Concept Agent
221 lines
8.8 KiB
Python
221 lines
8.8 KiB
Python
"""
|
|
Lahr Carpet Cleaning — Veo hero video generator.
|
|
5 shots x 4s = 20s reel. Concatenated by ffmpeg into hero-reel.mp4.
|
|
Saves clips to: assets/videos/hero/clips/
|
|
Saves final to: assets/videos/hero/hero-reel.mp4
|
|
Run: python3 tools/gen-video.py
|
|
"""
|
|
import os
|
|
import sys
|
|
import time
|
|
import subprocess
|
|
|
|
try:
|
|
from google import genai
|
|
from google.genai import types
|
|
except ImportError:
|
|
print("Installing google-genai...")
|
|
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__))
|
|
OUT_DIR = os.path.join(BASE_DIR, "assets", "videos", "hero", "clips")
|
|
REEL_OUT = os.path.join(BASE_DIR, "assets", "videos", "hero", "hero-reel.mp4")
|
|
os.makedirs(OUT_DIR, exist_ok=True)
|
|
|
|
client = genai.Client(api_key=API_KEY)
|
|
|
|
SHOTS = [
|
|
{
|
|
"name": "shot-01-door-opens",
|
|
"prompt": (
|
|
"Cinematic low-angle wide shot. A solid wood front door of an upstate New York home opens "
|
|
"inward smoothly. Bright golden afternoon sunlight pours through the doorway onto a carpeted "
|
|
"entryway floor. Camera is at floor level, looking toward the door. The door swings open "
|
|
"fully revealing light. No people visible. Photorealistic, warm inviting light, slow motion."
|
|
),
|
|
},
|
|
{
|
|
"name": "shot-02-pan-to-stains",
|
|
"prompt": (
|
|
"Slow cinematic camera pan from the front door entryway across a residential living room carpet "
|
|
"in an upstate New York home. The carpet shows visible dirt tracks, pet stains, and soiling "
|
|
"from daily use. Natural light. No people. Camera moves fluidly across the room revealing "
|
|
"the stained carpet. Photorealistic."
|
|
),
|
|
},
|
|
{
|
|
"name": "shot-03-stain-closeup",
|
|
"prompt": (
|
|
"Close-up shot of a stained beige carpet with visible pet stains, mud, and dark soiling. "
|
|
"Camera slowly pushes in on the dirty area. Dramatic side lighting emphasises the stain depth "
|
|
"and texture. Slow motion. Ultra-realistic macro photography style."
|
|
),
|
|
},
|
|
{
|
|
"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-05-extraction-couch",
|
|
"prompt": (
|
|
"Close-up cinematic shot of a professional technician's gloved hand holding a small flat "
|
|
"upholstery cleaning attachment tool, pressing it firmly against a dirty grey sofa cushion "
|
|
"and sliding it slowly across the fabric. The fabric visibly brightens and lifts as the tool "
|
|
"moves. No water pours out — suction draws moisture into the tool. Slow motion, natural light. "
|
|
"Photorealistic."
|
|
),
|
|
},
|
|
{
|
|
"name": "shot-06-extraction-stairs",
|
|
"prompt": (
|
|
"Cinematic shot of a professional technician's hands using a compact portable upright carpet "
|
|
"cleaner on a carpeted staircase — pushing the machine up a stair tread step by step. Each "
|
|
"tread brightens and looks freshly cleaned as the machine passes. No water pours out. Clean "
|
|
"bright carpet revealed on each step. Slow motion, warm interior light. Photorealistic."
|
|
),
|
|
},
|
|
{
|
|
"name": "shot-07-office-entryway",
|
|
"prompt": (
|
|
"Wide cinematic shot of a clean professional office building entryway with commercial grade "
|
|
"carpet. Modern corporate interior, glass doors, professional lighting. No people. Camera "
|
|
"slowly pushes forward through the entry. Photorealistic."
|
|
),
|
|
},
|
|
{
|
|
"name": "shot-08-showroom",
|
|
"prompt": (
|
|
"Wide cinematic shot of an upscale retail showroom or winery tasting room in the Finger Lakes "
|
|
"region. Rich carpet throughout, warm interior lighting, product displays. No people. Camera "
|
|
"glides forward through the space. Photorealistic, luxurious atmosphere."
|
|
),
|
|
},
|
|
{
|
|
"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(operation, timeout=420):
|
|
elapsed = 0
|
|
while not operation.done:
|
|
if elapsed >= timeout:
|
|
print(" Timed out.")
|
|
return None
|
|
print(f" Waiting... ({elapsed}s)")
|
|
time.sleep(15)
|
|
elapsed += 15
|
|
operation = client.operations.get(operation)
|
|
return operation
|
|
|
|
def download_video(video, out_path):
|
|
video_bytes = None
|
|
try:
|
|
video_bytes = client.files.download(file=video)
|
|
except Exception:
|
|
pass
|
|
if video_bytes:
|
|
with open(out_path, "wb") as f:
|
|
f.write(video_bytes)
|
|
return True
|
|
if hasattr(video, "uri") and video.uri:
|
|
import urllib.request
|
|
uri = video.uri + ("&" if "?" in video.uri else "?") + f"key={API_KEY}"
|
|
print(f" Fetching via URI...")
|
|
urllib.request.urlretrieve(uri, out_path)
|
|
return True
|
|
return False
|
|
|
|
def generate():
|
|
saved = []
|
|
for item in SHOTS:
|
|
out_path = os.path.join(OUT_DIR, f"{item['name']}.mp4")
|
|
print(f"\n[{SHOTS.index(item)+1}/{len(SHOTS)}] 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 is None:
|
|
continue
|
|
if op.response and op.response.generated_videos:
|
|
vid = op.response.generated_videos[0].video
|
|
if download_video(vid, out_path):
|
|
size_kb = os.path.getsize(out_path) // 1024
|
|
print(f" Saved {out_path} ({size_kb}KB)")
|
|
saved.append(out_path)
|
|
done = True
|
|
break
|
|
else:
|
|
print(f" Download failed for {model}")
|
|
else:
|
|
print(f" No video from {model}")
|
|
except Exception as e:
|
|
print(f" Error with {model}: {e}")
|
|
|
|
if not done:
|
|
print(f" FAILED: {item['name']}")
|
|
|
|
return saved
|
|
|
|
def concat(clips):
|
|
if len(clips) < 2:
|
|
print("Not enough clips to concatenate.")
|
|
return
|
|
list_file = os.path.join(OUT_DIR, "concat.txt")
|
|
with open(list_file, "w") as f:
|
|
for c in clips:
|
|
f.write(f"file '{c}'\n")
|
|
print(f"\nConcatenating {len(clips)} clips into hero-reel.mp4...")
|
|
result = subprocess.run(
|
|
["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", list_file,
|
|
"-c:v", "libx264", "-crf", "22", "-preset", "fast",
|
|
"-movflags", "+faststart", REEL_OUT],
|
|
capture_output=True, text=True
|
|
)
|
|
if result.returncode == 0:
|
|
size_kb = os.path.getsize(REEL_OUT) // 1024
|
|
print(f" Saved {REEL_OUT} ({size_kb}KB)")
|
|
else:
|
|
print(f" ffmpeg error: {result.stderr[-300:]}")
|
|
|
|
if __name__ == "__main__":
|
|
clips = generate()
|
|
if clips:
|
|
concat(clips)
|
|
print(f"\nDone. {len(clips)}/5 clips generated.")
|
|
if len(clips) == 5:
|
|
print("Hero reel ready: assets/videos/hero/hero-reel.mp4")
|