backup

tools/build-paced-reel.py

@@ -0,0 +1,87 @@
+"""
+Build paced hero reel from the browser-ordered clip list.
+- Shot 1 (family entry): trimmed to 3s — cuts before mud pan
+- Shots 2-7: full 6s (establish the story)
+- Shots 8-12: trimmed to 3.5s (building pace)
+- Shots 13-15: trimmed to 2.5s (rapid)
+- Shot 16 (final reveal): full 6s (hold on clean result)
+"""
+import os, subprocess, shutil
+
+BASE_DIR = os.path.dirname(os.path.dirname(__file__))
+VID_DIR  = os.path.join(BASE_DIR, "assets", "videos", "hero", "clips")
+REEL_OUT = os.path.join(BASE_DIR, "assets", "videos", "hero", "hero-reel.mp4")
+TMP_DIR  = os.path.join(VID_DIR, "paced-tmp")
+os.makedirs(TMP_DIR, exist_ok=True)
+
+# Clip order: wine spill > extraction > stain > technician, stairs later
+CLIPS = [
+    "v3-shot-02",              # 1  - wine spill on sofa
+    "shot-05-extraction-couch",# 2  - extraction couch
+    "shot-03-technician",      # 3  - technician
+    "v3-shot-03",              # 4  - dirty stained carpet close-up
+    "v3-shot-06",              # 5  - living room clean carpet pan
+    "v3-shot-07",              # 6  - restaurant carpet glide
+    "v3-shot-05",              # 7  - office lobby carpet pan
+    "v2-shot-05-clean-stairs", # 8  - clean bright staircase
+    "v2-shot-07-restaurant",   # 9  - restaurant carpet
+    "v2-shot-06-office",       # 10 - bright office carpet
+    "shot-01-wide-room",       # 11 - wide room establishing
+    "shot-05-clean-reveal",    # 12 - clean reveal
+    "shot-04-extraction-carpet",# 13 - final reveal
+]
+
+# Duration for each clip
+DURATIONS = [
+    4.5,   # 1  wine spill — cut at 4.5s
+    5.0,   # 2
+    5.0,   # 3
+    4.0,   # 4  --- building pace ---
+    4.0,   # 5
+    4.0,   # 6
+    3.5,   # 7
+    3.5,   # 8
+    2.5,   # 9  --- rapid ---
+    2.5,   # 10
+    2.5,   # 11
+    2.5,   # 12
+    6.0,   # 13 final reveal — hold
+]
+
+paced_clips = []
+for i, (name, dur) in enumerate(zip(CLIPS, DURATIONS)):
+    src = os.path.join(VID_DIR, f"{name}.mp4")
+    out = os.path.join(TMP_DIR, f"{i:02d}-{name}.mp4")
+    if not os.path.exists(src):
+        print(f"  MISSING: {src}")
+        continue
+    print(f"[{i+1:02d}] {name} → {dur}s")
+    result = subprocess.run(
+        ["ffmpeg", "-y", "-i", src, "-t", str(dur),
+         "-c:v", "libx264", "-crf", "22", "-preset", "fast", out],
+        capture_output=True, text=True
+    )
+    if result.returncode != 0:
+        print(f"  ffmpeg error: {result.stderr[-200:]}")
+    else:
+        paced_clips.append(out)
+
+print(f"\n{len(paced_clips)}/{len(CLIPS)} clips trimmed")
+
+concat_file = os.path.join(TMP_DIR, "concat.txt")
+with open(concat_file, "w") as f:
+    for p in paced_clips:
+        f.write(f"file '{p}'\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: {REEL_OUT} ({os.path.getsize(REEL_OUT)//1024}KB)")
+    # Clean up tmp
+    shutil.rmtree(TMP_DIR)
+    print("Done.")
+else:
+    print(f"ffmpeg error: {result.stderr[-400:]}")

tools/build-reel-server.py

@@ -0,0 +1,84 @@
+"""Local server: serves clip-browser.html + handles POST /build-reel to run ffmpeg."""
+import http.server, json, os, subprocess, urllib.parse
+
+BASE_DIR = os.path.dirname(os.path.dirname(__file__))
+VID_DIR  = os.path.join(BASE_DIR, "assets", "videos", "hero", "clips")
+REEL_OUT = os.path.join(BASE_DIR, "assets", "videos", "hero", "hero-reel.mp4")
+TOOLS_DIR = os.path.dirname(__file__)
+
+class Handler(http.server.BaseHTTPRequestHandler):
+    def log_message(self, fmt, *args): pass
+
+    def do_GET(self):
+        path = self.path.split('?')[0]
+        if path in ('/', '/tools/clip-browser.html'):
+            self._serve_file(os.path.join(TOOLS_DIR, 'clip-browser.html'), 'text/html')
+        elif path.startswith('/assets/videos/'):
+            rel = path.lstrip('/')
+            fpath = os.path.join(BASE_DIR, rel)
+            if os.path.exists(fpath):
+                self._serve_file(fpath, 'video/mp4')
+            else:
+                self._404()
+        else:
+            self._404()
+
+    def do_POST(self):
+        if self.path == '/tools/build-reel-api.py':
+            length = int(self.headers.get('Content-Length', 0))
+            body = json.loads(self.rfile.read(length))
+            clips = body.get('clips', [])
+            if not clips:
+                self._json({'message': 'No clips provided.'}, 400)
+                return
+            concat_file = os.path.join(VID_DIR, 'concat-browser.txt')
+            missing = []
+            with open(concat_file, 'w') as f:
+                for name in clips:
+                    p = os.path.join(VID_DIR, f'{name}.mp4')
+                    if not os.path.exists(p):
+                        missing.append(name)
+                    else:
+                        f.write(f"file '{p}'\n")
+            if missing:
+                self._json({'message': f'Missing clips: {missing}'}, 400)
+                return
+            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:
+                size = os.path.getsize(REEL_OUT) // 1024
+                self._json({'message': f'Reel built: hero-reel.mp4 ({size}KB). Now rebuild Docker: docker compose up -d --build'})
+            else:
+                self._json({'message': f'ffmpeg error: {result.stderr[-300:]}'}, 500)
+        else:
+            self._404()
+
+    def _serve_file(self, path, ctype):
+        with open(path, 'rb') as f:
+            data = f.read()
+        self.send_response(200)
+        self.send_header('Content-Type', ctype)
+        self.send_header('Content-Length', len(data))
+        self.end_headers()
+        self.wfile.write(data)
+
+    def _json(self, obj, code=200):
+        data = json.dumps(obj).encode()
+        self.send_response(code)
+        self.send_header('Content-Type', 'application/json')
+        self.send_header('Content-Length', len(data))
+        self.end_headers()
+        self.wfile.write(data)
+
+    def _404(self):
+        self.send_response(404)
+        self.end_headers()
+
+if __name__ == '__main__':
+    port = 8088
+    print(f'Clip browser running at http://localhost:{port}/')
+    http.server.HTTPServer(('', port), Handler).serve_forever()

