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import platform
import gradio as gr
from pathlib import Path
import logging
import asyncio
from typing import Any, Optional, Dict, List, Union, Tuple
from ..services import TrainingService, CaptioningService, SplittingService, ImportService, MonitoringService
from ..config import (
STORAGE_PATH, VIDEOS_TO_SPLIT_PATH, STAGING_PATH, OUTPUT_PATH,
TRAINING_PATH, LOG_FILE_PATH, TRAINING_PRESETS, TRAINING_VIDEOS_PATH, MODEL_PATH, OUTPUT_PATH,
MODEL_TYPES, SMALL_TRAINING_BUCKETS, TRAINING_TYPES,
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
)
from ..utils import (
get_recommended_precomputation_items,
count_media_files,
format_media_title,
TrainingLogParser
)
from ..tabs import ImportTab, SplitTab, CaptionTab, TrainTab, MonitorTab, ManageTab
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
httpx_logger = logging.getLogger('httpx')
httpx_logger.setLevel(logging.WARN)
class VideoTrainerUI:
def __init__(self):
"""Initialize services and tabs"""
# Initialize core services
self.trainer = TrainingService(self)
self.splitter = SplittingService()
self.importer = ImportService()
self.captioner = CaptioningService()
self.monitor = MonitoringService()
# Start the monitoring service on app creation
self.monitor.start_monitoring()
# Recovery status from any interrupted training
recovery_result = self.trainer.recover_interrupted_training()
# Add null check for recovery_result
if recovery_result is None:
recovery_result = {"status": "unknown", "ui_updates": {}}
self.recovery_status = recovery_result.get("status", "unknown")
self.ui_updates = recovery_result.get("ui_updates", {})
# Initialize log parser
self.log_parser = TrainingLogParser()
# Shared state for tabs
self.state = {
"recovery_result": recovery_result
}
# Initialize tabs dictionary (will be populated in create_ui)
self.tabs = {}
self.tabs_component = None
# Log recovery status
logger.info(f"Initialization complete. Recovery status: {self.recovery_status}")
def add_periodic_callback(self, callback_fn, interval=1.0):
"""Add a periodic callback function to the UI
Args:
callback_fn: Function to call periodically
interval: Time in seconds between calls (default: 1.0)
"""
try:
# Store a reference to the callback function
if not hasattr(self, "_periodic_callbacks"):
self._periodic_callbacks = []
self._periodic_callbacks.append(callback_fn)
# Add the callback to the Gradio app
self.app.add_callback(
interval, # Interval in seconds
callback_fn, # Function to call
inputs=None, # No inputs needed
outputs=list(self.components.values()) # All components as possible outputs
)
logger.info(f"Added periodic callback {callback_fn.__name__} with interval {interval}s")
except Exception as e:
logger.error(f"Error adding periodic callback: {e}", exc_info=True)
def create_ui(self):
"""Create the main Gradio UI"""
with gr.Blocks(title="🎥 Video Model Studio") as app:
gr.Markdown("# 🎥 Video Model Studio")
# Create main tabs component
with gr.Tabs() as self.tabs_component:
# Initialize tab objects
self.tabs["import_tab"] = ImportTab(self)
self.tabs["split_tab"] = SplitTab(self)
self.tabs["caption_tab"] = CaptionTab(self)
self.tabs["train_tab"] = TrainTab(self)
self.tabs["monitor_tab"] = MonitorTab(self)
self.tabs["manage_tab"] = ManageTab(self)
# Create tab UI components
for tab_id, tab_obj in self.tabs.items():
tab_obj.create(self.tabs_component)
# Connect event handlers
for tab_id, tab_obj in self.tabs.items():
tab_obj.connect_events()
# app-level timers for auto-refresh functionality
self._add_timers()
# Initialize app state on load
app.load(
fn=self.initialize_app_state,
outputs=[
self.tabs["split_tab"].components["video_list"],
self.tabs["caption_tab"].components["training_dataset"],
self.tabs["train_tab"].components["start_btn"],
self.tabs["train_tab"].components["stop_btn"],
self.tabs["train_tab"].components["pause_resume_btn"],
self.tabs["train_tab"].components["training_preset"],
self.tabs["train_tab"].components["model_type"],
self.tabs["train_tab"].components["training_type"],
self.tabs["train_tab"].components["lora_rank"],
self.tabs["train_tab"].components["lora_alpha"],
self.tabs["train_tab"].components["train_steps"],
self.tabs["train_tab"].components["batch_size"],
self.tabs["train_tab"].components["learning_rate"],
