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

device = "cuda"

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

@spaces.GPU
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)

    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)
    
    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



@spaces.GPU
def reject(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)

    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)
    
    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


@spaces.GPU
def accept(*args):
    
    conv_id_element = args[0]
    conv_id_element.visible=True
    '''
    markdown_blocks = list(args[1:-2])
    labels = args[-2]

    data = {'conv_id': conv_id, 'labels': labels}
    with open(f"./labels/conv_{conv_id}", 'w') as f:
        json.dump(data, f, indent=4)

    new_conversation = get_conversation()
    new_conv_id = new_conversation['conv_id']
    new_transcript = pad_transcript(new_conversation['transcript'], max_conversation_length)

    for i in range(max_conversation_length):
        if new_transcript[i]['speaker'] != '':
            markdown_blocks[i] = gr.Markdown(f"""<br>**{new_transcript[i]['speaker']}**: {new_transcript[i]['response']}""",
                                             visible=True)
        else:
            markdown_blocks[i] = gr.Markdown("", visible=False)

    new_labels = gr.CheckboxGroup(choices=checkbox_choices,
                                  label="",
                                  visible=False)
    conv_len = gr.Number(value=len(new_transcript), visible=False)

    return [new_conv_id] + list(markdown_blocks) + [labels] + [new_labels] + [conv_len]
    '''
 

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;
}
"""


power_device = "GPU"

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)

            left_button = gr.Button("Left", scale=0)
            right_button = gr.Button("Right", scale=0)
        
        result = gr.Image(label="Result", show_label=False)
        conv_id_element = gr.Text(value=1, visible=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]
        )

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

    right_button.click(
        fn = accept,
        inputs = [conv_id_element, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs = [conv_id_element]        
    )

demo.queue().launch()