app.py

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

pool = ProcessPoolExecutor(16)
pool.__enter__()

model_id = "runwayml/stable-diffusion-v1-5"
device = "cuda" if torch.cuda.is_available() else "cpu"

if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
    pipe = pipe.to(device)
else: 
    pipe = DiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
    pipe = pipe.to(device)

def translate(text,lang):
    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'
    ]
    resp = requests.post(
        url = "https://www.bing.com/ttranslatev3",
        params = {
            'isVertical': '1',
            'IG': '25FEDD45BDB744CF85D101C01E6596C0',
            'IID': 'translator.5026'
        },
        data = {
            'fromLang': 'auto-detect',
            'to': lang,
            'token': 'MLfPKZhAXMWZirPdlpeEfN8DB58dZKR-',
            'key': '1722119087131',
            'text': text,
            'tryFetchingGenderDebiasedTranslations': 'true'
        },
        headers = {'User-Agent': random.choice(user_agents)}
    )
    print(resp)
    jsn = resp.json()
    print(jsn)
    translated = jsn[0]["translations"][0]["text"]
    return translated

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

@spaces.GPU(duration=20)
def infer(prompt):
    name = generate_random_string(12)+".png"
    english_prompt = "Generate the most true and authentic and real and genuine single photograph, for " + translate(prompt,"en")
    print(f'Final prompt: {english_prompt}')
    image = pipe(english_prompt).images[0].save(name)
    return name

css="""
#col-container {
    margin: 0 auto;
    max-height: 90vh;
}
#image-container {
    aspect-ratio: 1 / 1;
}
"""

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

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
            # Image Generator
            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)
        result = gr.Image(elem_id="image-container", label="Result", show_label=False, type='filepath')
    run_button.click(
        fn = infer,
        inputs = [prompt],
        outputs = [result]
    )

demo.queue().launch()