app.py

import gradio as gr
import requests
import time
import json
import base64
import os
from io import BytesIO
import html
import re
from deep_translator import GoogleTranslator
from langdetect import detect



class Prodia:
    def __init__(self, api_key, base=None):
        self.base = base or "https://api.prodia.com/v1"
        self.headers = {
            "X-Prodia-Key": api_key
        }
    
    def generate(self, params):
        response = self._post(f"{self.base}/sd/generate", params)
        return response.json()
    
    def transform(self, params):
        response = self._post(f"{self.base}/sd/transform", params)
        return response.json()
    
    def controlnet(self, params):
        response = self._post(f"{self.base}/sd/controlnet", params)
        return response.json()
    
    def get_job(self, job_id):
        response = self._get(f"{self.base}/job/{job_id}")
        return response.json()

    def wait(self, job):
        job_result = job

        while job_result['status'] not in ['succeeded', 'failed']:
            time.sleep(0.25)
            job_result = self.get_job(job['job'])

        return job_result

    def list_models(self):
        response = self._get(f"{self.base}/sd/models")
        return response.json()

    def list_samplers(self):
        response = self._get(f"{self.base}/sd/samplers")
        return response.json()

    def _post(self, url, params):
        headers = {
            **self.headers,
            "Content-Type": "application/json"
        }
        response = requests.post(url, headers=headers, data=json.dumps(params))

        if response.status_code != 200:
            raise Exception(f"Bad Prodia Response: {response.status_code}")

        return response

    def _get(self, url):
        response = requests.get(url, headers=self.headers)

        if response.status_code != 200:
            raise Exception(f"Bad Prodia Response: {response.status_code}")

        return response


def image_to_base64(image):
    # Convert the image to bytes
    buffered = BytesIO()
    image.save(buffered, format="WEBP")  # You can change format to PNG if needed
    
    # Encode the bytes to base64
    img_str = base64.b64encode(buffered.getvalue())

    return img_str.decode('utf-8')  # Convert bytes to string


def remove_id_and_ext(text):
    text = re.sub(r'\[.*\]$', '', text)
    extension = text[-12:].strip()
    if extension == "safetensors":
        text = text[:-13]
    elif extension == "ckpt":
        text = text[:-4]
    return text


def get_data(text):
    results = {}
    patterns = {
        'prompt': r'(.*)',
        'negative_prompt': r'Negative prompt: (.*)',
        'steps': r'Steps: (\d+),',
        'seed': r'Seed: (\d+),',
        'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)', 
        'model': r'Model:\s*([^\s,]+)',
        'cfg_scale': r'CFG scale:\s*([\d\.]+)',
        'size': r'Size:\s*([0-9]+x[0-9]+)'
        }
    for key in ['prompt', 'negative_prompt', 'steps', 'seed', 'sampler', 'model', 'cfg_scale', 'size']:
        match = re.search(patterns[key], text)
        if match:
            results[key] = match.group(1)
        else:
            results[key] = None
    if results['size'] is not None:
        w, h = results['size'].split("x")
        results['w'] = w
        results['h'] = h
    else:
        results['w'] = None
        results['h'] = None
    return results




prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
model_list = prodia_client.list_models()
model_names = {}

for model_name in model_list:
    name_without_ext = remove_id_and_ext(model_name)
    model_names[name_without_ext] = model_name


def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
    language = detect(prompt)
    
    if language == 'ru':
        prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
        print(prompt)
    
    result = prodia_client.generate({
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "model": model,
        "steps": steps,
        "sampler": sampler,
        "cfg_scale": cfg_scale,
        "width": width,
        "height": height,
        "seed": seed
    })

    job = prodia_client.wait(result)

    return job["imageUrl"]


def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
    result = prodia_client.transform({
        "imageData": image_to_base64(input_image),
        "denoising_strength": denoising,
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "model": model,
        "steps": steps,
        "sampler": sampler,
        "cfg_scale": cfg_scale,
        "width": width,
        "height": height,
        "seed": seed
    })

    job = prodia_client.wait(result)

    return job["imageUrl"]


css = """
footer {visibility: hidden !important;}
#container{

margin: 0 auto;

max-width: 40rem;

