app.py

#!/usr/bin/env python
from __future__ import annotations
import os
import gradio as gr
import torch
from app_inference import create_inference_demo
from app_training import create_training_demo
from app_upload import create_upload_demo
from inference import InferencePipeline
from trainer import Trainer

TITLE = '# LoRA DreamBooth Training UI'
ORIGINAL_SPACE_ID = 'lora-library/LoRA-DreamBooth-Training-UI'
SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
SHARED_UI_WARNING = f'''
'''

if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
    SETTINGS = f'<a href="https://huggingface.co./spaces/{SPACE_ID}/settings">Settings</a>'
else:
    SETTINGS = 'Settings'
CUDA_NOT_AVAILABLE_WARNING = f'''
'''

HF_TOKEN_NOT_SPECIFIED_WARNING = f'''
'''

HF_TOKEN = os.getenv('HF_TOKEN')

def show_warning(warning_text: str) -> gr.Blocks:
    with gr.Blocks() as demo:
        with gr.Box():
            gr.Markdown(warning_text)
    return demo


pipe = InferencePipeline(HF_TOKEN)
trainer = Trainer(HF_TOKEN)

with gr.Blocks(css='style.css') as demo:
    if os.getenv('IS_SHARED_UI'):
        show_warning(SHARED_UI_WARNING)
    if not torch.cuda.is_available():
        show_warning(CUDA_NOT_AVAILABLE_WARNING)
    if not HF_TOKEN:
        show_warning(HF_TOKEN_NOT_SPECIFIED_WARNING)

    gr.Markdown(TITLE)
    with gr.Tabs():
        with gr.TabItem('Train'):
            create_training_demo(trainer, pipe)
        with gr.TabItem('Test'):
            create_inference_demo(pipe, HF_TOKEN)
        with gr.TabItem('Upload'):
            gr.Markdown('''
            - You can use this tab to upload models later if you choose not to upload models in training time or if upload in training time failed.
            ''')
            create_upload_demo(HF_TOKEN)

demo.queue(max_size=1).launch(share=False)