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85 lines
4.1 KiB
Python
85 lines
4.1 KiB
Python
#!/usr/bin/env python3
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"""Generate the book cover image with an image-generation model.
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This is, fittingly, the book eating its own dog food: the cover of a book about
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AI agents is produced by calling an image-generation model. Run it once; the
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cover (cover.tex) automatically switches to images/cover-image.png when present
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— no other change needed. You can then note on the colophon that the cover was
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generated by AI.
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Usage (OpenAI, the default):
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pip install openai
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export OPENAI_API_KEY=your-openai-api-key
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python gen_cover.py
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Swapping providers: edit generate() below. Stubs/notes are included for
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通義萬相 (DashScope)、即夢/可圖, and Flux (fal / Replicate) — pick whichever you
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have access to. The prompt is the important part and is provider-agnostic.
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"""
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import os
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# ── The prompt ────────────────────────────────────────────────────────────
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# O'Reilly "animal book" homage: a single woodcut/engraving animal on pure
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# white, which cover.tex composites under the serif title. The octopus suits an
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# AI-agent book — highly intelligent, a famous tool-user, eight semi-autonomous
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# arms ≈ one brain + many tools/hands (and even multi-agent). Swap the animal in
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# the prompt if you prefer another.
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PROMPT = (
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"Vintage scientific engraving illustration of an octopus, in the classic style of "
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"19th-century natural-history woodcuts and the O'Reilly animal book covers. Finely "
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"detailed black pen-and-ink crosshatching and fine line work; pure black line art, "
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"no color, no gray wash, no shading fills. The whole octopus rendered elegantly with "
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"gracefully curling tentacles, anatomically believable, slightly stylized. Perfectly "
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"clean pure white background, no scenery, no frame, no border, no text, no lettering, "
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"no numbers. Centered composition, crisp, high detail."
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)
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OUT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "images", "cover-image.png")
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def generate_openai(prompt, out):
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"""OpenAI Images API. Uses gpt-image-1 if available, else dall-e-3."""
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from openai import OpenAI
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import base64, urllib.request
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client = OpenAI()
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try:
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# gpt-image-1: best prompt adherence; returns b64. Portrait 1024x1536.
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r = client.images.generate(model="gpt-image-1", prompt=prompt,
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size="1024x1536", quality="high", n=1)
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data = base64.b64decode(r.data[0].b64_json)
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open(out, "wb").write(data)
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except Exception as e:
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print(f"gpt-image-1 unavailable ({e}); falling back to dall-e-3 …")
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r = client.images.generate(model="dall-e-3", prompt=prompt,
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size="1024x1792", quality="hd",
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style="natural", n=1)
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url = r.data[0].url
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urllib.request.urlretrieve(url, out)
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# ── Alternative providers (uncomment / adapt the one you use) ───────────────
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# def generate_dashscope(prompt, out): # 阿里 通義萬相 (wanx)
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# import dashscope # pip install dashscope ; export DASHSCOPE_API_KEY=...
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# rsp = dashscope.ImageSynthesis.call(model="wanx-v1", prompt=prompt,
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# n=1, size="1024*1536")
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# import urllib.request
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# urllib.request.urlretrieve(rsp.output.results[0].url, out)
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#
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# def generate_fal(prompt, out): # Flux via fal.ai
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# import fal_client, urllib.request # pip install fal-client ; export FAL_KEY=...
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# r = fal_client.run("fal-ai/flux-pro/v1.1",
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# arguments={"prompt": prompt, "image_size": "portrait_4_3"})
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# urllib.request.urlretrieve(r["images"][0]["url"], out)
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def generate(prompt, out):
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return generate_openai(prompt, out) # ← swap to your provider here
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if __name__ == "__main__":
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os.makedirs(os.path.dirname(OUT), exist_ok=True)
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print("Generating cover image …")
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generate(PROMPT, OUT)
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print(f"Saved {OUT}")
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print("Now rebuild: bash build_pdf.sh (cover.tex auto-detects the image)")
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