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"""
Manage tab for Video Model Studio UI
"""
import gradio as gr
import logging
import shutil
from pathlib import Path
from typing import Dict, Any, List, Optional
from .base_tab import BaseTab
from ..config import (
HF_API_TOKEN, VIDEOS_TO_SPLIT_PATH, STAGING_PATH, TRAINING_VIDEOS_PATH,
TRAINING_PATH, MODEL_PATH, OUTPUT_PATH, LOG_FILE_PATH
)
from ..utils import validate_model_repo
logger = logging.getLogger(__name__)
class ManageTab(BaseTab):
"""Manage tab for storage management and model publication"""
def __init__(self, app_state):
super().__init__(app_state)
self.id = "manage_tab"
self.title = "5️⃣ Manage"
def create(self, parent=None) -> gr.TabItem:
"""Create the Manage tab UI components"""
with gr.TabItem(self.title, id=self.id) as tab:
with gr.Column():
with gr.Row():
with gr.Column():
gr.Markdown("## Publishing")
gr.Markdown("You model can be pushed to Hugging Face (this will use HF_API_TOKEN)")
with gr.Row():
with gr.Column():
self.components["repo_id"] = gr.Textbox(
label="HuggingFace Model Repository",
placeholder="username/model-name",
info="The repository will be created if it doesn't exist"
)
self.components["make_public"] = gr.Checkbox(
label="Check this to make your model public (ie. visible and downloadable by anyone)",
info="You model is private by default"
)
self.components["push_model_btn"] = gr.Button(
"Push my model"
)
with gr.Row():
with gr.Column():
with gr.Row():
with gr.Column():
gr.Markdown("## Storage management")
with gr.Row():
self.components["download_dataset_btn"] = gr.DownloadButton(
"Download dataset (click again if DL doesn't start)",
variant="secondary",
size="lg"
)
self.components["download_model_btn"] = gr.DownloadButton(
"Download model (click again if DL doesn't start)",
variant="secondary",
size="lg"
)
with gr.Row():
self.components["global_stop_btn"] = gr.Button(
"Stop everything and delete my data",
variant="stop"
)
self.components["global_status"] = gr.Textbox(
label="Global Status",
interactive=False,
visible=False
)
return tab
def connect_events(self) -> None:
"""Connect event handlers to UI components"""
# Repository ID validation
self.components["repo_id"].change(
fn=self.validate_repo,
inputs=[self.components["repo_id"]],
outputs=[self.components["repo_id"]]
)
# Download buttons
self.components["download_dataset_btn"].click(
fn=self.app.trainer.create_training_dataset_zip,
outputs=[self.components["download_dataset_btn"]]
)
self.components["download_model_btn"].click(
fn=self.app.trainer.get_model_output_safetensors,
outputs=[self.components["download_model_btn"]]
)
# Global stop button
self.components["global_stop_btn"].click(
fn=self.handle_global_stop,
outputs=[
self.components["global_status"],
self.app.tabs["split_tab"].components["video_list"],
self.app.tabs["caption_tab"].components["training_dataset"],
self.app.tabs["train_tab"].components["status_box"],
self.app.tabs["train_tab"].components["log_box"],
self.app.tabs["split_tab"].components["detect_status"],
self.app.tabs["import_tab"].components["import_status"],
self.app.tabs["caption_tab"].components["preview_status"]
]
)
# Push model button
self.components["push_model_btn"].click(
fn=lambda repo_id: self.upload_to_hub(repo_id),
inputs=[self.components["repo_id"]],
outputs=[self.components["global_status"]]
)
def validate_repo(self, repo_id: str) -> gr.update:
"""Validate repository ID for HuggingFace Hub"""
validation = validate_model_repo(repo_id)
if validation["error"]:
return gr.update(value=repo_id, error=validation["error"])
return gr.update(value=repo_id, error=None)
def upload_to_hub(self, repo_id: str) -> str:
"""Upload model to HuggingFace Hub"""
if not repo_id:
return "Error: Repository ID is required"
# Validate repository name
validation = validate_model_repo(repo_id)
if validation["error"]:
return f"Error: {validation['error']}"
# Check if we have a model to upload
if not self.app.trainer.get_model_output_safetensors():
return "Error: No model found to upload"
# Upload model to hub
success = self.app.trainer.upload_to_hub(OUTPUT_PATH, repo_id)
if success:
return f"Successfully uploaded model to {repo_id}"
else:
return f"Failed to upload model to {repo_id}"
def handle_global_stop(self):
"""Handle the global stop button click"""
result = self.stop_all_and_clear()
# Format the details for display
status = result["status"]
details = "\n".join(f"{k}: {v}" for k, v in result["details"].items())
full_status = f"{status}\n\nDetails:\n{details}"
# Get fresh lists after cleanup
videos = self.app.tabs["split_tab"].list_unprocessed_videos()
clips = self.app.tabs["caption_tab"].list_training_files_to_caption()
return {
self.components["global_status"]: gr.update(value=full_status, visible=True),
self.app.tabs["split_tab"].components["video_list"]: videos,
self.app.tabs["caption_tab"].components["training_dataset"]: clips,
self.app.tabs["train_tab"].components["status_box"]: "Training stopped and data cleared",
self.app.tabs["train_tab"].components["log_box"]: "",
self.app.tabs["split_tab"].components["detect_status"]: "Scene detection stopped",
self.app.tabs["import_tab"].components["import_status"]: "All data cleared",
self.app.tabs["caption_tab"].components["preview_status"]: "Captioning stopped"
}
def stop_all_and_clear(self) -> Dict[str, str]:
"""Stop all running processes and clear data
Returns:
Dict with status messages for different components
"""
status_messages = {}
try:
# Stop training if running
if self.app.trainer.is_training_running():
training_result = self.app.trainer.stop_training()
status_messages["training"] = training_result["status"]
# Stop captioning if running
if self.app.captioner:
self.app.captioner.stop_captioning()
status_messages["captioning"] = "Captioning stopped"
# Stop scene detection if running
if self.app.splitter.is_processing():
self.app.splitter.processing = False
status_messages["splitting"] = "Scene detection stopped"
# Properly close logging before clearing log file
if self.app.trainer.file_handler:
self.app.trainer.file_handler.close()
logger.removeHandler(self.app.trainer.file_handler)
self.app.trainer.file_handler = None
if LOG_FILE_PATH.exists():
LOG_FILE_PATH.unlink()
# Clear all data directories
for path in [VIDEOS_TO_SPLIT_PATH, STAGING_PATH, TRAINING_VIDEOS_PATH, TRAINING_PATH,
MODEL_PATH, OUTPUT_PATH]:
if path.exists():
try:
shutil.rmtree(path)
path.mkdir(parents=True, exist_ok=True)
except Exception as e:
status_messages[f"clear_{path.name}"] = f"Error clearing {path.name}: {str(e)}"
else:
status_messages[f"clear_{path.name}"] = f"Cleared {path.name}"
# Reset any persistent state
self.app.tabs["caption_tab"]._should_stop_captioning = True
self.app.splitter.processing = False
# Recreate logging setup
self.app.trainer.setup_logging()
return {
"status": "All processes stopped and data cleared",
"details": status_messages
}
except Exception as e:
return {
"status": f"Error during cleanup: {str(e)}",
"details": status_messages
} |