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153
.docs/batch-import-design.md
Normal file
153
.docs/batch-import-design.md
Normal file
@@ -0,0 +1,153 @@
|
||||
# Recipe Batch Import Feature Design
|
||||
|
||||
## Overview
|
||||
Enable users to import multiple images as recipes in a single operation, rather than processing them individually. This feature addresses the need for efficient bulk recipe creation from existing image collections.
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ Frontend │
|
||||
├─────────────────────────────────────────────────────────────────┤
|
||||
│ BatchImportManager.js │
|
||||
│ ├── InputCollector (收集URL列表/目录路径) │
|
||||
│ ├── ConcurrencyController (自适应并发控制) │
|
||||
│ ├── ProgressTracker (进度追踪) │
|
||||
│ └── ResultAggregator (结果汇总) │
|
||||
├─────────────────────────────────────────────────────────────────┤
|
||||
│ batch_import_modal.html │
|
||||
│ └── 批量导入UI组件 │
|
||||
├─────────────────────────────────────────────────────────────────┤
|
||||
│ batch_import_progress.css │
|
||||
│ └── 进度显示样式 │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ Backend │
|
||||
├─────────────────────────────────────────────────────────────────┤
|
||||
│ py/routes/handlers/recipe_handlers.py │
|
||||
│ ├── start_batch_import() - 启动批量导入 │
|
||||
│ ├── get_batch_import_progress() - 查询进度 │
|
||||
│ └── cancel_batch_import() - 取消导入 │
|
||||
├─────────────────────────────────────────────────────────────────┤
|
||||
│ py/services/batch_import_service.py │
|
||||
│ ├── 自适应并发执行 │
|
||||
│ ├── 结果汇总 │
|
||||
│ └── WebSocket进度广播 │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## API Endpoints
|
||||
|
||||
| 端点 | 方法 | 说明 |
|
||||
|------|------|------|
|
||||
| `/api/lm/recipes/batch-import/start` | POST | 启动批量导入，返回 operation_id |
|
||||
| `/api/lm/recipes/batch-import/progress` | GET | 查询进度状态 |
|
||||
| `/api/lm/recipes/batch-import/cancel` | POST | 取消导入 |
|
||||
|
||||
## Backend Implementation Details
|
||||
|
||||
### BatchImportService
|
||||
|
||||
Location: `py/services/batch_import_service.py`
|
||||
|
||||
Key classes:
|
||||
- `BatchImportItem`: Dataclass for individual import item
|
||||
- `BatchImportProgress`: Dataclass for tracking progress
|
||||
- `BatchImportService`: Main service class
|
||||
|
||||
Features:
|
||||
- Adaptive concurrency control (adjusts based on success/failure rate)
|
||||
- WebSocket progress broadcasting
|
||||
- Graceful error handling (individual failures don't stop the batch)
|
||||
- Result aggregation
|
||||
|
||||
### WebSocket Message Format
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "batch_import_progress",
|
||||
"operation_id": "xxx",
|
||||
"total": 50,
|
||||
"completed": 23,
|
||||
"success": 21,
|
||||
"failed": 2,
|
||||
"skipped": 0,
|
||||
"current_item": "image_024.png",
|
||||
"status": "running"
|
||||
}
|
||||
```
|
||||
|
||||
### Input Types
|
||||
|
||||
1. **URL List**: Array of URLs (http/https)
|
||||
2. **Local Paths**: Array of local file paths
|
||||
3. **Directory**: Path to directory with optional recursive flag
|
||||
|
||||
### Error Handling
|
||||
|
||||
- Invalid URLs/paths: Skip and record error
|
||||
- Download failures: Record error, continue
|
||||
- Metadata extraction failures: Mark as "no metadata"
|
||||
- Duplicate detection: Option to skip duplicates
|
||||
|
||||
## Frontend Implementation Details (TODO)
|
||||
|
||||
### UI Components
|
||||
|
||||
1. **BatchImportModal**: Main modal with tabs for URLs/Directory input
|
||||
2. **ProgressDisplay**: Real-time progress bar and status
|
||||
3. **ResultsSummary**: Final results with success/failure breakdown
|
||||
|
||||
### Adaptive Concurrency Controller
|
||||
|
||||
```javascript
|
||||
class AdaptiveConcurrencyController {
|
||||
constructor(options = {}) {
|
||||
this.minConcurrency = options.minConcurrency || 1;
|
||||
this.maxConcurrency = options.maxConcurrency || 5;
|
||||
this.currentConcurrency = options.initialConcurrency || 3;
|
||||
}
|
||||
|
||||
adjustConcurrency(taskDuration, success) {
|
||||
if (success && taskDuration < 1000 && this.currentConcurrency < this.maxConcurrency) {
|
||||
this.currentConcurrency = Math.min(this.currentConcurrency + 1, this.maxConcurrency);
|
||||
}
|
||||
if (!success || taskDuration > 10000) {
|
||||
this.currentConcurrency = Math.max(this.currentConcurrency - 1, this.minConcurrency);
|
||||
}
|
||||
return this.currentConcurrency;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## File Structure
|
||||
|
||||
```
|
||||
Backend (implemented):
|
||||
├── py/services/batch_import_service.py # 后端服务
|
||||
├── py/routes/handlers/batch_import_handler.py # API处理器 (added to recipe_handlers.py)
|
||||
├── tests/services/test_batch_import_service.py # 单元测试
|
||||
└── tests/routes/test_batch_import_routes.py # API集成测试
|
||||
|
||||
Frontend (TODO):
|
||||
├── static/js/managers/BatchImportManager.js # 主管理器
|
||||
├── static/js/managers/batch/ # 子模块
|
||||
│ ├── ConcurrencyController.js # 并发控制
|
||||
│ ├── ProgressTracker.js # 进度追踪
|
||||
│ └── ResultAggregator.js # 结果汇总
|
||||
├── static/css/components/batch-import-modal.css # 样式
|
||||
└── templates/components/batch_import_modal.html # Modal模板
|
||||
```
|
||||
|
||||
## Implementation Status
|
||||
|
||||
- [x] Backend BatchImportService
|
||||
- [x] Backend API handlers
|
||||
- [x] WebSocket progress broadcasting
|
||||
- [x] Unit tests
|
||||
- [x] Integration tests
|
||||
- [ ] Frontend BatchImportManager
|
||||
- [ ] Frontend UI components
|
||||
- [ ] E2E tests
|
||||
464
.specs/metadata.schema.json
Normal file
464
.specs/metadata.schema.json
Normal file
@@ -0,0 +1,464 @@
|
||||
{
|
||||
"$schema": "http://json-schema.org/draft-07/schema#",
|
||||
"$id": "https://github.com/willmiao/ComfyUI-Lora-Manager/.specs/metadata.schema.json",
|
||||
"title": "ComfyUI LoRa Manager Model Metadata",
|
||||
"description": "Schema for .metadata.json sidecar files used by ComfyUI LoRa Manager",
|
||||
"type": "object",
|
||||
"oneOf": [
|
||||
{
|
||||
"title": "LoRA Model Metadata",
|
||||
"properties": {
|
||||
"file_name": {
|
||||
"type": "string",
|
||||
"description": "Filename without extension"
|
||||
},
|
||||
"model_name": {
|
||||
"type": "string",
|
||||
"description": "Display name of the model"
|
||||
},
|
||||
"file_path": {
|
||||
"type": "string",
|
||||
"description": "Full absolute path to the model file"
|
||||
},
|
||||
"size": {
|
||||
"type": "integer",
|
||||
"minimum": 0,
|
||||
"description": "File size in bytes at time of import/download"
|
||||
},
|
||||
"modified": {
|
||||
"type": "number",
|
||||
"description": "Unix timestamp when model was imported/added (Date Added)"
|
||||
},
|
||||
"sha256": {
|
||||
"type": "string",
|
||||
"pattern": "^[a-f0-9]{64}$",
|
||||
"description": "SHA256 hash of the model file (lowercase)"
|
||||
},
|
||||
"base_model": {
|
||||
"type": "string",
|
||||
"description": "Base model type (SD1.5, SD2.1, SDXL, SD3, Flux, Unknown, etc.)"
|
||||
},
|
||||
"preview_url": {
|
||||
"type": "string",
|
||||
"description": "Path to preview image file"
|
||||
},
|
||||
"preview_nsfw_level": {
|
||||
"type": "integer",
|
||||
"minimum": 0,
|
||||
"default": 0,
|
||||
"description": "NSFW level using bitmask values: 0 (none), 1 (PG), 2 (PG13), 4 (R), 8 (X), 16 (XXX), 32 (Blocked)"
|
||||
},
|
||||
"notes": {
|
||||
"type": "string",
|
||||
"default": "",
|
||||
"description": "User-defined notes"
|
||||
},
|
||||
"from_civitai": {
|
||||
"type": "boolean",
|
||||
"default": true,
|
||||
"description": "Whether the model originated from Civitai"
|
||||
},
|
||||
"civitai": {
|
||||
"$ref": "#/definitions/civitaiObject"
|
||||
},
|
||||
"tags": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"default": [],
|
||||
"description": "Model tags"
|
||||
},
|
||||
"modelDescription": {
|
||||
"type": "string",
|
||||
"default": "",
|
||||
"description": "Full model description"
|
||||
},
|
||||
"civitai_deleted": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "Whether the model was deleted from Civitai"
|
||||
},
|
||||
"favorite": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "Whether the model is marked as favorite"
|
||||
},
|
||||
"exclude": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "Whether to exclude from cache/scanning"
|
||||
},
|
||||
"db_checked": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "Whether checked against archive database"
|
||||
},
|
||||
"skip_metadata_refresh": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "Skip this model during bulk metadata refresh"
|
||||
},
|
||||
"metadata_source": {
|
||||
"type": ["string", "null"],
|
||||
"enum": ["civitai_api", "civarchive", "archive_db", null],
|
||||
"default": null,
|
||||
"description": "Last provider that supplied metadata"
|
||||
},
|
||||
"last_checked_at": {
|
||||
"type": "number",
|
||||
"default": 0,
|
||||
"description": "Unix timestamp of last metadata check"
|
||||
},
|
||||
"hash_status": {
|
||||
"type": "string",
|
||||
"enum": ["pending", "calculating", "completed", "failed"],
|
||||
"default": "completed",
|
||||
"description": "Hash calculation status"
|
||||
},
|
||||
"usage_tips": {
|
||||
"type": "string",
|
||||
"default": "{}",
|
||||
"description": "JSON string containing recommended usage parameters (LoRA only)"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"file_name",
|
||||
"model_name",
|
||||
"file_path",
|
||||
"size",
|
||||
"modified",
|
||||
"sha256",
|
||||
"base_model"
|
||||
],
|
||||
"additionalProperties": true
|
||||
},
|
||||
{
|
||||
"title": "Checkpoint Model Metadata",
|
||||
"properties": {
|
||||
"file_name": {
|
||||
"type": "string"
|
||||
},
|
||||
"model_name": {
|
||||
"type": "string"
|
||||
},
|
||||
"file_path": {
|
||||
"type": "string"
|
||||
},
|
||||
"size": {
|
||||
"type": "integer",
|
||||
"minimum": 0
|
||||
},
|
||||
"modified": {
|
||||
"type": "number"
|
||||
},
|
||||
"sha256": {
|
||||
"type": "string",
|
||||
"pattern": "^[a-f0-9]{64}$"
|
||||
},
|
||||
"base_model": {
|
||||
"type": "string"
|
||||
},
|
||||
"preview_url": {
|
||||
"type": "string"
|
||||
},
|
||||
"preview_nsfw_level": {
|
||||
"type": "integer",
|
||||
"minimum": 0,
|
||||
"maximum": 3,
|
||||
"default": 0
|
||||
},
|
||||
"notes": {
|
||||
"type": "string",
|
||||
"default": ""
|
||||
},
|
||||
"from_civitai": {
|
||||
"type": "boolean",
|
||||
"default": true
|
||||
},
|
||||
"civitai": {
|
||||
"$ref": "#/definitions/civitaiObject"
|
||||
},
|
||||
"tags": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"default": []
|
||||
},
|
||||
"modelDescription": {
|
||||
"type": "string",
|
||||
"default": ""
|
||||
},
|
||||
"civitai_deleted": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"favorite": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"exclude": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"db_checked": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"skip_metadata_refresh": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"metadata_source": {
|
||||
"type": ["string", "null"],
|
||||
"enum": ["civitai_api", "civarchive", "archive_db", null],
|
||||
"default": null
|
||||
},
|
||||
"last_checked_at": {
|
||||
"type": "number",
|
||||
"default": 0
|
||||
},
|
||||
"hash_status": {
|
||||
"type": "string",
|
||||
"enum": ["pending", "calculating", "completed", "failed"],
|
||||
"default": "completed"
|
||||
},
|
||||
"sub_type": {
|
||||
"type": "string",
|
||||
"default": "checkpoint",
|
||||
"description": "Model sub-type (checkpoint, diffusion_model, etc.)"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"file_name",
|
||||
"model_name",
|
||||
"file_path",
|
||||
"size",
|
||||
"modified",
|
||||
"sha256",
|
||||
"base_model"
|
||||
],
|
||||
"additionalProperties": true
|
||||
},
|
||||
{
|
||||
"title": "Embedding Model Metadata",
|
||||
"properties": {
|
||||
"file_name": {
|
||||
"type": "string"
|
||||
},
|
||||
"model_name": {
|
||||
"type": "string"
|
||||
},
|
||||
"file_path": {
|
||||
"type": "string"
|
||||
},
|
||||
"size": {
|
||||
"type": "integer",
|
||||
"minimum": 0
|
||||
},
|
||||
"modified": {
|
||||
"type": "number"
|
||||
},
|
||||
"sha256": {
|
||||
"type": "string",
|
||||
"pattern": "^[a-f0-9]{64}$"
|
||||
},
|
||||
"base_model": {
|
||||
"type": "string"
|
||||
},
|
||||
"preview_url": {
|
||||
"type": "string"
|
||||
},
|
||||
"preview_nsfw_level": {
|
||||
"type": "integer",
|
||||
"minimum": 0,
|
||||
"maximum": 3,
|
||||
"default": 0
|
||||
},
|
||||
"notes": {
|
||||
"type": "string",
|
||||
"default": ""
|
||||
},
|
||||
"from_civitai": {
|
||||
"type": "boolean",
|
||||
"default": true
|
||||
},
|
||||
"civitai": {
|
||||
"$ref": "#/definitions/civitaiObject"
|
||||
},
|
||||
"tags": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"default": []
|
||||
},
|
||||
"modelDescription": {
|
||||
"type": "string",
|
||||
"default": ""
|
||||
},
|
||||
"civitai_deleted": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"favorite": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"exclude": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"db_checked": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"skip_metadata_refresh": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
},
|
||||
"metadata_source": {
|
||||
"type": ["string", "null"],
|
||||
"enum": ["civitai_api", "civarchive", "archive_db", null],
|
||||
"default": null
|
||||
},
|
||||
"last_checked_at": {
|
||||
"type": "number",
|
||||
"default": 0
|
||||
},
|
||||
"hash_status": {
|
||||
"type": "string",
|
||||
"enum": ["pending", "calculating", "completed", "failed"],
|
||||
"default": "completed"
|
||||
},
|
||||
"sub_type": {
|
||||
"type": "string",
|
||||
"default": "embedding",
|
||||
"description": "Model sub-type"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"file_name",
|
||||
"model_name",
|
||||
"file_path",
|
||||
"size",
|
||||
"modified",
|
||||
"sha256",
|
||||
"base_model"
|
||||
],
|
||||
"additionalProperties": true
|
||||
}
|
||||
],
|
||||
"definitions": {
|
||||
"civitaiObject": {
|
||||
"type": "object",
|
||||
"default": {},
|
||||
"description": "Civitai/CivArchive API data and user-defined fields",
|
||||
"properties": {
|
||||
"id": {
|
||||
"type": "integer",
|
||||
"description": "Version ID from Civitai"
|
||||
},
|
||||
"modelId": {
|
||||
"type": "integer",
|
||||
"description": "Model ID from Civitai"
|
||||
},
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "Version name"
|
||||
},
|
||||
"description": {
|
||||
"type": "string",
|
||||
"description": "Version description"
|
||||
},
|
||||
"baseModel": {
|
||||
"type": "string",
|
||||
"description": "Base model type from Civitai"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"description": "Model type (checkpoint, embedding, etc.)"
|
||||
},
|
||||
"trainedWords": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"description": "Trigger words for the model (from API or user-defined)"
|
||||
},
|
||||
"customImages": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object"
|
||||
},
|
||||
"description": "Custom example images added by user"
|
||||
},
|
||||
"model": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string"
|
||||
},
|
||||
"description": {
|
||||
"type": "string"
|
||||
},
|
||||
"tags": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"files": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
"images": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
"creator": {
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
"additionalProperties": true
|
||||
},
|
||||
"usageTips": {
|
||||
"type": "object",
|
||||
"description": "Structure for usage_tips JSON string (LoRA models)",
|
||||
"properties": {
|
||||
"strength_min": {
|
||||
"type": "number",
|
||||
"description": "Minimum recommended model strength"
|
||||
},
|
||||
"strength_max": {
|
||||
"type": "number",
|
||||
"description": "Maximum recommended model strength"
|
||||
},
|
||||
"strength_range": {
|
||||
"type": "string",
|
||||
"description": "Human-readable strength range"
|
||||
},
|
||||
"strength": {
|
||||
"type": "number",
|
||||
"description": "Single recommended strength value"
|
||||
},
|
||||
"clip_strength": {
|
||||
"type": "number",
|
||||
"description": "Recommended CLIP/embedding strength"
|
||||
},
|
||||
"clip_skip": {
|
||||
"type": "integer",
|
||||
"description": "Recommended CLIP skip value"
|
||||
}
|
||||
},
|
||||
"additionalProperties": true
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -321,6 +321,12 @@ npm run test:coverage
|
||||
|
||||
---
|
||||
|
||||
## Documentation
|
||||
|
||||
- **[metadata.json Schema Documentation](docs/metadata-json-schema.md)** — Complete reference for the `.metadata.json` sidecar file format, including all fields, types, and examples for LoRA, Checkpoint, and Embedding models.