tools/clip-browser.html

@@ -0,0 +1,230 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<title>Lahr Clip Browser</title>
+<style>
+* { box-sizing: border-box; margin: 0; padding: 0; }
+body { background: #0a0a0b; color: #eee; font-family: Inter, sans-serif; padding: 24px; }
+h1 { font-size: 20px; color: #e8291b; margin-bottom: 6px; }
+p.sub { color: #888; font-size: 13px; margin-bottom: 24px; }
+.layout { display: grid; grid-template-columns: 1fr 340px; gap: 24px; }
+#clip-list { display: flex; flex-direction: column; gap: 10px; }
+.clip-item {
+  display: flex; align-items: center; gap: 12px;
+  background: #161618; border: 1px solid #2a2a2e; border-radius: 8px;
+  padding: 10px 12px; cursor: grab; user-select: none;
+  transition: border-color 0.15s;
+}
+.clip-item:hover { border-color: #e8291b; }
+.clip-item.dragging { opacity: 0.4; }
+.clip-item.drag-over { border-color: #e8291b; background: #1f1012; }
+.drag-handle { color: #555; font-size: 18px; flex-shrink: 0; cursor: grab; }
+.clip-thumb { width: 120px; height: 68px; object-fit: cover; border-radius: 4px; flex-shrink: 0; background: #222; }
+.clip-info { flex: 1; min-width: 0; }
+.clip-name { font-size: 13px; font-weight: 600; color: #ddd; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }
+.clip-size { font-size: 11px; color: #666; margin-top: 2px; }
+.clip-actions { display: flex; flex-direction: column; gap: 6px; flex-shrink: 0; }
+.btn-preview { background: #1e1e22; border: 1px solid #333; color: #ccc; padding: 5px 10px; border-radius: 4px; font-size: 11px; cursor: pointer; }
+.btn-preview:hover { border-color: #e8291b; color: #e8291b; }
+.btn-remove { background: transparent; border: 1px solid #333; color: #555; padding: 5px 10px; border-radius: 4px; font-size: 11px; cursor: pointer; }
+.btn-remove:hover { border-color: #e8291b; color: #e8291b; }
+
+.sidebar { position: sticky; top: 24px; }
+.preview-box { background: #161618; border: 1px solid #2a2a2e; border-radius: 8px; padding: 16px; margin-bottom: 16px; }
+.preview-box h3 { font-size: 13px; color: #888; margin-bottom: 10px; text-transform: uppercase; letter-spacing: 0.05em; }
+#preview-video { width: 100%; border-radius: 6px; background: #000; }
+#preview-name { font-size: 12px; color: #666; margin-top: 8px; text-align: center; }
+
+.reel-box { background: #161618; border: 1px solid #2a2a2e; border-radius: 8px; padding: 16px; }
+.reel-box h3 { font-size: 13px; color: #888; margin-bottom: 12px; text-transform: uppercase; letter-spacing: 0.05em; }
+#reel-order { font-size: 11px; color: #666; line-height: 1.8; margin-bottom: 14px; max-height: 220px; overflow-y: auto; }
+.btn-build { width: 100%; background: #e8291b; color: #fff; border: none; padding: 12px; border-radius: 6px; font-size: 14px; font-weight: 600; cursor: pointer; }
+.btn-build:hover { background: #c72216; }
+#build-output { margin-top: 12px; font-size: 11px; color: #888; background: #0d0d0f; border-radius: 4px; padding: 10px; display: none; white-space: pre-wrap; }
+
+.unused-section { margin-top: 32px; }
+.unused-section h2 { font-size: 14px; color: #555; margin-bottom: 12px; }
+#unused-list { display: flex; flex-direction: column; gap: 8px; }
+.unused-item { display: flex; align-items: center; gap: 12px; background: #111; border: 1px solid #1e1e22; border-radius: 8px; padding: 8px 12px; }
+.unused-item .clip-name { font-size: 12px; color: #666; }
+.btn-add { background: #1e1e22; border: 1px solid #333; color: #888; padding: 5px 10px; border-radius: 4px; font-size: 11px; cursor: pointer; margin-left: auto; }
+.btn-add:hover { border-color: #4a9; color: #4a9; }
+</style>
+</head>
+<body>
+
+<h1>Lahr Clip Browser</h1>
+<p class="sub">Drag clips to reorder. Click Preview to watch. Remove clips from reel. Build Reel when ready.</p>
+
+<div class="layout">
+  <div>
+    <div id="clip-list"></div>
+    <div class="unused-section">
+      <h2>Available (not in reel)</h2>
+      <div id="unused-list"></div>
+    </div>
+  </div>
+  <div class="sidebar">
+    <div class="preview-box">
+      <h3>Preview</h3>
+      <video id="preview-video" controls></video>
+      <div id="preview-name">Click Preview on any clip</div>
+    </div>
+    <div class="reel-box">
+      <h3>Current Reel Order</h3>
+      <div id="reel-order"></div>
+      <button class="btn-build" onclick="buildReel()">Build Reel</button>
+      <div id="build-output"></div>
+    </div>
+  </div>
+</div>
+
+<script>
+const BASE = '/assets/videos/hero/clips/';
+
+const ALL_CLIPS = [
+  { name: 'v3-shot-01', label: 'v3 · Family enters door, pans to carpet', size: '5.2MB' },
+  { name: 'v3-shot-02', label: 'v3 · Wine spill on sofa close-up', size: '6.0MB' },
+  { name: 'v3-shot-03', label: 'v3 · Dirty stained carpet close-up', size: '3.3MB' },
+  { name: 'v3-shot-04', label: 'v3 · Clean bright sofa pullback', size: '5.6MB' },
+  { name: 'v3-shot-05', label: 'v3 · Office lobby carpet pan', size: '4.7MB' },
+  { name: 'v3-shot-06', label: 'v3 · Living room clean carpet pan', size: '4.9MB' },
+  { name: 'v3-shot-07', label: 'v3 · Restaurant carpet glide', size: '3.1MB' },
+  { name: 'v2-shot-01-door-entry', label: 'v2 · Door entry muddy boots', size: '2.2MB' },
+  { name: 'v2-shot-02-mud-on-carpet', label: 'v2 · Mud boots on carpet floor level', size: '4.1MB' },
+  { name: 'v2-shot-03-stain-on-chair', label: 'v2 · Stain on chair close-up', size: '3.3MB' },
+  { name: 'v2-shot-04-extraction-carpet', label: 'v2 · Extraction machine on carpet', size: '4.2MB' },
+  { name: 'v2-shot-05-clean-stairs', label: 'v2 · Clean bright staircase', size: '5.6MB' },
+  { name: 'v2-shot-06-office', label: 'v2 · Bright office carpet', size: '4.7MB' },
+  { name: 'v2-shot-07-restaurant', label: 'v2 · Restaurant carpet', size: '4.7MB' },
+  { name: 'shot-01-door-opens-trimmed', label: 'v1 · Door opens (trimmed 2.5s)', size: '525KB' },
+  { name: 'shot-01-wide-room', label: 'v1 · Wide room establishing', size: '2.6MB' },
+  { name: 'shot-02-pan-to-stains', label: 'v1 · Pan to stains / muddy shoes', size: '2.8MB' },
+  { name: 'shot-02-staircase', label: 'v1 · Staircase', size: '1.4MB' },
+  { name: 'shot-03-stain-closeup', label: 'v1 · Stain close-up', size: '4.7MB' },
+  { name: 'shot-03-technician', label: 'v1 · Technician', size: '1.6MB' },
+  { name: 'shot-04-extraction-carpet', label: 'v1 · Extraction carpet (clean reveal)', size: '6.2MB' },
+  { name: 'shot-04-extraction-closeup', label: 'v1 · Extraction close-up', size: '2.0MB' },
+  { name: 'shot-05-clean-reveal', label: 'v1 · Clean reveal', size: '2.2MB' },
+  { name: 'shot-05-extraction-couch', label: 'v1 · Extraction couch', size: '3.5MB' },
+  { name: 'shot-06-extraction-stairs', label: 'v1 · Extraction stairs', size: '5.4MB' },
+  { name: 'shot-07-office-entryway', label: 'v1 · Office entryway', size: '5.9MB' },
+  { name: 'shot-08-showroom', label: 'v1 · Showroom', size: '4.1MB' },
+  { name: 'shot-09-technician-unloading', label: 'v1 · Technician unloading van', size: '3.7MB' },
+  { name: 'shot-01-door-opens', label: 'v1 · Door opens (full 6s)', size: '1.5MB' },
+];
+
+// Default reel = current v3 set
+let reelClips = ALL_CLIPS.slice(0, 7).map(c => c.name);
+
+function getClip(name) { return ALL_CLIPS.find(c => c.name === name); }
+
+function render() {
+  const list = document.getElementById('clip-list');
+  list.innerHTML = '';
+  reelClips.forEach((name, idx) => {
+    const clip = getClip(name);
+    if (!clip) return;
+    const div = document.createElement('div');
+    div.className = 'clip-item';
+    div.draggable = true;
+    div.dataset.name = name;
+    div.innerHTML = `
+      <span class="drag-handle">⠿</span>
+      <video class="clip-thumb" src="${BASE}${name}.mp4" muted preload="metadata"></video>
+      <div class="clip-info">
+        <div class="clip-name">${idx+1}. ${clip.label}</div>
+        <div class="clip-size">${clip.size}</div>
+      </div>
+      <div class="clip-actions">
+        <button class="btn-preview" onclick="preview('${name}', '${clip.label}')">Preview</button>
+        <button class="btn-remove" onclick="remove('${name}')">Remove</button>
+      </div>`;
+    div.addEventListener('dragstart', dragStart);
+    div.addEventListener('dragover', dragOver);
+    div.addEventListener('drop', drop);
+    div.addEventListener('dragend', dragEnd);
+    list.appendChild(div);
+  });
+
+  const unused = ALL_CLIPS.filter(c => !reelClips.includes(c.name));
+  const ulist = document.getElementById('unused-list');
+  ulist.innerHTML = '';
+  unused.forEach(clip => {
+    const div = document.createElement('div');
+    div.className = 'unused-item';
+    div.innerHTML = `
+      <div class="clip-name">${clip.label} <span style="color:#444">(${clip.size})</span></div>
+      <button class="btn-add" onclick="addToReel('${clip.name}')">+ Add</button>`;
+    ulist.appendChild(div);
+  });
+
+  const orderEl = document.getElementById('reel-order');
+  orderEl.innerHTML = reelClips.map((n,i) => {
+    const c = getClip(n);
+    return `<div>${i+1}. ${c ? c.label : n}</div>`;
+  }).join('');
+}
+
+function preview(name, label) {
+  const v = document.getElementById('preview-video');
+  v.src = BASE + name + '.mp4';
+  v.play();
+  document.getElementById('preview-name').textContent = label;
+}
+
+function remove(name) {
+  reelClips = reelClips.filter(n => n !== name);
+  render();
+}
+
+function addToReel(name) {
+  reelClips.push(name);
+  render();
+}
+
+let dragSrc = null;
+function dragStart(e) { dragSrc = this; this.classList.add('dragging'); }
+function dragOver(e) { e.preventDefault(); document.querySelectorAll('.clip-item').forEach(el => el.classList.remove('drag-over')); this.classList.add('drag-over'); }
+function drop(e) {
+  e.preventDefault();
+  if (dragSrc === this) return;
+  const fromName = dragSrc.dataset.name;
+  const toName = this.dataset.name;
+  const fi = reelClips.indexOf(fromName);
+  const ti = reelClips.indexOf(toName);
+  reelClips.splice(fi, 1);
+  reelClips.splice(ti, 0, fromName);
+  render();
+}
+function dragEnd() { document.querySelectorAll('.clip-item').forEach(el => el.classList.remove('dragging','drag-over')); }
+
+async function buildReel() {
+  const out = document.getElementById('build-output');
+  out.style.display = 'block';
+  out.textContent = 'Sending to server...';
+
+  const resp = await fetch('/tools/build-reel-api.py', {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ clips: reelClips })
+  });
+
+  if (!resp.ok) {
+    // Fallback: show the ffmpeg command to copy
+    const lines = reelClips.map(n => `file 'assets/videos/hero/clips/${n}.mp4'`).join('\n');
+    out.textContent = 'Server not available. Run this manually:\n\n' +
+      '# 1. Save as concat.txt:\n' + lines + '\n\n' +
+      '# 2. Run ffmpeg:\n' +
+      'ffmpeg -y -f concat -safe 0 -i concat.txt -c:v libx264 -crf 22 -preset fast -movflags +faststart assets/videos/hero/hero-reel.mp4';
+  } else {
+    const data = await resp.json();
+    out.textContent = data.message || 'Done.';
+  }
+}
+
+render();
+</script>
+</body>
+</html>

