app.py

import gradio as gr
import models

def train():
    return 'https://images.deepai.org/converted-papers/1803.09820/testLoss1.png'

def pred(engine):
    engine = engine-1
    pred_truth = models.lstm(engine)
    return 'https://www.owtrun.com/blog/wp-content/uploads/2023/01/RUL1.2-34-at-161-1024x527.png', f'{pred_truth[0]:.2f} | {pred_truth[1]:.0f}'

with gr.Blocks(css="footer {visibility: hidden}", title="Прогнозирование RUL") as demo:
    gr.Markdown("Выберите модель, набор данных и номер двигателя для прогнозирования оставшегося ресурса оборудования (RUL)")
    with gr.Tab('Демо'):
        with gr.Row():
            with gr.Column():
                model_list_demo = gr.Radio(["LSTM", "VAE"],label='Модель')
                gr.Radio(["FD001", "FD002", "FD003", "FD004"],label='Набор данных')
                engine_slider_demo = gr.Slider(1, 100, step=1, label='Двигатель', info='InFo', interactive=True)
                pred_bttn = gr.Button('Прогноз')
            with gr.Column():
                ill_chart = gr.Image(label='Прогноз')
                prediction1 = gr.Label(value='ххх | xxx', label='Прогноз | Истина')
        

    with gr.Tab('Обучение'):
        with gr.Row():
            with gr.Column():
                gr.File(file_types=['.csv'],label='Данные')
                gr.Radio(["LSTM", "VAE", "XGBoost"],label='Модель')
                train_bttn = gr.Button(value='Обучить')
                gr.Slider(1, 100, step=1, label='Двигатель', interactive=True)
                preed_bttn = gr.Button(value='Прогноз')

            with gr.Column():
                lc_chart = gr.Image(label='График обучения')
                prediction2 = gr.Label(value='ххх', label='Прогноз')

    pred_bttn.click(pred, inputs=[engine_slider_demo] , outputs=[ill_chart, prediction1])
    train_bttn.click(train, outputs=lc_chart)

demo.launch()