}

#intro{

max-width: 100%;

text-align: center;

margin: 0 auto;

}

div.svelte-vt1mxs {

display: flex;

position: relative;

flex-direction: column

}

div.svelte-vt1mxs>*,div.svelte-vt1mxs>.form > * {

width: var(--size-full)

}

.gap.svelte-vt1mxs {

gap: var(--layout-gap)

}

.hide.svelte-vt1mxs {

display: none

}

.compact.svelte-vt1mxs>*,.compact.svelte-vt1mxs .box {

border-radius: 0

}

.compact.svelte-vt1mxs,.panel.svelte-vt1mxs {

border: solid var(--panel-border-width) var(--panel-border-color);

border-radius: var(--container-radius);

background: var(--panel-background-fill);

padding: var(--spacing-lg)

}

div#component-24 {

display: none;

}

div#component-8 {background: #00000024;border: 0;color: #ffffff;backdrop-filter: blur(20px);-webkit-backdrop-filter: blur(20px);border-width: 0 !important;}

span.md.svelte-9tftx4 {

display: none;

}

.empty.svelte-lk9eg8.large.unpadded_box {

background: none !important;

}

div#component-26 {

display: none;

}

div#component-7 {

background: none;

}

.wrap.default.full.svelte-119qaqt.hide {

background: none !important;

}

.styler.svelte-iyf88w {

background: none !important;

}

div#component-3 {

background: none !important;

border: 0;

}

input.scroll-hide.svelte-1f354aw {

overflow: hidden !important;

}

div#component-5 {

border-radius: 40px 0px 0px 40px;

background: black !important;

opacity: 0.9;

}

#component-6 {

border-radius: 0px 40px 40px 0px;

background: linear-gradient(358deg, #ff4d0080, #fff0);

color: #ffffffe3;

border: 2px #ffffffc2 dashed;

border-left: 0;

font-size: 30px;

letter-spacing:-1px;

position: relative;

z-index: 1;

backdrop-filter: blur(18px);

-webkit-backdrop-filter: blur(18px);

}

div#component-0 {

max-width: 100% !important;

}

.grid-wrap.svelte-1b19cri.fixed-height {

max-height: 100% !important;

overflow: auto;

}

footer.svelte-1ax1toq {

display: none !important;

}

input.scroll-hide.svelte-1f354aw {

font-size: 26px;

padding: 25px;

}

div#component-4 {

margin-top: 230px;

margin-bottom: 30px;

}

gradio-app {

background-color: transparent !important;

background: url(https://vivawaves.com/wavesweaveslogo.svg) top center no-repeat !important;

margin-top: 260px;

}

label.svelte-1f354aw {

}

.styler.svelte-iyf88w {

}

body {

background: url(https://vivawaves.com/vivatodaybg2.jpg);

background-size: cover;

}

img.svelte-1b19cri {}

.preview.svelte-1b19cri {

background: #0000004d !important;

border-radius: 20px;

padding: 20px;

overflow: hidden;

}

button.svelte-1030q2h {

border-radius: 100%;

}

div.svelte-1030q2h svg {

}

svg path {

}

img.svelte-1b19cri {

border-radius: 10px;

}

.form.svelte-sfqy0y {

background: #fff0;

border-width: 0px;

opacity: 0.8;

}

.gradio-container-3-44-2,.gradio-container-3-44-2 *,.gradio-container-3-44-2 :before,.gradio-container-3-44-2 :after {

box-sizing: border-box;

border-width: 0;

border-style: solid;

}

div#component-13 {

display: none;

}

footer.svelte-mpyp5e {

display: none !important;

}

div#intro {

display: none;