|
||||
|
||||
---
|
||||
|
||||
## Contributing
|
||||
|
||||
Thank you for your interest in contributing to ComfyUI LoRA Manager! As this project is currently in its early stages and undergoing rapid development and refactoring, we are temporarily not accepting pull requests.
|
||||
|
||||
363
docs/metadata-json-schema.md
Normal file
363
docs/metadata-json-schema.md
Normal file
@@ -0,0 +1,363 @@
|
||||
# metadata.json Schema Documentation
|
||||
|
||||
This document defines the complete schema for `.metadata.json` files used by Lora Manager. These sidecar files store model metadata alongside model files (LoRA, Checkpoint, Embedding).
|
||||
|
||||
## Overview
|
||||
|
||||
- **File naming**: `<model_name>.metadata.json` (e.g., `my_lora.safetensors` → `my_lora.metadata.json`)
|
||||
- **Format**: JSON with UTF-8 encoding
|
||||
- **Purpose**: Store model metadata, tags, descriptions, preview images, and Civitai/CivArchive integration data
|
||||
- **Extensibility**: Unknown fields are preserved via `_unknown_fields` mechanism for forward compatibility
|
||||
|
||||
---
|
||||
|
||||
## Base Fields (All Model Types)
|
||||
|
||||
These fields are present in all model metadata files.
|
||||
|
||||
| Field | Type | Required | Auto-Updated | Description |
|
||||
|-------|------|----------|--------------|-------------|
|
||||
| `file_name` | string | ✅ Yes | ✅ Yes | Filename without extension (e.g., `"my_lora"`) |
|
||||
| `model_name` | string | ✅ Yes | ❌ No | Display name of the model. **Default**: `file_name` if no other source |
|
||||
| `file_path` | string | ✅ Yes | ✅ Yes | Full absolute path to the model file (normalized with `/` separators) |
|
||||
| `size` | integer | ✅ Yes | ❌ No | File size in bytes. **Set at**: Initial scan or download completion. Does not change thereafter. |
|
||||
| `modified` | float | ✅ Yes | ❌ No | **Import timestamp** — Unix timestamp when the model was first imported/added to the system. Used for "Date Added" sorting. Does not change after initial creation. |
|
||||
| `sha256` | string | ⚠️ Conditional | ✅ Yes | SHA256 hash of the model file (lowercase). **LoRA**: Required. **Checkpoint**: May be empty when `hash_status="pending"` (lazy hash calculation) |
|
||||
| `base_model` | string | ❌ No | ❌ No | Base model type. **Examples**: `"SD 1.5"`, `"SDXL 1.0"`, `"SDXL Lightning"`, `"Flux.1 D"`, `"Flux.1 S"`, `"Flux.1 Krea"`, `"Illustrious"`, `"Pony"`, `"AuraFlow"`, `"Kolors"`, `"ZImageTurbo"`, `"Wan Video"`, etc. **Default**: `"Unknown"` or `""` |
|
||||
| `preview_url` | string | ❌ No | ✅ Yes | Path to preview image file |
|
||||
| `preview_nsfw_level` | integer | ❌ No | ❌ No | NSFW level using **bitmask values** from Civitai: `1` (PG), `2` (PG13), `4` (R), `8` (X), `16` (XXX), `32` (Blocked). **Default**: `0` (none) |
|
||||
| `notes` | string | ❌ No | ❌ No | User-defined notes |
|
||||
| `from_civitai` | boolean | ❌ No (default: `true`) | ❌ No | Whether the model originated from Civitai |
|
||||
| `civitai` | object | ❌ No | ⚠️ Partial | Civitai/CivArchive API data and user-defined fields |
|
||||
| `tags` | array[string] | ❌ No | ⚠️ Partial | Model tags (merged from API and user input) |
|
||||
| `modelDescription` | string | ❌ No | ⚠️ Partial | Full model description (from API or user) |
|
||||
| `civitai_deleted` | boolean | ❌ No (default: `false`) | ❌ No | Whether the model was deleted from Civitai |
|
||||
| `favorite` | boolean | ❌ No (default: `false`) | ❌ No | Whether the model is marked as favorite |
|
||||
| `exclude` | boolean | ❌ No (default: `false`) | ❌ No | Whether to exclude from cache/scanning. User can set from `false` to `true` (currently no UI to revert) |
|
||||
| `db_checked` | boolean | ❌ No (default: `false`) | ❌ No | Whether checked against archive database |
|
||||
| `skip_metadata_refresh` | boolean | ❌ No (default: `false`) | ❌ No | Skip this model during bulk metadata refresh |
|
||||
| `metadata_source` | string\|null | ❌ No | ✅ Yes | Last provider that supplied metadata (see below) |
|
||||
| `last_checked_at` | float | ❌ No (default: `0`) | ✅ Yes | Unix timestamp of last metadata check |
|
||||
| `hash_status` | string | ❌ No (default: `"completed"`) | ✅ Yes | Hash calculation status: `"pending"`, `"calculating"`, `"completed"`, `"failed"` |
|
||||
|
||||
---
|
||||
|
||||
## Model-Specific Fields
|
||||
|
||||
### LoRA Models
|
||||
|
||||
LoRA models do not have a `model_type` field in metadata.json. The type is inferred from context or `civitai.type` (e.g., `"LoRA"`, `"LoCon"`, `"DoRA"`).
|
||||
|
||||
| Field | Type | Required | Auto-Updated | Description |
|
||||
|-------|------|----------|--------------|-------------|
|
||||
| `usage_tips` | string (JSON) | ❌ No (default: `"{}"`) | ❌ No | JSON string containing recommended usage parameters |
|
||||
|
||||
**`usage_tips` JSON structure:**
|
||||
|
||||
```json
|
||||
{
|
||||
"strength_min": 0.3,
|
||||
"strength_max": 0.8,
|
||||
"strength_range": "0.3-0.8",
|
||||
"strength": 0.6,
|
||||
"clip_strength": 0.5,
|
||||
"clip_skip": 2
|
||||
}
|
||||
```
|
||||
|
||||
| Key | Type | Description |
|
||||
|-----|------|-------------|
|
||||
| `strength_min` | number | Minimum recommended model strength |
|
||||
| `strength_max` | number | Maximum recommended model strength |
|
||||
| `strength_range` | string | Human-readable strength range |
|
||||
| `strength` | number | Single recommended strength value |
|
||||
| `clip_strength` | number | Recommended CLIP/embedding strength |
|
||||
| `clip_skip` | integer | Recommended CLIP skip value |
|
||||
|
||||
---
|
||||
|
||||
### Checkpoint Models
|
||||
|
||||
| Field | Type | Required | Auto-Updated | Description |
|
||||
|-------|------|----------|--------------|-------------|
|
||||
| `model_type` | string | ❌ No (default: `"checkpoint"`) | ❌ No | Model type: `"checkpoint"`, `"diffusion_model"` |
|
||||
|
||||
---
|
||||
|
||||
### Embedding Models
|
||||
|
||||
| Field | Type | Required | Auto-Updated | Description |
|
||||
|-------|------|----------|--------------|-------------|
|
||||
| `model_type` | string | ❌ No (default: `"embedding"`) | ❌ No | Model type: `"embedding"` |
|
||||
|
||||
---
|
||||
|
||||
## The `civitai` Field Structure
|
||||
|
||||
The `civitai` object stores the complete Civitai/CivArchive API response. Lora Manager preserves all fields from the API for future compatibility and extracts specific fields for use in the application.
|
||||
|
||||
### Version-Level Fields (Civitai API)
|
||||
|
||||
**Fields Used by Lora Manager:**
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `id` | integer | Version ID |
|
||||
| `modelId` | integer | Parent model ID |
|
||||
| `name` | string | Version name (e.g., `"v1.0"`, `"v2.0-pruned"`) |
|
||||
| `nsfwLevel` | integer | NSFW level (bitmask: 1=PG, 2=PG13, 4=R, 8=X, 16=XXX, 32=Blocked) |
|
||||
| `baseModel` | string | Base model (e.g., `"SDXL 1.0"`, `"Flux.1 D"`, `"Illustrious"`, `"Pony"`) |
|
||||
| `trainedWords` | array[string] | **Trigger words** for the model |
|
||||
| `type` | string | Model type (`"LoRA"`, `"Checkpoint"`, `"TextualInversion"`) |
|
||||
| `earlyAccessEndsAt` | string\|null | Early access end date (used for update notifications) |
|
||||
| `description` | string | Version description (HTML) |
|
||||
| `model` | object | Parent model object (see Model-Level Fields below) |
|
||||
| `creator` | object | Creator information (see Creator Fields below) |
|
||||
| `files` | array[object] | File list with hashes, sizes, download URLs (used for metadata extraction) |
|
||||
| `images` | array[object] | Image list with metadata, prompts, NSFW levels (used for preview/examples) |
|
||||
|
||||
**Fields Stored but Not Currently Used:**
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `createdAt` | string (ISO 8601) | Creation timestamp |
|
||||
| `updatedAt` | string (ISO 8601) | Last update timestamp |
|
||||
| `status` | string | Version status (e.g., `"Published"`, `"Draft"`) |
|
||||
| `publishedAt` | string (ISO 8601) | Publication timestamp |
|
||||
| `baseModelType` | string | Base model type (e.g., `"Standard"`, `"Inpaint"`, `"Refiner"`) |
|
||||
| `earlyAccessConfig` | object | Early access configuration |
|
||||
| `uploadType` | string | Upload type (`"Created"`, `"FineTuned"`, etc.) |
|
||||
| `usageControl` | string | Usage control setting |
|
||||
| `air` | string | Artifact ID (URN format: `urn:air:sdxl:lora:civitai:122359@135867`) |
|
||||
| `stats` | object | Download count, ratings, thumbs up count |
|
||||
| `videos` | array[object] | Video list |
|
||||
| `downloadUrl` | string | Direct download URL |
|
||||
| `trainingStatus` | string\|null | Training status (for on-site training) |
|
||||
| `trainingDetails` | object\|null | Training configuration |
|
||||
|
||||
### Model-Level Fields (`civitai.model.*`)
|
||||
|
||||
**Fields Used by Lora Manager:**
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `name` | string | Model name |
|
||||
| `type` | string | Model type (`"LoRA"`, `"Checkpoint"`, `"TextualInversion"`) |
|
||||
| `description` | string | Model description (HTML, used for `modelDescription`) |
|
||||
| `tags` | array[string] | Model tags (used for `tags` field) |
|
||||
| `allowNoCredit` | boolean | License: allow use without credit |
|
||||
| `allowCommercialUse` | array[string] | License: allowed commercial uses. **Values**: `"Image"` (sell generated images), `"Video"` (sell generated videos), `"RentCivit"` (rent on Civitai), `"Rent"` (rent elsewhere) |
|
||||
| `allowDerivatives` | boolean | License: allow derivatives |
|
||||
| `allowDifferentLicense` | boolean | License: allow different license |
|
||||
|
||||
**Fields Stored but Not Currently Used:**
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `nsfw` | boolean | Model NSFW flag |
|
||||
| `poi` | boolean | Person of Interest flag |
|
||||
|
||||
### Creator Fields (`civitai.creator.*`)
|
||||
|
||||
Both fields are used by Lora Manager:
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `username` | string | Creator username (used for author display and search) |
|
||||
| `image` | string | Creator avatar URL (used for display) |
|
||||
|
||||
### Model Type Field (Top-Level, Outside `civitai`)
|
||||
|
||||
| Field | Type | Values | Description |
|
||||
|-------|------|--------|-------------|
|
||||
| `model_type` | string | `"checkpoint"`, `"diffusion_model"`, `"embedding"` | Stored in metadata.json for Checkpoint and Embedding models. **Note**: LoRA models do not have this field; type is inferred from `civitai.type` or context. |
|
||||
|
||||
### User-Defined Fields (Within `civitai`)
|
||||
|
||||
For models not from Civitai or user-added data:
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `trainedWords` | array[string] | **Trigger words** — manually added by user |
|
||||
| `customImages` | array[object] | Custom example images added by user |
|
||||
|
||||
### customImages Structure
|
||||
|
||||
Each custom image entry has the following structure:
|
||||
|
||||
```json
|
||||
{
|
||||
"url": "",
|
||||
"id": "short_id",
|
||||
"nsfwLevel": 0,
|
||||
"width": 832,
|
||||
"height": 1216,
|
||||
"type": "image",
|
||||
"meta": {
|
||||
"prompt": "...",
|
||||
"negativePrompt": "...",
|
||||
"steps": 20,
|
||||
"cfgScale": 7,
|
||||
"seed": 123456
|
||||
},
|
||||
"hasMeta": true,
|
||||
"hasPositivePrompt": true
|
||||
}
|
||||
```
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `url` | string | Empty for local custom images |
|
||||
| `id` | string | Short ID or filename |
|
||||
| `nsfwLevel` | integer | NSFW level (bitmask) |
|
||||
| `width` | integer | Image width in pixels |
|
||||
| `height` | integer | Image height in pixels |
|
||||
| `type` | string | `"image"` or `"video"` |
|
||||
| `meta` | object\|null | Generation metadata (prompt, seed, etc.) extracted from image |
|
||||
| `hasMeta` | boolean | Whether metadata is available |
|
||||
| `hasPositivePrompt` | boolean | Whether a positive prompt is available |
|
||||
|
||||
### Minimal Non-Civitai Example
|
||||
|
||||
```json
|
||||
{
|
||||
"civitai": {
|
||||
"trainedWords": ["my_trigger_word"]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Non-Civitai Example Without Trigger Words
|
||||
|
||||
```json
|
||||
{
|
||||
"civitai": {}
|
||||
}
|
||||
```
|
||||
|
||||
### Example: User-Added Custom Images
|
||||
|
||||
```json
|
||||
{
|
||||
"civitai": {
|
||||
"trainedWords": ["custom_style"],
|
||||
"customImages": [
|
||||
{
|
||||
"url": "",
|
||||
"id": "example_1",
|
||||
"nsfwLevel": 0,
|
||||
"width": 832,
|
||||
"height": 1216,
|
||||
"type": "image",
|
||||
"meta": {
|
||||
"prompt": "example prompt",
|
||||
"seed": 12345
|
||||
},
|
||||
"hasMeta": true,
|
||||
"hasPositivePrompt": true
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Metadata Source Values
|
||||
|
||||
The `metadata_source` field indicates which provider last updated the metadata:
|
||||
|
||||
| Value | Source |
|
||||
|-------|--------|
|
||||
| `"civitai_api"` | Civitai API |
|
||||
| `"civarchive"` | CivArchive API |
|
||||
| `"archive_db"` | Metadata Archive Database |
|
||||
| `null` | No external source (user-defined only) |
|
||||
|
||||
---
|
||||
|
||||
## Auto-Update Behavior
|
||||
|
||||
### Fields Updated During Scanning
|
||||
|
||||
These fields are automatically synchronized with the filesystem:
|
||||
|
||||
- `file_name` — Updated if actual filename differs
|
||||
- `file_path` — Normalized and updated if path changes
|
||||
- `preview_url` — Updated if preview file is moved/removed
|
||||
- `sha256` — Updated during hash calculation (when `hash_status="pending"`)
|
||||
- `hash_status` — Updated during hash calculation
|
||||
- `last_checked_at` — Timestamp of scan
|
||||
- `metadata_source` — Set based on metadata provider
|
||||
|
||||
### Fields Set Once (Immutable After Import)
|
||||
|
||||
These fields are set when the model is first imported/scanned and **never change** thereafter:
|
||||
|
||||
- `modified` — Import timestamp (used for "Date Added" sorting)
|
||||
- `size` — File size at time of import/download
|
||||
|
||||
### User-Editable Fields
|
||||
|
||||
These fields can be edited by users at any time through the Lora Manager UI or by manually editing the metadata.json file:
|
||||
|
||||
- `model_name` — Display name
|
||||
- `tags` — Model tags
|
||||
- `modelDescription` — Model description
|
||||
- `notes` — User notes
|
||||
- `favorite` — Favorite flag
|
||||
- `exclude` — Exclude from scanning (user can set `false`→`true`, currently no UI to revert)
|
||||
- `skip_metadata_refresh` — Skip during bulk refresh
|
||||
- `civitai.trainedWords` — Trigger words
|
||||
- `civitai.customImages` — Custom example images
|
||||
- `usage_tips` — Usage recommendations (LoRA only)
|
||||
|
||||
---
|
||||
|
||||
|
||||
## Field Reference by Behavior
|
||||
|
||||
### Required Fields (Must Always Exist)
|
||||
|
||||
- `file_name`
|
||||
- `model_name` (defaults to `file_name` if not provided)
|
||||
- `file_path`
|
||||
- `size`
|
||||
- `modified`
|
||||
- `sha256` (LoRA: always required; Checkpoint: may be empty when `hash_status="pending"`)
|
||||
|
||||
### Optional Fields with Defaults
|
||||
|
||||
| Field | Default |
|
||||
|-------|---------|
|
||||
| `base_model` | `"Unknown"` or `""` |
|
||||
| `preview_nsfw_level` | `0` |
|
||||
| `from_civitai` | `true` |
|
||||
| `civitai` | `{}` |
|
||||
| `tags` | `[]` |
|
||||
| `modelDescription` | `""` |
|
||||
| `notes` | `""` |
|
||||
| `civitai_deleted` | `false` |
|
||||
| `favorite` | `false` |
|
||||
| `exclude` | `false` |
|
||||
| `db_checked` | `false` |
|
||||
| `skip_metadata_refresh` | `false` |
|
||||
| `metadata_source` | `null` |
|
||||
| `last_checked_at` | `0` |
|
||||
