Update

app.py

@@ -9,6 +9,7 @@ import gradio as gr
 import torch
 
 from app_inference import create_inference_demo
+from app_system_monitor import create_monitor_demo
 from app_training import create_training_demo
 from app_upload import create_upload_demo
 from inference import InferencePipeline
@@ -68,14 +69,23 @@ with gr.Blocks(css='style.css') as demo:
     gr.Markdown(TITLE)
     with gr.Tabs():
         with gr.TabItem('Train'):
-            create_training_demo(trainer, pipe, disable_training=IS_SHARED_UI)
+            create_training_demo(trainer,
+                                 pipe,
+                                 disable_run_button=IS_SHARED_UI)
         with gr.TabItem('Run'):
-            create_inference_demo(pipe, HF_TOKEN)
+            create_inference_demo(pipe,
+                                  HF_TOKEN,
+                                  disable_run_button=IS_SHARED_UI)
         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()
+            create_upload_demo(disable_run_button=IS_SHARED_UI)
+
+    with gr.Row():
+        if not IS_SHARED_UI and not os.getenv('DISABLE_SYSTEM_MONITOR'):
+            with gr.Accordion(label='System info', open=False):
+                create_monitor_demo()
 
     if not HF_TOKEN:
         show_warning(HF_TOKEN_NOT_SPECIFIED_WARNING)

app_inference.py

@@ -62,7 +62,8 @@ class InferenceUtil:
 
 
 def create_inference_demo(pipe: InferencePipeline,
-                          hf_token: str | None = None) -> gr.Blocks:
+                          hf_token: str | None = None,
+                          disable_run_button: bool = False) -> gr.Blocks:
     app = InferenceUtil(hf_token)
 
     with gr.Blocks() as demo:
@@ -117,7 +118,8 @@ def create_inference_demo(pipe: InferencePipeline,
                                                step=0.1,
                                                value=7.5)
 
-                run_button = gr.Button('Generate')
+                run_button = gr.Button('Generate',
+                                       interactive=not disable_run_button)
 
                 gr.Markdown('''
                 - After training, you can press "Reload Model List" button to load your trained model names.

app_training.py

@@ -6,7 +6,6 @@ import os
 
 import gradio as gr
 
-from app_system_monitor import create_monitor_demo
 from constants import UploadTarget
 from inference import InferencePipeline
 from trainer import Trainer
@@ -14,7 +13,7 @@ from trainer import Trainer
 
 def create_training_demo(trainer: Trainer,
                          pipe: InferencePipeline | None = None,
-                         disable_training: bool = False) -> gr.Blocks:
+                         disable_run_button: bool = False) -> gr.Blocks:
     def read_log() -> str:
         with open(trainer.log_file) as f:
             lines = f.readlines()
@@ -112,7 +111,7 @@ def create_training_demo(trainer: Trainer,
             interactive=bool(os.getenv('SPACE_ID')),
             visible=False)
         run_button = gr.Button('Start Training',
-                               interactive=not disable_training)
+                               interactive=not disable_run_button)
 
         with gr.Box():
             gr.Text(label='Log',
@@ -120,10 +119,6 @@ def create_training_demo(trainer: Trainer,
                     lines=10,
                     max_lines=10,
                     every=1)
-            if not disable_training and not os.getenv(
-                    'DISABLE_SYSTEM_MONITOR'):
-                with gr.Accordion(label='System info', open=False):
-                    create_monitor_demo()
 
         if pipe is not None:
             run_button.click(fn=pipe.clear)

app_upload.py

@@ -16,7 +16,7 @@ def load_local_model_list() -> dict:
     return gr.update(choices=choices, value=choices[0] if choices else None)
 
 
-def create_upload_demo() -> gr.Blocks:
+def create_upload_demo(disable_run_button: bool = False) -> gr.Blocks:
     model_dirs = find_exp_dirs()
 
     with gr.Blocks() as demo:
@@ -39,7 +39,7 @@ def create_upload_demo() -> gr.Blocks:
             model_name = gr.Textbox(label='Model Name')
             hf_token = gr.Text(label='Hugging Face Write Token',
                                visible=os.getenv('HF_TOKEN') is None)
-        upload_button = gr.Button('Upload')
+        upload_button = gr.Button('Upload', interactive=not disable_run_button)
         gr.Markdown(f'''
             - You can upload your trained model to your personal profile (i.e. https://huggingface.co/{{your_username}}/{{model_name}}) or to the public [Tune-A-Video Library](https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}) (i.e. https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}/{{model_name}}).
             ''')