ui.py

import gradio as gr
# from image_upload_renders import dreambooth_upload
from vision_tab import vision_tab
from view_creations import view_creations

from css_and_js import js

# dreambooth_upload_widget = dreambooth_upload()
# dreambooth_upload_widget.update(visibility=False)

def deploy(sa_file_loc, model_type):
    print("sa_file_loc:",sa_file_loc)
    print("model_type:",model_type)

with gr.Blocks() as demo:
    with gr.Tabs() as MainMenuTabs:
        with gr.TabItem('Batch job Creator & Viewer', id='view_tab'):
            view_creations()
        # with gr.TabItem('Train', id='train_tab'):
        #     gr.Markdown("# Train UI")
        #     with gr.Column():
        #         with gr.Accordion("Project setup"):
        #             gr.Markdown("""Train UI helps users easily train OSS models with little or no code using Vertex AI pipelines.
        #             All you need is a [GCP project](https://cloud.google.com/).
        #             """)
        #             gr.Markdown('## Project setup')
        #             sa_file_loc = gr.File(file_count=1, Interactive=True)
        #             gr.Markdown("""There are two ways to authenticateYou'll need a service account or an authenticated user with the following permissions:
        #             - Vertex AI
        #             - GCS storage admin
        #             """)
        #             gr.Markdown('Note: If this machine is already authenticated with GCP, this is not needed.')
        #     with gr.Row():
        #         with gr.Column():
        #             gr.Markdown('## Select the model type')
        #             with gr.Tabs() as tabs:
        #                 with gr.TabItem("Vision", id='vision_tab'):
        #                     vision_tab(sa_file_loc, deploy)
        #                 with gr.TabItem("Text", id='nlp_tab'):
        #                     with gr.Column():
        #                         nlp_dropdown = gr.Dropdown(label='Choose a model', choices=['text-classification', 'question answering'], interactive=True)
        #                         nlp_model_description = gr.Markdown('', label='model_description')
        #                     with gr.Row():
        #                         deploy_nlp_btn = gr.Button("deploy")
    # deploy_btn = gr.Button("deploy",)

    demo.load(_js=js())

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