| `hash_status` | `"completed"` |
|
||||
| `usage_tips` | `"{}"` (LoRA only) |
|
||||
| `model_type` | `"checkpoint"` or `"embedding"` (not present in LoRA models) |
|
||||
|
||||
---
|
||||
|
||||
## Version History
|
||||
|
||||
| Version | Date | Changes |
|
||||
|---------|------|---------|
|
||||
| 1.0 | 2026-03 | Initial schema documentation |
|
||||
|
||||
---
|
||||
|
||||
## See Also
|
||||
|
||||
- [JSON Schema Definition](../.specs/metadata.schema.json) — Formal JSON Schema for validation
|
||||
@@ -729,6 +729,64 @@
|
||||
"failed": "Rezept-Reparatur fehlgeschlagen: {message}",
|
||||
"missingId": "Rezept kann nicht repariert werden: Fehlende Rezept-ID"
|
||||
}
|
||||
},
|
||||
"batchImport": {
|
||||
"title": "[TODO: Translate] Batch Import Recipes",
|
||||
"action": "[TODO: Translate] Batch Import",
|
||||
"urlList": "[TODO: Translate] URL List",
|
||||
"directory": "[TODO: Translate] Directory",
|
||||
"urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
|
||||
"directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
|
||||
"urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
|
||||
"urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
|
||||
"urlsHint": "[TODO: Translate] Enter one URL or path per line",
|
||||
"directoryPath": "[TODO: Translate] Directory Path",
|
||||
"directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
|
||||
"browse": "[TODO: Translate] Browse",
|
||||
"recursive": "[TODO: Translate] Include subdirectories",
|
||||
"tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
|
||||
"tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
|
||||
"tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
|
||||
"skipNoMetadata": "[TODO: Translate] Skip images without metadata",
|
||||
"skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
|
||||
"start": "[TODO: Translate] Start Import",
|
||||
"startImport": "[TODO: Translate] Start Import",
|
||||
"importing": "[TODO: Translate] Importing...",
|
||||
"progress": "[TODO: Translate] Progress",
|
||||
"total": "[TODO: Translate] Total",
|
||||
"success": "[TODO: Translate] Success",
|
||||
"failed": "[TODO: Translate] Failed",
|
||||
"skipped": "[TODO: Translate] Skipped",
|
||||
"current": "[TODO: Translate] Current",
|
||||
"currentItem": "[TODO: Translate] Current",
|
||||
"preparing": "[TODO: Translate] Preparing...",
|
||||
"cancel": "[TODO: Translate] Cancel",
|
||||
"cancelImport": "[TODO: Translate] Cancel",
|
||||
"cancelled": "[TODO: Translate] Import cancelled",
|
||||
"completed": "[TODO: Translate] Import completed",
|
||||
"completedWithErrors": "[TODO: Translate] Completed with errors",
|
||||
"completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
|
||||
"successCount": "[TODO: Translate] Successful",
|
||||
"failedCount": "[TODO: Translate] Failed",
|
||||
"skippedCount": "[TODO: Translate] Skipped",
|
||||
"totalProcessed": "[TODO: Translate] Total processed",
|
||||
"viewDetails": "[TODO: Translate] View Details",
|
||||
"newImport": "[TODO: Translate] New Import",
|
||||
"manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
|
||||
"batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
|
||||
"backToParent": "[TODO: Translate] Back to parent directory",
|
||||
"folders": "[TODO: Translate] Folders",
|
||||
"folderCount": "[TODO: Translate] {count} folders",
|
||||
"imageFiles": "[TODO: Translate] Image Files",
|
||||
"images": "[TODO: Translate] images",
|
||||
"imageCount": "[TODO: Translate] {count} images",
|
||||
"selectFolder": "[TODO: Translate] Select This Folder",
|
||||
"errors": {
|
||||
"enterUrls": "[TODO: Translate] Please enter at least one URL or path",
|
||||
"enterDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"startFailed": "[TODO: Translate] Failed to start import: {message}"
|
||||
}
|
||||
}
|
||||
},
|
||||
"checkpoints": {
|
||||
@@ -1438,7 +1496,14 @@
|
||||
"recipeSaveFailed": "Fehler beim Speichern des Rezepts: {error}",
|
||||
"importFailed": "Import fehlgeschlagen: {message}",
|
||||
"folderTreeFailed": "Fehler beim Laden des Ordnerbaums",
|
||||
"folderTreeError": "Fehler beim Laden des Ordnerbaums"
|
||||
"folderTreeError": "Fehler beim Laden des Ordnerbaums",
|
||||
"batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
|
||||
"batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
|
||||
"batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
|
||||
"batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
|
||||
"batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
|
||||
},
|
||||
"models": {
|
||||
"noModelsSelected": "Keine Modelle ausgewählt",
|
||||
|
||||
@@ -729,6 +729,64 @@
|
||||
"failed": "Failed to repair recipe: {message}",
|
||||
"missingId": "Cannot repair recipe: Missing recipe ID"
|
||||
}
|
||||
},
|
||||
"batchImport": {
|
||||
"title": "Batch Import Recipes",
|
||||
"action": "Batch Import",
|
||||
"urlList": "URL List",
|
||||
"directory": "Directory",
|
||||
"urlDescription": "Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
|
||||
"directoryDescription": "Enter a directory path to import all images from that folder.",
|
||||
"urlsLabel": "Image URLs or Local Paths",
|
||||
"urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
|
||||
"urlsHint": "Enter one URL or path per line",
|
||||
"directoryPath": "Directory Path",
|
||||
"directoryPlaceholder": "/path/to/images/folder",
|
||||
"browse": "Browse",
|
||||
"recursive": "Include subdirectories",
|
||||
"tagsOptional": "Tags (optional, applied to all recipes)",
|
||||
"tagsPlaceholder": "Enter tags separated by commas",
|
||||
"tagsHint": "Tags will be added to all imported recipes",
|
||||
"skipNoMetadata": "Skip images without metadata",
|
||||
"skipNoMetadataHelp": "Images without LoRA metadata will be skipped automatically.",
|
||||
"start": "Start Import",
|
||||
"startImport": "Start Import",
|
||||
"importing": "Importing...",
|
||||
"progress": "Progress",
|
||||
"total": "Total",
|
||||
"success": "Success",
|
||||
"failed": "Failed",
|
||||
"skipped": "Skipped",
|
||||
"current": "Current",
|
||||
"currentItem": "Current",
|
||||
"preparing": "Preparing...",
|
||||
"cancel": "Cancel",
|
||||
"cancelImport": "Cancel",
|
||||
"cancelled": "Import cancelled",
|
||||
"completed": "Import completed",
|
||||
"completedWithErrors": "Completed with errors",
|
||||
"completedSuccess": "Successfully imported {count} recipe(s)",
|
||||
"successCount": "Successful",
|
||||
"failedCount": "Failed",
|
||||
"skippedCount": "Skipped",
|
||||
"totalProcessed": "Total processed",
|
||||
"viewDetails": "View Details",
|
||||
"newImport": "New Import",
|
||||
"manualPathEntry": "Please enter the directory path manually. File browser is not available in this browser.",
|
||||
"batchImportDirectorySelected": "Directory selected: {path}",
|
||||
"batchImportManualEntryRequired": "File browser not available. Please enter the directory path manually.",
|
||||
"backToParent": "Back to parent directory",
|
||||
"folders": "Folders",
|
||||
"folderCount": "{count} folders",
|
||||
"imageFiles": "Image Files",
|
||||
"images": "images",
|
||||
"imageCount": "{count} images",
|
||||
"selectFolder": "Select This Folder",
|
||||
"errors": {
|
||||
"enterUrls": "Please enter at least one URL or path",
|
||||
"enterDirectory": "Please enter a directory path",
|
||||
"startFailed": "Failed to start import: {message}"
|
||||
}
|
||||
}
|
||||
},
|
||||
"checkpoints": {
|
||||
@@ -1438,7 +1496,14 @@
|
||||
"recipeSaveFailed": "Failed to save recipe: {error}",
|
||||
"importFailed": "Import failed: {message}",
|
||||
"folderTreeFailed": "Failed to load folder tree",
|
||||
"folderTreeError": "Error loading folder tree"
|
||||
"folderTreeError": "Error loading folder tree",
|
||||
"batchImportFailed": "Failed to start batch import: {message}",
|
||||
"batchImportCancelling": "Cancelling batch import...",
|
||||
"batchImportCancelFailed": "Failed to cancel batch import: {message}",
|
||||
"batchImportNoUrls": "Please enter at least one URL or file path",
|
||||
"batchImportNoDirectory": "Please enter a directory path",
|
||||
"batchImportBrowseFailed": "Failed to browse directory: {message}",
|
||||
"batchImportDirectorySelected": "Directory selected: {path}"
|
||||
},
|
||||
"models": {
|
||||
"noModelsSelected": "No models selected",
|
||||
|
||||
@@ -729,6 +729,64 @@
|
||||
"failed": "Error al reparar la receta: {message}",
|
||||
"missingId": "No se puede reparar la receta: falta el ID de la receta"
|
||||
}
|
||||
},
|
||||
"batchImport": {
|
||||
"title": "[TODO: Translate] Batch Import Recipes",
|
||||
"action": "[TODO: Translate] Batch Import",
|
||||
"urlList": "[TODO: Translate] URL List",
|
||||
"directory": "[TODO: Translate] Directory",
|
||||
"urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
|
||||
"directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
|
||||
"urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
|
||||
"urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
|
||||
"urlsHint": "[TODO: Translate] Enter one URL or path per line",
|
||||
"directoryPath": "[TODO: Translate] Directory Path",
|
||||
"directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
|
||||
"browse": "[TODO: Translate] Browse",
|
||||
"recursive": "[TODO: Translate] Include subdirectories",
|
||||
"tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
|
||||
"tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
|
||||
"tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
|
||||
"skipNoMetadata": "[TODO: Translate] Skip images without metadata",
|
||||
"skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
|
||||
"start": "[TODO: Translate] Start Import",
|
||||
"startImport": "[TODO: Translate] Start Import",
|
||||
"importing": "[TODO: Translate] Importing...",
|
||||
"progress": "[TODO: Translate] Progress",
|
||||
"total": "[TODO: Translate] Total",
|
||||
"success": "[TODO: Translate] Success",
|
||||
"failed": "[TODO: Translate] Failed",
|
||||
"skipped": "[TODO: Translate] Skipped",
|
||||
"current": "[TODO: Translate] Current",
|
||||
"currentItem": "[TODO: Translate] Current",
|
||||
"preparing": "[TODO: Translate] Preparing...",
|
||||
"cancel": "[TODO: Translate] Cancel",
|
||||
"cancelImport": "[TODO: Translate] Cancel",
|
||||
"cancelled": "[TODO: Translate] Import cancelled",
|
||||
"completed": "[TODO: Translate] Import completed",
|
||||
"completedWithErrors": "[TODO: Translate] Completed with errors",
|
||||
"completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
|
||||
"successCount": "[TODO: Translate] Successful",
|
||||
"failedCount": "[TODO: Translate] Failed",
|
||||
"skippedCount": "[TODO: Translate] Skipped",
|
||||
"totalProcessed": "[TODO: Translate] Total processed",
|
||||
"viewDetails": "[TODO: Translate] View Details",
|
||||
"newImport": "[TODO: Translate] New Import",
|
||||
"manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
|
||||
"batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
|
||||
"backToParent": "[TODO: Translate] Back to parent directory",
|
||||
"folders": "[TODO: Translate] Folders",
|
||||
"folderCount": "[TODO: Translate] {count} folders",
|
||||
"imageFiles": "[TODO: Translate] Image Files",
|
||||
"images": "[TODO: Translate] images",
|
||||
"imageCount": "[TODO: Translate] {count} images",
|
||||
"selectFolder": "[TODO: Translate] Select This Folder",
|
||||
"errors": {
|
||||
"enterUrls": "[TODO: Translate] Please enter at least one URL or path",
|
||||
"enterDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"startFailed": "[TODO: Translate] Failed to start import: {message}"
|
||||
}
|
||||
}
|
||||
},
|
||||
"checkpoints": {
|
||||
@@ -1438,7 +1496,14 @@
|
||||
"recipeSaveFailed": "Error al guardar receta: {error}",
|
||||
"importFailed": "Importación falló: {message}",
|
||||
"folderTreeFailed": "Error al cargar árbol de carpetas",
|
||||
"folderTreeError": "Error cargando árbol de carpetas"
|
||||
"folderTreeError": "Error cargando árbol de carpetas",
|
||||
"batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
|
||||
"batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
|
||||
"batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
|
||||
"batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
|
||||
"batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
|
||||
},
|
||||
"models": {
|
||||
"noModelsSelected": "No hay modelos seleccionados",
|
||||
|
||||
@@ -729,6 +729,64 @@
|
||||
"failed": "Échec de la réparation de la recette : {message}",
|
||||
"missingId": "Impossible de réparer la recette : ID de recette manquant"
|
||||
}
|
||||
},
|
||||
"batchImport": {
|
||||
"title": "[TODO: Translate] Batch Import Recipes",
|
||||
"action": "[TODO: Translate] Batch Import",
|
||||
"urlList": "[TODO: Translate] URL List",
|
||||
"directory": "[TODO: Translate] Directory",
|
||||
"urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
|
||||
"directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
|
||||
"urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
|
||||
"urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
|
||||
"urlsHint": "[TODO: Translate] Enter one URL or path per line",
|
||||
"directoryPath": "[TODO: Translate] Directory Path",
|
||||
"directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
|
||||
"browse": "[TODO: Translate] Browse",
|
||||
"recursive": "[TODO: Translate] Include subdirectories",
|
||||
"tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
|
||||
"tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
|
||||
"tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
|
||||
"skipNoMetadata": "[TODO: Translate] Skip images without metadata",
|
||||
"skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
|
||||
"start": "[TODO: Translate] Start Import",
|
||||
"startImport": "[TODO: Translate] Start Import",
|
||||
"importing": "[TODO: Translate] Importing...",
|
||||
"progress": "[TODO: Translate] Progress",
|
||||
"total": "[TODO: Translate] Total",
|
||||
"success": "[TODO: Translate] Success",
|
||||
"failed": "[TODO: Translate] Failed",
|
||||
"skipped": "[TODO: Translate] Skipped",
|
||||
"current": "[TODO: Translate] Current",
|
||||
"currentItem": "[TODO: Translate] Current",
|
||||
"preparing": "[TODO: Translate] Preparing...",
|
||||
"cancel": "[TODO: Translate] Cancel",
|
||||
"cancelImport": "[TODO: Translate] Cancel",
|
||||
"cancelled": "[TODO: Translate] Import cancelled",
|
||||
"completed": "[TODO: Translate] Import completed",
|
||||
"completedWithErrors": "[TODO: Translate] Completed with errors",
|
||||
"completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
|
||||
"successCount": "[TODO: Translate] Successful",
|
||||
"failedCount": "[TODO: Translate] Failed",
|
||||
"skippedCount": "[TODO: Translate] Skipped",
|
||||
"totalProcessed": "[TODO: Translate] Total processed",
|
||||
"viewDetails": "[TODO: Translate] View Details",
|
||||
"newImport": "[TODO: Translate] New Import",
|
||||
"manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
|
||||
"batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
|
||||
"backToParent": "[TODO: Translate] Back to parent directory",
|
||||
"folders": "[TODO: Translate] Folders",
|
||||
"folderCount": "[TODO: Translate] {count} folders",
|
||||
"imageFiles": "[TODO: Translate] Image Files",
|
||||
"images": "[TODO: Translate] images",
|
||||
"imageCount": "[TODO: Translate] {count} images",
|
||||
"selectFolder": "[TODO: Translate] Select This Folder",
|
||||
"errors": {
|
||||
"enterUrls": "[TODO: Translate] Please enter at least one URL or path",
|
||||
"enterDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"startFailed": "[TODO: Translate] Failed to start import: {message}"
|
||||
}
|
||||
}
|
||||
},
|
||||
"checkpoints": {
|
||||