tools/gen-images-comfyui.py

@@ -0,0 +1,186 @@
+"""Generate replacement service images via ComfyUI SDXL (local, no API key needed)."""
+import json, time, urllib.request, urllib.error, os, sys
+
+COMFY = "http://localhost:8188"
+OUT_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "images", "services")
+CKPT = "sd_xl_base_1.0.safetensors"
+
+IMAGES = [
+    {
+        "filename": "vacation-rentals.jpg",
+        "positive": (
+            "bright cozy vacation rental living room interior, clean beige carpet, "
+            "comfortable furniture, large windows with natural light, Finger Lakes "
+            "style decor, warm inviting atmosphere, no people, no equipment, "
+            "professional interior photography, ultra-realistic"
+        ),
+        "negative": (
+            "people, person, human, worker, machine, vacuum, equipment, dirty, stain, "
+            "text, watermark, blurry, low quality, cartoon, dark"
+        ),
+    },
+    {
+        "filename": "office-spaces.jpg",
+        "positive": (
+            "modern corporate office interior, clean dark grey commercial carpet tiles, "
+            "open plan workspace, white desks, professional lighting, glass partitions, "
+            "no people, no equipment, architectural photography, ultra-realistic"
+        ),
+        "negative": (
+            "people, person, human, worker, machine, vacuum, equipment, dirty, stain, "
+            "text, watermark, blurry, low quality, cartoon"
+        ),
+    },
+    {
+        "filename": "hotels-inns.jpg",
+        "positive": (
+            "elegant hotel corridor interior, clean patterned carpet runner, warm wall "
+            "sconce lighting, white walls, numbered room doors along hallway, "
+            "hospitality interior design, no people, no equipment, "
+            "professional photography, ultra-realistic"
+        ),
+        "negative": (
+            "people, person, human, worker, machine, vacuum, equipment, dirty, stain, "
+            "text, watermark, blurry, low quality, cartoon"
+        ),
+    },
+    {
+        "filename": "retail-showrooms.jpg",
+        "positive": (
+            "upscale retail showroom interior, clean light grey carpet flooring, "
+            "modern display shelving, bright overhead track lighting, white walls, "
+            "customer-facing professional space, no people, no equipment, "
+            "architectural photography, ultra-realistic"
+        ),
+        "negative": (
+            "people, person, human, worker, machine, vacuum, equipment, dirty, stain, "
+            "text, watermark, blurry, low quality, cartoon"
+        ),
+    },
+    {
+        "filename": "property-management.jpg",
+        "positive": (
+            "clean apartment unit interior, fresh beige carpet throughout living room, "
+            "neutral walls, bright windows, move-in ready condition, residential "
+            "property management style, no people, no furniture, no equipment, "
+            "real estate photography, ultra-realistic"
+        ),
+        "negative": (
+            "people, person, human, worker, machine, vacuum, equipment, dirty, stain, "
+            "text, watermark, blurry, low quality, cartoon"
+        ),
+    },
+]
+
+def build_workflow(positive, negative, seed=None):
+    import random
+    if seed is None:
+        seed = random.randint(0, 2**32)
+    return {
+        "3": {
+            "class_type": "KSampler",
+            "inputs": {
+                "cfg": 7.0,
+                "denoise": 1.0,
+                "latent_image": ["5", 0],
+                "model": ["4", 0],
+                "negative": ["7", 0],
+                "positive": ["6", 0],
+                "sampler_name": "euler",
+                "scheduler": "normal",
+                "seed": seed,
+                "steps": 25,
+            },
+        },
+        "4": {
+            "class_type": "CheckpointLoaderSimple",
+            "inputs": {"ckpt_name": CKPT},
+        },
+        "5": {
+            "class_type": "EmptyLatentImage",
+            "inputs": {"batch_size": 1, "height": 768, "width": 1024},
+        },
+        "6": {
+            "class_type": "CLIPTextEncode",
+            "inputs": {"clip": ["4", 1], "text": positive},
+        },
+        "7": {
+            "class_type": "CLIPTextEncode",
+            "inputs": {"clip": ["4", 1], "text": negative},
+        },
+        "8": {
+            "class_type": "VAEDecode",
+            "inputs": {"samples": ["3", 0], "vae": ["4", 2]},
+        },
+        "9": {
+            "class_type": "SaveImage",
+            "inputs": {"filename_prefix": "lahr_gen", "images": ["8", 0]},
+        },
+    }
+
+
+def queue_prompt(workflow):
+    data = json.dumps({"prompt": workflow}).encode()
+    req = urllib.request.Request(
+        f"{COMFY}/prompt",
+        data=data,
+        headers={"Content-Type": "application/json"},
+    )
+    with urllib.request.urlopen(req) as resp:
+        return json.loads(resp.read())["prompt_id"]
+
+
+def wait_for_result(prompt_id, timeout=600):
+    start = time.time()
+    while time.time() - start < timeout:
+        try:
+            with urllib.request.urlopen(f"{COMFY}/history/{prompt_id}") as resp:
+                hist = json.loads(resp.read())
+            if prompt_id in hist:
+                outputs = hist[prompt_id].get("outputs", {})
+                for node_id, node_out in outputs.items():
+                    if "images" in node_out:
+                        return node_out["images"]
+        except Exception:
+            pass
+        print("  waiting...", flush=True)
+        time.sleep(5)
+    return None
+
+
+def download_image(img_info, out_path):
+    fname = img_info["filename"]
+    subfolder = img_info.get("subfolder", "")
+    img_type = img_info.get("type", "output")
+    params = f"filename={fname}&subfolder={subfolder}&type={img_type}"
+    url = f"{COMFY}/view?{params}"
+    with urllib.request.urlopen(url) as resp:
+        data = resp.read()
+    # Convert PNG to JPEG via PIL if available
+    try:
+        from PIL import Image
+        import io
+        img = Image.open(io.BytesIO(data)).convert("RGB")
+        img.save(out_path, "JPEG", quality=90)
+        print(f"  Saved JPEG ({len(data)//1024}KB raw -> {os.path.getsize(out_path)//1024}KB)")
+    except ImportError:
+        # Save as-is (PNG), rename accordingly
+        png_path = out_path.replace(".jpg", ".png")
+        with open(png_path, "wb") as f:
+            f.write(data)
+        print(f"  Saved PNG (PIL not available): {png_path}")
+
+
+for spec in IMAGES:
+    out_path = os.path.join(OUT_DIR, spec["filename"])
+    print(f"\nGenerating: {spec['filename']}")
+    workflow = build_workflow(spec["positive"], spec["negative"])
+    prompt_id = queue_prompt(workflow)
+    print(f"  Queued: {prompt_id}")
+    images = wait_for_result(prompt_id)
+    if images:
+        download_image(images[0], out_path)
+    else:
+        print("  FAILED: no output after timeout")
+
+print("\nDone.")

tools/gen-images.py

@@ -0,0 +1,106 @@
+"""
+Lahr Carpet Cleaning — Gemini Imagen hero image generator.
+Generates: hero living room, cleaning in progress, clean result.
+Saves to: assets/images/hero/
+Run: python3 tools/gen-images.py
+"""
+import os
+import sys
+
+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")
+OUT_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "images", "hero")
+os.makedirs(OUT_DIR, exist_ok=True)
+
+client = genai.Client(api_key=API_KEY)
+
+IMAGES = [
+    {
+        "name": "hero-living-room",
+        "prompt": (
+            "Luxurious modern living room with spotlessly clean cream carpet, "
+            "natural light streaming through large windows, contemporary furniture, "
+            "professional interior photography, warm inviting atmosphere, no people, "
+            "ultra-realistic, 8K quality"
+        ),
+        "aspect": "16:9",
+    },
+    {
+        "name": "hero-clean-result",
+        "prompt": (
+            "Pristine white plush carpet in a beautiful upscale residential home, "
+            "dramatic before-after transformation, deeply cleaned carpet with "
+            "vacuum lines visible, natural light, professional photography, no people"
+        ),
+        "aspect": "16:9",
+    },
+    {
+        "name": "hero-technician",
+        "prompt": (
+            "Professional carpet cleaning technician pushing a large upright hot water "
+            "extraction machine across residential carpet in a bright modern home interior. "
+            "Machine resembles an oversized upright vacuum cleaner with a cylindrical body. "
+            "No steam visible anywhere, no water spraying, no hoses visible, completely dry. "
+            "Technician shown from behind or side, no face, plain black shirt, no logo. "
+            "High-end professional photography."
+        ),
+        "aspect": "16:9",
+    },
+    {
+        "name": "hero-before-after",
+        "prompt": (
+            "Side-by-side residential living room carpet: left half heavily soiled with mud "
+            "and dark stains, right half same carpet after professional hot water extraction "
+            "cleaning, bright and pristine. No steam, no water, no machines visible anywhere. "
+            "Dramatic before-after contrast. Professional photography, no people."
+        ),
+        "aspect": "16:9",
+    },
+    {
+        "name": "hero-stairs",
+        "prompt": (
+            "Beautiful staircase with freshly cleaned plush carpet on stairs, "
+            "modern home interior, natural light from above, professional result, "
+            "no people, architectural photography, high-end residential"
+        ),
+        "aspect": "3:4",
+    },
+]
+
+def generate():
+    for item in IMAGES:
+        out_path = os.path.join(OUT_DIR, f"{item['name']}.jpg")
+        print(f"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=item["aspect"],
+                    output_mime_type="image/jpeg",
+                    safety_filter_level="block_low_and_above",
+                ),
+            )
+            if resp.generated_images:
+                image_bytes = resp.generated_images[0].image.image_bytes
+                with open(out_path, "wb") as f:
+                    f.write(image_bytes)
+                size_kb = len(image_bytes) // 1024
+                print(f"  Saved {out_path} ({size_kb}KB)")
+            else:
+                print(f"  No image returned for {item['name']}")
+        except Exception as e:
+            print(f"  Error on {item['name']}: {e}")
+
+if __name__ == "__main__":
+    generate()
+    print("\nDone. Images in assets/images/hero/")

