import gradio as gr
import random
import os
import io, base64
from PIL import Image
import numpy
import shortuuid

latent = gr.Interface.load("spaces/multimodalart/latentdiffusion")
rudalle = gr.Interface.load("spaces/multimodalart/rudalle")

#print(rudalle)
#guided = gr.Interface.load("spaces/EleutherAI/clip-guided-diffusion")
#print(guided)
def text2image_latent(text,steps,width,height,images,diversity):
    results = latent(text, steps, width, height, images, diversity)
    image_paths = []
    image_arrays = []
    for image in results[1]:
        image_str = image[0]
        image_str = image_str.replace("data:image/png;base64,","")
        decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
        img = Image.open(io.BytesIO(decoded_bytes))
        #image_arrays.append(numpy.asarray(img))
        url = shortuuid.uuid()
        temp_dir = './tmp'
        if not os.path.exists(temp_dir):
            os.makedirs(temp_dir, exist_ok=True)
        image_path = f'{temp_dir}/{url}.png'
        img.save(f'{temp_dir}/{url}.png')
        image_paths.append(image_path)
    return(results[0],image_paths)
  
def text2image_rudalle(text,aspect,model):
    image = rudalle(text,aspect,model)[0]
    return(image)

#def text2image_guided(text):
#    image = guided(text, None, 10, 600, 0, 0, 0, random.randint(0,2147483647), None, 50, 32)
#    print(image)
#    image = image[0]
#    return(image)

css_mt = {"margin-top": "1em"}

empty = gr.outputs.HTML()    

with gr.Blocks() as mindseye:
    gr.Markdown("<h1>MindsEye Lite <small><small>run multiple text-to-image models in one place</small></small></h1><p>MindsEye Lite orchestrates multiple text-to-image Hugging Face Spaces in one convenient space, so you can try different models. This work carries the spirit of <a href='https://multimodal.art/mindseye' target='_blank'>MindsEye Beta</a>, a tool to run multiple models with a single UI, but adjusted to the current hardware limitations of Spaces. MindsEye Lite was created by <a style='color: rgb(99, 102, 241);font-weight:bold' href='https://twitter.com/multimodalart' target='_blank'>@multimodalart</a>, keep up with the <a style='color: rgb(99, 102, 241);' href='https://multimodal.art/news' target='_blank'>latest multimodal ai art news here</a> and consider <a style='color: rgb(99, 102, 241);' href='https://www.patreon.com/multimodalart' target='_blank'>supporting us on Patreon</a></div></p>")
    gr.Markdown("<style>.mx-auto.container .gr-form-gap {flex-direction: row; gap: calc(1rem * calc(1 - var(--tw-space-y-reverse)));} .mx-auto.container .gr-form-gap .flex-col, .mx-auto.container .gr-form-gap .gr-box{width: 100%}</style>")
    text = gr.inputs.Textbox(placeholder="Try writing something..", label="Prompt")
    with gr.Column():
        with gr.Row():
            with gr.Tabs():
                with gr.TabItem("Latent Diffusion"):
                    steps = gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate",default=45,maximum=50,minimum=1,step=1)
                    width = gr.inputs.Slider(label="Width", default=256, step=32, maximum=256, minimum=32)
                    height = gr.inputs.Slider(label="Height", default=256, step=32, maximum = 256, minimum=32)
                    images = gr.inputs.Slider(label="Images - How many images you wish to generate", default=2, step=1, minimum=1, maximum=4)
                    diversity = gr.inputs.Slider(label="Diversity scale - How different from one another you wish the images to be",default=5.0, minimum=1.0, maximum=15.0)
                    get_image_latent = gr.Button("Generate Image",css=css_mt)
                    
                with gr.TabItem("ruDALLE"):
                    aspect = gr.inputs.Radio(label="Aspect Ratio", choices=["Square", "Horizontal", "Vertical"],default="Square")
                    model = gr.inputs.Dropdown(label="Model", choices=["Surrealism","Realism", "Emoji"], default="Surrealism")
                    get_image_rudalle = gr.Button("Generate Image",css=css_mt)
                with gr.TabItem("VQGAN+CLIP"):
                    pass
                with gr.TabItem("V-Diffusion"):
                    pass
        with gr.Row():
            with gr.Tabs():
                with gr.TabItem("Image output"):
                    image = gr.outputs.Image()
                with gr.TabItem("Gallery output"):
                    gallery = gr.Gallery(label="Individual images")
    
    get_image_latent.click(text2image_latent, inputs=[text,steps,width,height,images,diversity], outputs=[image,gallery])
    get_image_rudalle.click(text2image_rudalle, inputs=[text,aspect,model], outputs=image)
mindseye.launch()