}

div.svelte-15lo0d8 {

display: flex;

flex-wrap: wrap;

gap: 0;

width: var(--size-full);

flex-direction: initial;

justify-content: center;

align-items: baseline;

}

input.svelte-1f354aw.svelte-1f354aw, textarea.svelte-1f354aw.svelte-1f354aw {

display: block;

position: relative;

outline: none !important;

box-shadow: var(--input-shadow);

background: var(--input-background-fill);

padding: var(--input-padding);

width: 100%;

color: var(--body-text-color);

font-weight: var(--input-text-weight);

font-size: large;

line-height: initial;

border: none;

text-size-adjust: auto;

font-size: 23px !important;

}

div#component-24 {

display: none;

}

div#component-8 {background: #00000024;border: 0;color: #ffffff;backdrop-filter: blur(20px);-webkit-backdrop-filter: blur(20px);border-width: 0 !important;}

span.md.svelte-9tftx4 {

display: none;

}

.empty.svelte-lk9eg8.large.unpadded_box {

background: none !important;

}

div#component-26 {

display: none;

}

div#component-7 {

background: none;

}

.wrap.default.full.svelte-119qaqt.hide {

background: none !important;

}

.styler.svelte-iyf88w {

background: none !important;

}

div#component-3 {

background: none !important;

border: 0;

}

div#component-9 {
    border: 0 !important;
}


input.scroll-hide.svelte-1f354aw {

overflow: hidden !important;

}

div#component-5 {

border-radius: 40px;

background: transparent !important;

opacity: 1;

}

#component-6 {
    border-radius: 40px;
    background: #d7661500;
    border: none;
    border-left: 0;
    font-size: 30px;
    letter-spacing: -1px;
    position: relative;
    z-index: 1;
    backdrop-filter: none;
    -webkit-backdrop-filter: none;
    display: block;
}

div#component-0 {

max-width: 100% !important;

}

.grid-wrap.svelte-1b19cri.fixed-height {

max-height: 100% !important;

overflow: auto;

}

footer.svelte-1ax1toq {

display: none !important;

}

input.scroll-hide.svelte-1f354aw {

font-size: 26px;

padding: 25px;

}

div#component-4 {

margin-top: 230px;

margin-bottom: 30px;

}

gradio-app {

background-color: transparent !important;

background: url(https://vivawaves.com/wavesweaveslogo.svg) top center no-repeat !important;

margin-top: 77px;

}

label.svelte-1f354aw {

}

.styler.svelte-iyf88w {

}

body {

background: url(https://vivawaves.com/vivatodaybg2.jpg);

background-size: cover;

}

img.svelte-1b19cri {}

.preview.svelte-1b19cri {

background: #0000004d !important;

border-radius: 20px;

padding: 20px;

overflow: hidden;

}

button.svelte-1030q2h {

border-radius: 100%;

}

div.svelte-1030q2h svg {

}

svg path {

}

img.svelte-1b19cri {

border-radius: 10px;

}

.form.svelte-sfqy0y {

background: #fff0;

border-width: 0px;

opacity: 0.8;

}

.gradio-container-3-44-2,.gradio-container-3-44-2 *,.gradio-container-3-44-2 :before,.gradio-container-3-44-2 :after {

box-sizing: border-box;

border-width: 0;

border-style: solid;

}

div#component-13 {

display: none;

}

footer.svelte-mpyp5e {

display: none !important;

}

div#intro {

display: none;

}

div.svelte-15lo0d8 {

display: flex;

flex-wrap: wrap;

gap: 0 !important;

width: var(--size-full);

flex-direction: initial;

justify-content: center;

align-items: baseline;

}

input.svelte-1f354aw.svelte-1f354aw, textarea.svelte-1f354aw.svelte-1f354aw {

display: block;

position: relative;

outline: none !important;

box-shadow: var(--input-shadow);

background: var(--input-background-fill);

padding: var(--input-padding);

width: 100%;

color: var(--body-text-color);

font-weight: var(--input-text-weight);

font-size: large;

line-height: initial;

border: none;

text-size-adjust: auto;

font-size: 23px !important;

border-radius: 30px;

background: white !important;

text-align: center;