tools/gen-locations.py

@@ -0,0 +1,226 @@
+"""
+Lahr Carpet Cleaning — Location page generator.
+Creates /locations/<slug>/index.html for each city.
+Run: python3 tools/gen-locations.py
+"""
+import os
+
+BASE_DIR = os.path.dirname(os.path.dirname(__file__))
+LOC_DIR  = os.path.join(BASE_DIR, "locations")
+
+CITIES = [
+    {"name": "Waterloo",        "slug": "waterloo-ny",        "county": "Seneca County",  "note": "Our home base. Fastest response times in the area."},
+    {"name": "Geneva",          "slug": "geneva-ny",           "county": "Ontario County", "note": "Full residential and commercial services throughout Geneva."},
+    {"name": "Seneca Falls",    "slug": "seneca-falls-ny",     "county": "Seneca County",  "note": "Serving homes, vacation rentals, and businesses in Seneca Falls."},
+    {"name": "Canandaigua",     "slug": "canandaigua-ny",      "county": "Ontario County", "note": "Lakefront homes, rentals, and businesses along Canandaigua Lake."},
+    {"name": "Penn Yan",        "slug": "penn-yan-ny",         "county": "Yates County",   "note": "Homes, wineries, and short-term rentals in the Penn Yan area."},
+    {"name": "Newark",          "slug": "newark-ny",           "county": "Wayne County",   "note": "Carpet and upholstery cleaning for homes and businesses in Newark."},
+    {"name": "Clifton Springs", "slug": "clifton-springs-ny",  "county": "Ontario County", "note": "Residential and commercial cleaning throughout Clifton Springs."},
+    {"name": "Lodi",            "slug": "lodi-ny",             "county": "Seneca County",  "note": "Serving homes and vacation properties in Lodi and surrounding areas."},
+    {"name": "Himrod",          "slug": "himrod-ny",           "county": "Yates County",   "note": "Carpet and floor cleaning for homes and rentals in the Himrod area."},
+    {"name": "Phelps",          "slug": "phelps-ny",           "county": "Ontario County", "note": "Residential carpet and upholstery cleaning throughout Phelps."},
+    {"name": "Shortsville",     "slug": "shortsville-ny",      "county": "Ontario County", "note": "Home and business cleaning services in Shortsville, NY."},
+    {"name": "Victor",          "slug": "victor-ny",           "county": "Ontario County", "note": "Residential and commercial carpet cleaning throughout Victor."},
+    {"name": "Naples",          "slug": "naples-ny",           "county": "Ontario County", "note": "Serving homes, wineries, and vacation rentals in the Naples area."},
+    {"name": "Gorham",          "slug": "gorham-ny",           "county": "Ontario County", "note": "Carpet and floor cleaning for homes and properties in Gorham."},
+    {"name": "Manchester",      "slug": "manchester-ny",       "county": "Ontario County", "note": "Residential and commercial cleaning services in Manchester, NY."},
+    {"name": "Ovid",            "slug": "ovid-ny",             "county": "Seneca County",  "note": "Serving homes and rental properties throughout Ovid."},
+    {"name": "Clyde",           "slug": "clyde-ny",            "county": "Wayne County",   "note": "Carpet, upholstery, and floor cleaning for homes and businesses in Clyde."},
+    {"name": "Farmington",      "slug": "farmington-ny",       "county": "Ontario County", "note": "Residential and commercial carpet cleaning throughout Farmington."},
+    {"name": "East Bloomfield", "slug": "east-bloomfield-ny",  "county": "Ontario County", "note": "Serving homes and properties in East Bloomfield and surrounding areas."},
+    {"name": "Rushville",       "slug": "rushville-ny",        "county": "Yates County",   "note": "Carpet and upholstery cleaning for homes and rentals in Rushville."},
+    {"name": "Finger Lakes",    "slug": "finger-lakes-ny",     "county": "Region",         "note": "Serving vacation rentals, wineries, and homes across the Finger Lakes region."},
+]
+
+SERVICES = [
+    {"name": "Carpet Cleaning",    "slug": "/services/carpet-cleaning/", "img": "/assets/images/services/carpet-cleaning.jpg",    "sub": "In-Home Service",    "desc": "Hot water extraction removes deep-seated dirt, allergens, and stains from carpet fibers throughout your home."},
+    {"name": "Stairs Cleaning",    "slug": "/services/stairs/",          "img": "/assets/images/services/stairs-cleaning.jpg",    "sub": "Step by Step",       "desc": "Stairs collect more dirt per square inch than any flat surface. We clean every tread, riser, and landing."},
+    {"name": "Upholstery Cleaning","slug": "/services/upholstery/",      "img": "/assets/images/services/upholstery-cleaning.jpg","sub": "Furniture Refresh",  "desc": "Safe, effective cleaning for sofas, chairs, and mattresses. We work with all fabric types and leave no residue."},
+    {"name": "Floor Cleaning",     "slug": "/services/floors/",          "img": "/assets/images/services/floor-cleaning.jpg",     "sub": "Hard Surface Care",  "desc": "Wood floor cleaning and tile and grout restoration that brings hard surfaces back to their original condition."},
+    {"name": "Area Rug Cleaning",  "slug": "/services/area-rugs/",       "img": "/assets/images/services/area-rug-cleaning.jpg",  "sub": "Delicate Care",      "desc": "Gentle, specialized cleaning for oriental, Persian, and delicate rugs that restores color and removes embedded dirt."},
+    {"name": "Add-On Services",    "slug": "/services/add-ons/",         "img": "/assets/images/services/add-ons.jpg",            "sub": "Extra Care",         "desc": "Furniture moving, pet hair removal, odor treatment, and heavily soiled area care available alongside any service."},
+]
+
+HERO_IMAGES = [
+    "/assets/images/hero/hero-living-room.jpg",
+    "/assets/images/hero/hero-clean-result.jpg",
+    "/assets/images/hero/hero-technician.jpg",
+    "/assets/images/hero/hero-before-after.jpg",
+    "/assets/images/hero/hero-stairs.jpg",
+]
+
+
+def service_card(svc, city_name):
+    accent_word, rest = svc["name"].split(" ", 1) if " " in svc["name"] else (svc["name"], "")
+    h3 = f'<span class="text-accent">{accent_word}</span> {rest}'.strip()
+    return f"""                <div class="service-card">
+                    <div class="service-image"><img src="{svc['img']}" alt="{svc['name']} in {city_name}, NY"></div>
+                    <h3 class="heading-3">{h3}</h3>
+                    <div class="text-block-4">{svc['sub']}</div>
+                    <p class="paragraph-2">{svc['desc']}</p>
+                    <a href="{svc['slug']}" class="btn btn-primary">Learn More</a>
+                </div>"""
+
+
+def page_html(city, idx):
+    hero_img = HERO_IMAGES[idx % len(HERO_IMAGES)]
+    cards    = "\n".join(service_card(s, city["name"]) for s in SERVICES)
+    name     = city["name"]
+    county   = city["county"]
+    note     = city["note"]
+    slug     = city["slug"]
+
+    return f"""<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Carpet Cleaning in {name}, NY | Lahr Carpet Cleaning</title>
+    <meta name="description" content="Professional carpet, upholstery, and floor cleaning in {name}, NY. Lahr Carpet Cleaning serves {county} and the Finger Lakes region. Call 315-719-1218.">
+    <link rel="stylesheet" href="/assets/css/styles.css?v=5">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.5.1/css/all.min.css">
+</head>
+<body>
+    <header class="header" id="header">
+        <div class="container"><div id="site-nav"></div></div>
+    </header>
+
+    <section class="page-hero" style="background-image: url('{hero_img}');">
+        <div class="container">
+            <div class="page-hero-content">
+                <span class="hero-eyebrow">{county} &mdash; Finger Lakes</span>
+                <h1 class="hero-title">{name},<br><span class="text-accent">NY</span></h1>
+                <p>{note}</p>
+                <div class="hero-actions">
+                    <a href="/contact/" class="btn btn-primary btn-large">Book Now</a>
+                    <a href="tel:315-719-1218" class="btn btn-ghost btn-large"><i class="fas fa-phone"></i> 315-719-1218</a>
+                </div>
+            </div>
+        </div>
+    </section>
+
+    <section class="services-overview">
+        <div class="container">
+            <div class="section-header">
+                <span class="section-label">{name}, NY</span>
+                <h2 class="section-title">Services in {name}</h2>
+                <p class="section-subtitle">We serve {name} and the surrounding {county} communities. Call to confirm availability for your address.</p>
+            </div>
+            <div class="services-grid">
+{cards}
+            </div>
+        </div>
+    </section>
+
+    <section class="cta-banner">
+        <div class="cta-content">
+            <h2 class="heading-4"><strong>Serving </strong><span class="text-accent"><strong>{name}</strong></span></h2>
+            <p class="paragraph-4">Call 315-719-1218 or submit the form for a free estimate in {name}, NY.</p>
+            <a href="/contact/" class="btn btn-primary">Get a Free Estimate</a>
+        </div>
+    </section>
+
+    <div id="site-footer"></div>
+    <script src="/assets/js/components.js"></script>
+    <script src="/assets/js/main.js"></script>
+</body>
+</html>
+"""
+
+
+def locations_index():
+    city_cards = []
+    for i, city in enumerate(CITIES):
+        img = HERO_IMAGES[i % len(HERO_IMAGES)]
+        name = city["name"]
+        county = city["county"]
+        note = city["note"]
+        slug = city["slug"]
+        if " " in name:
+            accent_word, rest = name.split(" ", 1)
+            h3 = f'<span class="text-accent">{accent_word}</span> {rest}, NY'
+        else:
+            h3 = f'<span class="text-accent">{name}</span>, NY'
+        city_cards.append(f"""                <div class="service-card">
+                    <div class="service-image"><img src="{img}" alt="{name} NY"></div>
+                    <h3 class="heading-3">{h3}</h3>
+                    <div class="text-block-4">{county}</div>
+                    <p class="paragraph-2">{note}</p>
+                    <a href="/locations/{slug}/" class="btn btn-primary">View Services</a>
+                </div>""")
+
+    cards_html = "\n".join(city_cards)
+    return f"""<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Service Areas | Lahr Carpet Cleaning | Finger Lakes, NY</title>
+    <meta name="description" content="Lahr Carpet Cleaning serves Waterloo, Geneva, Seneca Falls, Canandaigua, Penn Yan, and 16 more cities across the Finger Lakes region. Call 315-719-1218.">
+    <link rel="stylesheet" href="/assets/css/styles.css?v=5">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.5.1/css/all.min.css">
+</head>
+<body>
+    <header class="header" id="header">
+        <div class="container"><div id="site-nav"></div></div>
+    </header>
+
+    <section class="page-hero" style="background-image: url('/assets/images/hero/hero-technician.jpg');">
+        <div class="container">
+            <div class="page-hero-content">
+                <span class="hero-eyebrow">Finger Lakes Region</span>
+                <h1 class="hero-title">Service<br><span class="text-accent">Areas</span></h1>
+                <p>We clean carpets, upholstery, rugs, and hard floors across 21 cities in Upstate New York. Select your city below.</p>
+                <div class="hero-actions">
+                    <a href="/contact/" class="btn btn-primary btn-large">Book Now</a>
+                    <a href="tel:315-719-1218" class="btn btn-ghost btn-large"><i class="fas fa-phone"></i> 315-719-1218</a>
+                </div>
+            </div>
+        </div>
+    </section>
+
+    <section class="services-overview">
+        <div class="container">
+            <div class="section-header">
+                <span class="section-label">Where We Work</span>
+                <h2 class="section-title">Cities We Serve</h2>
+                <p class="section-subtitle">Based in Waterloo, NY. We travel throughout Seneca, Ontario, Yates, Wayne, and Cayuga counties.</p>
+            </div>
+            <div class="services-grid">
+{cards_html}
+            </div>
+        </div>
+    </section>
+
+    <section class="cta-banner">
+        <div class="cta-content">
+            <h2 class="heading-4"><strong>Not sure if we cover your area?</strong></h2>
+            <p class="paragraph-4">Call 315-719-1218 or submit the form and we will confirm availability for your address.</p>
+            <a href="/contact/" class="btn btn-primary">Get a Free Estimate</a>
+        </div>
+    </section>
+
+    <div id="site-footer"></div>
+    <script src="/assets/js/components.js"></script>
+    <script src="/assets/js/main.js"></script>
+</body>
+</html>
+"""
+
+
+if __name__ == "__main__":
+    # Write locations index
+    with open(os.path.join(LOC_DIR, "index.html"), "w") as f:
+        f.write(locations_index())
+    print("Wrote locations/index.html")
+
+    # Write each city page
+    for i, city in enumerate(CITIES):
+        city_dir = os.path.join(LOC_DIR, city["slug"])
+        os.makedirs(city_dir, exist_ok=True)
+        out_path = os.path.join(city_dir, "index.html")
+        with open(out_path, "w") as f:
+            f.write(page_html(city, i))
+        print(f"Wrote locations/{city['slug']}/index.html")
+
+    print(f"\nDone. {len(CITIES)} city pages + index generated.")

