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refactor: Implement base model routes and services for LoRA and Checkpoint
- Added BaseModelRoutes class to handle common routes and logic for model types. - Created CheckpointRoutes class inheriting from BaseModelRoutes for checkpoint-specific routes. - Implemented CheckpointService class for handling checkpoint-related data and operations. - Developed LoraService class for managing LoRA-specific functionalities. - Introduced ModelServiceFactory to manage service and route registrations for different model types. - Established methods for fetching, filtering, and formatting model data across services. - Integrated CivitAI metadata handling within model routes and services. - Added pagination and filtering capabilities for model data retrieval.
This commit is contained in:
Will Miao
248
py/services/base_model_service.py
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248
py/services/base_model_service.py
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from abc import ABC, abstractmethod
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from typing import Dict, List, Optional, Type, Set
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import logging
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from ..utils.models import BaseModelMetadata
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from ..utils.constants import NSFW_LEVELS
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from .settings_manager import settings
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from ..utils.utils import fuzzy_match
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logger = logging.getLogger(__name__)
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class BaseModelService(ABC):
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"""Base service class for all model types"""
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def __init__(self, model_type: str, scanner, metadata_class: Type[BaseModelMetadata]):
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"""Initialize the service
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Args:
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model_type: Type of model (lora, checkpoint, etc.)
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scanner: Model scanner instance
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metadata_class: Metadata class for this model type
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"""
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self.model_type = model_type
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self.scanner = scanner
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self.metadata_class = metadata_class
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async def get_paginated_data(self, page: int, page_size: int, sort_by: str = 'name',
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folder: str = None, search: str = None, fuzzy_search: bool = False,
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base_models: list = None, tags: list = None,
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search_options: dict = None, hash_filters: dict = None,
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favorites_only: bool = False, **kwargs) -> Dict:
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"""Get paginated and filtered model data
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Args:
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page: Page number (1-based)
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page_size: Number of items per page
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sort_by: Sort criteria ('name' or 'date')
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folder: Folder filter
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search: Search term
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fuzzy_search: Whether to use fuzzy search
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base_models: List of base models to filter by
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tags: List of tags to filter by
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search_options: Search options dict
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hash_filters: Hash filtering options
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favorites_only: Filter for favorites only
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**kwargs: Additional model-specific filters
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Returns:
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Dict containing paginated results
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"""
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cache = await self.scanner.get_cached_data()
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# Get default search options if not provided
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if search_options is None:
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search_options = {
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'filename': True,
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'modelname': True,
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'tags': False,
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'recursive': False,
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}
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# Get the base data set
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filtered_data = cache.sorted_by_date if sort_by == 'date' else cache.sorted_by_name
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# Apply hash filtering if provided (highest priority)
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if hash_filters:
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filtered_data = await self._apply_hash_filters(filtered_data, hash_filters)
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# Jump to pagination for hash filters
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return self._paginate(filtered_data, page, page_size)
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# Apply common filters
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filtered_data = await self._apply_common_filters(
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filtered_data, folder, base_models, tags, favorites_only, search_options
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)
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# Apply search filtering
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if search:
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filtered_data = await self._apply_search_filters(
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filtered_data, search, fuzzy_search, search_options
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)
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# Apply model-specific filters
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filtered_data = await self._apply_specific_filters(filtered_data, **kwargs)
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return self._paginate(filtered_data, page, page_size)
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async def _apply_hash_filters(self, data: List[Dict], hash_filters: Dict) -> List[Dict]:
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"""Apply hash-based filtering"""
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single_hash = hash_filters.get('single_hash')
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multiple_hashes = hash_filters.get('multiple_hashes')
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if single_hash:
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# Filter by single hash
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single_hash = single_hash.lower()
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return [
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item for item in data
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if item.get('sha256', '').lower() == single_hash
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]
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elif multiple_hashes:
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# Filter by multiple hashes
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hash_set = set(hash.lower() for hash in multiple_hashes)
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return [
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item for item in data
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if item.get('sha256', '').lower() in hash_set
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]
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return data
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async def _apply_common_filters(self, data: List[Dict], folder: str = None,
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base_models: list = None, tags: list = None,
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favorites_only: bool = False, search_options: dict = None) -> List[Dict]:
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"""Apply common filters that work across all model types"""
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# Apply SFW filtering if enabled in settings
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if settings.get('show_only_sfw', False):
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data = [
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item for item in data
