vms/ui/project/tabs/train_tab.py

"""
Train tab for Video Model Studio UI with improved task progress display
"""

import gradio as gr
import logging
import os
import json
import shutil
from typing import Dict, Any, List, Optional, Tuple
from pathlib import Path

from vms.utils import BaseTab
from vms.config import (
    OUTPUT_PATH, ASK_USER_TO_DUPLICATE_SPACE,
    SMALL_TRAINING_BUCKETS,
    TRAINING_PRESETS, TRAINING_TYPES, MODEL_TYPES, MODEL_VERSIONS,
    DEFAULT_NB_TRAINING_STEPS, DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS,
    DEFAULT_BATCH_SIZE, DEFAULT_CAPTION_DROPOUT_P,
    DEFAULT_LEARNING_RATE,
    DEFAULT_LORA_RANK, DEFAULT_LORA_ALPHA,
    DEFAULT_LORA_RANK_STR, DEFAULT_LORA_ALPHA_STR,
    DEFAULT_SEED,
    DEFAULT_NUM_GPUS,
    DEFAULT_MAX_GPUS,
    DEFAULT_PRECOMPUTATION_ITEMS,
    DEFAULT_NB_TRAINING_STEPS,
    DEFAULT_NB_LR_WARMUP_STEPS,
    DEFAULT_AUTO_RESUME
)

logger = logging.getLogger(__name__)

class TrainTab(BaseTab):
    """Train tab for model training"""
    
    def __init__(self, app_state):
        super().__init__(app_state)
        self.id = "train_tab"
        self.title = "3️⃣ Train"
    
    def create(self, parent=None) -> gr.TabItem:
        """Create the Train tab UI components"""
        with gr.TabItem(self.title, id=self.id) as tab:
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        self.components["train_title"] = gr.Markdown("## 0 files in the training dataset")

                    with gr.Row():
                        with gr.Column():
                            self.components["training_preset"] = gr.Dropdown(
                                choices=list(TRAINING_PRESETS.keys()),
                                label="Training Preset",
                                value=list(TRAINING_PRESETS.keys())[0]
                            )
                        self.components["preset_info"] = gr.Markdown()

                    with gr.Row():
                        with gr.Column():
                            # Get the default model type from the first preset
                            default_model_type = list(MODEL_TYPES.keys())[0]

                            self.components["model_type"] = gr.Dropdown(
                                choices=list(MODEL_TYPES.keys()),
                                label="Model Type",
                                value=default_model_type,
                                interactive=True
                            )

                            # Get model versions for the default model type
                            default_model_versions = self.get_model_version_choices(default_model_type)
                            default_model_version = self.get_default_model_version(default_model_type)

                            # Ensure default_model_versions is not empty
                            if not default_model_versions:
                                # If no versions found for default model, use a fallback
                                internal_type = MODEL_TYPES.get(default_model_type)
                                if internal_type in MODEL_VERSIONS:
                                    default_model_versions = list(MODEL_VERSIONS[internal_type].keys())
                                else:
                                    # Last resort - collect all available versions from all models
                                    default_model_versions = []
                                    for model_versions in MODEL_VERSIONS.values():
                                        default_model_versions.extend(list(model_versions.keys()))
                                    
                                # If still empty, provide a placeholder
                                if not default_model_versions:
                                    default_model_versions = ["-- No versions available --"]
                                    
                                # Set default version to first in list if available
                                if default_model_versions:
                                    default_model_version = default_model_versions[0]
                                else:
                                    default_model_version = ""

                            self.components["model_version"] = gr.Dropdown(
                                choices=default_model_versions,
                                label="Model Version",
                                value=default_model_version,
                                interactive=True,
                                allow_custom_value=True  # Add this to avoid errors with custom values
                            )
                            
                            self.components["training_type"] = gr.Dropdown(
                                choices=list(TRAINING_TYPES.keys()),
                                label="Training Type",
                                value=list(TRAINING_TYPES.keys())[0]
                            )

                    with gr.Row():
                        self.components["model_info"] = gr.Markdown(
                            value=self.get_model_info(list(MODEL_TYPES.keys())[0], list(TRAINING_TYPES.keys())[0])
                        )

                    # LoRA specific parameters (will show/hide based on training type)
                    with gr.Row(visible=True) as lora_params_row:
                        self.components["lora_params_row"] = lora_params_row
                        self.components["lora_rank"] = gr.Dropdown(
                            label="LoRA Rank",
                            choices=["16", "32", "64", "128", "256", "512", "1024"],
                            value=DEFAULT_LORA_RANK_STR,
                            type="value"
                        )
                        self.components["lora_alpha"] = gr.Dropdown(
                            label="LoRA Alpha",
                            choices=["16", "32", "64", "128", "256", "512", "1024"],
                            value=DEFAULT_LORA_ALPHA_STR,
                            type="value"
                        )
                    
