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Concept Agent
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"""
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Lahr Carpet Cleaning — Veo hero video generator.
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5 shots x 4s = 20s reel. Concatenated by ffmpeg into hero-reel.mp4.
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Saves clips to: assets/videos/hero/clips/
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Saves final to: assets/videos/hero/hero-reel.mp4
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Run: python3 tools/gen-video.py
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"""
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import os
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import sys
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import time
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import 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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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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BASE_DIR = os.path.dirname(os.path.dirname(__file__))
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OUT_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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os.makedirs(OUT_DIR, exist_ok=True)
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client = genai.Client(api_key=API_KEY)
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SHOTS = [
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{
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"name": "shot-01-door-opens",
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"prompt": (
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"Cinematic low-angle wide shot. A solid wood front door of an upstate New York home opens "
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"inward smoothly. Bright golden afternoon sunlight pours through the doorway onto a carpeted "
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"entryway floor. Camera is at floor level, looking toward the door. The door swings open "
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"fully revealing light. No people visible. Photorealistic, warm inviting light, slow motion."
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),
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},
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{
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"name": "shot-02-pan-to-stains",
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"prompt": (
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"Slow cinematic camera pan from the front door entryway across a residential living room carpet "
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"in an upstate New York home. The carpet shows visible dirt tracks, pet stains, and soiling "
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"from daily use. Natural light. No people. Camera moves fluidly across the room revealing "
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"the stained carpet. Photorealistic."
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),
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},
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{
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"name": "shot-03-stain-closeup",
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"prompt": (
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"Close-up shot of a stained beige carpet with visible pet stains, mud, and dark soiling. "
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"Camera slowly pushes in on the dirty area. Dramatic side lighting emphasises the stain depth "
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"and texture. Slow motion. Ultra-realistic macro photography style."
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),
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},
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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-05-extraction-couch",
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"prompt": (
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"Close-up cinematic shot of a professional technician's gloved hand holding a small flat "
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"upholstery cleaning attachment tool, pressing it firmly against a dirty grey sofa cushion "
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"and sliding it slowly across the fabric. The fabric visibly brightens and lifts as the tool "
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"moves. No water pours out — suction draws moisture into the tool. Slow motion, natural light. "
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"Photorealistic."
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),
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},
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{
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"name": "shot-06-extraction-stairs",
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"prompt": (
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"Cinematic shot of a professional technician's hands using a compact portable upright carpet "
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"cleaner on a carpeted staircase — pushing the machine up a stair tread step by step. Each "
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"tread brightens and looks freshly cleaned as the machine passes. No water pours out. Clean "
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"bright carpet revealed on each step. Slow motion, warm interior light. Photorealistic."
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),
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},
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{
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"name": "shot-07-office-entryway",
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"prompt": (
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"Wide cinematic shot of a clean professional office building entryway with commercial grade "
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"carpet. Modern corporate interior, glass doors, professional lighting. No people. Camera "
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"slowly pushes forward through the entry. Photorealistic."
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),
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},
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{
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"name": "shot-08-showroom",
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"prompt": (
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"Wide cinematic shot of an upscale retail showroom or winery tasting room in the Finger Lakes "
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"region. Rich carpet throughout, warm interior lighting, product displays. No people. Camera "
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"glides forward through the space. Photorealistic, luxurious atmosphere."
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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 = [
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"veo-2.0-generate-001",
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"veo-3.0-generate-001",
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]
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def poll(operation, timeout=420):
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elapsed = 0
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while not operation.done:
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if elapsed >= timeout:
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print(" Timed out.")
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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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operation = client.operations.get(operation)
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return operation
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def download_video(video, out_path):
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video_bytes = None
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try:
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video_bytes = client.files.download(file=video)
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except Exception:
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pass
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if video_bytes:
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with open(out_path, "wb") as f:
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f.write(video_bytes)
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return True
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if hasattr(video, "uri") and video.uri:
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import urllib.request
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uri = video.uri + ("&" if "?" in video.uri else "?") + f"key={API_KEY}"
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print(f" Fetching via URI...")
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urllib.request.urlretrieve(uri, out_path)
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return True
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return False
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def generate():
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saved = []
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for item in SHOTS:
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out_path = os.path.join(OUT_DIR, f"{item['name']}.mp4")
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print(f"\n[{SHOTS.index(item)+1}/{len(SHOTS)}] 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,
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prompt=item["prompt"],
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config=types.GenerateVideosConfig(
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aspect_ratio="16:9",
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resolution="720p",
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duration_seconds=6,
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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 is None:
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continue
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if op.response and op.response.generated_videos:
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vid = op.response.generated_videos[0].video
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if download_video(vid, out_path):
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size_kb = os.path.getsize(out_path) // 1024
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print(f" Saved {out_path} ({size_kb}KB)")
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saved.append(out_path)
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done = True
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break
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else:
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print(f" Download failed for {model}")
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else:
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print(f" No video from {model}")
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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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return saved
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def concat(clips):
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if len(clips) < 2:
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print("Not enough clips to concatenate.")
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return
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list_file = os.path.join(OUT_DIR, "concat.txt")
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with open(list_file, "w") as f:
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for c in clips:
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f.write(f"file '{c}'\n")
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print(f"\nConcatenating {len(clips)} clips into hero-reel.mp4...")
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result = subprocess.run(
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["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", list_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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size_kb = os.path.getsize(REEL_OUT) // 1024
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print(f" Saved {REEL_OUT} ({size_kb}KB)")
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else:
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print(f" ffmpeg error: {result.stderr[-300:]}")
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if __name__ == "__main__":
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clips = generate()
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if clips:
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concat(clips)
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print(f"\nDone. {len(clips)}/5 clips generated.")
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if len(clips) == 5:
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print("Hero reel ready: assets/videos/hero/hero-reel.mp4")
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