lahrcarpetcleaning.com/tools/regen-industrial.py

"""Regenerate carpet-cleaning and commercial-overview service images with industrial extractor prompts."""
import os, sys, time, subprocess
try:
    from google import genai
    from google.genai import types
except ImportError:
    os.system(f"{sys.executable} -m pip install google-genai --quiet")
    from google import genai
    from google.genai import types

API_KEY  = os.environ.get("GEMINI_API_KEY", "AIzaSyB_1p8KvaT_rdNJGPs8HKk8bKsvUlcL6Kg")
BASE_DIR = os.path.dirname(os.path.dirname(__file__))
IMG_DIR  = os.path.join(BASE_DIR, "assets", "images", "services")
VID_DIR  = os.path.join(BASE_DIR, "assets", "videos", "hero", "clips")
REEL_OUT = os.path.join(BASE_DIR, "assets", "videos", "hero", "hero-reel.mp4")
client   = genai.Client(api_key=API_KEY)

# ── Service images ────────────────────────────────────────────────────────────
IMAGES = [
    {
        "name": "carpet-cleaning",
        "prompt": (
            "Wide shot of a large industrial stand-up hot water extraction machine being pushed across "
            "a plush beige residential carpet. The machine is a heavy commercial-grade upright extractor "
            "on wheels — tall, wide cleaning head at the base, long upright handle. "
            "The carpet behind it transitions from dirty and matted to clean, bright, and fluffy. "
            "Completely dry machine exterior, no steam, no water spraying anywhere. "
            "Warm natural interior light. Ultra-realistic professional photography."
        ),
    },
    {
        "name": "commercial-overview",
        "prompt": (
            "Professional carpet cleaning technician in a plain black shirt, shown from the side, "
            "pushing a large industrial stand-up hot water extraction machine through a bright commercial "
            "building lobby. The machine is a heavy commercial-grade upright extractor on wheels — "
            "tall, wide cleaning head, long handle. Clean carpet visible. No steam, no water spraying, "
            "no face visible. Professional editorial photography, ultra-realistic."
        ),
    },
]

for item in IMAGES:
    out_path = os.path.join(IMG_DIR, f"{item['name']}.jpg")
    print(f"[IMG] Generating {item['name']}...")
    try:
        resp = client.models.generate_images(
            model="imagen-4.0-generate-001",
            prompt=item["prompt"],
            config=types.GenerateImagesConfig(
                number_of_images=1, aspect_ratio="4:3",
                output_mime_type="image/jpeg",
                safety_filter_level="block_low_and_above",
            ),
        )
        if resp.generated_images:
            b = resp.generated_images[0].image.image_bytes
            with open(out_path, "wb") as f:
                f.write(b)
            print(f"  Saved {out_path} ({len(b)//1024}KB)")
        else:
            print(f"  No image returned")
    except Exception as e:
        print(f"  Error: {e}")

# ── Video shots ───────────────────────────────────────────────────────────────
SHOTS = [
    {
        "name": "shot-04-extraction-carpet",
        "prompt": (
            "Cinematic slow-motion wide shot: a large industrial stand-up hot water extraction machine "
            "being pushed steadily forward across a beige residential carpet. The machine is a tall "
            "professional-grade upright extractor — heavy-duty, commercial size, on wheels, with a wide "
            "cleaning head at the base and an upright handle. No steam, no spraying water, no visible "
            "liquid anywhere on the machine exterior. The carpet behind the machine transitions from dirty "
            "and matted to bright, clean, and fluffy as it passes. Warm natural room light. Photorealistic."
        ),
    },
    {
        "name": "shot-09-technician-unloading",
        "prompt": (
            "Wide shot of a professional carpet cleaning technician wearing a plain black shirt with no logo, "
            "rolling a large industrial stand-up hot water extraction machine out of a white service van "
            "parked in a residential driveway in upstate New York. The machine is a heavy commercial-grade "
            "upright extractor on wheels — tall, industrial size. Autumn trees in background, bright day. "
            "Technician shown from side or behind, no face visible. Photorealistic."
        ),
    },
]

MODELS = ["veo-2.0-generate-001", "veo-3.0-generate-001"]

def poll(op, timeout=420):
    elapsed = 0
    while not op.done:
        if elapsed >= timeout:
            return None
        print(f"  Waiting... ({elapsed}s)")
        time.sleep(15)
        elapsed += 15
        op = client.operations.get(op)
    return op

saved_clips = []
for item in SHOTS:
    out_path = os.path.join(VID_DIR, f"{item['name']}.mp4")
    print(f"\n[VID] Generating {item['name']}...")
    done = False
    for model in MODELS:
        try:
            print(f"  Model: {model}")
            op = client.models.generate_videos(
                model=model, prompt=item["prompt"],
                config=types.GenerateVideosConfig(
                    aspect_ratio="16:9", resolution="720p",
                    duration_seconds=6, number_of_videos=1,
                ),
            )
            op = poll(op)
            if op and op.response and op.response.generated_videos:
                vid = op.response.generated_videos[0].video
                try:
                    b = client.files.download(file=vid)
                except Exception:
                    b = None
                if b:
                    with open(out_path, "wb") as f:
                        f.write(b)
                    print(f"  Saved ({os.path.getsize(out_path)//1024}KB)")
                    saved_clips.append(item["name"])
                    done = True
                    break
        except Exception as e:
            print(f"  Error with {model}: {e}")
    if not done:
        print(f"  FAILED: {item['name']}")

# ── Reconcat reel if both shots regenerated ───────────────────────────────────
if len(saved_clips) == 2:
    print("\nReconcatenating reel...")
    concat_file = os.path.join(VID_DIR, "concat.txt")
    order = [
        "shot-01-door-opens-trimmed",
        "shot-02-pan-to-stains",
        "shot-03-stain-closeup",
        "shot-04-extraction-carpet",
        "shot-05-extraction-couch",
        "shot-06-extraction-stairs",
        "shot-07-office-entryway",
        "shot-08-showroom",
        "shot-09-technician-unloading",
    ]
    with open(concat_file, "w") as f:
        for name in order:
            f.write(f"file '{os.path.join(VID_DIR, name)}.mp4'\n")
    result = subprocess.run(
        ["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", concat_file,
         "-c:v", "libx264", "-crf", "22", "-preset", "fast",
         "-movflags", "+faststart", REEL_OUT],
        capture_output=True, text=True
    )
    if result.returncode == 0:
        print(f"  Reel saved ({os.path.getsize(REEL_OUT)//1024}KB)")
    else:
        print(f"  ffmpeg error: {result.stderr[-300:]}")
else:
    print(f"\nOnly {len(saved_clips)}/2 video shots regenerated — skipping reconcat.")

print("\nDone.")