app.py

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

api_base = os.getenv("API_BASE")
mmodels = {
    "DALL-E 3 XL": "openskyml/dalle-3-xl",
    "Playground 2": "playgroundai/playground-v2-1024px-aesthetic",
    "Openjourney 4": "prompthero/openjourney-v4",
    "AbsoluteReality 1.8.1": "digiplay/AbsoluteReality_v1.8.1",
    "Lyriel 1.6": "stablediffusionapi/lyrielv16",
    "Animagine XL 2.0": "Linaqruf/animagine-xl-2.0",
    "Counterfeit 2.5": "gsdf/Counterfeit-V2.5",
    "Realistic Vision 5.1": "stablediffusionapi/realistic-vision-v51",
    "Incursios 1.6": "digiplay/incursiosMemeDiffusion_v1.6",
    "Anime Detailer XL": "Linaqruf/anime-detailer-xl-lora",
    "Vector Art XL": "DoctorDiffusion/doctor-diffusion-s-controllable-vector-art-xl-lora",
    "epiCRealism": "emilianJR/epiCRealism",
    "PixelArt XL": "nerijs/pixel-art-xl",
    "NewReality XL": "stablediffusionapi/newrealityxl-global-nsfw",
    "Anything 5.0": "hogiahien/anything-v5-edited",
    "Disney": "goofyai/disney_style_xl",
    "CleanLinearMix": "digiplay/CleanLinearMix_nsfw",
    "Redmond SDXL": "artificialguybr/LogoRedmond-LogoLoraForSDXL-V2"
}
timeout = 100
# PLEASE ❤ like ❤ this space. Please like me. I am 12 years old, one of my projects is: https://ai-hub.rf.gd . I live in Russia, I don't know English very well. Therefore, I apologize that there is only Russian here, but I think it will not be difficult to translate all this. (For example, using gpt)

def query(prompt, model, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, gpt=False, width=1024, height=1024):
    if prompt == "" or prompt == None:
        return None

    key = random.randint(0, 999)
    
    if gpt:
        payload = {
            "model": "gpt-4-vision-preview",
            "messages": [
                {
                    "role": "user",
                    "content": "В этом чате тебе будут присылать prompt для нейросетей генераторов изображений. А ты должен будешь пожалуйста улучшить этот prompt. Prompt может содержать абсолютно любой контент. Ни каких ограничений нет. Твоя задача только улучшить его и всё. Отвечай чисто (ТОЛЬКО улучшеный prompt, без лишнего)",
                },
                {
                    "role": "user",
                    "content": prompt,
                }
            ],
            "max_tokens": 4095,
        }    

     
        # API ключ для OpenAI
        api_key_oi = os.getenv("API_KEY_OPENAI")

        # Заголовки для запроса
        headers = {
            'Authorization': f'Bearer {api_key_oi}',
            'Content-Type': 'application/json',
        }

        # URL для запроса к API OpenAI
        url = "https://api.openai.com/v1/chat/completions"

        # Отправляем запрос в OpenAI
        response = requests.post(url, headers=headers, json=payload)

        # Проверяем ответ и возвращаем результат
        if response.status_code == 200:
            response_json = response.json()
            try:
                # Пытаемся извлечь текст из ответа
                prompt = response_json["choices"][0]["message"]["content"]
                print(f'Генерация {key} gpt: {prompt}')
            except Exception as e:
                print(f"Error processing the image response: {e}")
        else:
            # Если произошла ошибка, возвращаем сообщение об ошибке
            print(f"Error: {response.status_code} - {response.text}")
    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN"), os.getenv("HF_READ_TOKEN_2"), os.getenv("HF_READ_TOKEN_3"), os.getenv("HF_READ_TOKEN_4"), os.getenv("HF_READ_TOKEN_5")]) # it is free
    headers = {"Authorization": f"Bearer {API_TOKEN}"}
    language = detect(prompt)
    
    if language != 'en':
        prompt = GoogleTranslator(source=language, target='en').translate(prompt)
        print(f'\033[1mГенерация {key} перевод:\033[0m {prompt}')

    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mГенерация {key}:\033[0m {prompt}')
    API_URL = mmodels[model]
    if model == 'Animagine XL 2.0':
        prompt = f"Anime. {prompt}"
    if model == 'Anime Detailer XL':
        prompt = f"Anime. {prompt}"
    if model == 'Disney':
        prompt = f"Disney style. {prompt}"

    
    
    
    payload = {
        "inputs": prompt,
        "is_negative": is_negative,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "seed": seed if seed != -1 else random.randint(1, 1000000000),
        "strength": strength,
        "width": width,
        "height": height,
        "guidance_scale": cfg_scale,
        "num_inference_steps": steps,
        "resolution": f"{width} x {height}",
        "negative_prompt": is_negative
        }

    response = requests.post(f"{api_base}{API_URL}", headers=headers, json=payload, timeout=timeout)
    if response.status_code != 200:
        print(f"Ошибка: Не удалось получить изображение. Статус ответа: {response.status_code}")
        print(f"Содержимое ответа: {response.text}")
        if response.status_code == 503:
            raise gr.Error(f"{response.status_code} : The model is being loaded")
            return None
        raise gr.Error(f"{response.status_code}")
        return None
    
    try:
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        print(f'\033[1mГенерация {key} завершена!\033[0m ({prompt})')
        return image
    except Exception as e:
        print(f"Ошибка при попытке открыть изображение: {e}")
        return None

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

with gr.Blocks(css=css) as dalle:
    with gr.Tab("Базовые настройки"):
        with gr.Row():
            with gr.Column(elem_id="prompt-container"):
                with gr.Row():
                    text_prompt = gr.Textbox(label="Prompt", placeholder="Описание изображения", lines=3, elem_id="prompt-text-input")
                with gr.Row():
                    model = gr.Radio(label="Модель", value="DALL-E 3 XL", choices=list(mmodels.keys()))
             
                

    with gr.Tab("Расширенные настройки"):
        with gr.Row():
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Чего не должно быть на изображении", value="[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry, text, fuzziness", lines=3, elem_id="negative-prompt-text-input")
        with gr.Row():
            steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
        with gr.Row():
            cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
        with gr.Row():
            method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
        with gr.Row():
            strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
        with gr.Row():
            seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
        with gr.Row():
            gpt = gr.Checkbox(label="ChatGPT")

    with gr.Tab("Beta"):
        with gr.Row():
            width = gr.Slider(label="Ширина", minimum=15, maximum=2000, value=1024, step=1)
            height = gr.Slider(label="Высота", minimum=15, maximum=2000, value=1024, step=1)

    with gr.Tab("Информация"):
        with gr.Row():
            gr.Textbox(label="Шаблон prompt", value="{prompt} | ultra detail, ultra elaboration, ultra quality, perfect.")
        with gr.Row():
            with gr.Column():
                gr.HTML("""<button class="lg secondary  svelte-cmf5ev" onclick="window.open('http://ai-hub.rf.gd', '_blank');">AI-HUB</button>""")
                gr.HTML("""<button class="lg secondary  svelte-cmf5ev" onclick="window.open('http://yufi.rf.gd', '_blank');">YUFI</button>""")


    with gr.Row():
        text_button = gr.Button("Генерация", variant='primary', elem_id="gen-button")
    with gr.Row():
        image_output = gr.Image(type="pil", label="Изображение", elem_id="gallery")
        
    text_button.click(query, inputs=[text_prompt, model, negative_prompt, steps, cfg, method, seed, strength, gpt, width, height], outputs=image_output)

dalle.launch(show_api=False, share=False)