tools/gen-missing-2.py

@@ -0,0 +1,60 @@
+"""Generate the 2 missing service images."""
+import os, sys
+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")
+OUT_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "images", "services")
+client  = genai.Client(api_key=API_KEY)
+
+TARGETS = [
+    {
+        "name": "property-management",
+        "prompt": (
+            "View across three clean empty apartment living rooms, each with spotlessly clean "
+            "beige carpet showing fresh extraction lines after professional hot water extraction cleaning. "
+            "Bright neutral interiors ready for new tenants. Natural light, no furniture, "
+            "no people, no equipment. Professional real estate photography, ultra-realistic."
+        ),
+    },
+    {
+        "name": "commercial-overview",
+        "prompt": (
+            "Professional carpet cleaning technician in a plain black shirt, shown from the side, "
+            "pushing a large upright extraction machine through a bright commercial building lobby. "
+            "Clean bright carpet behind the machine. No steam, no water spraying, no face visible. "
+            "Professional editorial photography, ultra-realistic."
+        ),
+    },
+]
+
+for item in TARGETS:
+    out_path = os.path.join(OUT_DIR, f"{item['name']}.jpg")
+    print(f"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 ({len(b)//1024}KB)")
+        else:
+            print(f"  No image returned")
+    except Exception as e:
+        print(f"  Error: {e}")
+
+print("Done.")

tools/gen-service-images.py

@@ -0,0 +1,179 @@
+"""
+Lahr Carpet Cleaning — Service card image generator.
+Generates 12 unique images for residential and commercial service cards.
+Saves to: assets/images/services/
+Run: python3 tools/gen-service-images.py
+"""
+import os
+import sys
+
+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")
+OUT_DIR  = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "images", "services")
+os.makedirs(OUT_DIR, exist_ok=True)
+
+client = genai.Client(api_key=API_KEY)
+
+IMAGES = [
+    # ── Residential ──────────────────────────────────────────────────────────
+    {
+        "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": "stairs-cleaning",
+        "prompt": (
+            "Wide cinematic shot of a carpeted residential staircase. Each step has clean, "
+            "bright plush carpet that looks freshly cleaned with visible extraction lines. "
+            "Modern upstate New York home interior, natural light from above, "
+            "warm wood banisters, no people, no equipment visible. "
+            "Professional interior photography, ultra-realistic."
+        ),
+    },
+    {
+        "name": "upholstery-cleaning",
+        "prompt": (
+            "Close-up of a clean grey linen sofa cushion showing bright, lifted fabric texture "
+            "after professional upholstery cleaning. Half the cushion shows the before "
+            "(slightly soiled, flat fabric) and half shows the cleaned result (bright, fluffy, refreshed). "
+            "Natural window light, residential living room in background, no people, no equipment. "
+            "Ultra-realistic product photography."
+        ),
+    },
+    {
+        "name": "floor-cleaning",
+        "prompt": (
+            "Wide shot of a gleaming hardwood floor in a modern residential home after professional "
+            "cleaning. The floor reflects soft natural window light, showing deep grain detail. "
+            "Contemporary furniture in background, no people, no cleaning equipment visible. "
+            "Professional interior photography, ultra-realistic."
+        ),
+    },
+    {
+        "name": "area-rug-cleaning",
+        "prompt": (
+            "Overhead flat-lay shot of a large vibrant Persian or oriental area rug "
+            "with rich red, navy, and cream geometric patterns, looking freshly cleaned — "
+            "colors vivid, fibers lifted and bright. Hardwood floor beneath. "
+            "No people, no equipment, no water. Professional product photography, ultra-realistic."
+        ),
+    },
+    {
+        "name": "add-ons",
+        "prompt": (
+            "Close-up macro shot of clean carpet fibers being lifted by a professional "
+            "grooming brush after hot water extraction cleaning. The fibers are bright, "
+            "fluffy, and standing upright. Warm light catches the texture. "
+            "No steam, no water, no people. Ultra-realistic macro photography."
+        ),
+    },
+    # ── Commercial ───────────────────────────────────────────────────────────
+    {
+        "name": "vacation-rentals",
+        "prompt": (
+            "Bright, airy vacation rental living room in the Finger Lakes region of upstate New York. "
+            "Spotlessly clean cream carpet, contemporary furniture, large windows with lake views, "
+            "warm natural afternoon light. Inviting and fresh. No people, no equipment. "
+            "Professional real estate photography, ultra-realistic."
+        ),
+    },
+    {
+        "name": "office-spaces",
+        "prompt": (
+            "Wide shot of a clean modern corporate office with freshly cleaned dark charcoal carpet "
+            "throughout. Open plan workspace, glass partitions, professional lighting. "
+            "Carpet shows neat vacuum lines indicating recent professional cleaning. "
+            "No people, no equipment. Professional architectural photography, ultra-realistic."
+        ),
+    },
+    {
+        "name": "hotels-inns",
+        "prompt": (
+            "Elegant hotel corridor in a boutique upstate New York inn. Clean, plush patterned "
+            "carpet runner down the hallway with fresh vacuum lines. Warm sconce lighting, "
+            "wood paneling, framed art on walls. No people, no equipment. "
+            "Professional hospitality photography, ultra-realistic."
+        ),
+    },
+    {
+        "name": "retail-showrooms",
+        "prompt": (
+            "Wide shot of an upscale retail showroom or winery tasting room in the Finger Lakes. "
+            "Clean, rich carpet throughout, warm lighting, product displays on shelves. "
+            "Carpet looks freshly extracted — bright and spotless. "
+            "No people, no equipment. Professional commercial interior photography, ultra-realistic."
+        ),
+    },
+    {
+        "name": "property-management",
+        "prompt": (
+            "View across three clean apartment living rooms in sequence, each showing "
+            "spotlessly clean beige carpet with fresh vacuum lines after professional cleaning. "
+            "Bright, neutral interiors ready for new tenants. Natural light, no furniture, "
+            "no people, no equipment. Professional real estate photography, ultra-realistic."
+        ),
+    },
+    {
+        "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."
+        ),
+    },
+]
+
+
+def generate():
+    saved = []
+    total = len(IMAGES)
+    for i, item in enumerate(IMAGES, 1):
+        out_path = os.path.join(OUT_DIR, f"{item['name']}.jpg")
+        print(f"[{i}/{total}] 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:
+                img_bytes = resp.generated_images[0].image.image_bytes
+                with open(out_path, "wb") as f:
+                    f.write(img_bytes)
+                print(f"  Saved {out_path} ({len(img_bytes)//1024}KB)")
+                saved.append(item["name"])
+            else:
+                print(f"  No image returned for {item['name']}")
+        except Exception as e:
+            print(f"  Error on {item['name']}: {e}")
+
+    return saved
+
+
+if __name__ == "__main__":
+    saved = generate()
+    print(f"\nDone. {len(saved)}/{len(IMAGES)} images saved to assets/images/services/")
+    if saved:
+        print("Generated:", ", ".join(saved))

tools/gen-video.py

@@ -0,0 +1,220 @@
+"""
+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")

tools/regen-3shots.py

@@ -0,0 +1,142 @@
+"""
+Regen shot-04, shot-06, shot-07 with corrected scenes.
+shot-04: carpet before/after reveal, no machine
+shot-06: clean bright staircase, no machine
+shot-07: bright modern office, no dark tones
+"""
+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__))
+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)
+
+SHOTS = [
+    {
+        "name": "shot-04-extraction-carpet",
+        "prompt": (
+            "Cinematic slow-motion wide shot. Camera glides low across a residential living room carpet. "
+            "The left half of the carpet is visibly dirty, stained, and matted. "
+            "The right half is bright, clean, fluffy, and freshly extracted. "
+            "The boundary between dirty and clean is sharp and dramatic. "
+            "Warm natural afternoon light. No people. No machines. No equipment. Photorealistic."
+        ),
+    },
+    {
+        "name": "shot-06-extraction-stairs",
+        "prompt": (
+            "Cinematic slow-motion shot looking up a bright residential carpeted staircase. "
+            "Each step has clean, bright, plush beige carpet with fresh extraction lines. "
+            "Warm natural light from above illuminates the stairs. Wood banisters on each side. "
+            "No people. No machines. No equipment anywhere in frame. Photorealistic."
+        ),
+    },
+    {
+        "name": "shot-07-office-entryway",
+        "prompt": (
+            "Wide cinematic shot of a bright modern commercial office building lobby. "
+            "Large windows let in abundant natural daylight. Clean beige or grey commercial carpet throughout. "
+            "White walls, professional lighting, glass doors, contemporary furniture. "
+            "The carpet looks spotlessly clean with neat vacuum lines. "
+            "No people. No machines. No dark tones — the space is bright and well-lit. Photorealistic."
+        ),
+    },
+]
+
+MODEL = "veo-3.1-generate-preview"
+
+def poll(op, timeout=600):
+    elapsed = 0
+    while not op.done:
+        if elapsed >= timeout:
+            print(f"  Timed out after {timeout}s.")
+            return None
+        print(f"  Waiting... ({elapsed}s)")
+        time.sleep(15)
+        elapsed += 15
+        op = client.operations.get(op)
+    return op
+
+saved = []
+for item in SHOTS:
+    out_path = os.path.join(VID_DIR, f"{item['name']}.mp4")
+    print(f"\n[VID] Generating {item['name']}...")
+    try:
+        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:
+            print(f"  FAILED (timeout)")
+            continue
+        if op.response and op.response.generated_videos:
+            vid = op.response.generated_videos[0].video
+            video_bytes = client.files.download(file=vid)
+            if video_bytes:
+                with open(out_path, "wb") as f:
+                    f.write(video_bytes)
+                print(f"  Saved ({os.path.getsize(out_path)//1024}KB)")
+                saved.append(item["name"])
+            else:
+                try:
+                    vid.save(out_path)
+                    print(f"  Saved via .save() ({os.path.getsize(out_path)//1024}KB)")
+                    saved.append(item["name"])
+                except Exception as e2:
+                    print(f"  Download failed: {e2}")
+        else:
+            print(f"  No video returned")
+    except Exception as e:
+        print(f"  Error: {e}")
+
+print(f"\n{len(saved)}/{len(SHOTS)} shots saved: {saved}")
+
+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",
+]
+
+missing = [n for n in ORDER if not os.path.exists(os.path.join(VID_DIR, f"{n}.mp4"))]
+if missing:
+    print(f"\nSkipping reconcat — missing: {missing}")
+else:
+    print("\nReconcatenating hero-reel.mp4...")
+    concat_file = os.path.join(VID_DIR, "concat.txt")
+    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[-400:]}")
+
+print("\nDone.")