@@ -1438,7 +1496,14 @@
|
||||
"recipeSaveFailed": "Échec de la sauvegarde de la recipe : {error}",
|
||||
"importFailed": "Échec de l'importation : {message}",
|
||||
"folderTreeFailed": "Échec du chargement de l'arborescence des dossiers",
|
||||
"folderTreeError": "Erreur lors du chargement de l'arborescence des dossiers"
|
||||
"folderTreeError": "Erreur lors du chargement de l'arborescence des dossiers",
|
||||
"batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
|
||||
"batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
|
||||
"batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
|
||||
"batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
|
||||
"batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
|
||||
},
|
||||
"models": {
|
||||
"noModelsSelected": "Aucun modèle sélectionné",
|
||||
|
||||
@@ -729,6 +729,64 @@
|
||||
"failed": "תיקון המתכון נכשל: {message}",
|
||||
"missingId": "לא ניתן לתקן את המתכון: חסר מזהה מתכון"
|
||||
}
|
||||
},
|
||||
"batchImport": {
|
||||
"title": "[TODO: Translate] Batch Import Recipes",
|
||||
"action": "[TODO: Translate] Batch Import",
|
||||
"urlList": "[TODO: Translate] URL List",
|
||||
"directory": "[TODO: Translate] Directory",
|
||||
"urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
|
||||
"directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
|
||||
"urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
|
||||
"urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
|
||||
"urlsHint": "[TODO: Translate] Enter one URL or path per line",
|
||||
"directoryPath": "[TODO: Translate] Directory Path",
|
||||
"directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
|
||||
"browse": "[TODO: Translate] Browse",
|
||||
"recursive": "[TODO: Translate] Include subdirectories",
|
||||
"tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
|
||||
"tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
|
||||
"tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
|
||||
"skipNoMetadata": "[TODO: Translate] Skip images without metadata",
|
||||
"skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
|
||||
"start": "[TODO: Translate] Start Import",
|
||||
"startImport": "[TODO: Translate] Start Import",
|
||||
"importing": "[TODO: Translate] Importing...",
|
||||
"progress": "[TODO: Translate] Progress",
|
||||
"total": "[TODO: Translate] Total",
|
||||
"success": "[TODO: Translate] Success",
|
||||
"failed": "[TODO: Translate] Failed",
|
||||
"skipped": "[TODO: Translate] Skipped",
|
||||
"current": "[TODO: Translate] Current",
|
||||
"currentItem": "[TODO: Translate] Current",
|
||||
"preparing": "[TODO: Translate] Preparing...",
|
||||
"cancel": "[TODO: Translate] Cancel",
|
||||
"cancelImport": "[TODO: Translate] Cancel",
|
||||
"cancelled": "[TODO: Translate] Import cancelled",
|
||||
"completed": "[TODO: Translate] Import completed",
|
||||
"completedWithErrors": "[TODO: Translate] Completed with errors",
|
||||
"completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
|
||||
"successCount": "[TODO: Translate] Successful",
|
||||
"failedCount": "[TODO: Translate] Failed",
|
||||
"skippedCount": "[TODO: Translate] Skipped",
|
||||
"totalProcessed": "[TODO: Translate] Total processed",
|
||||
"viewDetails": "[TODO: Translate] View Details",
|
||||
"newImport": "[TODO: Translate] New Import",
|
||||
"manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
|
||||
"batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
|
||||
"backToParent": "[TODO: Translate] Back to parent directory",
|
||||
"folders": "[TODO: Translate] Folders",
|
||||
"folderCount": "[TODO: Translate] {count} folders",
|
||||
"imageFiles": "[TODO: Translate] Image Files",
|
||||
"images": "[TODO: Translate] images",
|
||||
"imageCount": "[TODO: Translate] {count} images",
|
||||
"selectFolder": "[TODO: Translate] Select This Folder",
|
||||
"errors": {
|
||||
"enterUrls": "[TODO: Translate] Please enter at least one URL or path",
|
||||
"enterDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"startFailed": "[TODO: Translate] Failed to start import: {message}"
|
||||
}
|
||||
}
|
||||
},
|
||||
"checkpoints": {
|
||||
@@ -1438,7 +1496,14 @@
|
||||
"recipeSaveFailed": "שמירת המתכון נכשלה: {error}",
|
||||
"importFailed": "הייבוא נכשל: {message}",
|
||||
"folderTreeFailed": "טעינת עץ התיקיות נכשלה",
|
||||
"folderTreeError": "שגיאה בטעינת עץ התיקיות"
|
||||
"folderTreeError": "שגיאה בטעינת עץ התיקיות",
|
||||
"batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
|
||||
"batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
|
||||
"batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
|
||||
"batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
|
||||
"batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
|
||||
},
|
||||
"models": {
|
||||
"noModelsSelected": "לא נבחרו מודלים",
|
||||
|
||||
@@ -729,6 +729,64 @@
|
||||
"failed": "レシピの修復に失敗しました: {message}",
|
||||
"missingId": "レシピを修復できません: レシピIDがありません"
|
||||
}
|
||||
},
|
||||
"batchImport": {
|
||||
"title": "[TODO: Translate] Batch Import Recipes",
|
||||
"action": "[TODO: Translate] Batch Import",
|
||||
"urlList": "[TODO: Translate] URL List",
|
||||
"directory": "[TODO: Translate] Directory",
|
||||
"urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
|
||||
"directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
|
||||
"urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
|
||||
"urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
|
||||
"urlsHint": "[TODO: Translate] Enter one URL or path per line",
|
||||
"directoryPath": "[TODO: Translate] Directory Path",
|
||||
"directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
|
||||
"browse": "[TODO: Translate] Browse",
|
||||
"recursive": "[TODO: Translate] Include subdirectories",
|
||||
"tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
|
||||
"tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
|
||||
"tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
|
||||
"skipNoMetadata": "[TODO: Translate] Skip images without metadata",
|
||||
"skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
|
||||
"start": "[TODO: Translate] Start Import",
|
||||
"startImport": "[TODO: Translate] Start Import",
|
||||
"importing": "[TODO: Translate] Importing...",
|
||||
"progress": "[TODO: Translate] Progress",
|
||||
"total": "[TODO: Translate] Total",
|
||||
"success": "[TODO: Translate] Success",
|
||||
"failed": "[TODO: Translate] Failed",
|
||||
"skipped": "[TODO: Translate] Skipped",
|
||||
"current": "[TODO: Translate] Current",
|
||||
"currentItem": "[TODO: Translate] Current",
|
||||
"preparing": "[TODO: Translate] Preparing...",
|
||||
"cancel": "[TODO: Translate] Cancel",
|
||||
"cancelImport": "[TODO: Translate] Cancel",
|
||||
"cancelled": "[TODO: Translate] Import cancelled",
|
||||
"completed": "[TODO: Translate] Import completed",
|
||||
"completedWithErrors": "[TODO: Translate] Completed with errors",
|
||||
"completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
|
||||
"successCount": "[TODO: Translate] Successful",
|
||||
"failedCount": "[TODO: Translate] Failed",
|
||||
"skippedCount": "[TODO: Translate] Skipped",
|
||||
"totalProcessed": "[TODO: Translate] Total processed",
|
||||
"viewDetails": "[TODO: Translate] View Details",
|
||||
"newImport": "[TODO: Translate] New Import",
|
||||
"manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
|
||||
"batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
|
||||
"backToParent": "[TODO: Translate] Back to parent directory",
|
||||
"folders": "[TODO: Translate] Folders",
|
||||
"folderCount": "[TODO: Translate] {count} folders",
|
||||
"imageFiles": "[TODO: Translate] Image Files",
|
||||
"images": "[TODO: Translate] images",
|
||||
"imageCount": "[TODO: Translate] {count} images",
|
||||
"selectFolder": "[TODO: Translate] Select This Folder",
|
||||
"errors": {
|
||||
"enterUrls": "[TODO: Translate] Please enter at least one URL or path",
|
||||
"enterDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"startFailed": "[TODO: Translate] Failed to start import: {message}"
|
||||
}
|
||||
}
|
||||
},
|
||||
"checkpoints": {
|
||||
@@ -1438,7 +1496,14 @@
|
||||
"recipeSaveFailed": "レシピの保存に失敗しました：{error}",
|
||||
"importFailed": "インポートに失敗しました：{message}",
|
||||
"folderTreeFailed": "フォルダツリーの読み込みに失敗しました",
|
||||
"folderTreeError": "フォルダツリー読み込みエラー"
|
||||
"folderTreeError": "フォルダツリー読み込みエラー",
|
||||
"batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
|
||||
"batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
|
||||
"batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
|
||||
"batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
|
||||
"batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
|
||||
},
|
||||
"models": {
|
||||
"noModelsSelected": "モデルが選択されていません",
|
||||
|
||||
@@ -729,6 +729,64 @@
|
||||
"failed": "레시피 복구 실패: {message}",
|
||||
"missingId": "레시피를 복구할 수 없음: 레시피 ID 누락"
|
||||
}
|
||||
},
|
||||
"batchImport": {
|
||||
"title": "[TODO: Translate] Batch Import Recipes",
|
||||
"action": "[TODO: Translate] Batch Import",
|
||||
"urlList": "[TODO: Translate] URL List",
|
||||
"directory": "[TODO: Translate] Directory",
|
||||
"urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
|
||||
"directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
|
||||
"urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
|
||||
"urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
|
||||
"urlsHint": "[TODO: Translate] Enter one URL or path per line",
|
||||
"directoryPath": "[TODO: Translate] Directory Path",
|
||||
"directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
|
||||
"browse": "[TODO: Translate] Browse",
|
||||
"recursive": "[TODO: Translate] Include subdirectories",
|
||||
"tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
|
||||
"tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
|
||||
"tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
|
||||
"skipNoMetadata": "[TODO: Translate] Skip images without metadata",
|
||||
"skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
|
||||
"start": "[TODO: Translate] Start Import",
|
||||
"startImport": "[TODO: Translate] Start Import",
|
||||
"importing": "[TODO: Translate] Importing...",
|
||||
"progress": "[TODO: Translate] Progress",
|
||||
"total": "[TODO: Translate] Total",
|
||||
"success": "[TODO: Translate] Success",
|
||||
"failed": "[TODO: Translate] Failed",
|
||||
"skipped": "[TODO: Translate] Skipped",
|
||||
"current": "[TODO: Translate] Current",
|
||||
"currentItem": "[TODO: Translate] Current",
|
||||
"preparing": "[TODO: Translate] Preparing...",
|
||||
"cancel": "[TODO: Translate] Cancel",
|
||||
"cancelImport": "[TODO: Translate] Cancel",
|
||||
"cancelled": "[TODO: Translate] Import cancelled",
|
||||
"completed": "[TODO: Translate] Import completed",
|
||||
"completedWithErrors": "[TODO: Translate] Completed with errors",
|
||||
"completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
|
||||
"successCount": "[TODO: Translate] Successful",
|
||||
"failedCount": "[TODO: Translate] Failed",
|
||||
"skippedCount": "[TODO: Translate] Skipped",
|
||||
"totalProcessed": "[TODO: Translate] Total processed",
|
||||
"viewDetails": "[TODO: Translate] View Details",
|
||||
"newImport": "[TODO: Translate] New Import",
|
||||
"manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
|
||||
"batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
|
||||
"backToParent": "[TODO: Translate] Back to parent directory",
|
||||
"folders": "[TODO: Translate] Folders",
|
||||
"folderCount": "[TODO: Translate] {count} folders",
|
||||
"imageFiles": "[TODO: Translate] Image Files",
|
||||
"images": "[TODO: Translate] images",
|
||||
"imageCount": "[TODO: Translate] {count} images",
|
||||
"selectFolder": "[TODO: Translate] Select This Folder",
|
||||
"errors": {
|
||||
"enterUrls": "[TODO: Translate] Please enter at least one URL or path",
|
||||
"enterDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"startFailed": "[TODO: Translate] Failed to start import: {message}"
|
||||
}
|
||||
}
|
||||
},
|
||||
"checkpoints": {
|
||||
@@ -1438,7 +1496,14 @@
|
||||
"recipeSaveFailed": "레시피 저장 실패: {error}",
|
||||
"importFailed": "가져오기 실패: {message}",
|
||||
"folderTreeFailed": "폴더 트리 로딩 실패",
|
||||
"folderTreeError": "폴더 트리 로딩 오류"
|
||||
"folderTreeError": "폴더 트리 로딩 오류",
|
||||
"batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
|
||||
"batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
|
||||
"batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
|
||||
"batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
|
||||
"batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
|
||||
},
|
||||
"models": {
|
||||
"noModelsSelected": "선택된 모델이 없습니다",
|
||||
|
||||
@@ -729,6 +729,64 @@
|
||||
"failed": "Не удалось восстановить рецепт: {message}",
|
||||
"missingId": "Не удалось восстановить рецепт: отсутствует ID рецепта"
|
||||
}
|
||||
},
|
||||
"batchImport": {
|
||||
"title": "[TODO: Translate] Batch Import Recipes",
|
||||
"action": "[TODO: Translate] Batch Import",
|
||||
"urlList": "[TODO: Translate] URL List",
|
||||
"directory": "[TODO: Translate] Directory",
|
||||
"urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
|
||||
"directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
|
||||
"urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
|
||||
"urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
|
||||
"urlsHint": "[TODO: Translate] Enter one URL or path per line",
|
||||
"directoryPath": "[TODO: Translate] Directory Path",
|
||||
"directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
|
||||
"browse": "[TODO: Translate] Browse",
|
||||
"recursive": "[TODO: Translate] Include subdirectories",
|
||||
"tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
|
||||
"tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
|
||||
"tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
|
||||
"skipNoMetadata": "[TODO: Translate] Skip images without metadata",
|
||||
"skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
|
||||
"start": "[TODO: Translate] Start Import",
|
||||
"startImport": "[TODO: Translate] Start Import",
|
||||
"importing": "[TODO: Translate] Importing...",
|
||||
"progress": "[TODO: Translate] Progress",
|
||||
"total": "[TODO: Translate] Total",
|
||||
"success": "[TODO: Translate] Success",
|
||||
"failed": "[TODO: Translate] Failed",
|
||||
"skipped": "[TODO: Translate] Skipped",
|
||||
"current": "[TODO: Translate] Current",
|
||||
"currentItem": "[TODO: Translate] Current",
|
||||
"preparing": "[TODO: Translate] Preparing...",
|
||||
"cancel": "[TODO: Translate] Cancel",
|
||||
"cancelImport": "[TODO: Translate] Cancel",
|
||||
"cancelled": "[TODO: Translate] Import cancelled",
|
||||
"completed": "[TODO: Translate] Import completed",
|
||||
"completedWithErrors": "[TODO: Translate] Completed with errors",
|
||||
"completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
|
||||
"successCount": "[TODO: Translate] Successful",
|
||||
"failedCount": "[TODO: Translate] Failed",
|
||||
"skippedCount": "[TODO: Translate] Skipped",
|
||||
"totalProcessed": "[TODO: Translate] Total processed",
|
||||
"viewDetails": "[TODO: Translate] View Details",
|
||||
"newImport": "[TODO: Translate] New Import",
|
||||
"manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
|
||||
"batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
|
||||
"backToParent": "[TODO: Translate] Back to parent directory",
|
||||
"folders": "[TODO: Translate] Folders",
|
||||
"folderCount": "[TODO: Translate] {count} folders",
|
||||
"imageFiles": "[TODO: Translate] Image Files",
|
||||
"images": "[TODO: Translate] images",
|
||||
"imageCount": "[TODO: Translate] {count} images",
|
||||
"selectFolder": "[TODO: Translate] Select This Folder",
