mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-24 06:32:12 -03:00
Enhance Checkpoints Manager: Implement API integration for checkpoints, add filtering and sorting options, and improve UI components for better user experience
This commit is contained in:
Will Miao
@@ -10,6 +10,7 @@ from datetime import datetime
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from ..services.checkpoint_scanner import CheckpointScanner
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from ..config import config
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from ..services.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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@@ -25,9 +26,12 @@ class CheckpointsRoutes:
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def setup_routes(self, app):
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"""Register routes with the aiohttp app"""
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app.router.add_get('/lora_manager/api/checkpoints', self.get_checkpoints)
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app.router.add_get('/lora_manager/api/checkpoints/scan', self.scan_checkpoints)
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app.router.add_get('/lora_manager/api/checkpoints/info/{name}', self.get_checkpoint_info)
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app.router.add_get('/checkpoints', self.handle_checkpoints_page)
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app.router.add_get('/api/checkpoints', self.get_checkpoints)
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app.router.add_get('/api/checkpoints/base-models', self.get_base_models)
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app.router.add_get('/api/checkpoints/top-tags', self.get_top_tags)
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app.router.add_get('/api/checkpoints/scan', self.scan_checkpoints)
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app.router.add_get('/api/checkpoints/info/{name}', self.get_checkpoint_info)
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async def get_checkpoints(self, request):
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"""Get paginated checkpoint data"""
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@@ -76,8 +80,17 @@ class CheckpointsRoutes:
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hash_filters=hash_filters
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)
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# Format response items
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formatted_result = {
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'items': [self._format_checkpoint_response(cp) for cp in result['items']],
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'total': result['total'],
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'page': result['page'],
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'page_size': result['page_size'],
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'total_pages': result['total_pages']
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}
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# Return as JSON
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return web.json_response(result)
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return web.json_response(formatted_result)
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except Exception as e:
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logger.error(f"Error in get_checkpoints: {e}", exc_info=True)
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@@ -90,28 +103,122 @@ class CheckpointsRoutes:
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"""Get paginated and filtered checkpoint data"""
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cache = await self.scanner.get_cached_data()
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# Implement similar filtering logic as in LoraScanner
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# (Adapt code from LoraScanner.get_paginated_data)
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# ...
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# For now, a simplified implementation:
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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 basic folder filtering if needed
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# Apply hash filtering if provided (highest priority)
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if hash_filters:
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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() # Ensure lowercase for matching
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filtered_data = [
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cp for cp in filtered_data
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if cp.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) # Convert to set for faster lookup
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filtered_data = [
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cp for cp in filtered_data
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if cp.get('sha256', '').lower() in hash_set
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]
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# Jump to pagination
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total_items = len(filtered_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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result = {
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'items': filtered_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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return result
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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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filtered_data = [
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cp for cp in filtered_data
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if not cp.get('preview_nsfw_level') or cp.get('preview_nsfw_level') < NSFW_LEVELS['R']
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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.get('recursive', False):
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# Recursive folder filtering - include all subfolders
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filtered_data = [
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cp for cp in filtered_data
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if cp['folder'].startswith(folder)
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]
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else:
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# Exact folder filtering
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filtered_data = [
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cp for cp in filtered_data
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if cp['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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filtered_data = [
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cp for cp in filtered_data
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if cp['folder'] == folder
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if cp.get('base_model') in base_models
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]
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# Apply basic search if needed
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# Apply tag filtering
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if tags and len(tags) > 0:
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filtered_data = [
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cp for cp in filtered_data
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if any(tag in cp.get('tags', []) for tag in tags)
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]
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# Apply search filtering
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if search:
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filtered_data = [
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cp for cp in filtered_data
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if search.lower() in cp['file_name'].lower() or
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search.lower() in cp['model_name'].lower()
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]
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search_results = []
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for cp in filtered_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(cp.get('file_name', ''), search):
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search_results.append(cp)
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continue
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elif search.lower() in cp.get('file_name', '').lower():
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search_results.append(cp)
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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(cp.get('model_name', ''), search):
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search_results.append(cp)
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continue
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elif search.lower() in cp.get('model_name', '').lower():
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search_results.append(cp)
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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 cp:
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if any((fuzzy_match(tag, search) if fuzzy_search else search.lower() in tag.lower()) for tag in cp['tags']):
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search_results.append(cp)
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continue
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filtered_data = search_results
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# Calculate pagination
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total_items = len(filtered_data)
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start_idx = (page - 1) * page_size
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@@ -127,6 +234,88 @@ class CheckpointsRoutes:
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return result
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def _format_checkpoint_response(self, checkpoint):
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"""Format checkpoint data for API response"""
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return {
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"model_name": checkpoint["model_name"],
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"file_name": checkpoint["file_name"],
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"preview_url": config.get_preview_static_url(checkpoint.get("preview_url", "")),
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"preview_nsfw_level": checkpoint.get("preview_nsfw_level", 0),
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"base_model": checkpoint.get("base_model", ""),
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"folder": checkpoint["folder"],
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"sha256": checkpoint.get("sha256", ""),
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"file_path": checkpoint["file_path"].replace(os.sep, "/"),
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"file_size": checkpoint.get("size", 0),
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"modified": checkpoint.get("modified", ""),
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"tags": checkpoint.get("tags", []),
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"modelDescription": checkpoint.get("modelDescription", ""),
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"from_civitai": checkpoint.get("from_civitai", True),
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"notes": checkpoint.get("notes", ""),
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"model_type": checkpoint.get("model_type", "checkpoint"),
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"civitai": self._filter_civitai_data(checkpoint.get("civitai", {}))
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}
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def _filter_civitai_data(self, data):
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"""Filter relevant fields from CivitAI data"""
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if not data:
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return {}
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fields = [
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"id", "modelId", "name", "createdAt", "updatedAt",
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"publishedAt", "trainedWords", "baseModel", "description",
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"model", "images"
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]
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return {k: data[k] for k in fields if k in data}
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async def get_top_tags(self, request: web.Request) -> web.Response:
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"""Handle request for top tags sorted by frequency"""
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try:
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# Parse query parameters
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limit = int(request.query.get('limit', '20'))
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# Validate limit
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if limit < 1 or limit > 100:
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limit = 20 # Default to a reasonable limit
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# Get top tags
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top_tags = await self.scanner.get_top_tags(limit)
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return web.json_response({
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'success': True,
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'tags': top_tags
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})
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except Exception as e:
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logger.error(f"Error getting top tags: {str(e)}", exc_info=True)
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return web.json_response({
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'success': False,
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'error': 'Internal server error'
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}, status=500)
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async def get_base_models(self, request: web.Request) -> web.Response:
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"""Get base models used in loras"""
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try:
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# Parse query parameters
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limit = int(request.query.get('limit', '20'))
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# Validate limit
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if limit < 1 or limit > 100:
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limit = 20 # Default to a reasonable limit
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# Get base models
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base_models = await self.scanner.get_base_models(limit)
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return web.json_response({
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'success': True,
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'base_models': base_models
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})
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except Exception as e:
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logger.error(f"Error retrieving base models: {e}")
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return web.json_response({
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'success': False,
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'error': str(e)
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}, status=500)
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async def scan_checkpoints(self, request):
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"""Force a rescan of checkpoint files"""
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try:
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