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#1	#!/usr/bin/env python3
#2	import argparse
#3	import base64
#4	import datetime as dt
#5	import json
#6	import os
#7	import random
#8	import re
#9	import sys
#10	import urllib.error
#11	import urllib.request
#12	from pathlib import Path
#13
#14
#15	def slugify(text: str) -> str:
#16	text = text.lower().strip()
#17	text = re.sub(r"[^a-z0-9]+", "-", text)
#18	text = re.sub(r"-{2,}", "-", text).strip("-")
#19	return text or "image"
#20
#21
#22	def default_out_dir() -> Path:
#23	now = dt.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
#24	preferred = Path.home() / "Projects" / "tmp"
#25	base = preferred if preferred.is_dir() else Path("./tmp")
#26	base.mkdir(parents=True, exist_ok=True)
#27	return base / f"openai-image-gen-{now}"
#28
#29
#30	def pick_prompts(count: int) -> list[str]:
#31	subjects = [
#32	"a lobster astronaut",
#33	"a brutalist lighthouse",
#34	"a cozy reading nook",
#35	"a cyberpunk noodle shop",
#36	"a Vienna street at dusk",
#37	"a minimalist product photo",
#38	"a surreal underwater library",
#39	]
#40	styles = [
#41	"ultra-detailed studio photo",
#42	"35mm film still",
#43	"isometric illustration",
#44	"editorial photography",
#45	"soft watercolor",
#46	"architectural render",
#47	"high-contrast monochrome",
#48	]
#49	lighting = [
#50	"golden hour",
#51	"overcast soft light",
#52	"neon lighting",
#53	"dramatic rim light",
#54	"candlelight",
#55	"foggy atmosphere",
#56	]
#57	prompts: list[str] = []
#58	for _ in range(count):
#59	prompts.append(
#60	f"{random.choice(styles)} of {random.choice(subjects)}, {random.choice(lighting)}"
#61	)
#62	return prompts
#63
#64
#65	def get_model_defaults(model: str) -> tuple[str, str]:
#66	"""Return (default_size, default_quality) for the given model."""
#67	if model == "dall-e-2":
#68	# quality will be ignored
#69	return ("1024x1024", "standard")
#70	elif model == "dall-e-3":
#71	return ("1024x1024", "standard")
#72	else:
#73	# GPT image or future models
#74	return ("1024x1024", "high")
#75
#76
#77	def request_images(
#78	api_key: str,
#79	prompt: str,
#80	model: str,
#81	size: str,
#82	quality: str,
#83	background: str = "",
#84	output_format: str = "",
#85	style: str = "",
#86	) -> dict:
#87	url = "https://api.openai.com/v1/images/generations"
#88	args = {
#89	"model": model,
#90	"prompt": prompt,
#91	"size": size,
#92	"n": 1,
#93	}
#94
#95	# Quality parameter - dall-e-2 doesn't accept this parameter
#96	if model != "dall-e-2":
#97	args["quality"] = quality
#98
#99	# Note: response_format no longer supported by OpenAI Images API
#100	# dall-e models now return URLs by default
#101
#102	if model.startswith("gpt-image"):
#103	if background:
#104	args["background"] = background
#105	if output_format:
#106	args["output_format"] = output_format
#107
#108	if model == "dall-e-3" and style:
#109	args["style"] = style
#110
#111	body = json.dumps(args).encode("utf-8")
#112	req = urllib.request.Request(
#113	url,
#114	method="POST",
#115	headers={
#116	"Authorization": f"Bearer {api_key}",
#117	"Content-Type": "application/json",
#118	},
#119	data=body,
#120	)
#121	try:
#122	with urllib.request.urlopen(req, timeout=300) as resp:
#123	return json.loads(resp.read().decode("utf-8"))
#124	except urllib.error.HTTPError as e:
#125	payload = e.read().decode("utf-8", errors="replace")
#126	raise RuntimeError(f"OpenAI Images API failed ({e.code}): {payload}") from e
#127
#128
#129	def write_gallery(out_dir: Path, items: list[dict]) -> None:
#130	thumbs = "\n".join(
#131	[
#132	f"""
#133	<figure>
#134	<a href="{it["file"]}"><img src="{it["file"]}" loading="lazy" /></a>
#135	<figcaption>{it["prompt"]}</figcaption>
#136	</figure>
#137	""".strip()
#138	for it in items
#139	]
#140	)
#141	html = f"""<!doctype html>
#142	<meta charset="utf-8" />
#143	<title>openai-image-gen</title>
#144	<style>
#145	:root {{ color-scheme: dark; }}
#146	body {{ margin: 24px; font: 14px/1.4 ui-sans-serif, system-ui; background: #0b0f14; color: #e8edf2; }}
#147	h1 {{ font-size: 18px; margin: 0 0 16px; }}
#148	.grid {{ display: grid; grid-template-columns: repeat(auto-fill, minmax(240px, 1fr)); gap: 16px; }}
#149	figure {{ margin: 0; padding: 12px; border: 1px solid #1e2a36; border-radius: 14px; background: #0f1620; }}
#150	img {{ width: 100%; height: auto; border-radius: 10px; display: block; }}
#151	figcaption {{ margin-top: 10px; color: #b7c2cc; }}
#152	code {{ color: #9cd1ff; }}
#153	</style>
#154	<h1>openai-image-gen</h1>
#155	<p>Output: <code>{out_dir.as_posix()}</code></p>
#156	<div class="grid">
#157	{thumbs}
#158	</div>
#159	"""
#160	(out_dir / "index.html").write_text(html, encoding="utf-8")
#161
#162
#163	def main() -> int:
#164	ap = argparse.ArgumentParser(description="Generate images via OpenAI Images API.")