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if not item.get('preview_nsfw_level') or item.get('preview_nsfw_level') < NSFW_LEVELS['R']
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]
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# Apply favorites filtering if enabled
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if favorites_only:
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data = [
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item for item in data
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if item.get('favorite', False) is True
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]
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# Apply folder filtering
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if folder is not None:
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if search_options and search_options.get('recursive', False):
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# Recursive folder filtering - include all subfolders
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data = [
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item for item in data
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if item['folder'].startswith(folder)
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]
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else:
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# Exact folder filtering
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data = [
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item for item in data
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if item['folder'] == folder
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]
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# Apply base model filtering
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if base_models and len(base_models) > 0:
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data = [
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item for item in data
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if item.get('base_model') in base_models
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]
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# Apply tag filtering
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if tags and len(tags) > 0:
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data = [
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item for item in data
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if any(tag in item.get('tags', []) for tag in tags)
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]
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return data
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async def _apply_search_filters(self, data: List[Dict], search: str,
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fuzzy_search: bool, search_options: dict) -> List[Dict]:
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"""Apply search filtering"""
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search_results = []
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for item in data:
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# Search by file name
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if search_options.get('filename', True):
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if fuzzy_search:
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if fuzzy_match(item.get('file_name', ''), search):
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search_results.append(item)
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continue
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elif search.lower() in item.get('file_name', '').lower():
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search_results.append(item)
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continue
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# Search by model name
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if search_options.get('modelname', True):
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if fuzzy_search:
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if fuzzy_match(item.get('model_name', ''), search):
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search_results.append(item)
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continue
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elif search.lower() in item.get('model_name', '').lower():
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search_results.append(item)
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continue
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# Search by tags
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if search_options.get('tags', False) and 'tags' in item:
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if any((fuzzy_match(tag, search) if fuzzy_search else search.lower() in tag.lower())
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for tag in item['tags']):
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search_results.append(item)
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continue
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return search_results
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async def _apply_specific_filters(self, data: List[Dict], **kwargs) -> List[Dict]:
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"""Apply model-specific filters - to be overridden by subclasses if needed"""
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return data
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def _paginate(self, data: List[Dict], page: int, page_size: int) -> Dict:
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"""Apply pagination to filtered data"""
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total_items = len(data)
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start_idx = (page - 1) * page_size
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end_idx = min(start_idx + page_size, total_items)
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return {
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'items': data[start_idx:end_idx],
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'total': total_items,
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'page': page,
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'page_size': page_size,
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'total_pages': (total_items + page_size - 1) // page_size
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}
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@abstractmethod
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async def format_response(self, model_data: Dict) -> Dict:
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"""Format model data for API response - must be implemented by subclasses"""
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pass
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# Common service methods that delegate to scanner
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async def get_top_tags(self, limit: int = 20) -> List[Dict]:
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"""Get top tags sorted by frequency"""
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return await self.scanner.get_top_tags(limit)
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async def get_base_models(self, limit: int = 20) -> List[Dict]:
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"""Get base models sorted by frequency"""
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return await self.scanner.get_base_models(limit)
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def has_hash(self, sha256: str) -> bool:
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"""Check if a model with given hash exists"""
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return self.scanner.has_hash(sha256)
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def get_path_by_hash(self, sha256: str) -> Optional[str]:
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"""Get file path for a model by its hash"""
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return self.scanner.get_path_by_hash(sha256)
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def get_hash_by_path(self, file_path: str) -> Optional[str]:
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"""Get hash for a model by its file path"""
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return self.scanner.get_hash_by_path(file_path)
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async def scan_models(self, force_refresh: bool = False, rebuild_cache: bool = False):
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"""Trigger model scanning"""
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return await self.scanner.get_cached_data(force_refresh=force_refresh, rebuild_cache=rebuild_cache)
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async def get_model_info_by_name(self, name: str):
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"""Get model information by name"""
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return await self.scanner.get_model_info_by_name(name)
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def get_model_roots(self) -> List[str]:
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"""Get model root directories"""
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return self.scanner.get_model_roots()
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