                    with gr.Row():
                        self.components["train_steps"] = gr.Number(
                            label="Number of Training Steps",
                            value=DEFAULT_NB_TRAINING_STEPS,
                            minimum=1,
                            precision=0
                        )
                        self.components["batch_size"] = gr.Number(
                            label="Batch Size",
                            value=1,
                            minimum=1,
                            precision=0
                        )
                    with gr.Row():
                        self.components["learning_rate"] = gr.Number(
                            label="Learning Rate",
                            value=DEFAULT_LEARNING_RATE,
                            minimum=1e-8
                        )
                        self.components["save_iterations"] = gr.Number(
                            label="Save checkpoint every N iterations",
                            value=DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS,
                            minimum=1,
                            precision=0,
                            info="Model will be saved periodically after these many steps"
                        )
                    with gr.Row():
                        self.components["num_gpus"] = gr.Slider(
                            label="Number of GPUs to use",
                            value=DEFAULT_NUM_GPUS,
                            minimum=1,
                            maximum=DEFAULT_MAX_GPUS,
                            step=1,
                            info="Number of GPUs to use for training"
                        )
                        self.components["precomputation_items"] = gr.Number(
                            label="Precomputation Items",
                            value=DEFAULT_PRECOMPUTATION_ITEMS,
                            minimum=1,
                            precision=0,
                            info="Should be more or less the number of total items (ex: 200 videos), divided by the number of GPUs"
                        )
                    with gr.Row():
                        self.components["lr_warmup_steps"] = gr.Number(
                            label="Learning Rate Warmup Steps",
                            value=DEFAULT_NB_LR_WARMUP_STEPS,
                            minimum=0,
                            precision=0,
                            info="Number of warmup steps (typically 20-40% of total training steps). This helps reducing the impact of early training examples as well as giving time to optimizers to compute accurate statistics."
                        )

                with gr.Row():
                    with gr.Column():

                        with gr.Row():  
                            with gr.Column():
                                # Add description of the training buttons
                                self.components["training_buttons_info"] = gr.Markdown("""
                                ## ⚗️ Train your model on your dataset
                                - **🚀 Start new training**: Begins training from scratch (clears previous checkpoints)
                                - **🛸 Start from latest checkpoint**: Continues training from the most recent checkpoint
                                """)
                                
                                with gr.Row():
                                    # Check for existing checkpoints to determine button text
                                    checkpoints = list(OUTPUT_PATH.glob("finetrainers_step_*"))
                                    has_checkpoints = len(checkpoints) > 0

                                    self.components["start_btn"] = gr.Button(
                                        "🚀 Start new training",
                                        variant="primary",
                                        interactive=not ASK_USER_TO_DUPLICATE_SPACE
                                    )
                                    
                                    # Add new button for continuing from checkpoint
                                    self.components["resume_btn"] = gr.Button(
                                        "🛸 Start from latest checkpoint",
                                        variant="primary", 
                                        interactive=has_checkpoints and not ASK_USER_TO_DUPLICATE_SPACE
                                    )
                                    
                                with gr.Row():
                                    # Just use stop and pause buttons for now to ensure compatibility
                                    self.components["stop_btn"] = gr.Button(
                                        "Stop at Last Checkpoint",
                                        variant="primary",
                                        interactive=False
                                    )
                                    
                                    self.components["pause_resume_btn"] = gr.Button(
                                        "Resume Training",
                                        variant="secondary",
                                        interactive=False,
                                        visible=False
                                    )
                                    
                                    # Add delete checkpoints button
                                    self.components["delete_checkpoints_btn"] = gr.Button(
                                        "Delete All Checkpoints",
                                        variant="stop",
                                        interactive=has_checkpoints
                                    )

                                with gr.Row():
                                    self.components["auto_resume"] = gr.Checkbox(
                                        label="Automatically continue training in case of server reboot.",
                                        value=DEFAULT_AUTO_RESUME,
                                        info="When enabled, training will automatically resume from the latest checkpoint after app restart"
                                    )

                        with gr.Row():
                            with gr.Column():
                                self.components["status_box"] = gr.Textbox(
                                    label="Training Status",
                                    interactive=False,
                                    lines=4
                                )
                                
                                # Add new component for current task progress
                                self.components["current_task_box"] = gr.Textbox(
                                    label="Current Task Progress",
                                    interactive=False,
                                    lines=3,
                                    elem_id="current_task_display"
                                )
                                
                                with gr.Accordion("Finetrainers output (or see app logs for more details)", open=False):
                                    self.components["log_box"] = gr.TextArea(
                                        #label="",
                                        interactive=False,
                                        lines=60,
                                        max_lines=600,
                                        autoscroll=True
                                    )
                    
        return tab
    
    def update_model_type_and_version(self, model_type: str, model_version: str):
        """Update both model type and version together to keep them in sync"""
        # Get internal model type
        internal_type = MODEL_TYPES.get(model_type)
        