self.tabs["train_tab"].components["save_iterations"],
self.tabs["train_tab"].components["current_task_box"],
self.tabs["train_tab"].components["num_gpus"],
self.tabs["train_tab"].components["precomputation_items"],
self.tabs["train_tab"].components["lr_warmup_steps"]
]
)
return app
def _add_timers(self):
"""Add auto-refresh timers to the UI"""
# Status update timer for text components (every 1 second)
status_timer = gr.Timer(value=1)
status_timer.tick(
fn=self.tabs["train_tab"].get_status_updates, # Use a new function that returns appropriate updates
outputs=[
self.tabs["train_tab"].components["status_box"],
self.tabs["train_tab"].components["log_box"],
self.tabs["train_tab"].components["current_task_box"] if "current_task_box" in self.tabs["train_tab"].components else None
]
)
# Button update timer for button components (every 1 second)
button_timer = gr.Timer(value=1)
button_outputs = [
self.tabs["train_tab"].components["start_btn"],
self.tabs["train_tab"].components["stop_btn"]
]
# Add delete_checkpoints_btn or pause_resume_btn as the third button
if "delete_checkpoints_btn" in self.tabs["train_tab"].components:
button_outputs.append(self.tabs["train_tab"].components["delete_checkpoints_btn"])
elif "pause_resume_btn" in self.tabs["train_tab"].components:
button_outputs.append(self.tabs["train_tab"].components["pause_resume_btn"])
button_timer.tick(
fn=self.tabs["train_tab"].get_button_updates, # Use a new function for button-specific updates
outputs=button_outputs
)
# Dataset refresh timer (every 5 seconds)
dataset_timer = gr.Timer(value=5)
dataset_timer.tick(
fn=self.refresh_dataset,
outputs=[
self.tabs["split_tab"].components["video_list"],
self.tabs["caption_tab"].components["training_dataset"]
]
)
# Titles update timer (every 6 seconds)
titles_timer = gr.Timer(value=6)
titles_timer.tick(
fn=self.update_titles,
outputs=[
self.tabs["split_tab"].components["split_title"],
self.tabs["caption_tab"].components["caption_title"],
self.tabs["train_tab"].components["train_title"]
]
)
def initialize_app_state(self):
"""Initialize all app state in one function to ensure correct output count"""
# Get dataset info
video_list = self.tabs["split_tab"].list_unprocessed_videos()
training_dataset = self.tabs["caption_tab"].list_training_files_to_caption()
# Get button states based on recovery status
button_states = self.get_initial_button_states()
start_btn = button_states[0]
stop_btn = button_states[1]
delete_checkpoints_btn = button_states[2] # This replaces pause_resume_btn in the response tuple
# Get UI form values - possibly from the recovery
if self.recovery_status in ["recovered", "ready_to_recover", "running"] and "ui_updates" in self.state["recovery_result"]:
recovery_ui = self.state["recovery_result"]["ui_updates"]
# If we recovered training parameters from the original session
ui_state = {}
# Handle model_type specifically - could be internal or display name
if "model_type" in recovery_ui:
model_type_value = recovery_ui["model_type"]
# Remove " (LoRA)" suffix if present
if " (LoRA)" in model_type_value:
model_type_value = model_type_value.replace(" (LoRA)", "")
logger.info(f"Removed (LoRA) suffix from model type: {model_type_value}")
# If it's an internal name, convert to display name
if model_type_value not in MODEL_TYPES:
# Find the display name for this internal model type
for display_name, internal_name in MODEL_TYPES.items():
if internal_name == model_type_value:
model_type_value = display_name
logger.info(f"Converted internal model type '{recovery_ui['model_type']}' to display name '{model_type_value}'")
break
ui_state["model_type"] = model_type_value
# Handle training_type
if "training_type" in recovery_ui:
training_type_value = recovery_ui["training_type"]
# If it's an internal name, convert to display name
if training_type_value not in TRAINING_TYPES:
for display_name, internal_name in TRAINING_TYPES.items():
if internal_name == training_type_value:
training_type_value = display_name
logger.info(f"Converted internal training type '{recovery_ui['training_type']}' to display name '{training_type_value}'")
break
ui_state["training_type"] = training_type_value
# Copy other parameters
for param in ["lora_rank", "lora_alpha", "train_steps",
"batch_size", "learning_rate", "save_iterations", "training_preset"]:
if param in recovery_ui:
ui_state[param] = recovery_ui[param]
# Merge with existing UI state if needed
if ui_state:
current_state = self.load_ui_values()
current_state.update(ui_state)
self.trainer.save_ui_state(current_state)
logger.info(f"Updated UI state from recovery: {ui_state}")
# Load values (potentially with recovery updates applied)
ui_state = self.load_ui_values()
# Ensure model_type is a valid display name
model_type_val = ui_state.get("model_type", list(MODEL_TYPES.keys())[0])
# Remove " (LoRA)" suffix if present
if " (LoRA)" in model_type_val:
model_type_val = model_type_val.replace(" (LoRA)", "")
logger.info(f"Removed (LoRA) suffix from model type: {model_type_val}")
# Ensure it's a valid model type in the dropdown
if model_type_val not in MODEL_TYPES:
# Convert from internal to display name or use default
model_type_found = False
for display_name, internal_name in MODEL_TYPES.items():
if internal_name == model_type_val:
model_type_val = display_name
model_type_found = True
break
# If still not found, use the first model type
if not model_type_found:
model_type_val = list(MODEL_TYPES.keys())[0]
logger.warning(f"Invalid model type '{model_type_val}', using default: {model_type_val}")
# Ensure training_type is a valid display name
training_type_val = ui_state.get("training_type", list(TRAINING_TYPES.keys())[0])
if training_type_val not in TRAINING_TYPES:
# Convert from internal to display name or use default
training_type_found = False
for display_name, internal_name in TRAINING_TYPES.items():
if internal_name == training_type_val:
training_type_val = display_name
training_type_found = True
break
# If still not found, use the first training type
if not training_type_found:
training_type_val = list(TRAINING_TYPES.keys())[0]
logger.warning(f"Invalid training type '{training_type_val}', using default: {training_type_val}")
# Validate training preset
training_preset = ui_state.get("training_preset", list(TRAINING_PRESETS.keys())[0])
if training_preset not in TRAINING_PRESETS:
training_preset = list(TRAINING_PRESETS.keys())[0]
logger.warning(f"Invalid training preset '{training_preset}', using default: {training_preset}")
# Rest of the function remains unchanged
lora_rank_val = ui_state.get("lora_rank", DEFAULT_LORA_RANK_STR)
lora_alpha_val = ui_state.get("lora_alpha", DEFAULT_LORA_ALPHA_STR)
batch_size_val = int(ui_state.get("batch_size", DEFAULT_BATCH_SIZE))
learning_rate_val = float(ui_state.get("learning_rate", DEFAULT_LEARNING_RATE))
save_iterations_val = int(ui_state.get("save_iterations", DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS))
# Update for new UI components
num_gpus_val = int(ui_state.get("num_gpus", DEFAULT_NUM_GPUS))
# Calculate recommended precomputation items based on video count
video_count = len(list(TRAINING_VIDEOS_PATH.glob('*.mp4')))
recommended_precomputation = get_recommended_precomputation_items(video_count, num_gpus_val)
precomputation_items_val = int(ui_state.get("precomputation_items", recommended_precomputation))
# Ensure warmup steps are not more than training steps
train_steps_val = int(ui_state.get("train_steps", DEFAULT_NB_TRAINING_STEPS))
default_warmup = min(DEFAULT_NB_LR_WARMUP_STEPS, int(train_steps_val * 0.2))
lr_warmup_steps_val = int(ui_state.get("lr_warmup_steps", default_warmup))
# Ensure warmup steps <= training steps
lr_warmup_steps_val = min(lr_warmup_steps_val, train_steps_val)
# Initial current task value
current_task_val = ""
if hasattr(self, 'log_parser') and self.log_parser:
current_task_val = self.log_parser.get_current_task_display()
# Return all values in the exact order expected by outputs
return (
video_list,
training_dataset,
start_btn,
stop_btn,
delete_checkpoints_btn,
training_preset,
model_type_val,
training_type_val,
lora_rank_val,
lora_alpha_val,
train_steps_val,
batch_size_val,
learning_rate_val,
save_iterations_val,
current_task_val,
num_gpus_val,
precomputation_items_val,
lr_warmup_steps_val
)
def initialize_ui_from_state(self):
"""Initialize UI components from saved state"""
ui_state = self.load_ui_values()
# Return values in order matching the outputs in app.load
return (
ui_state.get("training_preset", list(TRAINING_PRESETS.keys())[0]),
ui_state.get("model_type", list(MODEL_TYPES.keys())[0]),
ui_state.get("training_type", list(TRAINING_TYPES.keys())[0]),
