app.py

# app.py
import gradio as gr
import spaces
from diffqrcoder_wrapper import generate_qr_art, load_pipeline
import torch

DEFAULT_PROMPT = (
    "whimsical biomimetic blueprint, iridescent inks swirling through "
    "mechanical petals, soft gears woven with luminescent filigree"
)

DEFAULT_NEG = "easynegative"

def warmup():
    """
    Run once on Space startup, on CPU only.
    Downloads models & builds pipeline into cache.
    """
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True
    torch.set_float32_matmul_precision("high")

    print("🔥 Warmup starting: downloading models & building pipeline on CPU...")
    pipe = load_pipeline()
    print("🔥 Warmup done. Pipeline ready on CPU.")

    # Optional: return a tiny status string for UI (doesn't have to be used)
    return "Warmup complete."

@spaces.GPU
def infer(
    url_or_text: str,
    prompt: str,
    num_inference_steps: int,
    controlnet_scale: float,
    scanning_robust_guidance_scale: float,
    perceptual_guidance_scale: float,
    srmpgd_iters: int,
    seed: int
):
    try:
        print("🔧 infer() starting")
        print("CUDA available?", torch.cuda.is_available())
        if torch.cuda.is_available():
            print("CUDA device count:", torch.cuda.device_count())
            print("Current device:", torch.cuda.current_device())
            print("Device name:", torch.cuda.get_device_name(0))

        pipe = load_pipeline()
        print("✅ pipeline loaded on CPU")

        # Attach to GPU in ZeroGPU context
        pipe = pipe.to("cuda")
        print("✅ pipeline moved to CUDA")

        srmpgd_num_iteration = None if srmpgd_iters == 0 else srmpgd_iters
        print(
            f"Params → steps={num_inference_steps}, "
            f"ctrl={controlnet_scale}, srg={scanning_robust_guidance_scale}, "
            f"pg={perceptual_guidance_scale}, iters={srmpgd_num_iteration}"
        )

        img = generate_qr_art(
            pipe,
            url_or_text=url_or_text,
            prompt=prompt,
            num_inference_steps=num_inference_steps,
            controlnet_conditioning_scale=controlnet_scale,
            scanning_robust_guidance_scale=scanning_robust_guidance_scale,
            perceptual_guidance_scale=perceptual_guidance_scale,
            srmpgd_num_iteration=srmpgd_num_iteration,
            seed=seed,
        )

        print("✅ generation complete")
        return img

    except Exception as e:
        print("❌ Error in infer():", repr(e))
        raise

with gr.Blocks() as demo:
    gr.Markdown(
        r"""
# DiffQRCoder – ZeroGPU demo

Generate aesthetic, scanning-robust QR codes using the **DiffQRCoder** pipeline
([WACV 2025](https://openaccess.thecvf.com/content/WACV2025/html/Liao_DiffQRCoder_Diffusion-Based_Aesthetic_QR_Code_Generation_with_Scanning_Robustness_Guided_WACV_2025_paper.html)) 🚀
        """
    )

    with gr.Row():
        url = gr.Textbox(
            label="QR contents (URL or text)",
            value="https://example.com",
        )

    prompt = gr.Textbox(
        label="Style prompt",
        value=DEFAULT_PROMPT,
        lines=3,
    )

    seed_input = gr.Slider(
        minimum=0,
        maximum=999999,
        value=42,
        step=1,
        label="Seed",
    )


    with gr.Accordion("Advanced parameters", open=False):
        steps = gr.Slider(
            minimum=10,
            maximum=60,
            value=40,
            step=1,
            label="Diffusion steps (num_inference_steps)",
        )
        control_scale = gr.Slider(
            minimum=0.5,
            maximum=2.0,
            value=1,
            step=0.05,
            label="ControlNet conditioning scale",
        )
        srg_scale = gr.Slider(
            minimum=0,
            maximum=800,
            value=500,
            step=10,
            label="Scanning-robust guidance scale (srg)",
        )
        pg_scale = gr.Slider(
            minimum=0,
            maximum=10,
            value=2,
            step=0.5,
            label="Perceptual guidance scale (pg)",
        )
        srmpgd_iters = gr.Slider(
            minimum=0,
            maximum=64,
            value=6,
            step=1,
            label="SR-MPGD iterations (0 = disabled)",
        )

    btn = gr.Button("Generate QR Art ✨", variant="primary")
    out = gr.Image(label="Output QR art", type="pil")

    btn.click(
        fn=infer,
        inputs=[
            url,
            prompt,
            steps,
            control_scale,
            srg_scale,
            pg_scale,
            srmpgd_iters,
            seed_input
        ],
        outputs=[out],
    )
    demo.load(fn=warmup, inputs=None, outputs=None)

demo.launch()