        # Make sure model_version is valid for this model type
        if internal_type and internal_type in MODEL_VERSIONS:
            valid_versions = list(MODEL_VERSIONS[internal_type].keys())
            if not model_version or model_version not in valid_versions:
                if valid_versions:
                    model_version = valid_versions[0]
        
        # Update UI state with both values to keep them in sync
        self.app.update_ui_state(model_type=model_type, model_version=model_version)
        return None

    def save_model_version(self, model_type: str, model_version: str):
        """Save model version ensuring it's consistent with model type"""
        internal_type = MODEL_TYPES.get(model_type)
        
        # Verify the model_version is compatible with the current model_type
        if internal_type and internal_type in MODEL_VERSIONS:
            valid_versions = MODEL_VERSIONS[internal_type].keys()
            if model_version not in valid_versions:
                # Don't save incompatible version
                return None
                
        # Save the model version along with current model type to ensure consistency
        self.app.update_ui_state(model_type=model_type, model_version=model_version)
        return None

    def handle_new_training_start(
        self, preset, model_type, model_version, training_type, 
        lora_rank, lora_alpha, train_steps, batch_size, learning_rate, 
        save_iterations, repo_id, progress=gr.Progress()
    ):
        """Handle new training start with checkpoint cleanup"""
        # Clear output directory to start fresh

        # Delete all checkpoint directories
        for checkpoint in OUTPUT_PATH.glob("finetrainers_step_*"):
            if checkpoint.is_dir():
                shutil.rmtree(checkpoint)
                
        # Also delete session.json which contains previous training info
        session_file = OUTPUT_PATH / "session.json"
        if session_file.exists():
            session_file.unlink()
        
        self.app.training.append_log("Cleared previous checkpoints for new training session")
        
        # Start training normally
        return self.handle_training_start(
            preset, model_type, model_version, training_type, 
            lora_rank, lora_alpha, train_steps, batch_size, learning_rate, 
            save_iterations, repo_id, progress
        )

    def handle_resume_training(
        self, preset, model_type, model_version, training_type, 
        lora_rank, lora_alpha, train_steps, batch_size, learning_rate, 
        save_iterations, repo_id, progress=gr.Progress()
    ):
        """Handle resuming training from the latest checkpoint"""
        # Find the latest checkpoint
        checkpoints = list(OUTPUT_PATH.glob("finetrainers_step_*"))

        if not checkpoints:
            return "No checkpoints found to resume from", "Please start a new training session instead"
        
        self.app.training.append_log(f"Resuming training from latest checkpoint")
    
        # Start training with the checkpoint
        return self.handle_training_start(
            preset, model_type, model_version, training_type, 
            lora_rank, lora_alpha, train_steps, batch_size, learning_rate, 
            save_iterations, repo_id, progress, 
            resume_from_checkpoint="latest"
        )

    def connect_events(self) -> None:
        """Connect event handlers to UI components"""
        # Model type change event - Update model version dropdown choices
        self.components["model_type"].change(
            fn=self.update_model_versions,
            inputs=[self.components["model_type"]],
            outputs=[self.components["model_version"]]
        ).then(
            fn=self.update_model_type_and_version,  # Add this new function
            inputs=[self.components["model_type"], self.components["model_version"]],
            outputs=[]
        ).then(
            # Use get_model_info instead of update_model_info
            fn=self.get_model_info,
            inputs=[self.components["model_type"], self.components["training_type"]],
            outputs=[self.components["model_info"]]
        )
        
        # Model version change event
        self.components["model_version"].change(
            fn=self.save_model_version,  # Replace with this new function
            inputs=[self.components["model_type"], self.components["model_version"]],
            outputs=[]
        )
            
        # Training type change event
        self.components["training_type"].change(
            fn=lambda v: self.app.update_ui_state(training_type=v),
            inputs=[self.components["training_type"]],
            outputs=[]
        ).then(
            fn=self.update_model_info,
            inputs=[self.components["model_type"], self.components["training_type"]],
            outputs=[
                self.components["model_info"],
                self.components["train_steps"],
                self.components["batch_size"],
                self.components["learning_rate"],
                self.components["save_iterations"],
                self.components["lora_params_row"]
            ]
        )

        self.components["auto_resume"].change(
            fn=lambda v: self.app.update_ui_state(auto_resume=v),
            inputs=[self.components["auto_resume"]],
            outputs=[]
        )

        # Add in the connect_events() method:
        self.components["num_gpus"].change(
            fn=lambda v: self.app.update_ui_state(num_gpus=v),
            inputs=[self.components["num_gpus"]],
            outputs=[]
        )

        self.components["precomputation_items"].change(
            fn=lambda v: self.app.update_ui_state(precomputation_items=v),
            inputs=[self.components["precomputation_items"]],
            outputs=[]
        )

        self.components["lr_warmup_steps"].change(
            fn=lambda v: self.app.update_ui_state(lr_warmup_steps=v),
            inputs=[self.components["lr_warmup_steps"]],
            outputs=[]
        )
                