|
||||
"errors": {
|
||||
"enterUrls": "[TODO: Translate] Please enter at least one URL or path",
|
||||
"enterDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"startFailed": "[TODO: Translate] Failed to start import: {message}"
|
||||
}
|
||||
}
|
||||
},
|
||||
"checkpoints": {
|
||||
@@ -1438,7 +1496,14 @@
|
||||
"recipeSaveFailed": "Не удалось сохранить рецепт: {error}",
|
||||
"importFailed": "Импорт не удался: {message}",
|
||||
"folderTreeFailed": "Не удалось загрузить дерево папок",
|
||||
"folderTreeError": "Ошибка загрузки дерева папок"
|
||||
"folderTreeError": "Ошибка загрузки дерева папок",
|
||||
"batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
|
||||
"batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
|
||||
"batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
|
||||
"batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
|
||||
"batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
|
||||
},
|
||||
"models": {
|
||||
"noModelsSelected": "Модели не выбраны",
|
||||
|
||||
@@ -729,6 +729,64 @@
|
||||
"failed": "修复配方失败：{message}",
|
||||
"missingId": "无法修复配方：缺少配方 ID"
|
||||
}
|
||||
},
|
||||
"batchImport": {
|
||||
"title": "批量导入配方",
|
||||
"action": "批量导入",
|
||||
"urlList": "[TODO: Translate] URL List",
|
||||
"directory": "[TODO: Translate] Directory",
|
||||
"urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
|
||||
"directoryDescription": "输入目录路径以导入该文件夹中的所有图片。",
|
||||
"urlsLabel": "图片 URL 或本地路径",
|
||||
"urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
|
||||
"urlsHint": "[TODO: Translate] Enter one URL or path per line",
|
||||
"directoryPath": "[TODO: Translate] Directory Path",
|
||||
"directoryPlaceholder": "/图片/文件夹/路径",
|
||||
"browse": "[TODO: Translate] Browse",
|
||||
"recursive": "[TODO: Translate] Include subdirectories",
|
||||
"tagsOptional": "标签（可选，应用于所有配方）",
|
||||
"tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
|
||||
"tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
|
||||
"skipNoMetadata": "跳过无元数据的图片",
|
||||
"skipNoMetadataHelp": "没有 LoRA 元数据的图片将自动跳过。",
|
||||
"start": "[TODO: Translate] Start Import",
|
||||
"startImport": "开始导入",
|
||||
"importing": "正在导入配方...",
|
||||
"progress": "进度",
|
||||
"total": "[TODO: Translate] Total",
|
||||
"success": "[TODO: Translate] Success",
|
||||
"failed": "[TODO: Translate] Failed",
|
||||
"skipped": "[TODO: Translate] Skipped",
|
||||
"current": "[TODO: Translate] Current",
|
||||
"currentItem": "当前",
|
||||
"preparing": "准备中...",
|
||||
"cancel": "[TODO: Translate] Cancel",
|
||||
"cancelImport": "取消",
|
||||
"cancelled": "批量导入已取消",
|
||||
"completed": "导入完成",
|
||||
"completedWithErrors": "[TODO: Translate] Completed with errors",
|
||||
"completedSuccess": "成功导入 {count} 个配方",
|
||||
"successCount": "成功",
|
||||
"failedCount": "失败",
|
||||
"skippedCount": "跳过",
|
||||
"totalProcessed": "总计处理",
|
||||
"viewDetails": "[TODO: Translate] View Details",
|
||||
"newImport": "[TODO: Translate] New Import",
|
||||
"manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
|
||||
"batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
|
||||
"backToParent": "[TODO: Translate] Back to parent directory",
|
||||
"folders": "[TODO: Translate] Folders",
|
||||
"folderCount": "[TODO: Translate] {count} folders",
|
||||
"imageFiles": "[TODO: Translate] Image Files",
|
||||
"images": "[TODO: Translate] images",
|
||||
"imageCount": "[TODO: Translate] {count} images",
|
||||
"selectFolder": "[TODO: Translate] Select This Folder",
|
||||
"errors": {
|
||||
"enterUrls": "请至少输入一个 URL 或路径",
|
||||
"enterDirectory": "请输入目录路径",
|
||||
"startFailed": "启动导入失败：{message}"
|
||||
}
|
||||
}
|
||||
},
|
||||
"checkpoints": {
|
||||
@@ -764,7 +822,7 @@
|
||||
"emptyFolderName": "请输入文件夹名称",
|
||||
"invalidFolderName": "文件夹名称包含无效字符",
|
||||
"noDragState": "未找到待处理的拖放操作"
|
||||
},
|
||||
},
|
||||
"empty": {
|
||||
"noFolders": "未找到文件夹",
|
||||
"dragHint": "拖拽项目到此处以创建文件夹"
|
||||
@@ -1438,7 +1496,14 @@
|
||||
"recipeSaveFailed": "保存配方失败：{error}",
|
||||
"importFailed": "导入失败：{message}",
|
||||
"folderTreeFailed": "加载文件夹树失败",
|
||||
"folderTreeError": "加载文件夹树出错"
|
||||
"folderTreeError": "加载文件夹树出错",
|
||||
"batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
|
||||
"batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
|
||||
"batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
|
||||
"batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
|
||||
"batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
|
||||
},
|
||||
"models": {
|
||||
"noModelsSelected": "未选中模型",
|
||||
|
||||
@@ -729,6 +729,64 @@
|
||||
"failed": "修復配方失敗：{message}",
|
||||
"missingId": "無法修復配方：缺少配方 ID"
|
||||
}
|
||||
},
|
||||
"batchImport": {
|
||||
"title": "[TODO: Translate] Batch Import Recipes",
|
||||
"action": "[TODO: Translate] Batch Import",
|
||||
"urlList": "[TODO: Translate] URL List",
|
||||
"directory": "[TODO: Translate] Directory",
|
||||
"urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
|
||||
"directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
|
||||
"urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
|
||||
"urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
|
||||
"urlsHint": "[TODO: Translate] Enter one URL or path per line",
|
||||
"directoryPath": "[TODO: Translate] Directory Path",
|
||||
"directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
|
||||
"browse": "[TODO: Translate] Browse",
|
||||
"recursive": "[TODO: Translate] Include subdirectories",
|
||||
"tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
|
||||
"tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
|
||||
"tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
|
||||
"skipNoMetadata": "[TODO: Translate] Skip images without metadata",
|
||||
"skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
|
||||
"start": "[TODO: Translate] Start Import",
|
||||
"startImport": "[TODO: Translate] Start Import",
|
||||
"importing": "[TODO: Translate] Importing...",
|
||||
"progress": "[TODO: Translate] Progress",
|
||||
"total": "[TODO: Translate] Total",
|
||||
"success": "[TODO: Translate] Success",
|
||||
"failed": "[TODO: Translate] Failed",
|
||||
"skipped": "[TODO: Translate] Skipped",
|
||||
"current": "[TODO: Translate] Current",
|
||||
"currentItem": "[TODO: Translate] Current",
|
||||
"preparing": "[TODO: Translate] Preparing...",
|
||||
"cancel": "[TODO: Translate] Cancel",
|
||||
"cancelImport": "[TODO: Translate] Cancel",
|
||||
"cancelled": "[TODO: Translate] Import cancelled",
|
||||
"completed": "[TODO: Translate] Import completed",
|
||||
"completedWithErrors": "[TODO: Translate] Completed with errors",
|
||||
"completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
|
||||
"successCount": "[TODO: Translate] Successful",
|
||||
"failedCount": "[TODO: Translate] Failed",
|
||||
"skippedCount": "[TODO: Translate] Skipped",
|
||||
"totalProcessed": "[TODO: Translate] Total processed",
|
||||
"viewDetails": "[TODO: Translate] View Details",
|
||||
"newImport": "[TODO: Translate] New Import",
|
||||
"manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
|
||||
"batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
|
||||
"backToParent": "[TODO: Translate] Back to parent directory",
|
||||
"folders": "[TODO: Translate] Folders",
|
||||
"folderCount": "[TODO: Translate] {count} folders",
|
||||
"imageFiles": "[TODO: Translate] Image Files",
|
||||
"images": "[TODO: Translate] images",
|
||||
"imageCount": "[TODO: Translate] {count} images",
|
||||
"selectFolder": "[TODO: Translate] Select This Folder",
|
||||
"errors": {
|
||||
"enterUrls": "[TODO: Translate] Please enter at least one URL or path",
|
||||
"enterDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"startFailed": "[TODO: Translate] Failed to start import: {message}"
|
||||
}
|
||||
}
|
||||
},
|
||||
"checkpoints": {
|
||||
@@ -1438,7 +1496,14 @@
|
||||
"recipeSaveFailed": "儲存配方失敗：{error}",
|
||||
"importFailed": "匯入失敗：{message}",
|
||||
"folderTreeFailed": "載入資料夾樹狀結構失敗",
|
||||
"folderTreeError": "載入資料夾樹狀結構錯誤"
|
||||
"folderTreeError": "載入資料夾樹狀結構錯誤",
|
||||
"batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
|
||||
"batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
|
||||
"batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
|
||||
"batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
|
||||
"batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
|
||||
"batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
|
||||
"batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
|
||||
},
|
||||
"models": {
|
||||
"noModelsSelected": "未選擇模型",
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
"""Base infrastructure shared across recipe routes."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
@@ -16,12 +17,14 @@ from ..services.recipes import (
|
||||
RecipePersistenceService,
|
||||
RecipeSharingService,
|
||||
)
|
||||
from ..services.batch_import_service import BatchImportService
|
||||
from ..services.server_i18n import server_i18n
|
||||
from ..services.service_registry import ServiceRegistry
|
||||
from ..services.settings_manager import get_settings_manager
|
||||
from ..utils.constants import CARD_PREVIEW_WIDTH
|
||||
from ..utils.exif_utils import ExifUtils
|
||||
from .handlers.recipe_handlers import (
|
||||
BatchImportHandler,
|
||||
RecipeAnalysisHandler,
|
||||
RecipeHandlerSet,
|
||||
RecipeListingHandler,
|
||||
@@ -116,7 +119,10 @@ class BaseRecipeRoutes:
|
||||
recipe_scanner_getter = lambda: self.recipe_scanner
|
||||
civitai_client_getter = lambda: self.civitai_client
|
||||
|
||||
standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
|
||||
standalone_mode = (
|
||||
os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"
|
||||
or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
|
||||
)
|
||||
if not standalone_mode:
|
||||
from ..metadata_collector import get_metadata # type: ignore[import-not-found]
|
||||
from ..metadata_collector.metadata_processor import ( # type: ignore[import-not-found]
|
||||
@@ -190,6 +196,22 @@ class BaseRecipeRoutes:
|
||||
sharing_service=sharing_service,
|
||||
)
|
||||
|
||||
from ..services.websocket_manager import ws_manager
|
||||
|
||||
batch_import_service = BatchImportService(
|
||||
analysis_service=analysis_service,
|
||||
persistence_service=persistence_service,
|
||||
ws_manager=ws_manager,
|
||||
logger=logger,
|
||||
)
|
||||
batch_import = BatchImportHandler(
|
||||
ensure_dependencies_ready=self.ensure_dependencies_ready,
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
logger=logger,
|
||||
batch_import_service=batch_import_service,
|
||||
)
|
||||
|
||||
return RecipeHandlerSet(
|
||||
page_view=page_view,
|
||||
listing=listing,
|
||||
@@ -197,4 +219,5 @@ class BaseRecipeRoutes:
|
||||
management=management,
|
||||
analysis=analysis,
|
||||
sharing=sharing,
|
||||
batch_import=batch_import,
|
||||
)
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
"""Dedicated handler objects for recipe-related routes."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
@@ -8,6 +9,7 @@ import re
|
||||
import asyncio
|
||||
import tempfile
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional
|
||||
|
||||
from aiohttp import web
|
||||
@@ -29,6 +31,7 @@ from ...utils.exif_utils import ExifUtils
|
||||
from ...recipes.merger import GenParamsMerger
|
||||
from ...recipes.enrichment import RecipeEnricher
|
||||
from ...services.websocket_manager import ws_manager as default_ws_manager
|
||||
from ...services.batch_import_service import BatchImportService
|
||||
|
||||
Logger = logging.Logger
|
||||
EnsureDependenciesCallable = Callable[[], Awaitable[None]]
|
||||
@@ -46,8 +49,11 @@ class RecipeHandlerSet:
|
||||
management: "RecipeManagementHandler"
|
||||
analysis: "RecipeAnalysisHandler"
|
||||
sharing: "RecipeSharingHandler"
|
||||
batch_import: "BatchImportHandler"
|
||||
|
||||
def to_route_mapping(self) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
|
||||
def to_route_mapping(
|
||||
self,
|
||||
) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
|
||||
"""Expose handler coroutines keyed by registrar handler names."""
|
||||
|
||||
return {
|
||||
@@ -81,6 +87,11 @@ class RecipeHandlerSet:
|
||||
"cancel_repair": self.management.cancel_repair,
|
||||
"repair_recipe": self.management.repair_recipe,
|
||||
"get_repair_progress": self.management.get_repair_progress,
|
||||
"start_batch_import": self.batch_import.start_batch_import,
|
||||
"get_batch_import_progress": self.batch_import.get_batch_import_progress,
|
||||
"cancel_batch_import": self.batch_import.cancel_batch_import,
|
||||
"start_directory_import": self.batch_import.start_directory_import,
|
||||
"browse_directory": self.batch_import.browse_directory,
|
||||
}
|
||||
|
||||
|
||||
@@ -170,8 +181,10 @@ class RecipeListingHandler:
|
||||
search_options = {
|
||||
"title": request.query.get("search_title", "true").lower() == "true",
|
||||
"tags": request.query.get("search_tags", "true").lower() == "true",
|
||||
"lora_name": request.query.get("search_lora_name", "true").lower() == "true",
|
||||
"lora_model": request.query.get("search_lora_model", "true").lower() == "true",
|
||||
"lora_name": request.query.get("search_lora_name", "true").lower()
|
||||
== "true",
|
||||
"lora_model": request.query.get("search_lora_model", "true").lower()
|
||||
== "true",
|
||||
"prompt": request.query.get("search_prompt", "true").lower() == "true",
|
||||
}
|
||||
|
||||
@@ -246,7 +259,9 @@ class RecipeListingHandler:
|
||||
return web.json_response({"error": "Recipe not found"}, status=404)
|
||||
return web.json_response(recipe)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error retrieving recipe details: %s", exc, exc_info=True)
|
||||
self._logger.error(
|
||||
"Error retrieving recipe details: %s", exc, exc_info=True
|
||||
)
|
||||
return web.json_response({"error": str(exc)}, status=500)
|
||||
|
||||
def format_recipe_file_url(self, file_path: str) -> str:
|
||||
@@ -256,7 +271,9 @@ class RecipeListingHandler:
|
||||
if static_url:
|
||||
return static_url
|
||||
except Exception as exc: # pragma: no cover - logging path
|
||||
self._logger.error("Error formatting recipe file URL: %s", exc, exc_info=True)
|
||||
self._logger.error(
|
||||
"Error formatting recipe file URL: %s", exc, exc_info=True
|
||||
)
|
||||
return "/loras_static/images/no-preview.png"
|
||||
|
||||
return "/loras_static/images/no-preview.png"
|
||||
@@ -293,7 +310,9 @@ class RecipeQueryHandler:
|
||||
for tag in recipe.get("tags", []) or []:
|
||||
tag_counts[tag] = tag_counts.get(tag, 0) + 1
|
||||
|
||||
sorted_tags = [{"tag": tag, "count": count} for tag, count in tag_counts.items()]
|
||||
sorted_tags = [
|
||||
{"tag": tag, "count": count} for tag, count in tag_counts.items()
|
||||
]
|
||||
sorted_tags.sort(key=lambda entry: entry["count"], reverse=True)
|
||||
return web.json_response({"success": True, "tags": sorted_tags[:limit]})
|
||||
except Exception as exc:
|
||||
@@ -313,9 +332,14 @@ class RecipeQueryHandler:
|
||||
for recipe in getattr(cache, "raw_data", []):
|
||||
base_model = recipe.get("base_model")
|
||||
if base_model:
|
||||
base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
|
||||
base_model_counts[base_model] = (
|
||||
base_model_counts.get(base_model, 0) + 1
|
||||
)
|
||||
|
||||
sorted_models = [{"name": model, "count": count} for model, count in base_model_counts.items()]
|
||||
sorted_models = [
|
||||
{"name": model, "count": count}
|
||||
for model, count in base_model_counts.items()
|
||||
]
|
||||
sorted_models.sort(key=lambda entry: entry["count"], reverse=True)
|
||||
return web.json_response({"success": True, "base_models": sorted_models})
|
||||
except Exception as exc:
|
||||
