app.py

import gradio as gr
import os
import re
#from tempfile import NamedTemporaryFile
import numpy as np
import spaces
import random
import string
from diffusers import StableDiffusion3Pipeline
import torch
from pathos.multiprocessing import ProcessingPool as ProcessPoolExecutor
import requests
from lxml.html import fromstring

pool = ProcessPoolExecutor(4)
pool.__enter__()

#model_id = "runwayml/stable-diffusion-v1-5"
model_id = "stabilityai/stable-diffusion-3-medium-diffusers"

device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = StableDiffusion3Pipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True, token=os.getenv('hf_token'))
    pipe = pipe.to(device)
else: 
    pipe = StableDiffusion3Pipeline.from_pretrained(model_id, use_safetensors=True, token=os.getenv('hf_token'))
    pipe = pipe.to(device)

def translate(text,lang):
    text = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', text)).lower().strip()
    lang = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', lang)).lower().strip()
    user_agents = [
        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 13_1) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15'
    ]
    url = 'https://www.google.com/search'
    print(url)
    resp = requests.get(
        url = url,
        params = {'q': f'{lang} translate {text}'},
        headers = {
            'User-Agent': random.choice(user_agents)
        }
    )
    content = resp.content
    html = fromstring(content)
    
    #src = html.xpath('//pre[@data-placeholder="Enter text"]/textarea')[0].text.strip()

    translated = text
    
    try:   
        trgt = html.xpath('//span[@class="target-language"]')[0].text.strip()
        rslt = html.xpath('//pre[@aria-label="Translated text"]/span')[0].text.strip()
        if trgt.lower() == lang.lower():
            translated = rslt
    except:
        raise Exception("Translation Error!")

    ret = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', translated)).lower().strip()
    print(ret)
    return ret

def generate_random_string(length):
    characters = string.ascii_letters + string.digits
    return ''.join(random.choice(characters) for _ in range(length))

@spaces.GPU(duration=120)
def Piper(_do):
    return pipe(
        _do,
        height=448,
        width=448,
        negative_prompt='Use: vivid colors, text, logo.',
        num_inference_steps=400,
        guidance_scale=10
    )

def infer(prompt):
    name = generate_random_string(12)+".png"
    _do = f'Use: a lot of realism, some pastel colors, { translate(prompt,"english") }, some realistic patterns.'.upper()
    image = Piper(_do).images[0].save(name)
    return name

css="""
#col-container {
    margin: 0 auto;
    max-width: 15cm;
}
#image-container {
    aspect-ratio: 1 / 1;
}
.dropdown-arrow {
    display: none !important;
}
"""

js="""
function custom(){
    document.querySelector("div#prompt input").setAttribute("maxlength","38");
}
"""

"""
    window.Wait = function (Test, Success, Fail = function () { }, timeout = Number.MAX_SAFE_INTEGER) {
    	let seconds = 0;
    	function Internal() {
    		if (!Test()) {
    		if (seconds >= timeout) {
    				Fail();
    				return;
    			}
    			setTimeout(function () {
    				seconds += 0.01;
    				Internal(...arguments);
    			}, 300);
    			return;
    	}
    		Success();
    	}
    	Internal();
    };
    
    Wait(function(){
        return document.querySelector("div#prompt input")
    },function(){
        document.querySelector("div#prompt input").setAttribute("maxlength","38");
    },function(){});
}
"""

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

with gr.Blocks(theme=gr.themes.Soft(),css=css,js=js) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
            # Image Generator
            Currently running on {power_device}.
        """)
        with gr.Row():
            prompt = gr.Textbox(
                elem_id="prompt",
                placeholder="Describe the photo",
                container=False,
                rtl=True,
                max_lines=1
            )
        with gr.Row():
            run_button = gr.Button("Run")
        result = gr.Image(elem_id="image-container", label="Result", show_label=False, type='filepath')
    prompt.submit(
        fn = infer,
        inputs = [prompt],
        outputs = [result]
    )
    run_button.click(
        fn = infer,
        inputs = [prompt],
        outputs = [result]
    )

demo.queue().launch()