        # Training parameters change events
        self.components["lora_rank"].change(
            fn=lambda v: self.app.update_ui_state(lora_rank=v),
            inputs=[self.components["lora_rank"]],
            outputs=[]
        )

        self.components["lora_alpha"].change(
            fn=lambda v: self.app.update_ui_state(lora_alpha=v),
            inputs=[self.components["lora_alpha"]],
            outputs=[]
        )

        self.components["train_steps"].change(
            fn=lambda v: self.app.update_ui_state(train_steps=v),
            inputs=[self.components["train_steps"]],
            outputs=[]
        )

        self.components["batch_size"].change(
            fn=lambda v: self.app.update_ui_state(batch_size=v),
            inputs=[self.components["batch_size"]],
            outputs=[]
        )

        self.components["learning_rate"].change(
            fn=lambda v: self.app.update_ui_state(learning_rate=v),
            inputs=[self.components["learning_rate"]],
            outputs=[]
        )

        self.components["save_iterations"].change(
            fn=lambda v: self.app.update_ui_state(save_iterations=v),
            inputs=[self.components["save_iterations"]],
            outputs=[]
        )
        
        # Training preset change event
        self.components["training_preset"].change(
            fn=lambda v: self.app.update_ui_state(training_preset=v),
            inputs=[self.components["training_preset"]],
            outputs=[]
        ).then(
            fn=self.update_training_params,
            inputs=[self.components["training_preset"]],
            outputs=[
                self.components["model_type"],
                self.components["training_type"],
                self.components["lora_rank"],
                self.components["lora_alpha"],
                self.components["train_steps"],
                self.components["batch_size"],
                self.components["learning_rate"],
                self.components["save_iterations"],
                self.components["preset_info"],
                self.components["lora_params_row"],
                self.components["num_gpus"],
                self.components["precomputation_items"],
                self.components["lr_warmup_steps"],
                # Add model_version to the outputs
                self.components["model_version"]
            ]
        )
        
        # Training control events
        self.components["start_btn"].click(
            fn=self.handle_new_training_start,
            inputs=[
                self.components["training_preset"],
                self.components["model_type"],
                self.components["model_version"],
                self.components["training_type"],
                self.components["lora_rank"],
                self.components["lora_alpha"],
                self.components["train_steps"],
                self.components["batch_size"],
                self.components["learning_rate"],
                self.components["save_iterations"],
                self.app.tabs["manage_tab"].components["repo_id"]
            ],
            outputs=[
                self.components["status_box"],
                self.components["log_box"]
            ]
        )

        self.components["resume_btn"].click(
            fn=self.handle_resume_training,
            inputs=[
                self.components["training_preset"],
                self.components["model_type"],
                self.components["model_version"],
                self.components["training_type"],
                self.components["lora_rank"],
                self.components["lora_alpha"],
                self.components["train_steps"],
                self.components["batch_size"],
                self.components["learning_rate"],
                self.components["save_iterations"],
                self.app.tabs["manage_tab"].components["repo_id"]
            ],
            outputs=[
                self.components["status_box"],
                self.components["log_box"]
            ]
        )
        

        # Use simplified event handlers for pause/resume and stop
        third_btn = self.components["delete_checkpoints_btn"] if "delete_checkpoints_btn" in self.components else self.components["pause_resume_btn"]
        
        self.components["pause_resume_btn"].click(
            fn=self.handle_pause_resume,
            outputs=[
                self.components["status_box"],
                self.components["log_box"],
                self.components["current_task_box"],
                self.components["start_btn"],
                self.components["stop_btn"],
                third_btn
            ]
        )

        self.components["stop_btn"].click(
            fn=self.handle_stop,
            outputs=[
                self.components["status_box"],
                self.components["log_box"],
                self.components["current_task_box"],
                self.components["start_btn"],
                self.components["stop_btn"],
                third_btn
            ]
        )

        # Add an event handler for delete_checkpoints_btn
        self.components["delete_checkpoints_btn"].click(
            fn=lambda: self.app.training.delete_all_checkpoints(),
            outputs=[self.components["status_box"]]
        )
    
    def update_model_versions(self, model_type: str) -> Dict:
        """Update model version choices based on selected model type"""
        try:
            # Get version choices for this model type
            model_versions = self.get_model_version_choices(model_type)
            
            # Get default version
            default_version = self.get_default_model_version(model_type)
            logger.info(f"update_model_versions({model_type}): default_version = {default_version}, available versions: {model_versions}")
            
            # Update UI state with proper model_type first
            self.app.update_ui_state(model_type=model_type)
            
            # Ensure model_versions is a simple list of strings
            model_versions = [str(version) for version in model_versions]
            
            # Create a new dropdown with the updated choices
            if not model_versions:
                logger.warning(f"No model versions available for {model_type}, using empty list")
                # Return empty dropdown to avoid errors
                return gr.Dropdown(choices=[], value=None)
                    
