import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch

# --- 1. CONSTANTS AND MODEL CONFIGURATION ---

# Styles and logical prompts from Perchance code
STYLES = {
    "Fotografia Realistyczna (Domyślny)": {
        "prompt": ", raw photo, realistic, candid shot, natural lighting, highly detailed face, dslr, sharp focus, 8k uhd, film grain, Fujifilm",
        "negative": "cartoon, anime, 3d render, painting, drawing, smooth skin, photoshop",
    },
    "Kinowy (Dramatyczny)": {
        "prompt": ", cinematic lighting, dramatic atmosphere, movie still, color graded, shallow depth of field, bokeh, volumetric fog, highly detailed, 8k, masterpiece",
        "negative": "bright, cheerful, flat lighting, amateur",
    },
    "Surowy (Raw)": {
        "prompt": ", high quality, detailed",
        "negative": "low quality",
    },
}

# Most comprehensive Negative Prompt (commonNegative)
COMMON_NEGATIVE = "(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, watermark, text, signature, sketch, poorly drawn face, low quality, worst quality, bad composition, blurry face, horror, grainy"

# Random description examples (shortened version)
RANDOM_DESCRIPTIONS = {
    "beautiful woman, tight dress, narrow waist, ethereal",
    "succubus, wind blowing hair, plunging neckline, bokeh",
    "catgirl, moonlit, high-angle shot, long exposure",
}

# Device detection for ZeroGPU compatibility
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"

# Model initialization with ZeroGPU compatibility
try:
    if torch.cuda.is_available():
        pipe = DiffusionPipeline.from_pretrained(
            model_repo_id,
            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
)
    pipe = pipe.to(device)
except Exception as e:
    print(f"Model loading failed: {e}")
    pipe = None

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

def infer(
    user_prompt,
    style_name,
    user_negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    if pipe is None:
        raise gr.Error("Model not loaded. Please check the logs for details.")
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    
    # 1. Compose Final Prompts (Perchance Logic)
    style = STYLES.get(style_name, {})
    
    # Final positive prompt = User Description + Style Prompt
    final_prompt = user_prompt + style.get("prompt", ""))
    
    # Final negative prompt = COMMON_NEGATIVE + Style Negative + User Negative
    final_negative_prompt = (
        COMMON_NEGATIVE +
        ", " + style.get("negative", "") +
        (", " + user_negative_prompt if user_negative_prompt else "")
    
    # 2. Call the model
    generator = torch.Generator().manual_seed(seed))
    
    with torch.inference_mode():
        image = pipe(
            prompt=final_prompt,
            negative_prompt=final_negative_prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            generator=generator,
        ).images[0]
    
    return image, seed

# Create examples
examples = [
    [random.choice(RANDOM_DESCRIPTIONS), "Fotografia Realistyczna (Domyślny)", ""],
    ["An astronaut riding a green horse, detailed, sci-fi", "Kinowy (Dramatyczny)", ""],
    ["A delicious ceviche cheesecake slice, studio lighting", "Fotografia Realistyczna (Domyślny)", "blurry, dark"],
]

# Custom CSS for better styling
custom_css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
.gradio-container {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
}
"""

# Gradio 6 Application
with gr.Blocks() as demo:
    gr.Markdown("# 🎨 AI Image Generator (High Quality) 🖼️")
    gr.Markdown(
        "Add a description, choose a style, and click **Generate**.<br>"
        "<span style='font-size:80%; color:grey;'>Engine: SDXL-Turbo. Implemented advanced prompts and common negative prompts.</span>"
    )
    
    with gr.Row():
        with gr.Column(scale=3):
            user_prompt = gr.Textbox(
                label="📝 Description (What do you want to see?)",
                lines=3,
                placeholder="high quality portrait photo. The more details, the better.",
            )
        with gr.Column(scale=1):
            style_name = gr.Dropdown(
                label="🎨 Style and Quality",
                choices=list(STYLES.keys())),
                value="Fotografia Realistyczna (Domyślny)",
            )
    
    with gr.Row():
        run_button = gr.Button("Generate", variant="primary")
    
    with gr.Row():
        result = gr.Image(label="Generated Image", height=512)
    
    with gr.Row():
        with gr.Column():
            seed = gr.Slider(
                label="🌱 Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
    
    with gr.Accordion("🎛 Advanced Settings", open=False):
            with gr.Row():
                width = gr.Slider(
                    label="📏 Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                step=32,
                value=1024,
            )
    
    with gr.Row():
        with gr.Column():
            height = gr.Slider(
                label="📐 Height",
                minimum=256,
                maximum=MAX_IMAGE_SIZE,
                step=32,
                value=1024,
            )
    
    with gr.Row():
        with gr.Column():
            user_negative_prompt = gr.Textbox(
                label="🚫 Additional Anti-description",
                lines=1,
                placeholder="Enter additional words to eliminate, e.g. 'cartoon, painting, drawing'",
            )
    
    with gr.Row():
        with gr.Column():
            guidance_scale = gr.Slider(
                label="🎛 Guidance Scale",
                minimum=0.0,
                maximum=10.0,
                step=0.1,
                value=0.0,
            )
            num_inference_steps = gr.Slider(
                label="⚡ Number of Inference Steps",
                minimum=1,
                maximum=50,
                step=1,
                value=2,
            )
    
    # Examples section
    gr.Examples(
        examples=examples,
        inputs=[user_prompt, style_name, user_negative_prompt],
        label="💡 Examples - Click to load",
    )
    
    # Event listeners
    run_button.click(
        fn=infer,
        inputs=[
            user_prompt,
            style_name,
            user_negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
        api_visibility="public",
    )

if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        theme=gr.themes.Soft(
            primary_hue="indigo",
            secondary_hue="blue",
            neutral_hue="slate",
            font=gr.themes.GoogleFont("Inter"),
            text_size="lg",
            spacing_size="md",
            radius_size="lg"
        ),
        css=custom_css,
        footer_links=[
            {
                "label": "Built with anycoder",
                "url": "https://huggingface.co/spaces/akhaliq/anycoder",
            },
            {
                "label": "Gradio",
                "url": "https://gradio.app",
            },
        ],
    )