@@ -345,7 +369,9 @@ class RecipeQueryHandler:
|
||||
folders = await recipe_scanner.get_folders()
|
||||
return web.json_response({"success": True, "folders": folders})
|
||||
except Exception as exc:
|
||||
self._logger.error("Error retrieving recipe folders: %s", exc, exc_info=True)
|
||||
self._logger.error(
|
||||
"Error retrieving recipe folders: %s", exc, exc_info=True
|
||||
)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
|
||||
async def get_folder_tree(self, request: web.Request) -> web.Response:
|
||||
@@ -358,7 +384,9 @@ class RecipeQueryHandler:
|
||||
folder_tree = await recipe_scanner.get_folder_tree()
|
||||
return web.json_response({"success": True, "tree": folder_tree})
|
||||
except Exception as exc:
|
||||
self._logger.error("Error retrieving recipe folder tree: %s", exc, exc_info=True)
|
||||
self._logger.error(
|
||||
"Error retrieving recipe folder tree: %s", exc, exc_info=True
|
||||
)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
|
||||
async def get_unified_folder_tree(self, request: web.Request) -> web.Response:
|
||||
@@ -371,7 +399,9 @@ class RecipeQueryHandler:
|
||||
folder_tree = await recipe_scanner.get_folder_tree()
|
||||
return web.json_response({"success": True, "tree": folder_tree})
|
||||
except Exception as exc:
|
||||
self._logger.error("Error retrieving unified recipe folder tree: %s", exc, exc_info=True)
|
||||
self._logger.error(
|
||||
"Error retrieving unified recipe folder tree: %s", exc, exc_info=True
|
||||
)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
|
||||
async def get_recipes_for_lora(self, request: web.Request) -> web.Response:
|
||||
@@ -383,7 +413,9 @@ class RecipeQueryHandler:
|
||||
|
||||
lora_hash = request.query.get("hash")
|
||||
if not lora_hash:
|
||||
return web.json_response({"success": False, "error": "Lora hash is required"}, status=400)
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Lora hash is required"}, status=400
|
||||
)
|
||||
|
||||
matching_recipes = await recipe_scanner.get_recipes_for_lora(lora_hash)
|
||||
return web.json_response({"success": True, "recipes": matching_recipes})
|
||||
@@ -400,7 +432,9 @@ class RecipeQueryHandler:
|
||||
|
||||
self._logger.info("Manually triggering recipe cache rebuild")
|
||||
await recipe_scanner.get_cached_data(force_refresh=True)
|
||||
return web.json_response({"success": True, "message": "Recipe cache refreshed successfully"})
|
||||
return web.json_response(
|
||||
{"success": True, "message": "Recipe cache refreshed successfully"}
|
||||
)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error refreshing recipe cache: %s", exc, exc_info=True)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
@@ -429,7 +463,9 @@ class RecipeQueryHandler:
|
||||
"id": recipe.get("id"),
|
||||
"title": recipe.get("title"),
|
||||
"file_url": recipe.get("file_url")
|
||||
or self._format_recipe_file_url(recipe.get("file_path", "")),
|
||||
or self._format_recipe_file_url(
|
||||
recipe.get("file_path", "")
|
||||
),
|
||||
"modified": recipe.get("modified"),
|
||||
"created_date": recipe.get("created_date"),
|
||||
"lora_count": len(recipe.get("loras", [])),
|
||||
@@ -437,7 +473,9 @@ class RecipeQueryHandler:
|
||||
)
|
||||
|
||||
if len(recipes) >= 2:
|
||||
recipes.sort(key=lambda entry: entry.get("modified", 0), reverse=True)
|
||||
recipes.sort(
|
||||
key=lambda entry: entry.get("modified", 0), reverse=True
|
||||
)
|
||||
response_data.append(
|
||||
{
|
||||
"type": "fingerprint",
|
||||
@@ -460,7 +498,9 @@ class RecipeQueryHandler:
|
||||
"id": recipe.get("id"),
|
||||
"title": recipe.get("title"),
|
||||
"file_url": recipe.get("file_url")
|
||||
or self._format_recipe_file_url(recipe.get("file_path", "")),
|
||||
or self._format_recipe_file_url(
|
||||
recipe.get("file_path", "")
|
||||
),
|
||||
"modified": recipe.get("modified"),
|
||||
"created_date": recipe.get("created_date"),
|
||||
"lora_count": len(recipe.get("loras", [])),
|
||||
@@ -468,7 +508,9 @@ class RecipeQueryHandler:
|
||||
)
|
||||
|
||||
if len(recipes) >= 2:
|
||||
recipes.sort(key=lambda entry: entry.get("modified", 0), reverse=True)
|
||||
recipes.sort(
|
||||
key=lambda entry: entry.get("modified", 0), reverse=True
|
||||
)
|
||||
response_data.append(
|
||||
{
|
||||
"type": "source_url",
|
||||
@@ -479,9 +521,13 @@ class RecipeQueryHandler:
|
||||
)
|
||||
|
||||
response_data.sort(key=lambda entry: entry["count"], reverse=True)
|
||||
return web.json_response({"success": True, "duplicate_groups": response_data})
|
||||
return web.json_response(
|
||||
{"success": True, "duplicate_groups": response_data}
|
||||
)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error finding duplicate recipes: %s", exc, exc_info=True)
|
||||
self._logger.error(
|
||||
"Error finding duplicate recipes: %s", exc, exc_info=True
|
||||
)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
|
||||
async def get_recipe_syntax(self, request: web.Request) -> web.Response:
|
||||
@@ -498,9 +544,13 @@ class RecipeQueryHandler:
|
||||
return web.json_response({"error": "Recipe not found"}, status=404)
|
||||
|
||||
if not syntax_parts:
|
||||
return web.json_response({"error": "No LoRAs found in this recipe"}, status=400)
|
||||
return web.json_response(
|
||||
{"error": "No LoRAs found in this recipe"}, status=400
|
||||
)
|
||||
|
||||
return web.json_response({"success": True, "syntax": " ".join(syntax_parts)})
|
||||
return web.json_response(
|
||||
{"success": True, "syntax": " ".join(syntax_parts)}
|
||||
)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error generating recipe syntax: %s", exc, exc_info=True)
|
||||
return web.json_response({"error": str(exc)}, status=500)
|
||||
@@ -561,11 +611,17 @@ class RecipeManagementHandler:
|
||||
await self._ensure_dependencies_ready()
|
||||
recipe_scanner = self._recipe_scanner_getter()
|
||||
if recipe_scanner is None:
|
||||
return web.json_response({"success": False, "error": "Recipe scanner unavailable"}, status=503)
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Recipe scanner unavailable"},
|
||||
status=503,
|
||||
)
|
||||
|
||||
# Check if already running
|
||||
if self._ws_manager.is_recipe_repair_running():
|
||||
return web.json_response({"success": False, "error": "Recipe repair already in progress"}, status=409)
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Recipe repair already in progress"},
|
||||
status=409,
|
||||
)
|
||||
|
||||
recipe_scanner.reset_cancellation()
|
||||
|
||||
@@ -579,11 +635,12 @@ class RecipeManagementHandler:
|
||||
progress_callback=progress_callback
|
||||
)
|
||||
except Exception as e:
|
||||
self._logger.error(f"Error in recipe repair task: {e}", exc_info=True)
|
||||
await self._ws_manager.broadcast_recipe_repair_progress({
|
||||
"status": "error",
|
||||
"error": str(e)
|
||||
})
|
||||
self._logger.error(
|
||||
f"Error in recipe repair task: {e}", exc_info=True
|
||||
)
|
||||
await self._ws_manager.broadcast_recipe_repair_progress(
|
||||
{"status": "error", "error": str(e)}
|
||||
)
|
||||
finally:
|
||||
# Keep the final status for a while so the UI can see it
|
||||
await asyncio.sleep(5)
|
||||
@@ -593,7 +650,9 @@ class RecipeManagementHandler:
|
||||
|
||||
asyncio.create_task(run_repair())
|
||||
|
||||
return web.json_response({"success": True, "message": "Recipe repair started"})
|
||||
return web.json_response(
|
||||
{"success": True, "message": "Recipe repair started"}
|
||||
)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error starting recipe repair: %s", exc, exc_info=True)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
@@ -603,10 +662,15 @@ class RecipeManagementHandler:
|
||||
await self._ensure_dependencies_ready()
|
||||
recipe_scanner = self._recipe_scanner_getter()
|
||||
if recipe_scanner is None:
|
||||
return web.json_response({"success": False, "error": "Recipe scanner unavailable"}, status=503)
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Recipe scanner unavailable"},
|
||||
status=503,
|
||||
)
|
||||
|
||||
recipe_scanner.cancel_task()
|
||||
return web.json_response({"success": True, "message": "Cancellation requested"})
|
||||
return web.json_response(
|
||||
{"success": True, "message": "Cancellation requested"}
|
||||
)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error cancelling recipe repair: %s", exc, exc_info=True)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
@@ -616,7 +680,10 @@ class RecipeManagementHandler:
|
||||
await self._ensure_dependencies_ready()
|
||||
recipe_scanner = self._recipe_scanner_getter()
|
||||
if recipe_scanner is None:
|
||||
return web.json_response({"success": False, "error": "Recipe scanner unavailable"}, status=503)
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Recipe scanner unavailable"},
|
||||
status=503,
|
||||
)
|
||||
|
||||
recipe_id = request.match_info["recipe_id"]
|
||||
result = await recipe_scanner.repair_recipe_by_id(recipe_id)
|
||||
@@ -632,25 +699,26 @@ class RecipeManagementHandler:
|
||||
progress = self._ws_manager.get_recipe_repair_progress()
|
||||
if progress:
|
||||
return web.json_response({"success": True, "progress": progress})
|
||||
return web.json_response({"success": False, "message": "No repair in progress"}, status=404)
|
||||
return web.json_response(
|
||||
{"success": False, "message": "No repair in progress"}, status=404
|
||||
)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error getting repair progress: %s", exc, exc_info=True)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
|
||||
|
||||
async def import_remote_recipe(self, request: web.Request) -> web.Response:
|
||||
try:
|
||||
await self._ensure_dependencies_ready()
|
||||
recipe_scanner = self._recipe_scanner_getter()
|
||||
if recipe_scanner is None:
|
||||
raise RuntimeError("Recipe scanner unavailable")
|
||||
|
||||
|
||||
# 1. Parse Parameters
|
||||
params = request.rel_url.query
|
||||
image_url = params.get("image_url")
|
||||
name = params.get("name")
|
||||
resources_raw = params.get("resources")
|
||||
|
||||
|
||||
if not image_url:
|
||||
raise RecipeValidationError("Missing required field: image_url")
|
||||
if not name:
|
||||
@@ -658,64 +726,80 @@ class RecipeManagementHandler:
|
||||
if not resources_raw:
|
||||
raise RecipeValidationError("Missing required field: resources")
|
||||
|
||||
checkpoint_entry, lora_entries = self._parse_resources_payload(resources_raw)
|
||||
checkpoint_entry, lora_entries = self._parse_resources_payload(
|
||||
resources_raw
|
||||
)
|
||||
gen_params_request = self._parse_gen_params(params.get("gen_params"))
|
||||
|
||||
|
||||
# 2. Initial Metadata Construction
|
||||
metadata: Dict[str, Any] = {
|
||||
"base_model": params.get("base_model", "") or "",
|
||||
"loras": lora_entries,
|
||||
"gen_params": gen_params_request or {},
|
||||
"source_url": image_url
|
||||
"source_url": image_url,
|
||||
}
|
||||
|
||||
|
||||
source_path = params.get("source_path")
|
||||
if source_path:
|
||||
metadata["source_path"] = source_path
|
||||
|
||||
|
||||
# Checkpoint handling
|
||||
if checkpoint_entry:
|
||||
metadata["checkpoint"] = checkpoint_entry
|
||||
# Ensure checkpoint is also in gen_params for consistency if needed by enricher?
|
||||
# Actually enricher looks at metadata['checkpoint'], so this is fine.
|
||||
|
||||
|
||||
# Try to resolve base model from checkpoint if not explicitly provided
|
||||
if not metadata["base_model"]:
|
||||
base_model_from_metadata = await self._resolve_base_model_from_checkpoint(checkpoint_entry)
|
||||
base_model_from_metadata = (
|
||||
await self._resolve_base_model_from_checkpoint(checkpoint_entry)
|
||||
)
|
||||
if base_model_from_metadata:
|
||||
metadata["base_model"] = base_model_from_metadata
|
||||
|
||||
tags = self._parse_tags(params.get("tags"))
|
||||
|
||||
|
||||
# 3. Download Image
|
||||
image_bytes, extension, civitai_meta_from_download = await self._download_remote_media(image_url)
|
||||
(
|
||||
image_bytes,
|
||||
extension,
|
||||
civitai_meta_from_download,
|
||||
) = await self._download_remote_media(image_url)
|
||||
|
||||
# 4. Extract Embedded Metadata
|
||||
# Note: We still extract this here because Enricher currently expects 'gen_params' to already be populated
|
||||
# with embedded data if we want it to merge it.
|
||||
# Note: We still extract this here because Enricher currently expects 'gen_params' to already be populated
|
||||
# with embedded data if we want it to merge it.
|
||||
# However, logic in Enricher merges: request > civitai > embedded.
|
||||
# So we should gather embedded params and put them into the recipe's gen_params (as initial state)
|
||||
# So we should gather embedded params and put them into the recipe's gen_params (as initial state)
|
||||
# OR pass them to enricher to handle?
|
||||
# The interface of Enricher.enrich_recipe takes `recipe` (with gen_params) and `request_params`.
|
||||
# So let's extract embedded and put it into recipe['gen_params'] but careful not to overwrite request params.
|
||||
# Actually, `GenParamsMerger` which `Enricher` uses handles 3 layers.
|
||||
# But `Enricher` interface is: recipe['gen_params'] (as embedded) + request_params + civitai (fetched internally).
|
||||
# Wait, `Enricher` fetches Civitai info internally based on URL.
|
||||
# Wait, `Enricher` fetches Civitai info internally based on URL.
|
||||
# `civitai_meta_from_download` is returned by `_download_remote_media` which might be useful if URL didn't have ID.
|
||||
|
||||
|
||||
# Let's extract embedded metadata first
|
||||
embedded_gen_params = {}
|
||||
try:
|
||||
with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as temp_img:
|
||||
with tempfile.NamedTemporaryFile(
|
||||
suffix=extension, delete=False
|
||||
) as temp_img:
|
||||
temp_img.write(image_bytes)
|
||||
temp_img_path = temp_img.name
|
||||
|
||||
|
||||
try:
|
||||
raw_embedded = ExifUtils.extract_image_metadata(temp_img_path)
|
||||
if raw_embedded:
|
||||
parser = self._analysis_service._recipe_parser_factory.create_parser(raw_embedded)
|
||||
parser = (
|
||||
self._analysis_service._recipe_parser_factory.create_parser(
|
||||
raw_embedded
|
||||
)
|
||||
)
|
||||
if parser:
|
||||
parsed_embedded = await parser.parse_metadata(raw_embedded, recipe_scanner=recipe_scanner)
|
||||
parsed_embedded = await parser.parse_metadata(
|
||||
raw_embedded, recipe_scanner=recipe_scanner
|
||||
)
|
||||
if parsed_embedded and "gen_params" in parsed_embedded:
|
||||
embedded_gen_params = parsed_embedded["gen_params"]
|
||||
else:
|
||||
@@ -724,7 +808,9 @@ class RecipeManagementHandler:
|
||||
if os.path.exists(temp_img_path):
|
||||
os.unlink(temp_img_path)
|
||||
except Exception as exc:
|
||||
self._logger.warning("Failed to extract embedded metadata during import: %s", exc)
|
||||
self._logger.warning(
|
||||
"Failed to extract embedded metadata during import: %s", exc
|
||||
)
|
||||
|
||||
# Pre-populate gen_params with embedded data so Enricher treats it as the "base" layer
|
||||
if embedded_gen_params:
|
||||
@@ -732,18 +818,18 @@ class RecipeManagementHandler:
|
||||
# But wait, we want request params to override everything.
|
||||
# So we should set recipe['gen_params'] = embedded, and pass request params to enricher.
|
||||
metadata["gen_params"] = embedded_gen_params
|
||||
|
||||
|
||||
# 5. Enrich with unified logic
|
||||
# This will fetch Civitai info (if URL matches) and merge: request > civitai > embedded
|
||||
civitai_client = self._civitai_client_getter()
|
||||
await RecipeEnricher.enrich_recipe(
|
||||
recipe=metadata,
|
||||
recipe=metadata,
|
||||
civitai_client=civitai_client,
|
||||
request_params=gen_params_request # Pass explicit request params here to override
|
||||
request_params=gen_params_request, # Pass explicit request params here to override
|
||||
)