            # Ensure default_version is in model_versions
            if default_version not in model_versions and model_versions:
                default_version = model_versions[0]
                logger.info(f"Default version not in choices, using first available: {default_version}")
            
            # Return the updated dropdown
            logger.info(f"Returning dropdown with {len(model_versions)} choices")
            return gr.Dropdown(choices=model_versions, value=default_version)
        except Exception as e:
            # Log any exceptions for debugging
            logger.error(f"Error in update_model_versions: {str(e)}")
            # Return empty dropdown to avoid errors
            return gr.Dropdown(choices=[], value=None)
        
    def handle_training_start(
        self, preset, model_type, model_version, training_type, 
        lora_rank, lora_alpha, train_steps, batch_size, learning_rate, 
        save_iterations, repo_id,
        progress=gr.Progress(),
        resume_from_checkpoint=None,
    ):
        """Handle training start with proper log parser reset and checkpoint detection"""
        # Safely reset log parser if it exists
        if hasattr(self.app, 'log_parser') and self.app.log_parser is not None:
            self.app.log_parser.reset()
        else:
            logger.warning("Log parser not initialized, creating a new one")
            from ..utils import TrainingLogParser
            self.app.log_parser = TrainingLogParser()
            
        # Check for latest checkpoint
        checkpoints = list(OUTPUT_PATH.glob("finetrainers_step_*"))
        has_checkpoints = len(checkpoints) > 0
        resume_from = resume_from_checkpoint  # Use the passed parameter
        
        if resume_from and checkpoints:
            # Find the latest checkpoint
            latest_checkpoint = max(checkpoints, key=os.path.getmtime)
            resume_from = str(latest_checkpoint)

            logger.info(f"Found checkpoint at {resume_from}, note from @julian: right now let's just resume training at 'latest'")
            result_from = "latest"
            
        # Convert model_type display name to internal name
        model_internal_type = MODEL_TYPES.get(model_type)
        
        if not model_internal_type:
            logger.error(f"Invalid model type: {model_type}")
            return f"Error: Invalid model type '{model_type}'", "Model type not recognized"
        
        # Convert training_type display name to internal name
        training_internal_type = TRAINING_TYPES.get(training_type)
        
        if not training_internal_type:
            logger.error(f"Invalid training type: {training_type}")
            return f"Error: Invalid training type '{training_type}'", "Training type not recognized"
        
        # Get other parameters from UI form
        num_gpus = int(self.components["num_gpus"].value)
        precomputation_items = int(self.components["precomputation_items"].value)
        lr_warmup_steps = int(self.components["lr_warmup_steps"].value)
        
        # Start training (it will automatically use the checkpoint if provided)
        try:
            return self.app.training.start_training(
                model_internal_type,
                lora_rank,
                lora_alpha,
                train_steps,
                batch_size,
                learning_rate,
                save_iterations,
                repo_id,
                preset_name=preset,
                training_type=training_internal_type,
                model_version=model_version,  # Pass the model version from dropdown
                resume_from_checkpoint=resume_from,
                num_gpus=num_gpus,
                precomputation_items=precomputation_items,
                lr_warmup_steps=lr_warmup_steps,
                progress=progress
            )
        except Exception as e:
            logger.exception("Error starting training")
            return f"Error starting training: {str(e)}", f"Exception: {str(e)}\n\nCheck the logs for more details."

    def get_model_version_choices(self, model_type: str) -> List[str]:
        """Get model version choices based on model type"""
        # Convert UI display name to internal name
        internal_type = MODEL_TYPES.get(model_type)
        if not internal_type or internal_type not in MODEL_VERSIONS:
            logger.warning(f"No model versions found for {model_type} (internal type: {internal_type})")
            return []
            
        # Return just the model IDs as a list of simple strings
        version_ids = list(MODEL_VERSIONS.get(internal_type, {}).keys())
        logger.info(f"Found {len(version_ids)} versions for {model_type}: {version_ids}")
        
        # Ensure they're all strings
        return [str(version) for version in version_ids]

    def get_default_model_version(self, model_type: str) -> str:
        """Get default model version for the given model type"""
        # Convert UI display name to internal name
        internal_type = MODEL_TYPES.get(model_type)
        logger.debug(f"get_default_model_version({model_type}) = {internal_type}")
        
        if not internal_type or internal_type not in MODEL_VERSIONS:
            logger.warning(f"No valid model versions found for {model_type}")
            return ""
            
        # Get the first version available for this model type
        versions = list(MODEL_VERSIONS.get(internal_type, {}).keys())
        if versions:
            default_version = versions[0]
            logger.debug(f"Default version for {model_type}: {default_version}")
            return default_version
        return ""
   
    def update_model_info(self, model_type: str, training_type: str) -> Dict:
        """Update model info and related UI components based on model type and training type"""
        # Get model info text
        model_info = self.get_model_info(model_type, training_type)
        
