#!/usr/bin/env python3
"""Generate the book cover image with an image-generation model.

This is, fittingly, the book eating its own dog food: the cover of a book about
AI agents is produced by calling an image-generation model. Run it once; the
cover (cover.tex) automatically switches to images/cover-image.png when present
— no other change needed. You can then note on the colophon that the cover was
generated by AI.

Usage (OpenAI, the default):
    pip install openai
    export OPENAI_API_KEY=your-openai-api-key
    python gen_cover.py

Swapping providers: edit generate() below. Stubs/notes are included for
通義萬相 (DashScope)、即夢/可圖, and Flux (fal / Replicate) — pick whichever you
have access to. The prompt is the important part and is provider-agnostic.
"""
import os

# ── The prompt ────────────────────────────────────────────────────────────
# O'Reilly "animal book" homage: a single woodcut/engraving animal on pure
# white, which cover.tex composites under the serif title. The octopus suits an
# AI-agent book — highly intelligent, a famous tool-user, eight semi-autonomous
# arms ≈ one brain + many tools/hands (and even multi-agent). Swap the animal in
# the prompt if you prefer another.
PROMPT = (
    "Vintage scientific engraving illustration of an octopus, in the classic style of "
    "19th-century natural-history woodcuts and the O'Reilly animal book covers. Finely "
    "detailed black pen-and-ink crosshatching and fine line work; pure black line art, "
    "no color, no gray wash, no shading fills. The whole octopus rendered elegantly with "
    "gracefully curling tentacles, anatomically believable, slightly stylized. Perfectly "
    "clean pure white background, no scenery, no frame, no border, no text, no lettering, "
    "no numbers. Centered composition, crisp, high detail."
)

OUT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "images", "cover-image.png")


def generate_openai(prompt, out):
    """OpenAI Images API. Uses gpt-image-1 if available, else dall-e-3."""
    from openai import OpenAI
    import base64, urllib.request
    client = OpenAI()
    try:
        # gpt-image-1: best prompt adherence; returns b64. Portrait 1024x1536.
        r = client.images.generate(model="gpt-image-1", prompt=prompt,
                                    size="1024x1536", quality="high", n=1)
        data = base64.b64decode(r.data[0].b64_json)
        open(out, "wb").write(data)
    except Exception as e:
        print(f"gpt-image-1 unavailable ({e}); falling back to dall-e-3 …")
        r = client.images.generate(model="dall-e-3", prompt=prompt,
                                    size="1024x1792", quality="hd",
                                    style="natural", n=1)
        url = r.data[0].url
        urllib.request.urlretrieve(url, out)


# ── Alternative providers (uncomment / adapt the one you use) ───────────────
# def generate_dashscope(prompt, out):   # 阿里 通義萬相 (wanx)
#     import dashscope  # pip install dashscope ; export DASHSCOPE_API_KEY=...
#     rsp = dashscope.ImageSynthesis.call(model="wanx-v1", prompt=prompt,
#                                         n=1, size="1024*1536")
#     import urllib.request
#     urllib.request.urlretrieve(rsp.output.results[0].url, out)
#
# def generate_fal(prompt, out):         # Flux via fal.ai
#     import fal_client, urllib.request   # pip install fal-client ; export FAL_KEY=...
#     r = fal_client.run("fal-ai/flux-pro/v1.1",
#                        arguments={"prompt": prompt, "image_size": "portrait_4_3"})
#     urllib.request.urlretrieve(r["images"][0]["url"], out)


def generate(prompt, out):
    return generate_openai(prompt, out)   # ← swap to your provider here


if __name__ == "__main__":
    os.makedirs(os.path.dirname(OUT), exist_ok=True)
    print("Generating cover image …")
    generate(PROMPT, OUT)
    print(f"Saved {OUT}")
    print("Now rebuild:  bash build_pdf.sh   (cover.tex auto-detects the image)")