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

# Загрузка моделей
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    controlnet=controlnet,
    # torch_dtype=torch.float16
).to("cuda")

def generate_background(image_path, prompt, negative_prompt):
    # Удаление фона
    image = Image.open(image_path).convert("RGBA")
    output_image = remove(image)
    
    # Преобразование изображения объекта в контурное изображение
    foreground = output_image.convert("L")
    _, contour = cv2.threshold(np.array(foreground), 127, 255, cv2.THRESH_BINARY)
    contour_image = Image.fromarray(contour)
    
    # Генерация фона
    generator = torch.Generator(device="cuda").manual_seed(1024)
    result = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        image=contour_image,
        generator=generator,
        num_inference_steps=50
    )
    
    background = result.images[0].convert("RGBA")
    
    # Изменение размера фона до размера переднего плана
    background = background.resize(output_image.size)
    
    # Наложение изображений
    composite = Image.alpha_composite(background, output_image)
    
    return composite

# Определение интерфейса Gradio
iface = gr.Interface(
    fn=generate_background,
    inputs=[
        gr.Image(type="filepath", label="Загрузите изображение"),
        gr.Textbox(lines=2, placeholder="Введите позитивный промт", label="Позитивный промт"),
        gr.Textbox(lines=2, placeholder="Введите негативный промт", label="Негативный промт")
    ],
    outputs=gr.Image(type="pil", label="Результат")
)

# Запуск интерфейса
iface.launch()

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

# device = "cuda" if torch.cuda.is_available() else "cpu"

# if torch.cuda.is_available():
#     torch.cuda.max_memory_allocated(device=device)
#     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
#     pipe.enable_xformers_memory_efficient_attention()
#     pipe = pipe.to(device)
# else: 
#     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
#     pipe = pipe.to(device)

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

# def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):

#     if randomize_seed:
#         seed = random.randint(0, MAX_SEED)
        
#     generator = torch.Generator().manual_seed(seed)
    
#     image = pipe(
#         prompt = prompt, 
#         negative_prompt = negative_prompt,
#         guidance_scale = guidance_scale, 
#         num_inference_steps = num_inference_steps, 
#         width = width, 
#         height = height,
#         generator = generator
#     ).images[0] 
    
#     return image

# examples = [
#     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
#     "An astronaut riding a green horse",
#     "A delicious ceviche cheesecake slice",
# ]

# css="""
# #col-container {
#     margin: 0 auto;
#     max-width: 520px;
# }
# """

# if torch.cuda.is_available():
#     power_device = "GPU"
# else:
#     power_device = "CPU"

# with gr.Blocks(css=css) as demo:
    
#     with gr.Column(elem_id="col-container"):
#         gr.Markdown(f"""
#         # Text-to-Image Gradio Template
#         Currently running on {power_device}.
#         """)
        
#         with gr.Row():
            
#             prompt = gr.Text(
#                 label="Prompt",
#                 show_label=False,
#                 max_lines=1,
#                 placeholder="Enter your prompt",
#                 container=False,
#             )
            
#             run_button = gr.Button("Run", scale=0)
        
#         result = gr.Image(label="Result", show_label=False)

#         with gr.Accordion("Advanced Settings", open=False):
            
#             negative_prompt = gr.Text(
#                 label="Negative prompt",
#                 max_lines=1,
#                 placeholder="Enter a negative prompt",
#                 visible=False,
#             )
            
#             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.Row():
                
#                 width = gr.Slider(
#                     label="Width",
#                     minimum=256,
#                     maximum=MAX_IMAGE_SIZE,
#                     step=32,
#                     value=512,
#                 )
                
#                 height = gr.Slider(
#                     label="Height",
#                     minimum=256,
#                     maximum=MAX_IMAGE_SIZE,
#                     step=32,
#                     value=512,
#                 )
            
#             with gr.Row():
                
#                 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=12,
#                     step=1,
#                     value=2,
#                 )
        
#         gr.Examples(
#             examples = examples,
#             inputs = [prompt]
#         )

#     run_button.click(
#         fn = infer,
#         inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
#         outputs = [result]
#     )

# demo.queue().launch()