        # Get default parameters for this model type and training type
        params = self.get_default_params(MODEL_TYPES.get(model_type), TRAINING_TYPES.get(training_type))
        
        # Check if LoRA params should be visible
        show_lora_params = training_type == "LoRA Finetune"
        
        # Return updates for UI components
        return {
            self.components["model_info"]: model_info,
            self.components["train_steps"]: params["train_steps"],
            self.components["batch_size"]: params["batch_size"],
            self.components["learning_rate"]: params["learning_rate"],
            self.components["save_iterations"]: params["save_iterations"],
            self.components["lora_params_row"]: gr.Row(visible=show_lora_params)
        }

    def get_model_info(self, model_type: str, training_type: str) -> str:
        """Get information about the selected model type and training method"""
        if model_type == "HunyuanVideo":
            base_info = """### HunyuanVideo
    - Required VRAM: ~48GB minimum
    - Recommended batch size: 1-2
    - Typical training time: 2-4 hours
    - Default resolution: 49x512x768"""
            
            if training_type == "LoRA Finetune":
                return base_info + "\n- Required VRAM: ~18GB minimum\n- Default LoRA rank: 128 (~400 MB)"
            else:
                return base_info + "\n- Required VRAM: ~48GB minimum\n- **Full finetune not recommended due to VRAM requirements**"
                
        elif model_type == "LTX-Video":
            base_info = """### LTX-Video
    - Recommended batch size: 1-4
    - Typical training time: 1-3 hours
    - Default resolution: 49x512x768"""
            
            if training_type == "LoRA Finetune":
                return base_info + "\n- Required VRAM: ~18GB minimum\n- Default LoRA rank: 128 (~400 MB)"
            else:
                return base_info + "\n- Required VRAM: ~21GB minimum\n- Full model size: ~8GB"
                
        elif model_type == "Wan":
            base_info = """### Wan
    - Recommended batch size: 1-4
    - Typical training time: 1-3 hours
    - Default resolution: 49x512x768"""
            
            if training_type == "LoRA Finetune":
                return base_info + "\n- Required VRAM: ~16GB minimum\n- Default LoRA rank: 32 (~120 MB)"
            else:
                return base_info + "\n- **Full finetune not recommended due to VRAM requirements**"
        
        # Default fallback
        return f"### {model_type}\nPlease check documentation for VRAM requirements and recommended settings."

    def get_default_params(self, model_type: str, training_type: str) -> Dict[str, Any]:
        """Get default training parameters for model type"""
        # Find preset that matches model type and training type
        matching_presets = [
            preset for preset_name, preset in TRAINING_PRESETS.items() 
            if preset["model_type"] == model_type and preset["training_type"] == training_type
        ]
        
        if matching_presets:
            # Use the first matching preset
            preset = matching_presets[0]
            return {
                "train_steps": preset.get("train_steps", DEFAULT_NB_TRAINING_STEPS),
                "batch_size": preset.get("batch_size", DEFAULT_BATCH_SIZE),
                "learning_rate": preset.get("learning_rate", DEFAULT_LEARNING_RATE),
                "save_iterations": preset.get("save_iterations", DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS),
                "lora_rank": preset.get("lora_rank", DEFAULT_LORA_RANK_STR),
                "lora_alpha": preset.get("lora_alpha", DEFAULT_LORA_ALPHA_STR)
            }
        
        # Default fallbacks
        if model_type == "hunyuan_video":
            return {
                "train_steps": DEFAULT_NB_TRAINING_STEPS,
                "batch_size": DEFAULT_BATCH_SIZE,
                "learning_rate": 2e-5,
                "save_iterations": DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS,
                "lora_rank": DEFAULT_LORA_RANK_STR,
                "lora_alpha": DEFAULT_LORA_ALPHA_STR
            }
        elif model_type == "ltx_video":
            return {
                "train_steps": DEFAULT_NB_TRAINING_STEPS,
                "batch_size": DEFAULT_BATCH_SIZE,
                "learning_rate": DEFAULT_LEARNING_RATE,
                "save_iterations": DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS,
                "lora_rank": DEFAULT_LORA_RANK_STR,
                "lora_alpha": DEFAULT_LORA_ALPHA_STR
            }
        elif model_type == "wan":
            return {
                "train_steps": DEFAULT_NB_TRAINING_STEPS,
                "batch_size": DEFAULT_BATCH_SIZE,
                "learning_rate": 5e-5,
                "save_iterations": DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS,
                "lora_rank": "32",
                "lora_alpha": "32"
            }
        else:
            # Generic defaults
            return {
                "train_steps": DEFAULT_NB_TRAINING_STEPS,
                "batch_size": DEFAULT_BATCH_SIZE,
                "learning_rate": DEFAULT_LEARNING_RATE,
                "save_iterations": DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS,
                "lora_rank": DEFAULT_LORA_RANK_STR,
                "lora_alpha": DEFAULT_LORA_ALPHA_STR
            }
            
    def update_training_params(self, preset_name: str) -> Tuple:
        """Update UI components based on selected preset while preserving custom settings"""
        preset = TRAINING_PRESETS[preset_name]
        