|
||||
|
||||
|
||||
# If we got civitai_meta from download but Enricher didn't fetch it (e.g. not a civitai URL or failed),
|
||||
# we might want to manually merge it?
|
||||
# we might want to manually merge it?
|
||||
# But usually `import_remote_recipe` is used with Civitai URLs.
|
||||
# For now, relying on Enricher's internal fetch is consistent with repair.
|
||||
|
||||
@@ -762,7 +848,9 @@ class RecipeManagementHandler:
|
||||
except RecipeDownloadError as exc:
|
||||
return web.json_response({"error": str(exc)}, status=400)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error importing recipe from remote source: %s", exc, exc_info=True)
|
||||
self._logger.error(
|
||||
"Error importing recipe from remote source: %s", exc, exc_info=True
|
||||
)
|
||||
return web.json_response({"error": str(exc)}, status=500)
|
||||
|
||||
async def delete_recipe(self, request: web.Request) -> web.Response:
|
||||
@@ -816,7 +904,11 @@ class RecipeManagementHandler:
|
||||
target_path = data.get("target_path")
|
||||
if not recipe_id or not target_path:
|
||||
return web.json_response(
|
||||
{"success": False, "error": "recipe_id and target_path are required"}, status=400
|
||||
{
|
||||
"success": False,
|
||||
"error": "recipe_id and target_path are required",
|
||||
},
|
||||
status=400,
|
||||
)
|
||||
|
||||
result = await self._persistence_service.move_recipe(
|
||||
@@ -845,7 +937,11 @@ class RecipeManagementHandler:
|
||||
target_path = data.get("target_path")
|
||||
if not recipe_ids or not target_path:
|
||||
return web.json_response(
|
||||
{"success": False, "error": "recipe_ids and target_path are required"}, status=400
|
||||
{
|
||||
"success": False,
|
||||
"error": "recipe_ids and target_path are required",
|
||||
},
|
||||
status=400,
|
||||
)
|
||||
|
||||
result = await self._persistence_service.move_recipes_bulk(
|
||||
@@ -934,7 +1030,9 @@ class RecipeManagementHandler:
|
||||
except RecipeValidationError as exc:
|
||||
return web.json_response({"error": str(exc)}, status=400)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error saving recipe from widget: %s", exc, exc_info=True)
|
||||
self._logger.error(
|
||||
"Error saving recipe from widget: %s", exc, exc_info=True
|
||||
)
|
||||
return web.json_response({"error": str(exc)}, status=500)
|
||||
|
||||
async def _parse_save_payload(self, reader) -> dict[str, Any]:
|
||||
@@ -1006,7 +1104,9 @@ class RecipeManagementHandler:
|
||||
raise RecipeValidationError("gen_params payload must be an object")
|
||||
return parsed
|
||||
|
||||
def _parse_resources_payload(self, payload_raw: str) -> tuple[Optional[Dict[str, Any]], List[Dict[str, Any]]]:
|
||||
def _parse_resources_payload(
|
||||
self, payload_raw: str
|
||||
) -> tuple[Optional[Dict[str, Any]], List[Dict[str, Any]]]:
|
||||
try:
|
||||
payload = json.loads(payload_raw)
|
||||
except json.JSONDecodeError as exc:
|
||||
@@ -1066,15 +1166,19 @@ class RecipeManagementHandler:
|
||||
civitai_match = re.match(r"https://civitai\.com/images/(\d+)", image_url)
|
||||
if civitai_match:
|
||||
if civitai_client is None:
|
||||
raise RecipeDownloadError("Civitai client unavailable for image download")
|
||||
raise RecipeDownloadError(
|
||||
"Civitai client unavailable for image download"
|
||||
)
|
||||
image_info = await civitai_client.get_image_info(civitai_match.group(1))
|
||||
if not image_info:
|
||||
raise RecipeDownloadError("Failed to fetch image information from Civitai")
|
||||
|
||||
raise RecipeDownloadError(
|
||||
"Failed to fetch image information from Civitai"
|
||||
)
|
||||
|
||||
media_url = image_info.get("url")
|
||||
if not media_url:
|
||||
raise RecipeDownloadError("No image URL found in Civitai response")
|
||||
|
||||
|
||||
# Use optimized preview URLs if possible
|
||||
media_type = image_info.get("type")
|
||||
rewritten_url, _ = rewrite_preview_url(media_url, media_type=media_type)
|
||||
@@ -1083,18 +1187,24 @@ class RecipeManagementHandler:
|
||||
else:
|
||||
download_url = media_url
|
||||
|
||||
success, result = await downloader.download_file(download_url, temp_path, use_auth=False)
|
||||
success, result = await downloader.download_file(
|
||||
download_url, temp_path, use_auth=False
|
||||
)
|
||||
if not success:
|
||||
raise RecipeDownloadError(f"Failed to download image: {result}")
|
||||
|
||||
|
||||
# Extract extension from URL
|
||||
url_path = download_url.split('?')[0].split('#')[0]
|
||||
url_path = download_url.split("?")[0].split("#")[0]
|
||||
extension = os.path.splitext(url_path)[1].lower()
|
||||
if not extension:
|
||||
extension = ".webp" # Default to webp if unknown
|
||||
extension = ".webp" # Default to webp if unknown
|
||||
|
||||
with open(temp_path, "rb") as file_obj:
|
||||
return file_obj.read(), extension, image_info.get("meta") if civitai_match and image_info else None
|
||||
return (
|
||||
file_obj.read(),
|
||||
extension,
|
||||
image_info.get("meta") if civitai_match and image_info else None,
|
||||
)
|
||||
except RecipeDownloadError:
|
||||
raise
|
||||
except RecipeValidationError:
|
||||
@@ -1108,14 +1218,15 @@ class RecipeManagementHandler:
|
||||
except FileNotFoundError:
|
||||
pass
|
||||
|
||||
|
||||
def _safe_int(self, value: Any) -> int:
|
||||
try:
|
||||
return int(value)
|
||||
except (TypeError, ValueError):
|
||||
return 0
|
||||
|
||||
async def _resolve_base_model_from_checkpoint(self, checkpoint_entry: Dict[str, Any]) -> str:
|
||||
async def _resolve_base_model_from_checkpoint(
|
||||
self, checkpoint_entry: Dict[str, Any]
|
||||
) -> str:
|
||||
version_id = self._safe_int(checkpoint_entry.get("modelVersionId"))
|
||||
|
||||
if not version_id:
|
||||
@@ -1134,7 +1245,9 @@ class RecipeManagementHandler:
|
||||
base_model = version_info.get("baseModel") or ""
|
||||
return str(base_model) if base_model is not None else ""
|
||||
except Exception as exc: # pragma: no cover - defensive logging
|
||||
self._logger.warning("Failed to resolve base model from checkpoint metadata: %s", exc)
|
||||
self._logger.warning(
|
||||
"Failed to resolve base model from checkpoint metadata: %s", exc
|
||||
)
|
||||
|
||||
return ""
|
||||
|
||||
@@ -1279,5 +1392,311 @@ class RecipeSharingHandler:
|
||||
except RecipeNotFoundError as exc:
|
||||
return web.json_response({"error": str(exc)}, status=404)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error downloading shared recipe: %s", exc, exc_info=True)
|
||||
self._logger.error(
|
||||
"Error downloading shared recipe: %s", exc, exc_info=True
|
||||
)
|
||||
return web.json_response({"error": str(exc)}, status=500)
|
||||
|
||||
|
||||
class BatchImportHandler:
|
||||
"""Handle batch import operations for recipes."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
ensure_dependencies_ready: EnsureDependenciesCallable,
|
||||
recipe_scanner_getter: RecipeScannerGetter,
|
||||
civitai_client_getter: CivitaiClientGetter,
|
||||
logger: Logger,
|
||||
batch_import_service: BatchImportService,
|
||||
) -> None:
|
||||
self._ensure_dependencies_ready = ensure_dependencies_ready
|
||||
self._recipe_scanner_getter = recipe_scanner_getter
|
||||
self._civitai_client_getter = civitai_client_getter
|
||||
self._logger = logger
|
||||
self._batch_import_service = batch_import_service
|
||||
|
||||
async def start_batch_import(self, request: web.Request) -> web.Response:
|
||||
try:
|
||||
await self._ensure_dependencies_ready()
|
||||
|
||||
if self._batch_import_service.is_import_running():
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Batch import already in progress"},
|
||||
status=409,
|
||||
)
|
||||
|
||||
data = await request.json()
|
||||
items = data.get("items", [])
|
||||
tags = data.get("tags", [])
|
||||
skip_no_metadata = data.get("skip_no_metadata", False)
|
||||
|
||||
if not items:
|
||||
return web.json_response(
|
||||
{"success": False, "error": "No items provided"},
|
||||
status=400,
|
||||
)
|
||||
|
||||
for item in items:
|
||||
if not item.get("source"):
|
||||
return web.json_response(
|
||||
{
|
||||
"success": False,
|
||||
"error": "Each item must have a 'source' field",
|
||||
},
|
||||
status=400,
|
||||
)
|
||||
|
||||
operation_id = await self._batch_import_service.start_batch_import(
|
||||
recipe_scanner_getter=self._recipe_scanner_getter,
|
||||
civitai_client_getter=self._civitai_client_getter,
|
||||
items=items,
|
||||
tags=tags,
|
||||
skip_no_metadata=skip_no_metadata,
|
||||
)
|
||||
|
||||
return web.json_response(
|
||||
{
|
||||
"success": True,
|
||||
"operation_id": operation_id,
|
||||
}
|
||||
)
|
||||
except RecipeValidationError as exc:
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=400)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error starting batch import: %s", exc, exc_info=True)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
|
||||
async def start_directory_import(self, request: web.Request) -> web.Response:
|
||||
try:
|
||||
await self._ensure_dependencies_ready()
|
||||
|
||||
if self._batch_import_service.is_import_running():
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Batch import already in progress"},
|
||||
status=409,
|
||||
)
|
||||
|
||||
data = await request.json()
|
||||
directory = data.get("directory")
|
||||
recursive = data.get("recursive", True)
|
||||
tags = data.get("tags", [])
|
||||
skip_no_metadata = data.get("skip_no_metadata", True)
|
||||
|
||||
if not directory:
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Directory path is required"},
|
||||
status=400,
|
||||
)
|
||||
|
||||
operation_id = await self._batch_import_service.start_directory_import(
|
||||
recipe_scanner_getter=self._recipe_scanner_getter,
|
||||
civitai_client_getter=self._civitai_client_getter,
|
||||
directory=directory,
|
||||
recursive=recursive,
|
||||
tags=tags,
|
||||
skip_no_metadata=skip_no_metadata,
|
||||
)
|
||||
|
||||
return web.json_response(
|
||||
{
|
||||
"success": True,
|
||||
"operation_id": operation_id,
|
||||
}
|
||||
)
|
||||
except RecipeValidationError as exc:
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=400)
|
||||
except Exception as exc:
|
||||
self._logger.error(
|
||||
"Error starting directory import: %s", exc, exc_info=True
|
||||
)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
|
||||
async def get_batch_import_progress(self, request: web.Request) -> web.Response:
|
||||
try:
|
||||
operation_id = request.query.get("operation_id")
|
||||
if not operation_id:
|
||||
return web.json_response(
|
||||
{"success": False, "error": "operation_id is required"},
|
||||
status=400,
|
||||
)
|
||||
|
||||
progress = self._batch_import_service.get_progress(operation_id)
|
||||
if not progress:
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Operation not found"},
|
||||
status=404,
|
||||
)
|
||||
|
||||
return web.json_response(
|
||||
{
|
||||
"success": True,
|
||||
"progress": progress.to_dict(),
|
||||
}
|
||||
)
|
||||
except Exception as exc:
|
||||
self._logger.error(
|
||||
"Error getting batch import progress: %s", exc, exc_info=True
|
||||
)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
|
||||
async def cancel_batch_import(self, request: web.Request) -> web.Response:
|
||||
try:
|
||||
data = await request.json()
|
||||
operation_id = data.get("operation_id")
|
||||
|
||||
if not operation_id:
|
||||
return web.json_response(
|
||||
{"success": False, "error": "operation_id is required"},
|
||||
status=400,
|
||||
)
|
||||
|
||||
cancelled = self._batch_import_service.cancel_import(operation_id)
|
||||
if not cancelled:
|
||||
return web.json_response(
|
||||
{
|
||||
"success": False,
|
||||
"error": "Operation not found or already completed",
|
||||
},
|
||||
status=404,
|
||||
)
|
||||
|
||||
return web.json_response(
|
||||
{"success": True, "message": "Cancellation requested"}
|
||||
)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error cancelling batch import: %s", exc, exc_info=True)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
|
||||
async def browse_directory(self, request: web.Request) -> web.Response:
|
||||
"""Browse a directory and return its contents (subdirectories and files)."""
|
||||
try:
|
||||
data = await request.json()
|
||||
directory_path = data.get("path", "")
|
||||
|
||||
if not directory_path:
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Directory path is required"},
|
||||
status=400,
|
||||
)
|
||||
|
||||
# Normalize the path
|
||||
path = Path(directory_path).expanduser().resolve()
|
||||
|
||||
# Security check: ensure path is within allowed directories
|
||||
# Allow common image/model directories
|
||||
allowed_roots = [
|
||||
Path.home(),
|
||||
Path("/"), # Allow browsing from root for flexibility
|
||||
]
|
||||
|
||||
# Check if path is within any allowed root
|
||||
is_allowed = False
|
||||
for root in allowed_roots:
|
||||
try:
|
||||
path.relative_to(root)
|
||||
is_allowed = True
|
||||
break
|
||||
except ValueError:
|
||||
continue
|
||||
|
||||
if not is_allowed:
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Access denied to this directory"},
|
||||
status=403,
|
||||
)
|
||||
|
||||
if not path.exists():
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Directory does not exist"},
|
||||
status=404,
|
||||
)
|
||||
|
||||
if not path.is_dir():
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Path is not a directory"},
|
||||
status=400,
|
||||
)
|
||||
|
||||
# List directory contents
|
||||
directories = []
|
||||
image_files = []
|
||||
|
||||
image_extensions = {
|
||||
".jpg",
|
||||
".jpeg",
|
||||
".png",
|
||||
".gif",
|
||||
".webp",
|
||||
".bmp",
|
||||
".tiff",
|
||||
".tif",
|
||||
}
|
||||
|
||||
try:
|
||||
for item in path.iterdir():
|
||||
try:
|
||||
if item.is_dir():
|
||||
# Skip hidden directories and common system folders
|
||||
if not item.name.startswith(".") and item.name not in [
|
||||
"__pycache__",
|
||||
"node_modules",
|
||||
]:
|
||||
directories.append(
|
||||
{
|
||||
"name": item.name,
|
||||
"path": str(item),
|
||||
"is_parent": False,
|
||||
}
|
||||
)
|
||||
elif item.is_file() and item.suffix.lower() in image_extensions:
|
||||
image_files.append(
|
||||
{
|
||||
"name": item.name,
|
||||
"path": str(item),
|
||||
"size": item.stat().st_size,
|
||||
}
|
||||
)
|
||||
except (PermissionError, OSError):
|
||||
# Skip files/directories we can't access
|
||||
continue
|
||||
|
||||
# Sort directories and files alphabetically
|
||||
directories.sort(key=lambda x: x["name"].lower())
|
||||
image_files.sort(key=lambda x: x["name"].lower())
|
||||
|
||||
# Add parent directory if not at root
|
||||
parent_path = path.parent
|
||||
show_parent = str(path) != str(path.root)
|
||||
|
||||
return web.json_response(
|
||||
{
|
||||
"success": True,
|
||||
"current_path": str(path),
|
||||
"parent_path": str(parent_path) if show_parent else None,
|
||||
"directories": directories,
|
||||
"image_files": image_files,
|
||||
"image_count": len(image_files),
|
||||
"directory_count": len(directories),
|
||||
}
|
||||
)
|
||||
|
||||
except PermissionError:
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Permission denied"},
|
||||
status=403,
|
||||
)
|
||||
except OSError as exc:
|
||||
return web.json_response(
|
||||
{"success": False, "error": f"Error reading directory: {str(exc)}"},
|
||||
status=500,
|
||||
)
|
||||
|
||||
except json.JSONDecodeError:
|
||||
return web.json_response(
|
||||
{"success": False, "error": "Invalid JSON"},
|
||||
status=400,
|
||||
)
|
||||
except Exception as exc:
|
||||
self._logger.error("Error browsing directory: %s", exc, exc_info=True)
|
||||
return web.json_response({"success": False, "error": str(exc)}, status=500)
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
"""Route registrar for recipe endpoints."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
@@ -22,7 +23,9 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
|
||||
RouteDefinition("GET", "/api/lm/recipe/{recipe_id}", "get_recipe"),
|
||||
RouteDefinition("GET", "/api/lm/recipes/import-remote", "import_remote_recipe"),
|
||||
RouteDefinition("POST", "/api/lm/recipes/analyze-image", "analyze_uploaded_image"),
|
||||
RouteDefinition("POST", "/api/lm/recipes/analyze-local-image", "analyze_local_image"),
|
||||
RouteDefinition(
|
||||
"POST", "/api/lm/recipes/analyze-local-image", "analyze_local_image"
|
||||
),
|
||||
RouteDefinition("POST", "/api/lm/recipes/save", "save_recipe"),
|
||||
RouteDefinition("DELETE", "/api/lm/recipe/{recipe_id}", "delete_recipe"),
|
||||
RouteDefinition("GET", "/api/lm/recipes/top-tags", "get_top_tags"),
|
||||
@@ -30,9 +33,13 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
|
||||
RouteDefinition("GET", "/api/lm/recipes/roots", "get_roots"),
|
||||
RouteDefinition("GET", "/api/lm/recipes/folders", "get_folders"),
|
||||
RouteDefinition("GET", "/api/lm/recipes/folder-tree", "get_folder_tree"),
|
||||
RouteDefinition("GET", "/api/lm/recipes/unified-folder-tree", "get_unified_folder_tree"),
|
||||
RouteDefinition(
|
||||
"GET", "/api/lm/recipes/unified-folder-tree", "get_unified_folder_tree"
|
||||
),
|
||||
RouteDefinition("GET", "/api/lm/recipe/{recipe_id}/share", "share_recipe"),
|
||||
RouteDefinition("GET", "/api/lm/recipe/{recipe_id}/share/download", "download_shared_recipe"),
|
||||
RouteDefinition(
|
||||
"GET", "/api/lm/recipe/{recipe_id}/share/download", "download_shared_recipe"
|
||||
),
|
||||
RouteDefinition("GET", "/api/lm/recipe/{recipe_id}/syntax", "get_recipe_syntax"),
|
||||
RouteDefinition("PUT", "/api/lm/recipe/{recipe_id}/update", "update_recipe"),
|
||||
RouteDefinition("POST", "/api/lm/recipe/move", "move_recipe"),
|
||||
@@ -40,13 +47,26 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
|
||||
RouteDefinition("POST", "/api/lm/recipe/lora/reconnect", "reconnect_lora"),
|
||||
RouteDefinition("GET", "/api/lm/recipes/find-duplicates", "find_duplicates"),
|
||||
RouteDefinition("POST", "/api/lm/recipes/bulk-delete", "bulk_delete"),
|
||||
RouteDefinition("POST", "/api/lm/recipes/save-from-widget", "save_recipe_from_widget"),
|
||||
RouteDefinition(
|
||||
"POST", "/api/lm/recipes/save-from-widget", "save_recipe_from_widget"
|
||||
),
|
||||
RouteDefinition("GET", "/api/lm/recipes/for-lora", "get_recipes_for_lora"),
|
||||
RouteDefinition("GET", "/api/lm/recipes/scan", "scan_recipes"),
|
||||
RouteDefinition("POST", "/api/lm/recipes/repair", "repair_recipes"),
|
||||
RouteDefinition("POST", "/api/lm/recipes/cancel-repair", "cancel_repair"),
|
||||
RouteDefinition("POST", "/api/lm/recipe/{recipe_id}/repair", "repair_recipe"),
|
||||
RouteDefinition("GET", "/api/lm/recipes/repair-progress", "get_repair_progress"),
|
||||
RouteDefinition("POST", "/api/lm/recipes/batch-import/start", "start_batch_import"),
|
||||
RouteDefinition(
|
||||
"GET", "/api/lm/recipes/batch-import/progress", "get_batch_import_progress"
|
||||
),
|
||||
RouteDefinition(
|
||||
"POST", "/api/lm/recipes/batch-import/cancel", "cancel_batch_import"
|
||||
),
|
||||
RouteDefinition(
|
||||
"POST", "/api/lm/recipes/batch-import/directory", "start_directory_import"
|
||||
),
|
||||
RouteDefinition("POST", "/api/lm/recipes/browse-directory", "browse_directory"),
|
||||
)
|
||||
|
||||
|
||||
@@ -63,7 +83,9 @@ class RecipeRouteRegistrar:
|
||||
def __init__(self, app: web.Application) -> None:
|
||||
self._app = app
|
||||
|
||||
def register_routes(self, handler_lookup: Mapping[str, Callable[[web.Request], object]]) -> None:
|
||||
def register_routes(
|
||||
self, handler_lookup: Mapping[str, Callable[[web.Request], object]]
|
||||
) -> None:
|
||||
for definition in ROUTE_DEFINITIONS:
|
||||
handler = handler_lookup[definition.handler_name]
|
||||
self._bind_route(definition.method, definition.path, handler)
|
||||
|
||||
593
py/services/batch_import_service.py
Normal file
593
py/services/batch_import_service.py
Normal file
@@ -0,0 +1,593 @@
|
||||
"""Batch import service for importing multiple images as recipes."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
import uuid
|
||||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
from typing import Any, Callable, Dict, List, Optional, Set
|
||||
|
||||
from aiohttp import web
|
||||
|
||||
from .recipes import (
|
||||
RecipeAnalysisService,
|
||||
RecipePersistenceService,
|
||||
RecipeValidationError,
|
||||
RecipeDownloadError,
|
||||
RecipeNotFoundError,
|
||||
)
|
||||
|
||||
|
||||
class ImportItemType(Enum):
|
||||
"""Type of import item."""