tools/regen-commercial-overview.py

@@ -0,0 +1,55 @@
+"""Regenerate commercial-overview.jpg with a clear commercial carpet scene."""
+import os, sys
+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")
+OUT_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "images", "services")
+client  = genai.Client(api_key=API_KEY)
+
+PROMPTS = [
+    (
+        "Wide shot of a modern commercial office building lobby with clean grey carpet throughout. "
+        "Professional corporate interior, glass doors, white walls, overhead lighting. "
+        "The carpet is spotless and freshly cleaned — uniform, well-maintained. "
+        "No people. No machines. No equipment. Professional architectural photography, ultra-realistic."
+    ),
+    (
+        "Wide interior shot of a bright commercial building corridor with clean, dark grey commercial carpet. "
+        "Modern office environment, glass partitions, professional lighting. "
+        "The carpet looks freshly cleaned and spotless. "
+        "No people, no equipment. Professional photography, ultra-realistic."
+    ),
+]
+
+out_path = os.path.join(OUT_DIR, "commercial-overview.jpg")
+
+for i, prompt in enumerate(PROMPTS):
+    print(f"Attempt {i+1}...")
+    try:
+        resp = client.models.generate_images(
+            model="imagen-4.0-generate-001",
+            prompt=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 ({len(b)//1024}KB)")
+            break
+        else:
+            print("No image returned")
+    except Exception as e:
+        print(f"Error: {e}")
+
+print("Done.")

tools/regen-full-reel.py

@@ -0,0 +1,163 @@
+"""
+Full hero reel regeneration — 7-shot narrative arc.
+1. Door opens, muddy boots run in
+2. Mud tracked across carpet
+3. Stain on upholstered chair
+4. Carpet cleaning machine extracting dirt
+5. Clean bright staircase
+6. Office building wide carpet
+7. Restaurant with carpet
+"""
+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__))
+VID_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(VID_DIR, exist_ok=True)
+client   = genai.Client(api_key=API_KEY)
+
+SHOTS = [
+    {
+        "name": "v2-shot-01-door-entry",
+        "prompt": (
+            "Cinematic slow-motion wide shot. A wooden front door of an upstate New York home swings open. "
+            "A child and an adult walk inside wearing muddy boots. Camera stays low at floor level. "
+            "The boots leave dark muddy tracks across the beige carpet in the entryway with each step. "
+            "Warm afternoon light pours through the open door. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-02-mud-on-carpet",
+        "prompt": (
+            "Extreme close-up slow-motion shot at carpet level. Muddy boot soles press into clean beige carpet, "
+            "leaving dark brown mud stains and wet footprints with each step. "
+            "Camera is low, tight on the boots and the mud soaking into carpet fibers. "
+            "Dramatic side lighting. No faces visible. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-03-stain-on-chair",
+        "prompt": (
+            "Close-up cinematic shot of a light grey upholstered armchair. "
+            "A visible dark stain spreads across one cushion. "
+            "Camera slowly pushes in on the stain, showing the soiled fabric texture. "
+            "Warm natural light from a window. No people. No equipment. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-04-extraction-carpet",
+        "prompt": (
+            "Cinematic slow-motion wide shot. A technician pushes a Rug Doctor style carpet cleaning machine "
+            "steadily forward across a beige living room carpet. The machine is a tall upright unit with a handle "
+            "and flat rectangular cleaning head — like a large upright vacuum cleaner. "
+            "The carpet behind the machine is visibly brighter and cleaner than the carpet ahead of it. "
+            "No steam. No water spraying. Warm room light. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-05-clean-stairs",
+        "prompt": (
+            "Cinematic slow-motion shot looking up a bright residential carpeted staircase. "
+            "Each step has clean, fresh, plush beige carpet. Warm natural light from above. "
+            "Wood banisters on the sides. The carpet looks spotless and freshly cleaned. "
+            "No people. No machines. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-06-office",
+        "prompt": (
+            "Wide cinematic shot of a bright modern commercial office lobby. "
+            "Large windows, abundant natural daylight. Clean grey commercial carpet covers the entire floor. "
+            "White walls, glass partitions, professional lighting. Carpet looks spotlessly clean. "
+            "No people. No machines. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-07-restaurant",
+        "prompt": (
+            "Wide cinematic shot of an upscale restaurant dining room with carpeted floors. "
+            "Warm ambient lighting, white tablecloths, wood accents. "
+            "The carpet is clean, rich, and well-maintained throughout the space. "
+            "No people. No machines. Photorealistic, luxurious atmosphere."
+        ),
+    },
+]
+
+MODEL = "veo-3.1-generate-preview"
+
+def poll(op, timeout=600):
+    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 = []
+for item in SHOTS:
+    out_path = os.path.join(VID_DIR, f"{item['name']}.mp4")
+    print(f"\n[{SHOTS.index(item)+1}/{len(SHOTS)}] {item['name']}...")
+    try:
+        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
+            video_bytes = client.files.download(file=vid)
+            if video_bytes:
+                with open(out_path, "wb") as f:
+                    f.write(video_bytes)
+                print(f"  Saved ({os.path.getsize(out_path)//1024}KB)")
+                saved.append(out_path)
+            else:
+                try:
+                    vid.save(out_path)
+                    print(f"  Saved via .save()")
+                    saved.append(out_path)
+                except Exception as e2:
+                    print(f"  Download failed: {e2}")
+        else:
+            print(f"  No video returned")
+    except Exception as e:
+        print(f"  Error: {e}")
+
+print(f"\n{len(saved)}/{len(SHOTS)} shots saved")
+
+if len(saved) < 2:
+    print("Not enough clips — skipping reconcat.")
+else:
+    order = [os.path.join(VID_DIR, f"{s['name']}.mp4") for s in SHOTS
+             if os.path.exists(os.path.join(VID_DIR, f"{s['name']}.mp4"))]
+    concat_file = os.path.join(VID_DIR, "concat-v2.txt")
+    with open(concat_file, "w") as f:
+        for p in order:
+            f.write(f"file '{p}'\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:]}")
+
+print("\nDone.")

tools/regen-images-targeted.py

@@ -0,0 +1,64 @@
+"""Regenerate only hero-technician and hero-before-after images."""
+import os, sys
+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")
+OUT_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "images", "hero")
+client  = genai.Client(api_key=API_KEY)
+
+TARGETS = [
+    {
+        "name": "hero-technician",
+        "aspect": "16:9",
+        "prompt": (
+            "Professional carpet cleaning technician pushing a large upright hot water "
+            "extraction machine across residential carpet in a bright modern home interior. "
+            "Machine resembles an oversized upright vacuum cleaner with a cylindrical body. "
+            "No steam visible anywhere, no water spraying, no hoses visible, completely dry. "
+            "Technician shown from behind or side, no face, plain black shirt, no logo. "
+            "High-end professional photography."
+        ),
+    },
+    {
+        "name": "hero-before-after",
+        "aspect": "16:9",
+        "prompt": (
+            "Side-by-side residential living room carpet: left half heavily soiled with mud "
+            "and dark stains, right half same carpet after professional hot water extraction "
+            "cleaning, bright and pristine. No steam, no water, no machines visible anywhere. "
+            "Dramatic before-after contrast. Professional photography, no people."
+        ),
+    },
+]
+
+for item in TARGETS:
+    out_path = os.path.join(OUT_DIR, f"{item['name']}.jpg")
+    print(f"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=item["aspect"],
+                output_mime_type="image/jpeg",
+                safety_filter_level="block_low_and_above",
+            ),
+        )
+        if resp.generated_images:
+            img_bytes = resp.generated_images[0].image.image_bytes
+            with open(out_path, "wb") as f:
+                f.write(img_bytes)
+            print(f"  Saved {out_path} ({len(img_bytes)//1024}KB)")
+        else:
+            print(f"  No image returned for {item['name']}")
+    except Exception as e:
+        print(f"  Error: {e}")
+
+print("\nDone.")

tools/regen-industrial.py

@@ -0,0 +1,169 @@
+"""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.")

tools/regen-shot.py

@@ -0,0 +1,87 @@
+"""
+Regenerate one specific shot and re-concatenate the hero reel.
+Usage: python3 tools/regen-shot.py shot-02-staircase
+"""
+import os, sys, time, subprocess
+from google import genai
+from google.genai import types
+import urllib.request
+
+API_KEY  = os.environ.get("GEMINI_API_KEY", "")
+BASE_DIR = os.path.dirname(os.path.dirname(__file__))
+CLIPS_DIR = os.path.join(BASE_DIR, "assets", "videos", "hero", "clips")
+REEL_OUT  = os.path.join(BASE_DIR, "assets", "videos", "hero", "hero-reel.mp4")
+
+if not API_KEY:
+    print("Set GEMINI_API_KEY env var"); sys.exit(1)
+
+# Import SHOTS from gen-video.py (dash in name requires importlib)
+import importlib.util
+spec = importlib.util.spec_from_file_location(
+    "gen_video", os.path.join(os.path.dirname(__file__), "gen-video.py")
+)
+_mod = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(_mod)
+SHOTS = _mod.SHOTS
+
+shot_name = sys.argv[1] if len(sys.argv) > 1 else None
+target = next((s for s in SHOTS if s["name"] == shot_name), None)
+if not target:
+    print(f"Shot '{shot_name}' not found. Available: {[s['name'] for s in SHOTS]}")
+    sys.exit(1)
+
+client = genai.Client(api_key=API_KEY)
+out_path = os.path.join(CLIPS_DIR, f"{target['name']}.mp4")
+
+print(f"Regenerating {target['name']}...")
+op = client.models.generate_videos(
+    model="veo-3.0-generate-001",
+    prompt=target["prompt"],
+    config=types.GenerateVideosConfig(
+        aspect_ratio="16:9", resolution="720p",
+        duration_seconds=6, number_of_videos=1,
+    ),
+)
+elapsed = 0
+while not op.done:
+    print(f"  Waiting... ({elapsed}s)")
+    time.sleep(15); elapsed += 15
+    op = client.operations.get(op)
+
+if not (op.response and op.response.generated_videos):
+    print("No video returned"); sys.exit(1)
+
+vid = op.response.generated_videos[0].video
+video_bytes = None
+try:
+    video_bytes = client.files.download(file=vid)
+except Exception:
+    pass
+if video_bytes:
+    with open(out_path, "wb") as f: f.write(video_bytes)
+elif hasattr(vid, "uri") and vid.uri:
+    uri = vid.uri + ("&" if "?" in vid.uri else "?") + f"key={API_KEY}"
+    urllib.request.urlretrieve(uri, out_path)
+else:
+    print("Download failed"); sys.exit(1)
+
+print(f"Saved {out_path} ({os.path.getsize(out_path)//1024}KB)")
+
+# Re-concat in shot order
+clips = [os.path.join(CLIPS_DIR, f"{s['name']}.mp4") for s in SHOTS
+         if os.path.exists(os.path.join(CLIPS_DIR, f"{s['name']}.mp4"))]
+list_file = os.path.join(CLIPS_DIR, "concat.txt")
+with open(list_file, "w") as f:
+    for c in clips: f.write(f"file '{c}'\n")
+
+print(f"Concatenating {len(clips)} clips...")
+r = 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 r.returncode == 0:
+    print(f"Reel updated: {REEL_OUT} ({os.path.getsize(REEL_OUT)//1024}KB)")
+else:
+    print(f"ffmpeg error: {r.stderr[-200:]}")

