import gradio as gr
import torch
import numpy as np
from PIL import Image
import cv2
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler

# ---------------------------------------------------------------------------
# Device setup (works on free CPU Spaces)
# ---------------------------------------------------------------------------
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32

# ---------------------------------------------------------------------------
# Model loading (cached – runs once on Space startup)
# ---------------------------------------------------------------------------
def load_pipeline():
    controlnet = ControlNetModel.from_pretrained(
        "lllyasviel/sd-controlnet-canny",
        torch_dtype=DTYPE,
    )
    pipe = StableDiffusionControlNetPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        controlnet=controlnet,
        torch_dtype=DTYPE,
        safety_checker=None,
    )
    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
    pipe = pipe.to(DEVICE)
    if DEVICE == "cuda":
        pipe.enable_model_cpu_offload()
    return pipe

pipe = load_pipeline()

# ---------------------------------------------------------------------------
# Helper: extract Canny edges
# ---------------------------------------------------------------------------
def extract_canny(image: Image.Image, low: int, high: int) -> Image.Image:
    img_array = np.array(image.convert("RGB"))
    edges = cv2.Canny(img_array, low, high)
    edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
    return Image.fromarray(edges_rgb)

# ---------------------------------------------------------------------------
# Main generation function
# ---------------------------------------------------------------------------
def generate(input_image, prompt, negative_prompt, canny_low, canny_high,
             guidance_scale, steps, seed):
    if input_image is None:
        raise gr.Error("Bitte lade ein Bild hoch.")
    if not prompt.strip():
        raise gr.Error("Bitte gib einen Prompt ein.")

    pil_image = Image.fromarray(input_image).resize((512, 512))
    control_image = extract_canny(pil_image, int(canny_low), int(canny_high))

    generator = torch.manual_seed(int(seed)) if seed >= 0 else None

    result = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt or None,
        image=control_image,
        num_inference_steps=int(steps),
        guidance_scale=float(guidance_scale),
        generator=generator,
    ).images[0]

    return control_image, result

# ---------------------------------------------------------------------------
# Gradio UI
# ---------------------------------------------------------------------------
css = """
body { font-family: 'Inter', sans-serif; background: #0f0f11; color: #e8e8f0; }
.gradio-container { max-width: 1100px; margin: 0 auto; }
#title { text-align: center; padding: 2rem 0 0.5rem; }
#title h1 { font-size: 2rem; font-weight: 700; letter-spacing: -0.5px;
             background: linear-gradient(90deg, #a78bfa, #60a5fa);
             -webkit-background-clip: text; -webkit-text-fill-color: transparent; }
#title p  { color: #9090a8; font-size: 0.95rem; margin-top: 0.25rem; }
.panel    { background: #1a1a22; border: 1px solid #2a2a38; border-radius: 12px; padding: 1.25rem; }
.generate-btn { background: linear-gradient(135deg, #7c3aed, #2563eb) !important;
                color: white !important; border: none !important;
                font-weight: 600 !important; font-size: 1rem !important;
                border-radius: 8px !important; height: 48px !important; }
.generate-btn:hover { opacity: 0.9 !important; }
"""

with gr.Blocks(css=css, title="ControlNet Canny") as demo:
    gr.HTML("""
        <div id="title">
            <h1>⚡ ControlNet · Canny Edge</h1>
            <p>Lade ein Bild hoch, schreib einen Prompt – und erzeuge ein neues Bild, das die Struktur deines Originals übernimmt.</p>
        </div>
    """)

    gr.Markdown(f"> 🖥️ Läuft auf: **{DEVICE.upper()}** — auf CPU dauert eine Generierung ca. 2–5 Minuten. Bitte Geduld.")

    with gr.Row():
        with gr.Column(scale=1, elem_classes="panel"):
            gr.Markdown("### 📥 Eingabe")
            input_image = gr.Image(label="Referenzbild", type="numpy", height=300)
            prompt = gr.Textbox(label="Prompt",
                placeholder="a futuristic city at night, neon lights, photorealistic, 8k", lines=3)
            negative_prompt = gr.Textbox(label="Negative Prompt (optional)",
                placeholder="blurry, low quality, watermark, deformed", lines=2)

            with gr.Accordion("⚙️ Erweiterte Einstellungen", open=False):
                with gr.Row():
                    canny_low  = gr.Slider(0, 255, value=100, step=1, label="Canny Low Threshold")
                    canny_high = gr.Slider(0, 255, value=200, step=1, label="Canny High Threshold")
                with gr.Row():
                    guidance_scale = gr.Slider(1, 20, value=7.5, step=0.5, label="Guidance Scale")
                    steps          = gr.Slider(10, 30, value=15, step=1, label="Inference Steps")
                seed = gr.Number(value=42, label="Seed (-1 = zufällig)", precision=0)

            run_btn = gr.Button("🎨  Generieren", elem_classes="generate-btn")

        with gr.Column(scale=1, elem_classes="panel"):
            gr.Markdown("### 📤 Ergebnis")
            canny_out  = gr.Image(label="Canny-Kantenbild", height=250)
            result_out = gr.Image(label="Generiertes Bild", height=350)

    run_btn.click(
        fn=generate,
        inputs=[input_image, prompt, negative_prompt, canny_low, canny_high,
                guidance_scale, steps, seed],
        outputs=[canny_out, result_out],
    )

demo.queue().launch()