curl -s "https://generativelanguage.googleapis.com/v1beta/models?key=$GEMINI_API_KEY" | python3 -c "import sys,json; [print(m['name']) for m in json.load(sys.stdin)['models'] if 'image' in m['name'].lower()]"

BAD — keyword soup:
"professional woman, spa, warm lighting, high quality, 4K"

GOOD — narrative direction:
"A professional skin treatment scene in a warm clinical setting.
A practitioner wearing blue medical gloves uses a microneedling pen
on the client's forehead. The client lies on a white treatment bed,
eyes closed, relaxed. Warm golden-hour light from a window to the
left. Terracotta-toned wall visible in the background. Shot at
85mm f/2.0, shallow depth of field. No text, no watermarks."

python3 << 'PYEOF'
import json, base64, urllib.request, os, sys

GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
if not GEMINI_API_KEY:
    print("Set GEMINI_API_KEY environment variable"); sys.exit(1)

model = "gemini-3.1-flash-image-preview"
url = f"https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent?key={GEMINI_API_KEY}"

prompt = """A professional photograph of a modern co-working space in
Newcastle, Australia. Natural light floods through floor-to-ceiling
windows. Three people collaborate at a standing desk — one pointing
at a laptop screen. Exposed brick wall, potted fiddle-leaf fig,
coffee cups on the desk. Shot at 35mm f/4.0, environmental portrait
style. No text, no watermarks, no logos."""

payload = json.dumps({
    "contents": [{"parts": [{"text": prompt}]}],
    "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"],
        "temperature": 0.8
    }
}).encode()

req = urllib.request.Request(url, data=payload, headers={
    "Content-Type": "application/json",
    "User-Agent": "ImageGen/1.0"
})

resp = urllib.request.urlopen(req, timeout=120)
result = json.loads(resp.read())

# Extract image from response
for part in result["candidates"][0]["content"]["parts"]:
    if "inlineData" in part:
        img_data = base64.b64decode(part["inlineData"]["data"])
        output_path = "hero-image.png"
        with open(output_path, "wb") as f:
            f.write(img_data)
        print(f"Saved: {output_path} ({len(img_data):,} bytes)")
        break
PYEOF

python3 << 'PYEOF'
import json, base64, urllib.request, os, sys

OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    print("Set OPENAI_API_KEY environment variable"); sys.exit(1)

url = "https://api.openai.com/v1/images/generations"

payload = json.dumps({
    "model": "gpt-image-1.5",
    "prompt": "A minimal, clean plumbing wrench icon. Flat design, single consistent stroke weight, modern style. On a transparent background.",
    "n": 1,
    "size": "1024x1024",
    "background": "transparent",
    "output_format": "png"
}).encode()

req = urllib.request.Request(url, data=payload, headers={
    "Content-Type": "application/json",
    "Authorization": f"Bearer {OPENAI_API_KEY}"
})

resp = urllib.request.urlopen(req, timeout=120)
result = json.loads(resp.read())

img_data = base64.b64decode(result["data"][0]["b64_json"])
with open("icon-wrench.png", "wb") as f:
    f.write(img_data)
print(f"Saved: icon-wrench.png ({len(img_data):,} bytes)")
PYEOF

# Convert to WebP for web use
python3 -c "
from PIL import Image
img = Image.open('hero-image.png')
img.save('hero-image.webp', 'WEBP', quality=85)
print(f'WebP: {img.size[0]}x{img.size[1]}')
"

# Trim whitespace from transparent icons
python3 -c "
from PIL import Image
img = Image.open('icon.png')
trimmed = img.crop(img.getbbox())
trimmed.save('icon-trimmed.png')
"

# Send to Gemini Flash for critique
critique_prompt = """Review this image for:
1. AI artifacts (extra fingers, floating objects, text errors)
2. Technical accuracy (wrong equipment, unsafe positioning)
3. Composition issues (awkward cropping, cluttered background)
4. Style consistency with a professional stock photo

List any issues found, or say 'PASS' if the image is production-ready."""

# Turn 1: Generate base image
contents = [{"role": "user", "parts": [{"text": "Scene prompt..."}]}]

# The response includes thoughtSignature on parts — preserve them ALL

# Turn 2: Edit the image
contents = [
    {"role": "user", "parts": [{"text": "Original prompt"}]},
    {"role": "model", "parts": response_parts_with_signatures},  # Keep intact
    {"role": "user", "parts": [{"text": "Edit: change the wall colour to blue. Keep everything else exactly the same."}]}
]

GOOD: "Edit this image: keep the people, desk, and window unchanged.
Only change: wall colour from terracotta to ocean blue."

BAD: "Now make the wall blue."
(Model may change everything else too)

export GEMINI_API_KEY="your-key-here"
export OPENAI_API_KEY="your-key-here"