tools/regen-shot02.py

@@ -0,0 +1,102 @@
+"""Generate shot-02 replacement: kid runs in with muddy shoes, focus on feet and mud tracks."""
+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__))
+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)
+
+PROMPTS = [
+    (
+        "Slow cinematic shot at floor level, looking across a beige residential carpet. "
+        "A child's feet in muddy sneakers run into frame from the front door and across the carpet, "
+        "leaving clear muddy footprints with each step. Then adult feet in boots walk in behind, "
+        "adding more dirt and mud tracks. Camera stays low, focused on the feet and the mud on the carpet. "
+        "Warm indoor light. Photorealistic, slow motion."
+    ),
+    (
+        "Low cinematic camera angle at carpet level inside a home entryway. "
+        "A child runs in wearing muddy shoes, close-up on their feet stomping mud into the beige carpet. "
+        "Adult legs follow behind, tracking in more dirt. "
+        "The carpet shows fresh mud and dirty footprints after each step. "
+        "Camera stays at floor level throughout. Warm natural light. Slow motion. Photorealistic."
+    ),
+]
+
+MODEL = "veo-3.1-generate-preview"
+out_path = os.path.join(VID_DIR, "shot-02-pan-to-stains.mp4")
+
+def poll(op, timeout=600):
+    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 = False
+for i, prompt in enumerate(PROMPTS):
+    print(f"\n[VID] shot-02 attempt {i+1}...")
+    try:
+        op = client.models.generate_videos(
+            model=MODEL,
+            prompt=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
+            video_bytes = client.files.download(file=vid)
+            if video_bytes:
+                with open(out_path, "wb") as f:
+                    f.write(video_bytes)
+                print(f"  Saved ({os.path.getsize(out_path)//1024}KB)")
+                saved = True
+                break
+        else:
+            print(f"  No video returned, trying next prompt...")
+    except Exception as e:
+        print(f"  Error: {e}")
+
+if not saved:
+    print("All attempts failed — original shot-02 kept.")
+else:
+    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",
+    ]
+    missing = [n for n in ORDER if not os.path.exists(os.path.join(VID_DIR, f"{n}.mp4"))]
+    if missing:
+        print(f"Skipping reconcat — missing: {missing}")
+    else:
+        concat_file = os.path.join(VID_DIR, "concat.txt")
+        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:]}")
+
+print("\nDone.")

tools/regen-shot04.py

@@ -0,0 +1,95 @@
+"""Retry shot-04 with a simple clean carpet scene."""
+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__))
+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)
+
+PROMPTS = [
+    (
+        "Cinematic slow dolly shot moving forward through a bright residential living room. "
+        "Clean, plush beige carpet fills the floor. Warm afternoon sunlight comes through large windows. "
+        "Contemporary furniture, neutral walls. The carpet looks freshly cleaned — bright, fluffy, spotless. "
+        "No people. No machines. Photorealistic."
+    ),
+    (
+        "Slow cinematic camera pan across a clean beige residential carpet in a bright living room. "
+        "The carpet fibers are lifted and bright after professional cleaning. "
+        "Warm natural light. Modern home interior. No people, no equipment. Photorealistic."
+    ),
+]
+
+MODEL = "veo-3.1-generate-preview"
+out_path = os.path.join(VID_DIR, "shot-04-extraction-carpet.mp4")
+
+def poll(op, timeout=600):
+    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 = False
+for i, prompt in enumerate(PROMPTS):
+    print(f"\n[VID] shot-04 attempt {i+1}...")
+    try:
+        op = client.models.generate_videos(
+            model=MODEL,
+            prompt=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
+            video_bytes = client.files.download(file=vid)
+            if video_bytes:
+                with open(out_path, "wb") as f:
+                    f.write(video_bytes)
+                print(f"  Saved ({os.path.getsize(out_path)//1024}KB)")
+                saved = True
+                break
+        else:
+            print(f"  No video returned, trying next prompt...")
+    except Exception as e:
+        print(f"  Error: {e}")
+
+if not saved:
+    print("All attempts failed.")
+else:
+    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",
+    ]
+    concat_file = os.path.join(VID_DIR, "concat.txt")
+    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:]}")
+
+print("\nDone.")

tools/regen-v3-shot04.py

@@ -0,0 +1,98 @@
+"""Replace v3-shot-04 with a clean sofa result — no cleaning action, no equipment, no steam."""
+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__))
+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)
+
+PROMPTS = [
+    (
+        "Close-up cinematic shot slowly pulling back from a clean, bright grey upholstered sofa cushion. "
+        "The fabric is fresh, fluffy, and spotless. Warm natural window light. "
+        "No people. No machines. No equipment of any kind. No steam. No water. "
+        "Just a beautiful clean sofa in a bright living room. Photorealistic."
+    ),
+    (
+        "Slow cinematic pan across a clean living room. A light grey sofa with pristine cushions. "
+        "Bright clean beige carpet on the floor. Warm afternoon light. "
+        "No people. No machines. No equipment. Photorealistic."
+    ),
+]
+
+MODEL = "veo-3.1-generate-preview"
+out_path = os.path.join(VID_DIR, "v3-shot-04.mp4")
+
+def poll(op, timeout=600):
+    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 = False
+for i, prompt in enumerate(PROMPTS):
+    print(f"\n[v3-shot-04] attempt {i+1}...")
+    try:
+        op = client.models.generate_videos(
+            model=MODEL,
+            prompt=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
+            video_bytes = client.files.download(file=vid)
+            if video_bytes:
+                with open(out_path, "wb") as f:
+                    f.write(video_bytes)
+                print(f"  Saved ({os.path.getsize(out_path)//1024}KB)")
+                saved = True
+                break
+        else:
+            print(f"  No video returned")
+    except Exception as e:
+        print(f"  Error: {e}")
+
+if saved:
+    clips = [
+        "v3-shot-01", "v3-shot-02", "v3-shot-03", "v3-shot-04",
+        "v3-shot-05", "v3-shot-06", "v3-shot-07",
+    ]
+    missing = [n for n in clips if not os.path.exists(os.path.join(VID_DIR, f"{n}.mp4"))]
+    if missing:
+        print(f"Skipping reconcat — missing: {missing}")
+    else:
+        concat_file = os.path.join(VID_DIR, "concat-v3.txt")
+        with open(concat_file, "w") as f:
+            for n in clips:
+                f.write(f"file '{os.path.join(VID_DIR, n)}.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("Failed — shot-04 unchanged.")
+
+print("\nDone.")

tools/regen-v3.py

@@ -0,0 +1,153 @@
+"""
+Hero reel v3 — 7 shots with corrected 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__))
+VID_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(VID_DIR, exist_ok=True)
+client   = genai.Client(api_key=API_KEY)
+
+SHOTS = [
+    {
+        "name": "v3-shot-01",
+        "prompt": (
+            "Medium shot. A family of four — two adults and two children — walks through the front door "
+            "of a warm residential home. Camera is inside the home facing them as they enter. "
+            "After they walk in, the camera slowly pans down to the carpet, revealing dirty footprints "
+            "and mud tracked onto the beige carpet. Warm natural light. Photorealistic."
+        ),
+    },
+    {
+        "name": "v3-shot-02",
+        "prompt": (
+            "Medium shot, slow zoom in. A glass of red wine has spilled onto a light grey upholstered sofa. "
+            "The camera starts wide on the sofa then slowly zooms in on the dark wine stain spreading "
+            "across the fabric. The stain is clearly visible, soaked into the cushion. "
+            "Warm living room light. No people. Photorealistic."
+        ),
+    },
+    {
+        "name": "v3-shot-03",
+        "prompt": (
+            "Medium shot at low angle, camera near floor level. Inside a bright commercial office entryway. "
+            "A person wearing work boots walks through the entry door toward the camera and past it. "
+            "The camera is low, showing the boots and lower legs as they walk across the carpet past the lens. "
+            "Office carpet visible, natural light from glass doors behind. Photorealistic."
+        ),
+    },
+    {
+        "name": "v3-shot-04",
+        "prompt": (
+            "Close-up cinematic shot. A technician's gloved hand holds a small upholstery extraction wand — "
+            "a flat rectangular handheld tool. The technician presses the wand firmly onto a wine-stained "
+            "sofa cushion and draws it slowly across the fabric. The stain visibly lifts as the wand moves. "
+            "Suction only — no water sprays out. Slow motion. Natural light. Photorealistic."
+        ),
+    },
+    {
+        "name": "v3-shot-05",
+        "prompt": (
+            "Wide cinematic shot. Camera slowly pans across the entrance of a modern commercial office building. "
+            "Clean grey commercial carpet stretches across the lobby floor. Large windows, glass doors, "
+            "professional lighting. The carpet looks freshly cleaned and spotless. "
+            "No people. No machines. Photorealistic."
+        ),
+    },
+    {
+        "name": "v3-shot-06",
+        "prompt": (
+            "Wide cinematic shot. Camera slowly pans across a bright, clean residential living room. "
+            "Plush clean beige carpet throughout. Comfortable furniture — sofa, armchairs, coffee table. "
+            "Warm natural afternoon light through large windows. The room looks fresh and inviting. "
+            "No people. No cleaning equipment. Photorealistic."
+        ),
+    },
+    {
+        "name": "v3-shot-07",
+        "prompt": (
+            "Wide cinematic shot. Camera moves slowly forward through an upscale restaurant dining room. "
+            "Rich carpet covers the floor. White tablecloths, warm ambient lighting, wood accents. "
+            "The carpet looks clean, deep, and well-maintained as the camera glides through the space. "
+            "No people. Photorealistic, luxurious atmosphere."
+        ),
+    },
+]
+
+MODEL = "veo-3.1-generate-preview"
+
+def poll(op, timeout=600):
+    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 = []
+for i, item in enumerate(SHOTS):
+    out_path = os.path.join(VID_DIR, f"{item['name']}.mp4")
+    print(f"\n[{i+1}/{len(SHOTS)}] {item['name']}...")
+    try:
+        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
+            video_bytes = client.files.download(file=vid)
+            if video_bytes:
+                with open(out_path, "wb") as f:
+                    f.write(video_bytes)
+                print(f"  Saved ({os.path.getsize(out_path)//1024}KB)")
+                saved.append(out_path)
+            else:
+                try:
+                    vid.save(out_path)
+                    print(f"  Saved via .save()")
+                    saved.append(out_path)
+                except Exception as e2:
+                    print(f"  Download failed: {e2}")
+        else:
+            print(f"  No video returned")
+    except Exception as e:
+        print(f"  Error: {e}")
+
+print(f"\n{len(saved)}/{len(SHOTS)} shots saved")
+
+if len(saved) >= 2:
+    concat_file = os.path.join(VID_DIR, "concat-v3.txt")
+    clips = [os.path.join(VID_DIR, f"{s['name']}.mp4") for s in SHOTS
+             if os.path.exists(os.path.join(VID_DIR, f"{s['name']}.mp4"))]
+    with open(concat_file, "w") as f:
+        for p in clips:
+            f.write(f"file '{p}'\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:]}")
+
+print("\nDone.")

