""" 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 gradio_modal import Modal from vms.utils import BaseTab, validate_model_repo from vms.config import ( HF_API_TOKEN, VIDEOS_TO_SPLIT_PATH, STAGING_PATH, USE_LARGE_DATASET ) 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 = "5๏ธโƒฃ Storage" def get_download_button_text(self) -> str: """Get the dynamic text for the download button based on current model state""" try: model_info = self.app.training.get_model_output_info() if model_info["path"] and model_info["steps"]: return f"๐Ÿง  Download weights ({model_info['steps']} steps)" elif model_info["path"]: return "๐Ÿง  Download weights (.safetensors)" else: return "๐Ÿง  Download weights (not available)" except Exception as e: logger.warning(f"Error getting model info for button text: {e}") return "๐Ÿง  Download weights (.safetensors)" def get_checkpoint_button_text(self) -> str: """Get the dynamic text for the download checkpoint button""" try: return self.app.training.get_checkpoint_button_text() except Exception as e: logger.warning(f"Error getting checkpoint button text: {e}") return "๐Ÿ“ฅ Download checkpoints (not available)" def update_download_button_text(self) -> gr.update: """Update the download button text""" return gr.update(value=self.get_download_button_text()) def download_and_update_button(self): """Handle download and return updated button with current text""" # Get the safetensors path for download path = self.app.training.get_model_output_safetensors() # For DownloadButton, we need to return the file path directly for download # The button text will be updated on next render return path 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.Row(): with gr.Column(): gr.Markdown("## ๐Ÿฆ Backup your model") gr.Markdown("There is currently a bug, you might have to click multiple times to trigger a download.") with gr.Row(): self.components["download_dataset_btn"] = gr.DownloadButton( "๐Ÿ“ฆ Download training dataset (.zip)", variant="secondary", size="lg", visible=not USE_LARGE_DATASET ) # If we have a large dataset, display a message explaining why download is disabled if USE_LARGE_DATASET: gr.Markdown("๐Ÿ“ฆ Training dataset download disabled for large datasets") self.components["download_model_btn"] = gr.DownloadButton( self.get_download_button_text(), variant="secondary", size="lg" ) self.components["download_checkpoint_btn"] = gr.DownloadButton( self.get_checkpoint_button_text(), variant="secondary", size="lg" ) self.components["download_output_btn"] = gr.DownloadButton( "๐Ÿ“ Download output directory (.zip)", variant="secondary", size="lg", visible=False ) with gr.Row(): with gr.Column(): gr.Markdown("## ๐Ÿ“ก Publish your model") 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(): gr.Markdown("## ๐Ÿงน Maintenance") gr.Markdown("Clean up old files to free disk space.") with gr.Row(): self.components["cleanup_lora_btn"] = gr.Button( "๐Ÿ”„ Keep last 2 LoRA weights and clean up older ones", variant="secondary", size="lg" ) with gr.Row(): with gr.Column(): gr.Markdown("## โ™ป๏ธ Delete your data") gr.Markdown("Make sure you have made a backup first.") gr.Markdown("If you are deleting because of a bug, remember you can use the Developer Mode on HF to inspect the working directory (in /data or .data)") with gr.Row(): with gr.Column(): gr.Markdown("### ๐Ÿงฝ Delete specific data") gr.Markdown("You can selectively delete either the dataset and/or the last model data.") with gr.Row(): with gr.Column(scale=1): self.components["delete_dataset_btn"] = gr.Button( "๐Ÿšจ Delete dataset (images, video, captions)", variant="secondary" ) self.components["delete_dataset_status"] = gr.Textbox( label="Delete Dataset Status", interactive=False, visible=False ) # Modal for dataset deletion confirmation with Modal(visible=False) as dataset_delete_modal: gr.Markdown("## โš ๏ธ Confirm Deletion") gr.Markdown("Are you sure you want to delete all dataset files (images, videos, captions)?") gr.Markdown("This action cannot be undone!") with gr.Row(): cancel_dataset_btn = gr.Button("๐Ÿซข No, cancel", variant="secondary") confirm_dataset_btn = gr.Button("๐Ÿšจ Yes, delete", variant="primary") self.components["dataset_delete_modal"] = dataset_delete_modal self.components["cancel_dataset_btn"] = cancel_dataset_btn self.components["confirm_dataset_btn"] = confirm_dataset_btn with gr.Column(scale=1): self.components["delete_model_btn"] = gr.Button( "๐Ÿšจ Delete model (checkpoints, weights, config)", variant="secondary" ) self.components["delete_model_status"] = gr.Textbox( label="Delete Model Status", interactive=False, visible=False ) # Modal for model deletion confirmation with Modal(visible=False) as model_delete_modal: gr.Markdown("## โš ๏ธ Confirm Deletion") gr.Markdown("Are you sure you want to delete all model files (checkpoints, weights, config)?") gr.Markdown("This action cannot be undone!") with gr.Row(): cancel_model_btn = gr.Button("๐Ÿซข No, cancel", variant="secondary") confirm_model_btn = gr.Button("๐Ÿšจ Yes, delete", variant="primary") self.components["model_delete_modal"] = model_delete_modal self.components["cancel_model_btn"] = cancel_model_btn self.components["confirm_model_btn"] = confirm_model_btn with gr.Row(): with gr.Column(): gr.Markdown("### โ˜ข๏ธ Nuke all project data") gr.Markdown("This will nuke the original dataset (all images, videos and captions), the training dataset, and the model outputs (weights, checkpoints, settings). So use with care!") with gr.Row(): self.components["global_stop_btn"] = gr.Button( "๐Ÿšจ Delete all project data and models (are you sure?!)", variant="stop" ) self.components["global_status"] = gr.Textbox( label="Global Status", interactive=False, visible=False ) # Modal for global deletion confirmation with Modal(visible=False) as global_delete_modal: gr.Markdown("## โš ๏ธ Confirm Complete Data Deletion") gr.Markdown("Are you sure you want to delete ALL project data and models?") gr.Markdown("This includes:") gr.Markdown("- All original datasets (images, videos, captions)") gr.Markdown("- All training datasets") gr.Markdown("- All model outputs (weights, checkpoints, settings)") gr.Markdown("This action cannot be undone!") with gr.Row(): cancel_global_btn = gr.Button("๐Ÿซข No, cancel", variant="secondary") confirm_global_btn = gr.Button("๐Ÿšจ Yes, delete", variant="primary") self.components["global_delete_modal"] = global_delete_modal self.components["cancel_global_btn"] = cancel_global_btn self.components["confirm_global_btn"] = confirm_global_btn 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.training.create_training_dataset_zip, outputs=[self.components["download_dataset_btn"]] ) self.components["download_model_btn"].click( fn=self.app.training.get_model_output_safetensors, outputs=[self.components["download_model_btn"]] ) self.components["download_checkpoint_btn"].click( fn=self.app.training.create_checkpoint_zip, outputs=[self.components["download_checkpoint_btn"]] ) self.components["download_output_btn"].click( fn=self.app.training.create_output_directory_zip, outputs=[self.components["download_output_btn"]] ) # LoRA cleanup button self.components["cleanup_lora_btn"].click( fn=self.cleanup_old_lora_weights, outputs=[] ) # Dataset deletion with modal self.components["delete_dataset_btn"].click( fn=lambda: Modal(visible=True), inputs=[], outputs=[self.components["dataset_delete_modal"]] ) # Modal cancel button self.components["cancel_dataset_btn"].click( fn=lambda: Modal(visible=False), inputs=[], outputs=[self.components["dataset_delete_modal"]] ) # Modal confirm button self.components["confirm_dataset_btn"].click( fn=self.delete_dataset, outputs=[ self.components["delete_dataset_status"], self.app.tabs["caption_tab"].components["training_dataset"] ] ).then( fn=lambda: Modal(visible=False), inputs=[], outputs=[self.components["dataset_delete_modal"]] ) # Model deletion with modal self.components["delete_model_btn"].click( fn=lambda: Modal(visible=True), inputs=[], outputs=[self.components["model_delete_modal"]] ) # Modal cancel button self.components["cancel_model_btn"].click( fn=lambda: Modal(visible=False), inputs=[], outputs=[self.components["model_delete_modal"]] ) # Modal confirm button self.components["confirm_model_btn"].click( fn=self.delete_model, outputs=[ self.components["delete_model_status"], self.app.tabs["train_tab"].components["status_box"] ] ).then( fn=lambda: Modal(visible=False), inputs=[], outputs=[self.components["model_delete_modal"]] ) # Global stop button with modal self.components["global_stop_btn"].click( fn=lambda: Modal(visible=True), inputs=[], outputs=[self.components["global_delete_modal"]] ) # Modal cancel button self.components["cancel_global_btn"].click( fn=lambda: Modal(visible=False), inputs=[], outputs=[self.components["global_delete_modal"]] ) # Modal confirm button self.components["confirm_global_btn"].click( fn=self.handle_global_stop, outputs=[ self.components["global_status"], 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["import_tab"].components["import_status"], self.app.tabs["caption_tab"].components["preview_status"] ] ).then( fn=lambda: Modal(visible=False), inputs=[], outputs=[self.components["global_delete_modal"]] ) # 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.training.get_model_output_safetensors(): return "Error: No model found to upload" # Upload model to hub success = self.app.training.upload_to_hub(self.app.