#165	ap.add_argument("--prompt", help="Single prompt. If omitted, random prompts are generated.")
#166	ap.add_argument("--count", type=int, default=8, help="How many images to generate.")
#167	ap.add_argument("--model", default="gpt-image-1", help="Image model id.")
#168	ap.add_argument("--size", default="", help="Image size (e.g. 1024x1024, 1536x1024). Defaults based on model if not specified.")
#169	ap.add_argument("--quality", default="", help="Image quality (e.g. high, standard). Defaults based on model if not specified.")
#170	ap.add_argument("--background", default="", help="Background transparency (GPT models only): transparent, opaque, or auto.")
#171	ap.add_argument("--output-format", default="", help="Output format (GPT models only): png, jpeg, or webp.")
#172	ap.add_argument("--style", default="", help="Image style (dall-e-3 only): vivid or natural.")
#173	ap.add_argument("--out-dir", default="", help="Output directory (default: ./tmp/openai-image-gen-<ts>).")
#174	args = ap.parse_args()
#175
#176	api_key = (os.environ.get("OPENAI_API_KEY") or "").strip()
#177	if not api_key:
#178	print("Missing OPENAI_API_KEY", file=sys.stderr)
#179	return 2
#180
#181	# Apply model-specific defaults if not specified
#182	default_size, default_quality = get_model_defaults(args.model)
#183	size = args.size or default_size
#184	quality = args.quality or default_quality
#185
#186	count = args.count
#187	if args.model == "dall-e-3" and count > 1:
#188	print(f"Warning: dall-e-3 only supports generating 1 image at a time. Reducing count from {count} to 1.", file=sys.stderr)
#189	count = 1
#190
#191	out_dir = Path(args.out_dir).expanduser() if args.out_dir else default_out_dir()
#192	out_dir.mkdir(parents=True, exist_ok=True)
#193
#194	prompts = [args.prompt] * count if args.prompt else pick_prompts(count)
#195
#196	# Determine file extension based on output format
#197	if args.model.startswith("gpt-image") and args.output_format:
#198	file_ext = args.output_format
#199	else:
#200	file_ext = "png"
#201
#202	items: list[dict] = []
#203	for idx, prompt in enumerate(prompts, start=1):
#204	print(f"[{idx}/{len(prompts)}] {prompt}")
#205	res = request_images(
#206	api_key,
#207	prompt,
#208	args.model,
#209	size,
#210	quality,
#211	args.background,
#212	args.output_format,
#213	args.style,
#214	)
#215	data = res.get("data", [{}])[0]
#216	image_b64 = data.get("b64_json")
#217	image_url = data.get("url")
#218	if not image_b64 and not image_url:
#219	raise RuntimeError(f"Unexpected response: {json.dumps(res)[:400]}")
#220
#221	filename = f"{idx:03d}-{slugify(prompt)[:40]}.{file_ext}"
#222	filepath = out_dir / filename
#223	if image_b64:
#224	filepath.write_bytes(base64.b64decode(image_b64))
#225	else:
#226	try:
#227	urllib.request.urlretrieve(image_url, filepath)
#228	except urllib.error.URLError as e:
#229	raise RuntimeError(f"Failed to download image from {image_url}: {e}") from e
#230
#231	items.append({"prompt": prompt, "file": filename})
#232
#233	(out_dir / "prompts.json").write_text(json.dumps(items, indent=2), encoding="utf-8")
#234	write_gallery(out_dir, items)
#235	print(f"\nWrote: {(out_dir / 'index.html').as_posix()}")
#236	return 0
#237
#238
#239	if __name__ == "__main__":
#240	raise SystemExit(main())
#241

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