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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
from ..config import (
STORAGE_PATH, VIDEOS_TO_SPLIT_PATH, STAGING_PATH,
TRAINING_PATH, LOG_FILE_PATH, TRAINING_PRESETS, TRAINING_VIDEOS_PATH, MODEL_PATH, OUTPUT_PATH,
MODEL_TYPES, SMALL_TRAINING_BUCKETS
)
from ..utils import count_media_files, format_media_title, TrainingLogParser
from ..tabs import ImportTab, SplitTab, CaptionTab, TrainTab, 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.splitter = SplittingService()
self.importer = ImportService()
self.captioner = CaptioningService()
# Recovery status from any interrupted training
recovery_result = self.trainer.recover_interrupted_training()
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
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["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()
# Add 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["lora_rank"],
self.tabs["train_tab"].components["lora_alpha"],
self.tabs["train_tab"].components["num_epochs"],
self.tabs["train_tab"].components["batch_size"],
self.tabs["train_tab"].components["learning_rate"],
self.tabs["train_tab"].components["save_iterations"]
]
)
return app
def _add_timers(self):
"""Add auto-refresh timers to the UI"""
# Status update timer (every 1 second)
status_timer = gr.Timer(value=1)
status_timer.tick(
fn=self.tabs["train_tab"].get_latest_status_message_logs_and_button_labels,
outputs=[
self.tabs["train_tab"].components["status_box"],
self.tabs["train_tab"].components["log_box"],
self.tabs["train_tab"].components["start_btn"],
self.tabs["train_tab"].components["stop_btn"],
self.tabs["train_tab"].components["pause_resume_btn"]
]
)
# 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
button_states = self.get_initial_button_states()
start_btn = button_states[0]
stop_btn = button_states[1]
pause_resume_btn = button_states[2]
# Get UI form values
ui_state = self.load_ui_values()
training_preset = ui_state.get("training_preset", list(TRAINING_PRESETS.keys())[0])
model_type_val = ui_state.get("model_type", list(MODEL_TYPES.keys())[0])
lora_rank_val = ui_state.get("lora_rank", "128")
lora_alpha_val = ui_state.get("lora_alpha", "128")
num_epochs_val = int(ui_state.get("num_epochs", 70))
batch_size_val = int(ui_state.get("batch_size", 1))
learning_rate_val = float(ui_state.get("learning_rate", 3e-5))
save_iterations_val = int(ui_state.get("save_iterations", 500))
# Return all values in the exact order expected by outputs
return (
video_list,
training_dataset,
start_btn,
stop_btn,
pause_resume_btn,
training_preset,
model_type_val,
lora_rank_val,
lora_alpha_val,
num_epochs_val,
batch_size_val,
learning_rate_val,
save_iterations_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("lora_rank", "128"),
ui_state.get("lora_alpha", "128"),
ui_state.get("num_epochs", 70),
ui_state.get("batch_size", 1),
ui_state.get("learning_rate", 3e-5),
ui_state.get("save_iterations", 500)
)
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", "128")
ui_state["lora_alpha"] = ui_state.get("lora_alpha", "128")
ui_state["num_epochs"] = int(ui_state.get("num_epochs", 70))
ui_state["batch_size"] = int(ui_state.get("batch_size", 1))
ui_state["learning_rate"] = float(ui_state.get("learning_rate", 3e-5))
ui_state["save_iterations"] = int(ui_state.get("save_iterations", 500))
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.trainer.recover_interrupted_training()
ui_updates = recovery_result.get("ui_updates", {})
# Return button states in the correct order
return (
gr.Button(**ui_updates.get("start_btn", {"interactive": True, "variant": "primary"})),
gr.Button(**ui_updates.get("stop_btn", {"interactive": False, "variant": "secondary"})),
gr.Button(**ui_updates.get("pause_resume_btn", {"interactive": False, "variant": "secondary"}))
)
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
) |