        # Load current UI state to check if user has customized values
        current_state = self.app.load_ui_values()
        
        # Find the display name that maps to our model type
        model_display_name = next(
            key for key, value in MODEL_TYPES.items() 
            if value == preset["model_type"]
        )
        
        # Find the display name that maps to our training type
        training_display_name = next(
            key for key, value in TRAINING_TYPES.items() 
            if value == preset["training_type"]
        )
            
        # Get preset description for display
        description = preset.get("description", "")
        
        # Get max values from buckets
        buckets = preset["training_buckets"]
        max_frames = max(frames for frames, _, _ in buckets)
        max_height = max(height for _, height, _ in buckets)
        max_width = max(width for _, _, width in buckets)
        bucket_info = f"\nMaximum video size: {max_frames} frames at {max_width}x{max_height} resolution"
        
        info_text = f"{description}{bucket_info}"
        
        # Check if LoRA params should be visible
        show_lora_params = preset["training_type"] == "lora"
        
        # Use preset defaults but preserve user-modified values if they exist
        lora_rank_val = current_state.get("lora_rank") if current_state.get("lora_rank") != preset.get("lora_rank", DEFAULT_LORA_RANK_STR) else preset.get("lora_rank", DEFAULT_LORA_RANK_STR)
        lora_alpha_val = current_state.get("lora_alpha") if current_state.get("lora_alpha") != preset.get("lora_alpha", DEFAULT_LORA_ALPHA_STR) else preset.get("lora_alpha", DEFAULT_LORA_ALPHA_STR)
        train_steps_val = current_state.get("train_steps") if current_state.get("train_steps") != preset.get("train_steps", DEFAULT_NB_TRAINING_STEPS) else preset.get("train_steps", DEFAULT_NB_TRAINING_STEPS)
        batch_size_val = current_state.get("batch_size") if current_state.get("batch_size") != preset.get("batch_size", DEFAULT_BATCH_SIZE) else preset.get("batch_size", DEFAULT_BATCH_SIZE)
        learning_rate_val = current_state.get("learning_rate") if current_state.get("learning_rate") != preset.get("learning_rate", DEFAULT_LEARNING_RATE) else preset.get("learning_rate", DEFAULT_LEARNING_RATE)
        save_iterations_val = current_state.get("save_iterations") if current_state.get("save_iterations") != preset.get("save_iterations", DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS) else preset.get("save_iterations", DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS)
        num_gpus_val = current_state.get("num_gpus") if current_state.get("num_gpus") != preset.get("num_gpus", DEFAULT_NUM_GPUS) else preset.get("num_gpus", DEFAULT_NUM_GPUS)
        precomputation_items_val = current_state.get("precomputation_items") if current_state.get("precomputation_items") != preset.get("precomputation_items", DEFAULT_PRECOMPUTATION_ITEMS) else preset.get("precomputation_items", DEFAULT_PRECOMPUTATION_ITEMS)
        lr_warmup_steps_val = current_state.get("lr_warmup_steps") if current_state.get("lr_warmup_steps") != preset.get("lr_warmup_steps", DEFAULT_NB_LR_WARMUP_STEPS) else preset.get("lr_warmup_steps", DEFAULT_NB_LR_WARMUP_STEPS)
        
        # Get the appropriate model version for the selected model type
        model_versions = self.get_model_version_choices(model_display_name)
        default_model_version = self.get_default_model_version(model_display_name)

        # Ensure we have valid choices and values
        if not model_versions:
            logger.warning(f"No versions found for {model_display_name}, using empty list")
            model_versions = []
            default_model_version = None
        elif default_model_version not in model_versions and model_versions:
            default_model_version = model_versions[0]
            logger.info(f"Reset default version to first available: {default_model_version}")

        # Ensure model_versions is a simple list of strings
        model_versions = [str(version) for version in model_versions]

        # Create the model version dropdown update
        model_version_update = gr.Dropdown(choices=model_versions, value=default_model_version)

        # Return values in the same order as the output components
        return (
            model_display_name,
            training_display_name,
            lora_rank_val,
            lora_alpha_val,
            train_steps_val,
            batch_size_val,
            learning_rate_val,
            save_iterations_val,
            info_text,
            gr.Row(visible=show_lora_params),
            num_gpus_val,
            precomputation_items_val,
            lr_warmup_steps_val,
            model_version_update,
        )


    def get_latest_status_message_and_logs(self) -> Tuple[str, str, str]:
        """Get latest status message, log content, and status code in a safer way"""
        state = self.app.training.get_status()
        logs = self.app.training.get_logs()

        # Check if training process died unexpectedly
        training_died = False
        
        if state["status"] == "training" and not self.app.training.is_training_running():
            state["status"] = "error"
            state["message"] = "Training process terminated unexpectedly."
            training_died = True
            
