from __future__ import annotations

from pathlib import Path

import gradio as gr
import torch
from model import ConditionalGenerator
from PIL import Image, ImageDraw
from safetensors.torch import load_file

ARTIFACT_DIR = Path(__file__).resolve().parent / "artifacts" / "pocket-wgan"
MODEL = ConditionalGenerator()
MODEL.load_state_dict(load_file(ARTIFACT_DIR / "generator.safetensors"))
MODEL.eval()


def generate_gallery(label: int, seed: int, temperature: float) -> tuple[Image.Image, dict]:
    labels = torch.full((12,), int(label), dtype=torch.long)
    generated = MODEL.generate(labels, seed=int(seed), temperature=float(temperature))
    canvas = Image.new("L", (4 * 128, 3 * 128), color=0)
    for index, pixels in enumerate(generated):
        image = (
            Image.fromarray(
                pixels.reshape(8, 8).mul(255).clamp(0, 255).to(torch.uint8).numpy(),
                mode="L",
            )
            .resize((120, 120), Image.Resampling.NEAREST)
        )
        canvas.paste(image, ((index % 4) * 128 + 4, (index // 4) * 128 + 4))
    draw = ImageDraw.Draw(canvas)
    for column in range(1, 4):
        draw.line((column * 128, 0, column * 128, 384), fill=64, width=1)
    for row in range(1, 3):
        draw.line((0, row * 128, 512, row * 128), fill=64, width=1)
    metadata = {
        "digit": int(label),
        "seed": int(seed),
        "temperature": float(temperature),
        "samples": 12,
        "mean_pixel_variance": float(generated.var(dim=0).mean()),
    }
    return canvas, metadata


with gr.Blocks(title="Pocket WGAN-GP") as demo:
    gr.Markdown(
        "# Pocket WGAN-GP\n"
        "Explore a compact adversarial generator trained with Wasserstein distance, "
        "gradient penalty, projection conditioning, and explicit collapse checks."
    )
    with gr.Row():
        label = gr.Slider(0, 9, value=7, step=1, label="Digit class")
        seed = gr.Slider(0, 100_000, value=2047, step=1, label="Noise seed")
        temperature = gr.Slider(
            0.25, 1.75, value=1.0, step=0.05, label="Latent temperature"
        )
    gallery, metadata = generate_gallery(7, 2047, 1.0)
    output = gr.Image(value=gallery, label="Twelve adversarial samples")
    metrics = gr.JSON(value=metadata, label="Live diversity readout")
    button = gr.Button("Generate a new batch", variant="primary")
    button.click(
        generate_gallery,
        inputs=[label, seed, temperature],
        outputs=[output, metrics],
    )


if __name__ == "__main__":
    demo.launch()