ui_state.get("lora_rank", DEFAULT_LORA_RANK_STR),
ui_state.get("lora_alpha", DEFAULT_LORA_ALPHA_STR),
ui_state.get("train_steps", DEFAULT_NB_TRAINING_STEPS),
ui_state.get("batch_size", DEFAULT_BATCH_SIZE),
ui_state.get("learning_rate", DEFAULT_LEARNING_RATE),
ui_state.get("save_iterations", DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS)
)
def update_ui_state(self, **kwargs):
"""Update UI state with new values"""
current_state = self.trainer.load_ui_state()
current_state.update(kwargs)
self.trainer.save_ui_state(current_state)
# Don't return anything to avoid Gradio warnings
return None
def load_ui_values(self):
"""Load UI state values for initializing form fields"""
ui_state = self.trainer.load_ui_state()
# Ensure proper type conversion for numeric values
ui_state["lora_rank"] = ui_state.get("lora_rank", DEFAULT_LORA_RANK_STR)
ui_state["lora_alpha"] = ui_state.get("lora_alpha", DEFAULT_LORA_ALPHA_STR)
ui_state["train_steps"] = int(ui_state.get("train_steps", DEFAULT_NB_TRAINING_STEPS))
ui_state["batch_size"] = int(ui_state.get("batch_size", DEFAULT_BATCH_SIZE))
ui_state["learning_rate"] = float(ui_state.get("learning_rate", DEFAULT_LEARNING_RATE))
ui_state["save_iterations"] = int(ui_state.get("save_iterations", DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS))
return ui_state
# Add this new method to get initial button states:
def get_initial_button_states(self):
"""Get the initial states for training buttons based on recovery status"""
recovery_result = self.state.get("recovery_result") or self.trainer.recover_interrupted_training()
ui_updates = recovery_result.get("ui_updates", {})
# Check for checkpoints to determine start button text
has_checkpoints = len(list(OUTPUT_PATH.glob("checkpoint-*"))) > 0
# Default button states if recovery didn't provide any
if not ui_updates or not ui_updates.get("start_btn"):
is_training = self.trainer.is_training_running()
if is_training:
# Active training detected
start_btn_props = {"interactive": False, "variant": "secondary", "value": "Continue Training" if has_checkpoints else "Start Training"}
stop_btn_props = {"interactive": True, "variant": "primary", "value": "Stop at Last Checkpoint"}
delete_btn_props = {"interactive": False, "variant": "stop", "value": "Delete All Checkpoints"}
else:
# No active training
start_btn_props = {"interactive": True, "variant": "primary", "value": "Continue Training" if has_checkpoints else "Start Training"}
stop_btn_props = {"interactive": False, "variant": "secondary", "value": "Stop at Last Checkpoint"}
delete_btn_props = {"interactive": has_checkpoints, "variant": "stop", "value": "Delete All Checkpoints"}
else:
# Use button states from recovery
start_btn_props = ui_updates.get("start_btn", {"interactive": True, "variant": "primary", "value": "Start Training"})
stop_btn_props = ui_updates.get("stop_btn", {"interactive": False, "variant": "secondary", "value": "Stop at Last Checkpoint"})
delete_btn_props = ui_updates.get("delete_checkpoints_btn", {"interactive": has_checkpoints, "variant": "stop", "value": "Delete All Checkpoints"})
# Return button states in the correct order
return (
gr.Button(**start_btn_props),
gr.Button(**stop_btn_props),
gr.Button(**delete_btn_props)
)
def update_titles(self) -> Tuple[Any]:
"""Update all dynamic titles with current counts
Returns:
Dict of Gradio updates
"""
# Count files for splitting
split_videos, _, split_size = count_media_files(VIDEOS_TO_SPLIT_PATH)
split_title = format_media_title(
"split", split_videos, 0, split_size
)
# Count files for captioning
caption_videos, caption_images, caption_size = count_media_files(STAGING_PATH)
caption_title = format_media_title(
"caption", caption_videos, caption_images, caption_size
)
# Count files for training
train_videos, train_images, train_size = count_media_files(TRAINING_VIDEOS_PATH)
train_title = format_media_title(
"train", train_videos, train_images, train_size
)
return (
gr.Markdown(value=split_title),
gr.Markdown(value=caption_title),
gr.Markdown(value=f"{train_title} available for training")
)
def refresh_dataset(self):
"""Refresh all dynamic lists and training state"""
video_list = self.tabs["split_tab"].list_unprocessed_videos()
training_dataset = self.tabs["caption_tab"].list_training_files_to_caption()
return (
video_list,
training_dataset
) |