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"""
Main Import Service for Video Model Studio.
Delegates to specialized handler classes for different import types.
"""
import logging
from typing import List, Dict, Optional, Tuple, Any, Union, Callable
from pathlib import Path
import gradio as gr
from huggingface_hub import HfApi
from vms.config import HF_API_TOKEN
from vms.ui.project.services.importing.file_upload import FileUploadHandler
from vms.ui.project.services.importing.youtube import YouTubeDownloader
from vms.ui.project.services.importing.hub_dataset import HubDatasetBrowser
logger = logging.getLogger(__name__)
class ImportingService:
"""Main service class for handling imports from various sources"""
def __init__(self):
"""Initialize the import service and handlers"""
self.hf_api = HfApi(token=HF_API_TOKEN)
self.file_handler = FileUploadHandler()
self.youtube_handler = YouTubeDownloader()
self.hub_browser = HubDatasetBrowser(self.hf_api)
def process_uploaded_files(self, file_paths: List[str], enable_splitting: bool) -> str:
"""Process uploaded file (ZIP, TAR, MP4, or image)
Args:
file_paths: File paths to the uploaded files from Gradio
enable_splitting: Whether to enable automatic video splitting
Returns:
Status message string
"""
print(f"process_uploaded_files(..., enable_splitting = { enable_splitting})")
if not file_paths or len(file_paths) == 0:
logger.warning("No files provided to process_uploaded_files")
return "No files provided"
print(f"process_uploaded_files(..., enable_splitting = {enable_splitting:})")
print(f"process_uploaded_files: calling self.file_handler.process_uploaded_files")
return self.file_handler.process_uploaded_files(file_paths, enable_splitting)
def download_youtube_video(self, url: str, enable_splitting: bool, progress=None) -> str:
"""Download a video from YouTube
Args:
url: YouTube video URL
enable_splitting: Whether to enable automatic video splitting
progress: Optional Gradio progress indicator
Returns:
Status message string
"""
return self.youtube_handler.download_video(url, enable_splitting, progress)
def search_datasets(self, query: str) -> List[List[str]]:
"""Search for datasets on the Hugging Face Hub
Args:
query: Search query string
Returns:
List of datasets matching the query [id, title, downloads]
"""
return self.hub_browser.search_datasets(query)
def get_dataset_info(self, dataset_id: str) -> Tuple[str, Dict[str, int], Dict[str, List[str]]]:
"""Get detailed information about a dataset
Args:
dataset_id: The dataset ID to get information for
Returns:
Tuple of (markdown_info, file_counts, file_groups)
"""
return self.hub_browser.get_dataset_info(dataset_id)
async def download_dataset(
self,
dataset_id: str,
enable_splitting: bool,
progress_callback: Optional[Callable] = None
) -> Tuple[str, str]:
"""Download a dataset and process its video/image content
Args:
dataset_id: The dataset ID to download
enable_splitting: Whether to enable automatic video splitting
progress_callback: Optional callback for progress tracking
Returns:
Tuple of (loading_msg, status_msg)
"""
return await self.hub_browser.download_dataset(dataset_id, enable_splitting, progress_callback)
async def download_file_group(
self,
dataset_id: str,
file_type: str,
enable_splitting: bool,
progress_callback: Optional[Callable] = None
) -> str:
"""Download a group of files (videos or WebDatasets)
Args:
dataset_id: The dataset ID
file_type: Type of file ("video" or "webdataset")
enable_splitting: Whether to enable automatic video splitting
progress_callback: Optional callback for progress tracking
Returns:
Status message
"""
return await self.hub_browser.download_file_group(dataset_id, file_type, enable_splitting, progress_callback) |