app.py

import gradio as gr
import requests
import time
import json
from deep_translator import GoogleTranslator
from langdetect import detect
import os
from PIL import Image
from io import BytesIO


def flip_text(prompt, key):
    c = 0
    
    url_id1 = os.getenv("url_id1")
    url_id2 = os.getenv("url_id2")
    url_id3 = os.getenv("url_id3")
    key_os = os.getenv("key_os")

    if str(key).lower() == str(key_os).lower():
    
        r_m = requests.post("https://myneuralnetworks.ru/get_key_a/", data={"key": url_id1})
        key_a = r_m.json()['result']
    
        try:
            language = detect(prompt)
            if language == 'ru':
                prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
                print(prompt)
        except:
            pass
            
        headers = {'authorization': key_a}
        json_data = {'prompt': prompt, 'model': 'V5', 'size': '1:1'}
        
        response = requests.post(f"{url_id2}", headers=headers, json=json_data)
        data_id = response.json()['data']['recordUuid']
        
        while True:
            time.sleep(2)
            r_check = requests.get(f"{url_id3}{data_id}")
            status = r_check.json()['data']['picState']
            if status == 'waitingStart':
                continue
            elif status == 'queuing':
                continue
            elif status == "generating":
                continue
            elif status == "fail":
                return ["https://myneuralnetworks.ru/static/img/zp3.jpg"]
            elif status == "success":
                photo = r_check.json()['data']['picUrl']
                ph_url = eval(photo)[0]["picUrl"]
                response2 = requests.get(ph_url)
                img = Image.open(BytesIO(response2.content))
                new_size = (1024, 1024)
                
                top_left = img.crop((0, 0, new_size[0], new_size[1]))
                top_right = img.crop((new_size[0], 0, new_size[0]*2, new_size[1]))
                bottom_left = img.crop((0, new_size[1], new_size[0], new_size[1]*2))
                a = [top_left, top_right, bottom_left]
                return a
            else:
                break

    else:
        return ["https://myneuralnetworks.ru/static/img/zp2.jpg"]

    
css = """
#generate {
    width: 100%;
    background: #e253dd !important;
    border: none;
    border-radius: 50px;
    outline: none !important;
    color: white;
}
#generate:hover {
    background: #de6bda !important;
    outline: none !important;
    color: #fff;
    }
footer {visibility: hidden !important;}

#image_output {
min-height: 900px !important;
}
"""

with gr.Blocks(css=css) as demo:

    with gr.Row():
        prompt = gr.Textbox(placeholder="Введите описание изображения...", show_label=True, label='Описание изображения:', lines=3)
    with gr.Row():
        tg = gr.HTML("Ключ доступа можно найти в моём <a href='https://t.me/myneuralnetworks' target='_blank'>Telegram-канале</a>")
    with gr.Row():
        key = gr.Textbox(placeholder="Введите ключ доступа...", show_label=True, label='Ключ доступа:', lines=1)
    with gr.Row():
        text_button = gr.Button("Сгенерировать изображение", variant='primary', elem_id="generate")
    with gr.Row():
        image_output = gr.Gallery(show_label=True, label='Результат:', elem_id='image_output', columns=[1], rows=[3], object_fit="contain")
        text_button.click(flip_text, inputs=[prompt, key], outputs=image_output)
    
demo.queue(concurrency_count=12)
demo.launch()