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 cleanup_old_lora_weights(self): """Clean up old LoRA weight directories, keeping only the latest 2""" try: self.app.training.cleanup_old_lora_weights(max_to_keep=2) gr.Info("โœ… Successfully cleaned up old LoRA weights") except Exception as e: error_msg = f"โŒ Failed to cleanup LoRA weights: {str(e)}" gr.Error(error_msg) logger.error(f"LoRA cleanup failed: {e}") def delete_dataset(self): """Delete dataset files (images, videos, captions)""" status_messages = {} try: # Stop captioning if running if self.app.captioning: self.app.captioning.stop_captioning() status_messages["captioning"] = "Captioning stopped" # Stop scene detection if running if self.app.splitting.is_processing(): self.app.splitting.processing = False status_messages["splitting"] = "Scene detection stopped" # Clear dataset directories for path in [VIDEOS_TO_SPLIT_PATH, STAGING_PATH, self.app.training_videos_path, self.app.training_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 relevant persistent state self.app.tabs["caption_tab"]._should_stop_captioning = True self.app.splitting.processing = False # Format response details = "\n".join(f"{k}: {v}" for k, v in status_messages.items()) message = f"Dataset deleted successfully\n\nDetails:\n{details}" # Get fresh lists after cleanup clips = self.app.tabs["caption_tab"].list_training_files_to_caption() return gr.update(value=message, visible=True), clips except Exception as e: error_message = f"Error deleting dataset: {str(e)}\n\nDetails:\n{status_messages}" return gr.update(value=error_message, visible=True), self.app.tabs["caption_tab"].list_training_files_to_caption() def delete_model(self): """Delete model files (checkpoints, weights, configuration)""" status_messages = {} try: # Stop training if running if self.app.training.is_training_running(): training_result = self.app.training.stop_training() status_messages["training"] = training_result["status"] # Clear model output directory if self.app.output_path.exists(): try: shutil.rmtree(self.app.output_path) self.app.output_path.mkdir(parents=True, exist_ok=True) except Exception as e: status_messages[f"clear_{self.app.output_path.name}"] = f"Error clearing {self.app.output_path.name}: {str(e)}" else: status_messages[f"clear_{self.app.output_path.name}"] = f"Cleared {self.app.output_path.name}" # Properly close logging before clearing log file if self.app.training.file_handler: self.app.training.file_handler.close() logger.removeHandler(self.app.training.file_handler) self.app.training.file_handler = None if self.app.log_file_path.exists(): self.app.log_file_path.unlink() # Reset training UI state self.app.training.setup_logging() # Format response details = "\n".join(f"{k}: {v}" for k, v in status_messages.items()) message = f"Model deleted successfully\n\nDetails:\n{details}" return gr.update(value=message, visible=True), "Model files have been deleted" except Exception as e: error_message = f"Error deleting model: {str(e)}\n\nDetails:\n{status_messages}" return gr.update(value=error_message, visible=True), f"Error deleting model: {str(e)}" 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 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["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["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.training.is_training_running(): training_result = self.app.training.stop_training() status_messages["training"] = training_result["status"] # Stop captioning if running if self.app.captioning: self.app.captioning.stop_captioning() status_messages["captioning"] = "Captioning stopped" # Stop scene detection if running if self.app.splitting.is_processing(): self.app.splitting.processing = False status_messages["splitting"] = "Scene detection stopped" # Properly close logging before clearing log file if self.app.training.file_handler: self.app.training.file_handler.close() logger.removeHandler(self.app.training.file_handler) self.app.training.file_handler = None if self.app.log_file_path.exists(): self.app.log_file_path.unlink() # Clear all data directories for path in [VIDEOS_TO_SPLIT_PATH, STAGING_PATH, self.app.training_videos_path, self.app.training_path, self.app.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.splitting.processing = False # Recreate logging setup self.app.training.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 }