tools/regen-v4.py

@@ -0,0 +1,154 @@
+"""Hero reel v4 — 6 precise shots."""
+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__))
+VID_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(VID_DIR, exist_ok=True)
+client   = genai.Client(api_key=API_KEY)
+
+SHOTS = [
+    {
+        "name": "v4-shot-01",
+        "prompt": (
+            "Medium cinematic shot. A family — two adults and two children — walks through a front door "
+            "into a residential home. The camera follows their feet as they step onto the beige carpet "
+            "in the entryway, then slowly pans down to show their shoes leaving dirty tracks on the carpet. "
+            "Warm afternoon light. Photorealistic, slow motion."
+        ),
+    },
+    {
+        "name": "v4-shot-02",
+        "prompt": (
+            "Slow-motion close-up cinematic shot. A wine glass tips over on a light grey fabric sofa cushion. "
+            "Red wine pours out of the glass and spreads across the sofa cushion, soaking into the fabric. "
+            "The dark red stain expands slowly across the grey upholstery. "
+            "Warm living room light. No people visible. Photorealistic, dramatic slow motion."
+        ),
+    },
+    {
+        "name": "v4-shot-03",
+        "prompt": (
+            "Cinematic close-up shot slowly pushing in on a section of heavily soiled residential carpet. "
+            "The beige carpet has multiple visible stains — dark spots, discoloration, pet stains, "
+            "and general dirt buildup embedded in the fibers. "
+            "Dramatic side lighting emphasizes the depth of the stains. No people. No equipment. Photorealistic."
+        ),
+    },
+    {
+        "name": "v4-shot-04",
+        "prompt": (
+            "Close-up cinematic shot. A carpet cleaning technician in a plain black shirt pushes "
+            "a large upright carpet cleaning machine — like a Rug Doctor — across a dirty beige carpet. "
+            "Tight shot focused on the wide flat cleaning head at the base of the machine pressing against "
+            "the carpet and moving forward. The carpet behind the machine looks visibly cleaner and brighter. "
+            "The machine only pulls dirt and moisture INTO itself — nothing comes out. "
+            "No steam. No liquid leaving the machine. Photorealistic slow motion."
+        ),
+    },
+    {
+        "name": "v4-shot-05",
+        "prompt": (
+            "Wide cinematic shot slowly pushing forward through the main entrance of a modern commercial "
+            "office building. Clean grey carpet covers the entire lobby floor. Glass doors, white walls, "
+            "professional overhead lighting. The carpet is the visual centerpiece — clean, uniform, well-maintained. "
+            "No people. No machines. Photorealistic."
+        ),
+    },
+    {
+        "name": "v4-shot-06",
+        "prompt": (
+            "Cinematic wide shot inside a bright clean residential living room. "
+            "The camera slowly pans upward from the clean plush beige carpet to reveal the whole room. "
+            "A family — two adults and a child — walks in at different moments and relaxes on the sofa. "
+            "Everyone is wearing socks. The carpet is spotless and fluffy. Warm natural light. "
+            "Comfortable, inviting atmosphere. Photorealistic."
+        ),
+    },
+]
+
+MODELS = ["veo-3.1-generate-preview", "veo-2.0-generate-001"]
+
+def poll(op, timeout=600):
+    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 = []
+for i, item in enumerate(SHOTS):
+    out_path = os.path.join(VID_DIR, f"{item['name']}.mp4")
+    print(f"\n[{i+1}/{len(SHOTS)}] {item['name']}...")
+    done = False
+    for model in MODELS:
+        try:
+            print(f"  Trying {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
+                video_bytes = client.files.download(file=vid)
+                if video_bytes:
+                    with open(out_path, "wb") as f:
+                        f.write(video_bytes)
+                    print(f"  Saved ({os.path.getsize(out_path)//1024}KB)")
+                    saved.append(out_path)
+                    done = True
+                    break
+                else:
+                    try:
+                        vid.save(out_path)
+                        print(f"  Saved via .save()")
+                        saved.append(out_path)
+                        done = True
+                        break
+                    except Exception as e2:
+                        print(f"  Download failed: {e2}")
+            else:
+                print(f"  No video returned from {model}")
+        except Exception as e:
+            print(f"  Error with {model}: {e}")
+    if not done:
+        print(f"  FAILED: {item['name']}")
+
+print(f"\n{len(saved)}/{len(SHOTS)} shots saved")
+
+if len(saved) >= 2:
+    clips = [os.path.join(VID_DIR, f"{s['name']}.mp4") for s in SHOTS
+             if os.path.exists(os.path.join(VID_DIR, f"{s['name']}.mp4"))]
+    concat_file = os.path.join(VID_DIR, "concat-v4.txt")
+    with open(concat_file, "w") as f:
+        for p in clips:
+            f.write(f"file '{p}'\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:]}")
+
+print("\nDone.")

tools/regen-veo31.py

@@ -0,0 +1,167 @@
+"""
+Regenerate carpet extraction shots using Veo 3.1.
+Fixes: buffer/rotary/steamer machines replaced with explicit stand-up extractor descriptions.
+Reconcats hero-reel.mp4 when done.
+Run: python3 tools/regen-veo31.py
+"""
+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__))
+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)
+
+# Machine description block used across all shots
+MACHINE = (
+    "The carpet cleaning machine looks exactly like a Rug Doctor or Bissell Big Green carpet cleaner — "
+    "the large upright machines you rent at grocery stores. Tall rectangular body, upright handle, "
+    "wide flat cleaning foot at the base. It is pushed forward in straight lines like a vacuum cleaner. "
+    "NOT a floor buffer. NOT a rotary disc scrubber. NOT a steam cleaner. NOT circular. "
+    "NO spinning parts. NO steam. NO water spraying out. The carpet is cleaned by suction alone."
+)
+
+SHOTS = [
+    {
+        "name": "shot-04-extraction-carpet",
+        "prompt": (
+            f"Cinematic slow-motion wide shot. A man pushes a Rug Doctor style carpet cleaning machine "
+            f"steadily forward across a beige residential carpet in a living room. {MACHINE} "
+            f"The carpet behind the machine transitions from dirty and matted to bright, clean, and fluffy. "
+            f"Warm natural room light. Photorealistic professional video."
+        ),
+    },
+    {
+        "name": "shot-05-extraction-couch",
+        "prompt": (
+            "Close-up cinematic slow-motion shot. A professional technician's gloved hand holds a small "
+            "flat handheld upholstery cleaning attachment — a rectangular suction wand, flat on the bottom, "
+            "no moving parts, no spinning disc. "
+            "The technician presses it firmly against a dirty grey sofa cushion and slides it slowly across. "
+            "The fabric visibly brightens as the wand moves across it. "
+            "NO rotary machine anywhere in frame. NO spinning. NO steam. NO water pouring out. "
+            "Natural light. Photorealistic."
+        ),
+    },
+    {
+        "name": "shot-06-extraction-stairs",
+        "prompt": (
+            "Cinematic slow-motion shot. A professional technician pushes a Rug Doctor style upright carpet "
+            "cleaning machine up a carpeted residential staircase, one stair tread at a time. "
+            "The machine is a tall rectangular upright unit with a handle and flat cleaning head — "
+            "NOT a buffer, NOT circular, NOT rotating. "
+            "Each stair tread brightens and looks freshly cleaned as the machine passes over it. "
+            "NO steam, NO water visible. Warm interior light. Photorealistic."
+        ),
+    },
+    {
+        "name": "shot-09-technician-unloading",
+        "prompt": (
+            f"Wide cinematic shot. A professional carpet cleaning technician in a plain black shirt "
+            f"rolls a Rug Doctor style carpet cleaning machine down a ramp out of a white service van "
+            f"parked in a residential driveway in upstate New York. {MACHINE} "
+            f"Autumn trees in background, bright daylight. "
+            f"Technician shown from the side or behind, no face visible. Photorealistic."
+        ),
+    },
+]
+
+MODEL = "veo-3.1-generate-preview"
+
+def poll(op, timeout=600):
+    elapsed = 0
+    while not op.done:
+        if elapsed >= timeout:
+            print(f"  Timed out after {timeout}s.")
+            return None
+        print(f"  Waiting... ({elapsed}s)")
+        time.sleep(15)
+        elapsed += 15
+        op = client.operations.get(op)
+    return op
+
+saved = []
+for item in SHOTS:
+    out_path = os.path.join(VID_DIR, f"{item['name']}.mp4")
+    print(f"\n[VID] Generating {item['name']} with {MODEL}...")
+    try:
+        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:
+            print(f"  FAILED (timeout): {item['name']}")
+            continue
+        if op.response and op.response.generated_videos:
+            vid = op.response.generated_videos[0].video
+            # Veo 3.1 download pattern
+            video_bytes = client.files.download(file=vid)
+            if video_bytes:
+                with open(out_path, "wb") as f:
+                    f.write(video_bytes)
+                print(f"  Saved {out_path} ({os.path.getsize(out_path)//1024}KB)")
+                saved.append(item["name"])
+            else:
+                # fallback: try .save() method
+                try:
+                    vid.save(out_path)
+                    print(f"  Saved via .save() ({os.path.getsize(out_path)//1024}KB)")
+                    saved.append(item["name"])
+                except Exception as e2:
+                    print(f"  Download failed: {e2}")
+        else:
+            print(f"  No video returned for {item['name']}")
+    except Exception as e:
+        print(f"  Error: {e}")
+
+print(f"\n{len(saved)}/{len(SHOTS)} shots saved: {saved}")
+
+# Reconcat full reel
+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",
+]
+
+missing = [n for n in ORDER if not os.path.exists(os.path.join(VID_DIR, f"{n}.mp4"))]
+if missing:
+    print(f"\nSkipping reconcat — missing clips: {missing}")
+else:
+    print("\nReconcatenating hero-reel.mp4...")
+    concat_file = os.path.join(VID_DIR, "concat.txt")
+    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: {REEL_OUT} ({os.path.getsize(REEL_OUT)//1024}KB)")
+    else:
+        print(f"  ffmpeg error: {result.stderr[-400:]}")
+
+print("\nDone.")