Kokoro-API-5 / app.py
Yaron Koresh
Update app.py
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import os
import re
import spaces
import random
import string
import torch
import requests
import gradio as gr
import numpy as np
from lxml.html import fromstring
from diffusers import AutoPipelineForText2Image
#from tempfile import NamedTemporaryFile
from pathos.threading import ThreadPool as Pool
#model_id = "runwayml/stable-diffusion-v1-5"
#model_id = "kandinsky-community/kandinsky-3"
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 = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True, token=os.getenv('hf_token'))
pipe = pipe.to(device)
else:
pipe = AutoPipelineForText2Image.from_pretrained(model_id, use_safetensors=True, token=os.getenv('hf_token'))
pipe = pipe.to(device)
def translate(text,lang):
if text == None or lang == None:
return ""
text = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', text)).lower().strip()
lang = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', lang)).lower().strip()
if text == "" or lang == "":
return ""
if len(text) > 38:
raise Exception("Translation Error: Too long text!")
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 (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'
]
padded_chars = re.sub("[(^\-)(\-$)]","",text.replace("","-").replace("- -"," ")).strip()
query_text = f'Please translate {padded_chars}, into {lang}'
url = f'https://www.google.com/search?q={query_text}'
print(url)
resp = requests.get(
url = url,
headers = {
'User-Agent': random.choice(user_agents)
}
)
content = resp.content
html = fromstring(content)
translated = text
try:
src_lang = html.xpath('//*[@class="source-language"]')[0].text_content().lower().strip()
trgt_lang = html.xpath('//*[@class="target-language"]')[0].text_content().lower().strip()
src_text = html.xpath('//*[@id="tw-source-text"]/*')[0].text_content().lower().strip()
trgt_text = html.xpath('//*[@id="tw-target-text"]/*')[0].text_content().lower().strip()
if trgt_lang == lang:
translated = trgt_text
except:
print(f'Translation Warning: Failed To Translate!')
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=50)
def Piper(_do,_dont):
return pipe(
_do,
height=320,
width=640,
negative_prompt=_dont,
num_inference_steps=200,
guidance_scale=10
)
def infer(prompt_en,prompt2_en):
name = generate_random_string(12)+".png"
if prompt_en == "":
_do = 'natural coloring, epic scene, reasonable proportions, realistic content, highly detailed photograph'
else:
_do = f'photographed { prompt_en }, natural coloring, epic scene, reasonable proportions, realistic content, highly detailed photograph'
if prompt2_en == "":
_dont = 'smooth texture, fictional proportions, blurred content, distorted items, deformed palms, logos and signs, texts and prints'
else:
_dont = f'{prompt2_en} where in {prompt_en}, smooth texture, fictional proportions, blurred content, distorted items, deformed palms, logos and signs, texts and prints'
image = Piper(_do,_dont).images[0].save(name)
return name
css="""
input, input::placeholder {
text-align: center !important;
}
*, *::placeholder {
direction: rtl !important;
font-family: Suez One !important;
}
h1,h2,h3,h4,h5,h6,span,p,pre {
width: 100% !important;
text-align: center !important;
display: block !important;
}
footer {
display: none !important;
}
#col-container {
margin: 0 auto !important;
max-width: 15cm !important;
}
.image-container {
aspect-ratio: 640 / 320 !important;
}
.dropdown-arrow {
display: none !important;
}
"""
js="""
function custom(){
document.querySelector("div#prompt input").setAttribute("maxlength","27");
document.querySelector("div#prompt2 input").setAttribute("maxlength","27");
}
"""
if torch.cuda.is_available():
power_device = "诪注讘讚 讙专驻讬"
else:
power_device = "诪注讘讚 诇讜讙讬"
with gr.Blocks(theme=gr.themes.Soft(),css=css,js=js) as demo:
result = []
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# 诪讞讜诇诇 转诪讜谞讜转 - {power_device}
""")
with gr.Row():
prompt = gr.Textbox(
elem_id="prompt",
placeholder="诪讛 *讻谉* 诇讛讜住讬祝",
container=False,
rtl=True,
max_lines=1
)
with gr.Row():
prompt2 = gr.Textbox(
elem_id="prompt2",
placeholder="诪讛 *诇讗* 诇讛讜住讬祝",
container=False,
rtl=True,
max_lines=1
)
with gr.Row():
run_button = gr.Button("讛转讞诇讛")
with gr.Row():
result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
def _ret(idx,p1,p2):
print(f'Starting {idx}: {p1} {p2}')
v = infer(p1,p2)
print(f'Finished {idx}: {v}')
return v
def _rets(p1,p2):
p1_en = translate(p1,"english")
p2_en = translate(p2,"english")
ln = len(result)
idxs = list(range(ln))
p1s = [p1_en for _ in idxs]
p2s = [p2_en for _ in idxs]
return list(Pool(ln).imap( _ret, idxs, p1s, p2s ))
run_button.click(fn=_rets,inputs=[prompt,prompt2],outputs=result)
demo.queue().launch()