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

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("codermert/zehra_flux", torch_dtype=dtype).to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, num_images=4, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    
    generator = torch.Generator().manual_seed(seed)
    images = []
    
    for _ in range(num_images):
        image = pipe(
            prompt=prompt,
            width=width,
            height=height,
            num_inference_steps=num_inference_steps,
            generator=generator,
            guidance_scale=0.0
        ).images[0]
        images.append(image)
        # Her görsel için farklı seed kullan
        seed = random.randint(0, MAX_SEED)
        generator = torch.Generator().manual_seed(seed)
    
    return images, seed

examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cat holding a sign that says hello world",
    "an anime illustration of a wiener schnitzel",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 900px;
}
.generated-images {
    display: grid;
    grid-template-columns: repeat(2, 1fr);
    gap: 10px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("""# Zehra Flux Image Generator
        4 farklı görsel üreten AI görsel oluşturucu
        """)
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Görseliniz için prompt girin",
                container=False,
            )
            run_button = gr.Button("Oluştur", scale=0)
        
        # 4 görsel için grid layout
        with gr.Row(elem_classes="generated-images"):
            results = [gr.Image(label=f"Sonuç {i+1}", show_label=True) for i in range(4)]
        
        with gr.Accordion("Gelişmiş Ayarlar", open=False):
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="Rastgele seed", value=True)
            
            with gr.Row():
                width = gr.Slider(
                    label="Genişlik",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
                
                height = gr.Slider(
                    label="Yükseklik",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
            
            num_inference_steps = gr.Slider(
                label="Inference adım sayısı",
                minimum=1,
                maximum=50,
                step=1,
                value=4,
            )
        
        gr.Examples(
            examples=examples,
            fn=infer,
            inputs=[prompt],
            outputs=[*results, seed],
            cache_examples="lazy"
        )
        
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
        outputs=[*results, seed]
    )

demo.launch()