            # Look for error in logs
            error_lines = []
            for line in logs.splitlines():
                if "Error:" in line or "Exception:" in line or "Traceback" in line:
                    error_lines.append(line)
            
            if error_lines:
                state["message"] += f"\n\nPossible error: {error_lines[-1]}"

        # Ensure log parser is initialized
        if not hasattr(self.app, 'log_parser') or self.app.log_parser is None:
            from ..utils import TrainingLogParser
            self.app.log_parser = TrainingLogParser()
            logger.info("Initialized missing log parser")

        # Parse new log lines
        if logs and not training_died:
            last_state = None
            for line in logs.splitlines():
                try:
                    state_update = self.app.log_parser.parse_line(line)
                    if state_update:
                        last_state = state_update
                except Exception as e:
                    logger.error(f"Error parsing log line: {str(e)}")
                    continue
            
            if last_state:
                ui_updates = self.update_training_ui(last_state)
                state["message"] = ui_updates.get("status_box", state["message"])
        
        # Parse status for training state
        if "completed" in state["message"].lower():
            state["status"] = "completed"
        elif "error" in state["message"].lower():
            state["status"] = "error"
        elif "failed" in state["message"].lower():
            state["status"] = "error"
        elif "stopped" in state["message"].lower():
            state["status"] = "stopped"

        # Add the current task info if available
        if hasattr(self.app, 'log_parser') and self.app.log_parser is not None:
            state["current_task"] = self.app.log_parser.get_current_task_display()

        return (state["status"], state["message"], logs)

    def get_status_updates(self):
        """Get status updates for text components (no variant property)"""
        status, message, logs = self.get_latest_status_message_and_logs()
        
        # Get current task if available
        current_task = ""
        if hasattr(self.app, 'log_parser') and self.app.log_parser is not None:
            current_task = self.app.log_parser.get_current_task_display()
        
        return message, logs, current_task

    def get_button_updates(self):
        """Get button updates (with variant property)"""
        status, _, _ = self.get_latest_status_message_and_logs()
        
        # Add checkpoints detection
        checkpoints = list(OUTPUT_PATH.glob("finetrainers_step_*"))
        has_checkpoints = len(checkpoints) > 0
        
        is_training = status in ["training", "initializing"]
        is_completed = status in ["completed", "error", "stopped"]
        
        # Create button updates
        start_btn = gr.Button(
            value="🚀 Start new training",
            interactive=not is_training,
            variant="primary" if not is_training else "secondary"
        )
        
        resume_btn = gr.Button(
            value="🛸 Start from latest checkpoint",
            interactive=has_checkpoints and not is_training,
            variant="primary" if not is_training else "secondary"
        )
        
        stop_btn = gr.Button(
            value="Stop at Last Checkpoint",
            interactive=is_training,
            variant="primary" if is_training else "secondary"
        )
        
        # Add delete_checkpoints_btn
        delete_checkpoints_btn = gr.Button(
            "Delete All Checkpoints",
            interactive=has_checkpoints and not is_training,
            variant="stop"
        )
        
        return start_btn, resume_btn, stop_btn, delete_checkpoints_btn
            
    def update_training_ui(self, training_state: Dict[str, Any]):
        """Update UI components based on training state"""
        updates = {}
        
        # Update status box with high-level information
        status_text = []
        if training_state["status"] != "idle":
            status_text.extend([
                f"Status: {training_state['status']}",
                f"Progress: {training_state['progress']}",
                f"Step: {training_state['current_step']}/{training_state['total_steps']}",
                f"Time elapsed: {training_state['elapsed']}",
                f"Estimated remaining: {training_state['remaining']}",
                "",
                f"Current loss: {training_state['step_loss']}",
                f"Learning rate: {training_state['learning_rate']}",
                f"Gradient norm: {training_state['grad_norm']}",
                f"Memory usage: {training_state['memory']}"
            ])
            
            if training_state["error_message"]:
                status_text.append(f"\nError: {training_state['error_message']}")
                
        updates["status_box"] = "\n".join(status_text)
        
        # Add current task information to the dedicated box
        if training_state.get("current_task"):
            updates["current_task_box"] = training_state["current_task"]
        else:
            updates["current_task_box"] = "No active task" if training_state["status"] != "training" else "Waiting for task information..."
        
        return updates
        
    def handle_pause_resume(self):
        """Handle pause/resume button click"""
        status, _, _ = self.get_latest_status_message_and_logs()
        
        if status == "paused":
            self.app.training.resume_training()
        else:
            self.app.training.pause_training()
            
        # Return the updates separately for text and buttons
        return (*self.get_status_updates(), *self.get_button_updates())

    def handle_stop(self):
        """Handle stop button click"""
        self.app.training.stop_training()
        
        # Return the updates separately for text and buttons
        return (*self.get_status_updates(), *self.get_button_updates())