Implement paginated LoRA data fetching with caching and infinite scroll

routes/api_routes.py

@@ -6,12 +6,16 @@ from typing import Dict, List
 from ..services.civitai_client import CivitaiClient
 from ..utils.file_utils import update_civitai_metadata, load_metadata
 from ..config import config
+from ..services.lora_scanner import LoraScanner
 
 logger = logging.getLogger(__name__)
 
 class ApiRoutes:
     """API route handlers for LoRA management"""
 
+    def __init__(self):
+        self.scanner = LoraScanner()
+
     @classmethod
     def setup_routes(cls, app: web.Application):
         """Register API routes"""
@@ -19,6 +23,7 @@ class ApiRoutes:
         app.router.add_post('/api/delete_model', routes.delete_model)
         app.router.add_post('/api/fetch-civitai', routes.fetch_civitai)
         app.router.add_post('/api/replace_preview', routes.replace_preview)
+        app.router.add_get('/api/loras', routes.get_loras)
 
     async def delete_model(self, request: web.Request) -> web.Response:
         """Handle model deletion request"""
@@ -88,6 +93,86 @@ class ApiRoutes:
             logger.error(f"Error replacing preview: {e}", exc_info=True)
             return web.Response(text=str(e), status=500)
 
+    async def get_loras(self, request: web.Request) -> web.Response:
+        """Handle paginated LoRA data request"""
+        try:
+            # Parse query parameters
+            page = int(request.query.get('page', '1'))
+            page_size = int(request.query.get('page_size', '20'))
+            sort_by = request.query.get('sort_by', 'name')
+            folder = request.query.get('folder')
+            
+            # Validate parameters
+            if page < 1 or page_size < 1 or page_size > 100:
+                return web.json_response({
+                    'error': 'Invalid pagination parameters'
+                }, status=400)
+            
+            if sort_by not in ['date', 'name']:
+                return web.json_response({
+                    'error': 'Invalid sort parameter'
+                }, status=400)
+            
+            # Get paginated data
+            result = await self.scanner.get_paginated_data(
+                page=page,
+                page_size=page_size,
+                sort_by=sort_by,
+                folder=folder
+            )
+            
+            # Format the response data
+            formatted_items = [
+                self._format_lora_response(item) 
+                for item in result['items']
+            ]
+            
+            return web.json_response({
+                'items': formatted_items,
+                'total': result['total'],
+                'page': result['page'],
+                'page_size': result['page_size'],
+                'total_pages': result['total_pages']
+            })
+            
+        except ValueError as e:
+            return web.json_response({
+                'error': 'Invalid parameters'
+            }, status=400)
+            
+        except Exception as e:
+            logger.error(f"Error fetching loras: {e}", exc_info=True)
+            return web.json_response({
+                'error': 'Internal server error'
+            }, status=500)
+
+    def _format_lora_response(self, lora: Dict) -> Dict:
+        """Format LoRA data for API response"""
+        return {
+            "model_name": lora["model_name"],
+            "file_name": lora["file_name"],
+            "preview_url": config.get_preview_static_url(lora["preview_url"]),
+            "base_model": lora["base_model"],
+            "folder": lora["folder"],
+            "sha256": lora["sha256"],
+            "file_path": lora["file_path"].replace(os.sep, "/"),
+            "modified": lora["modified"],
+            "from_civitai": lora.get("from_civitai", True),
+            "civitai": self._filter_civitai_data(lora.get("civitai", {}))
+        }
+
+    def _filter_civitai_data(self, data: Dict) -> Dict:
+        """Filter relevant fields from CivitAI data"""
+        if not data:
+            return {}
+            
+        fields = [
+            "id", "modelId", "name", "createdAt", "updatedAt", 
+            "publishedAt", "trainedWords", "baseModel", "description",
+            "model", "images"
+        ]
+        return {k: data[k] for k in fields if k in data}
+
     # Private helper methods
     async def _delete_model_files(self, target_dir: str, file_name: str) -> List[str]:
         """Delete model and associated files"""