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"being pushed steadily forward across a beige residential carpet. The machine is a tall "
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"professional-grade upright extractor — heavy-duty, commercial size, on wheels, with a wide "
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"cleaning head at the base and an upright handle. No steam, no spraying water, no visible "
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"liquid anywhere on the machine exterior. The carpet behind the machine transitions from dirty "
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"and matted to bright, clean, and fluffy as it passes. Warm natural room light. Photorealistic."
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),
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},
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{
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"name": "shot-09-technician-unloading",
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"prompt": (
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"Wide shot of a professional carpet cleaning technician wearing a plain black shirt with no logo, "
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"rolling a large industrial stand-up hot water extraction machine out of a white service van "
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"parked in a residential driveway in upstate New York. The machine is a heavy commercial-grade "
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"upright extractor on wheels — tall, industrial size. Autumn trees in background, bright day. "
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"Technician shown from side or behind, no face visible. Photorealistic."
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),
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},
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]
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MODELS = ["veo-2.0-generate-001", "veo-3.0-generate-001"]
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def poll(op, timeout=420):
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elapsed = 0
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while not op.done:
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if elapsed >= timeout:
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return None
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print(f" Waiting... ({elapsed}s)")
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time.sleep(15)
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elapsed += 15
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op = client.operations.get(op)
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return op
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saved_clips = []
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for item in SHOTS:
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out_path = os.path.join(VID_DIR, f"{item['name']}.mp4")
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print(f"\n[VID] Generating {item['name']}...")
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done = False
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for model in MODELS:
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try:
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print(f" Model: {model}")
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op = client.models.generate_videos(
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model=model, prompt=item["prompt"],
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config=types.GenerateVideosConfig(
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aspect_ratio="16:9", resolution="720p",
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duration_seconds=6, number_of_videos=1,
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),
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)
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op = poll(op)
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if op and op.response and op.response.generated_videos:
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vid = op.response.generated_videos[0].video
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try:
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b = client.files.download(file=vid)
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except Exception:
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b = None
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if b:
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with open(out_path, "wb") as f:
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f.write(b)
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print(f" Saved ({os.path.getsize(out_path)//1024}KB)")
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saved_clips.append(item["name"])
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done = True
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break
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except Exception as e:
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print(f" Error with {model}: {e}")
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if not done:
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print(f" FAILED: {item['name']}")
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# ── Reconcat reel if both shots regenerated ───────────────────────────────────
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if len(saved_clips) == 2:
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print("\nReconcatenating reel...")
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concat_file = os.path.join(VID_DIR, "concat.txt")
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order = [
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"shot-01-door-opens-trimmed",
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"shot-02-pan-to-stains",
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"shot-03-stain-closeup",
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"shot-04-extraction-carpet",
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"shot-05-extraction-couch",
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"shot-06-extraction-stairs",
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"shot-07-office-entryway",
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"shot-08-showroom",
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"shot-09-technician-unloading",
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]
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with open(concat_file, "w") as f:
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for name in order:
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f.write(f"file '{os.path.join(VID_DIR, name)}.mp4'\n")
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result = subprocess.run(
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["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", concat_file,
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"-c:v", "libx264", "-crf", "22", "-preset", "fast",
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"-movflags", "+faststart", REEL_OUT],
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capture_output=True, text=True
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)
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if result.returncode == 0:
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print(f" Reel saved ({os.path.getsize(REEL_OUT)//1024}KB)")
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else:
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print(f" ffmpeg error: {result.stderr[-300:]}")
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else:
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print(f"\nOnly {len(saved_clips)}/2 video shots regenerated — skipping reconcat.")
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print("\nDone.")
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