}

div#component-8 {

margin-bottom: 70px;
margin-top: 210px;

}
    div#component-15 {display: none;}

div#component-18 {
    display: none;
}

div#component-1 {
    display: none;
}

button.selected.svelte-kqij2n {
    display: none;
}

button.svelte-kqij2n {
    display: none;
}

.tab-nav.scroll-hide.svelte-kqij2n {
    display: none;
}

.svelte-vt1mxs.gap {
    border-radius: 20px;
}

div#component-6 {padding: 26px;}

button#generate {
    background: #eb7623;
    border-radius: 40px;
    padding: 16px;
    color: #FFF;
    FONT-SIZE: large;
    border: 2px solid #ff7600;
    border-top: 0px solid;
    box-shadow: 0px 12px 10px -10px #ff7600;
}
"""
        
with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Accordion(label="Models", open=False):
            model = gr.Radio(interactive=False, value="epicrealism_naturalSinRC1VAE.safetensors [90a4c676]", show_label=False, choices=prodia_client.list_models())

    with gr.Tabs() as tabs:
        
        with gr.Tab("txt2img", id='t2i'):
                    
            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Tab("Основные настройки"):
                        with gr.Column(scale=6, min_width=600):
                            prompt = gr.Textbox("", placeholder="Take a deep breath and take your time describing your weave... Be as vague or specific as you want. 💜✨ ", show_label=False, lines=3) 
                            negative_prompt = gr.Textbox(placeholder="Here you can describe anything that you would like NOT to see. ", show_label=False, lines=1, value="")
                                        
                        with gr.Row():
                            with gr.Column(scale=1):
                                steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=50, value=40, step=1)
    
                        with gr.Row():
                            with gr.Column(scale=1):
                                width = gr.Slider(label="Ширина", minimum=15, maximum=1024, value=1024, step=8)
                                height = gr.Slider(label="Длина", minimum=15, maximum=1024, value=1024, step=8)
                            
                    with gr.Tab("Расширенные настройки"):
                        with gr.Row():
                            with gr.Column(scale=1):
                                sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
                            
                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
                        seed = gr.Slider(label="Seed", minimum=-1, maximum=10000000, value=-1)
                    text_button = gr.Button("Weave", variant='primary', elem_id="generate")
                    image_output = gr.Image()
    
            text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output)
        
        with gr.Tab("img2img", id='i2i'):
            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Tab("Основные настройки"):
                        i2i_image_input = gr.Image(type="pil")
                        with gr.Column(scale=6, min_width=600):
                            i2i_prompt = gr.Textbox("", placeholder="Prompt", show_label=False, lines=3)
                            i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry")
                            
                        with gr.Row():
                                
                            with gr.Column(scale=1):
                                i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=50, value=30, step=1)
    
                        with gr.Row():
                            with gr.Column(scale=1):
                                i2i_width = gr.Slider(label="Ширина", minimum=15, maximum=1024, value=512, step=8)
                                i2i_height = gr.Slider(label="Высота", minimum=15, maximum=1024, value=512, step=8)
                            
    
                    with gr.Tab("Расширенные настройки"):

                        with gr.Row():
                            with gr.Column(scale=1):
                                i2i_sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
                                
                           
                        i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
                        i2i_denoising = gr.Slider(label="Схожесть с оригиналом", minimum=0, maximum=1, value=0.7, step=0.1)
                        i2i_seed = gr.Slider(label="Seed", minimum=-1, maximum=10000000, value=-1)

                
                    i2i_text_button = gr.Button("Генерация", variant='primary', elem_id="generate")
                    i2i_image_output = gr.Image()

            i2i_text_button.click(img2img, inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt, model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height, i2i_seed], outputs=i2i_image_output)
demo.queue(concurrency_count=64, max_size=80, api_open=False).launch(max_threads=256)