import gradio as gr
import torch
import numpy as np
import diffusers
import os
import random
from PIL import Image

hf_token = os.environ.get("HF_TOKEN")
from diffusers import AutoPipelineForText2Image

device = "cuda"  # if torch.cuda.is_available() else "cpu"
pipe = AutoPipelineForText2Image.from_pretrained("briaai/BRIA-2.3", torch_dtype=torch.float16, force_zeros_for_empty_prompt=False).to(device)
pipe.load_ip_adapter("briaai/Image-Prompt", subfolder='models', weight_name="ip_adapter_bria.bin")
pipe.to(device)

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

@spaces.GPU(enable_queue=True)
def predict(prompt, ip_adapter_images, ip_adapter_scale=0.5, negative_prompt="", seed=100, randomize_seed=False, center_crop=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=50, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    
    # Optionally resize images if center crop is not selected
    if not center_crop:
        ip_adapter_images = [image.resize((224, 224)) for image in ip_adapter_images]
    
    # Create a generator for reproducible random seed
    generator = torch.Generator(device="cuda").manual_seed(seed)
    pipe.set_ip_adapter_scale([ip_adapter_scale])
    
    # Pass all images at once to the pipe
    result_images = pipe(
        prompt=prompt,
        ip_adapter_image=ip_adapter_images,  # Pass the list of images
        negative_prompt=negative_prompt,
        height=height,
        width=width,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        num_images_per_prompt=1,
        generator=generator,
    ).images
    
    return result_images, seed

examples = [
    ["high quality", ["example1.png", "example2.png"], 1.0, "", 1000, False, False, 1152, 896],
]

css = """
#col-container {
    display: flex;
    flex-direction: column;
    align-items: center;
    padding: 10px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column():
        with gr.Row():
            prompt = gr.Textbox(label="Prompt", lines=1)
            ip_adapter_images = gr.Gallery(label="Input Images", elem_id="image-gallery").style(grid=[2], preview=True)
        
        ip_adapter_scale = gr.Slider(label="IP Adapter Scale", minimum=0.0, maximum=1.0, step=0.1, value=0.5)
        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Optional", lines=1)
        
        with gr.Row():
            seed = gr.Number(label="Seed", value=100)
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            center_crop = gr.Checkbox(label="Center Crop Image", value=False, info="If not checked, the images will be resized.")
        
        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=7.0
            )
            num_inference_steps = gr.Slider(
                label="Number of Inference Steps", minimum=1, maximum=100, step=1, value=25
            )
        
        result = gr.Gallery(label="Generated Images").style(grid=[2], preview=True)
        
        run_button = gr.Button("Run")
    
    run_button.click(
        predict,
        inputs=[prompt, ip_adapter_images, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height, guidance_scale, num_inference_steps],
        outputs=[result, seed]
    )

    gr.Examples(
        examples=examples,
        fn=predict,
        inputs=[prompt, ip_adapter_images, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height],
        outputs=[result, seed],
        cache_examples="lazy"
    )

demo.queue(max_size=25, api_open=False).launch(show_api=False)