app.py

import os
import gradio as gr
import logging

LOG_FORMAT = '%(asctime)s - %(levelname)s  [%(name)s] %(funcName)s -> %(message)s'
logging.basicConfig(level = logging.DEBUG, format = LOG_FORMAT)
logger = logging.getLogger(__name__)
logger.debug('Logging started')

API_KEY=os.environ.get('HUGGING_FACE_HUB_TOKEN', None)
article = """---
This space was created using [SD Space Creator](https://huggingface.co/spaces/anzorq/sd-space-creator)."""

class Model:
    def __init__(self, name, path="", prefix=""):
        self.name = name
        self.path = path
        self.prefix = prefix
        self.pipe_t2i = None
        self.pipe_i2i = None

models = [
   Model("Marvel","models/ItsJayQz/Marvel_WhatIf_Diffusion", "whatif style"), 
   Model("Cyberpunk Anime Diffusion", "models/DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style"),
   Model("Guan Yu Diffusion", "models/DGSpitzer/Guan-Yu-Diffusion", "Guan-Yu style"),
   Model("Portrait plus", "models/wavymulder/portraitplus", "portrait+ style"),
   Model("classic Disney", "models/nitrosocke/classic-anim-diffusion", "classic disney style"),
   Model("SD21","models/stabilityai/stable-diffusion-2-1", "sd21 default style")
]

custom_model = "models/stabilityai/stable-diffusion-2-1"

def selectModel(message):
    message = message.lower()
    for i in range(len(models)):
        if message.find(models[i].prefix)!=-1:
            c_model=models[i].path
            logging.warning('model selected = '+c_model)
            return c_model
    c_model=models[i].path
    logging.warning('model selected = '+c_model)
    return c_model

sandbox = gr.Interface.load(
    fn= selectModel,
    name= custom_model, 
    title="""AlStable sandbox""",
    inputs = gr.Textbox(label="Prompt", show_label=False, max_lines=2, placeholder="Enter your prompt", elem_id="input-prompt"), 
    description="""Demo for <a href="https://huggingface.co/stabilityai/stable-diffusion-2-1">AlStable</a> Stable Diffusion model.""",
    article=article,
    api_key=API_KEY
)
logging.warning('model chosen = '+custom_model)
sandbox.queue(concurrency_count=20).launch()