|
||||
|
||||
URL = "url"
|
||||
LOCAL_PATH = "local_path"
|
||||
|
||||
|
||||
class ImportStatus(Enum):
|
||||
"""Status of an individual import item."""
|
||||
|
||||
PENDING = "pending"
|
||||
PROCESSING = "processing"
|
||||
SUCCESS = "success"
|
||||
FAILED = "failed"
|
||||
SKIPPED = "skipped"
|
||||
|
||||
|
||||
@dataclass
|
||||
class BatchImportItem:
|
||||
"""Represents a single item to import."""
|
||||
|
||||
id: str
|
||||
source: str
|
||||
item_type: ImportItemType
|
||||
status: ImportStatus = ImportStatus.PENDING
|
||||
error_message: Optional[str] = None
|
||||
recipe_name: Optional[str] = None
|
||||
recipe_id: Optional[str] = None
|
||||
duration: float = 0.0
|
||||
|
||||
|
||||
@dataclass
|
||||
class BatchImportProgress:
|
||||
"""Tracks progress of a batch import operation."""
|
||||
|
||||
operation_id: str
|
||||
total: int
|
||||
completed: int = 0
|
||||
success: int = 0
|
||||
failed: int = 0
|
||||
skipped: int = 0
|
||||
current_item: str = ""
|
||||
status: str = "pending"
|
||||
started_at: float = field(default_factory=time.time)
|
||||
finished_at: Optional[float] = None
|
||||
items: List[BatchImportItem] = field(default_factory=list)
|
||||
tags: List[str] = field(default_factory=list)
|
||||
skip_no_metadata: bool = False
|
||||
skip_duplicates: bool = False
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"operation_id": self.operation_id,
|
||||
"total": self.total,
|
||||
"completed": self.completed,
|
||||
"success": self.success,
|
||||
"failed": self.failed,
|
||||
"skipped": self.skipped,
|
||||
"current_item": self.current_item,
|
||||
"status": self.status,
|
||||
"started_at": self.started_at,
|
||||
"finished_at": self.finished_at,
|
||||
"progress_percent": round((self.completed / self.total) * 100, 1)
|
||||
if self.total > 0
|
||||
else 0,
|
||||
"items": [
|
||||
{
|
||||
"id": item.id,
|
||||
"source": item.source,
|
||||
"item_type": item.item_type.value,
|
||||
"status": item.status.value,
|
||||
"error_message": item.error_message,
|
||||
"recipe_name": item.recipe_name,
|
||||
"recipe_id": item.recipe_id,
|
||||
"duration": item.duration,
|
||||
}
|
||||
for item in self.items
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
class AdaptiveConcurrencyController:
|
||||
"""Adjusts concurrency based on task performance."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
min_concurrency: int = 1,
|
||||
max_concurrency: int = 5,
|
||||
initial_concurrency: int = 3,
|
||||
) -> None:
|
||||
self.min_concurrency = min_concurrency
|
||||
self.max_concurrency = max_concurrency
|
||||
self.current_concurrency = initial_concurrency
|
||||
self._task_durations: List[float] = []
|
||||
self._recent_errors = 0
|
||||
self._recent_successes = 0
|
||||
|
||||
def record_result(self, duration: float, success: bool) -> None:
|
||||
self._task_durations.append(duration)
|
||||
if len(self._task_durations) > 10:
|
||||
self._task_durations.pop(0)
|
||||
|
||||
if success:
|
||||
self._recent_successes += 1
|
||||
if duration < 1.0 and self.current_concurrency < self.max_concurrency:
|
||||
self.current_concurrency = min(
|
||||
self.current_concurrency + 1, self.max_concurrency
|
||||
)
|
||||
elif duration > 10.0 and self.current_concurrency > self.min_concurrency:
|
||||
self.current_concurrency = max(
|
||||
self.current_concurrency - 1, self.min_concurrency
|
||||
)
|
||||
else:
|
||||
self._recent_errors += 1
|
||||
if self.current_concurrency > self.min_concurrency:
|
||||
self.current_concurrency = max(
|
||||
self.current_concurrency - 1, self.min_concurrency
|
||||
)
|
||||
|
||||
def reset_counters(self) -> None:
|
||||
self._recent_errors = 0
|
||||
self._recent_successes = 0
|
||||
|
||||
def get_semaphore(self) -> asyncio.Semaphore:
|
||||
return asyncio.Semaphore(self.current_concurrency)
|
||||
|
||||
|
||||
class BatchImportService:
|
||||
"""Service for batch importing images as recipes."""
|
||||
|
||||
SUPPORTED_EXTENSIONS: Set[str] = {".jpg", ".jpeg", ".png", ".webp", ".gif", ".bmp"}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
analysis_service: RecipeAnalysisService,
|
||||
persistence_service: RecipePersistenceService,
|
||||
ws_manager: Any,
|
||||
logger: logging.Logger,
|
||||
) -> None:
|
||||
self._analysis_service = analysis_service
|
||||
self._persistence_service = persistence_service
|
||||
self._ws_manager = ws_manager
|
||||
self._logger = logger
|
||||
self._active_operations: Dict[str, BatchImportProgress] = {}
|
||||
self._cancellation_flags: Dict[str, bool] = {}
|
||||
self._concurrency_controller = AdaptiveConcurrencyController()
|
||||
|
||||
def is_import_running(self, operation_id: Optional[str] = None) -> bool:
|
||||
if operation_id:
|
||||
progress = self._active_operations.get(operation_id)
|
||||
return progress is not None and progress.status in ("pending", "running")
|
||||
return any(
|
||||
p.status in ("pending", "running") for p in self._active_operations.values()
|
||||
)
|
||||
|
||||
def get_progress(self, operation_id: str) -> Optional[BatchImportProgress]:
|
||||
return self._active_operations.get(operation_id)
|
||||
|
||||
def cancel_import(self, operation_id: str) -> bool:
|
||||
if operation_id in self._active_operations:
|
||||
self._cancellation_flags[operation_id] = True
|
||||
return True
|
||||
return False
|
||||
|
||||
def _validate_url(self, url: str) -> bool:
|
||||
import re
|
||||
|
||||
url_pattern = re.compile(
|
||||
r"^https?://"
|
||||
r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+[A-Z]{2,6}\.?|"
|
||||
r"localhost|"
|
||||
r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})"
|
||||
r"(?::\d+)?"
|
||||
r"(?:/?|[/?]\S+)$",
|
||||
re.IGNORECASE,
|
||||
)
|
||||
return url_pattern.match(url) is not None
|
||||
|
||||
def _validate_local_path(self, path: str) -> bool:
|
||||
try:
|
||||
normalized = os.path.normpath(path)
|
||||
if not os.path.isabs(normalized):
|
||||
return False
|
||||
if ".." in normalized:
|
||||
return False
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _is_duplicate_source(
|
||||
self,
|
||||
source: str,
|
||||
item_type: ImportItemType,
|
||||
recipe_scanner: Any,
|
||||
) -> bool:
|
||||
try:
|
||||
cache = recipe_scanner.get_cached_data_sync()
|
||||
if not cache:
|
||||
return False
|
||||
|
||||
for recipe in getattr(cache, "raw_data", []):
|
||||
source_path = recipe.get("source_path") or recipe.get("source_url")
|
||||
if source_path and source_path == source:
|
||||
return True
|
||||
return False
|
||||
except Exception:
|
||||
self._logger.warning("Failed to check for duplicates", exc_info=True)
|
||||
return False
|
||||
|
||||
async def start_batch_import(
|
||||
self,
|
||||
*,
|
||||
recipe_scanner_getter: Callable[[], Any],
|
||||
civitai_client_getter: Callable[[], Any],
|
||||
items: List[Dict[str, str]],
|
||||
tags: Optional[List[str]] = None,
|
||||
skip_no_metadata: bool = False,
|
||||
skip_duplicates: bool = False,
|
||||
) -> str:
|
||||
operation_id = str(uuid.uuid4())
|
||||
|
||||
import_items = []
|
||||
for idx, item in enumerate(items):
|
||||
source = item.get("source", "")
|
||||
item_type_str = item.get("type", "url")
|
||||
|
||||
if item_type_str == "url" or source.startswith(("http://", "https://")):
|
||||
item_type = ImportItemType.URL
|
||||
else:
|
||||
item_type = ImportItemType.LOCAL_PATH
|
||||
|
||||
batch_import_item = BatchImportItem(
|
||||
id=f"{operation_id}_{idx}",
|
||||
source=source,
|
||||
item_type=item_type,
|
||||
)
|
||||
import_items.append(batch_import_item)
|
||||
|
||||
progress = BatchImportProgress(
|
||||
operation_id=operation_id,
|
||||
total=len(import_items),
|
||||
items=import_items,
|
||||
tags=tags or [],
|
||||
skip_no_metadata=skip_no_metadata,
|
||||
skip_duplicates=skip_duplicates,
|
||||
)
|
||||
|
||||
self._active_operations[operation_id] = progress
|
||||
self._cancellation_flags[operation_id] = False
|
||||
|
||||
asyncio.create_task(
|
||||
self._run_batch_import(
|
||||
operation_id=operation_id,
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
)
|
||||
)
|
||||
|
||||
return operation_id
|
||||
|
||||
async def start_directory_import(
|
||||
self,
|
||||
*,
|
||||
recipe_scanner_getter: Callable[[], Any],
|
||||
civitai_client_getter: Callable[[], Any],
|
||||
directory: str,
|
||||
recursive: bool = True,
|
||||
tags: Optional[List[str]] = None,
|
||||
skip_no_metadata: bool = False,
|
||||
skip_duplicates: bool = False,
|
||||
) -> str:
|
||||
image_paths = await self._discover_images(directory, recursive)
|
||||
|
||||
items = [{"source": path, "type": "local_path"} for path in image_paths]
|
||||
|
||||
return await self.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=items,
|
||||
tags=tags,
|
||||
skip_no_metadata=skip_no_metadata,
|
||||
skip_duplicates=skip_duplicates,
|
||||
)
|
||||
|
||||
async def _discover_images(
|
||||
self,
|
||||
directory: str,
|
||||
recursive: bool = True,
|
||||
) -> List[str]:
|
||||
if not os.path.isdir(directory):
|
||||
raise RecipeValidationError(f"Directory not found: {directory}")
|
||||
|
||||
image_paths: List[str] = []
|
||||
|
||||
if recursive:
|
||||
for root, _, files in os.walk(directory):
|
||||
for filename in files:
|
||||
if self._is_supported_image(filename):
|
||||
image_paths.append(os.path.join(root, filename))
|
||||
else:
|
||||
for filename in os.listdir(directory):
|
||||
filepath = os.path.join(directory, filename)
|
||||
if os.path.isfile(filepath) and self._is_supported_image(filename):
|
||||
image_paths.append(filepath)
|
||||
|
||||
return sorted(image_paths)
|
||||
|
||||
def _is_supported_image(self, filename: str) -> bool:
|
||||
ext = os.path.splitext(filename)[1].lower()
|
||||
return ext in self.SUPPORTED_EXTENSIONS
|
||||
|
||||
async def _run_batch_import(
|
||||
self,
|
||||
*,
|
||||
operation_id: str,
|
||||
recipe_scanner_getter: Callable[[], Any],
|
||||
civitai_client_getter: Callable[[], Any],
|
||||
) -> None:
|
||||
progress = self._active_operations.get(operation_id)
|
||||
if not progress:
|
||||
return
|
||||
|
||||
progress.status = "running"
|
||||
await self._broadcast_progress(progress)
|
||||
|
||||
self._concurrency_controller = AdaptiveConcurrencyController()
|
||||
|
||||
async def process_item(item: BatchImportItem) -> None:
|
||||
if self._cancellation_flags.get(operation_id, False):
|
||||
return
|
||||
|
||||
progress.current_item = (
|
||||
os.path.basename(item.source)
|
||||
if item.item_type == ImportItemType.LOCAL_PATH
|
||||
else item.source[:50]
|
||||
)
|
||||
item.status = ImportStatus.PROCESSING
|
||||
await self._broadcast_progress(progress)
|
||||
|
||||
start_time = time.time()
|
||||
try:
|
||||
result = await self._import_single_item(
|
||||
item=item,
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
tags=progress.tags,
|
||||
skip_no_metadata=progress.skip_no_metadata,
|
||||
skip_duplicates=progress.skip_duplicates,
|
||||
semaphore=self._concurrency_controller.get_semaphore(),
|
||||
)
|
||||
|
||||
duration = time.time() - start_time
|
||||
item.duration = duration
|
||||
self._concurrency_controller.record_result(
|
||||
duration, result.get("success", False)
|
||||
)
|
||||
|
||||
if result.get("success"):
|
||||
item.status = ImportStatus.SUCCESS
|
||||
item.recipe_name = result.get("recipe_name")
|
||||
item.recipe_id = result.get("recipe_id")
|
||||
progress.success += 1
|
||||
elif result.get("skipped"):
|
||||
item.status = ImportStatus.SKIPPED
|
||||
item.error_message = result.get("error")
|
||||
progress.skipped += 1
|
||||
else:
|
||||
item.status = ImportStatus.FAILED
|
||||
item.error_message = result.get("error")
|
||||
progress.failed += 1
|
||||
|
||||
except Exception as e:
|
||||
self._logger.error(f"Error importing {item.source}: {e}")
|
||||
item.status = ImportStatus.FAILED
|
||||
item.error_message = str(e)
|
||||
item.duration = time.time() - start_time
|
||||
progress.failed += 1
|
||||
self._concurrency_controller.record_result(item.duration, False)
|
||||
|
||||
progress.completed += 1
|
||||
await self._broadcast_progress(progress)
|
||||
|
||||
tasks = [process_item(item) for item in progress.items]
|
||||
await asyncio.gather(*tasks, return_exceptions=True)
|
||||
|
||||
if self._cancellation_flags.get(operation_id, False):
|
||||
progress.status = "cancelled"
|
||||
else:
|
||||
progress.status = "completed"
|
||||
|
||||
progress.finished_at = time.time()
|
||||
progress.current_item = ""
|
||||
await self._broadcast_progress(progress)
|
||||
|
||||
await asyncio.sleep(5)
|
||||
self._cleanup_operation(operation_id)
|
||||
|
||||
async def _import_single_item(
|
||||
self,
|
||||
*,
|
||||
item: BatchImportItem,
|
||||
recipe_scanner_getter: Callable[[], Any],
|
||||
civitai_client_getter: Callable[[], Any],
|
||||
tags: List[str],
|
||||
skip_no_metadata: bool,
|
||||
skip_duplicates: bool,
|
||||
semaphore: asyncio.Semaphore,
|
||||
) -> Dict[str, Any]:
|
||||
async with semaphore:
|
||||
recipe_scanner = recipe_scanner_getter()
|
||||
if recipe_scanner is None:
|
||||
return {"success": False, "error": "Recipe scanner unavailable"}
|
||||
|
||||
try:
|
||||
if item.item_type == ImportItemType.URL:
|
||||
if not self._validate_url(item.source):
|
||||
return {
|
||||
"success": False,
|
||||
"error": f"Invalid URL format: {item.source}",
|
||||
}
|
||||
|
||||
if skip_duplicates:
|
||||
if self._is_duplicate_source(
|
||||
item.source, item.item_type, recipe_scanner
|
||||
):
|
||||
return {
|
||||
"success": False,
|
||||
"skipped": True,
|
||||
"error": "Duplicate source URL",
|
||||
}
|
||||
|
||||
civitai_client = civitai_client_getter()
|
||||
analysis_result = await self._analysis_service.analyze_remote_image(
|
||||
url=item.source,
|
||||
recipe_scanner=recipe_scanner,
|
||||
civitai_client=civitai_client,
|
||||
)
|
||||
else:
|
||||
if not self._validate_local_path(item.source):
|
||||
return {
|
||||
"success": False,
|
||||
"error": f"Invalid or unsafe path: {item.source}",
|
||||
}
|
||||
|
||||
if not os.path.exists(item.source):
|
||||
return {
|
||||
"success": False,
|
||||
"error": f"File not found: {item.source}",
|
||||
}
|
||||
|
||||
if skip_duplicates:
|
||||
if self._is_duplicate_source(
|
||||
item.source, item.item_type, recipe_scanner
|
||||
):
|
||||
return {
|
||||
"success": False,
|
||||
"skipped": True,
|
||||
"error": "Duplicate source path",
|
||||
}
|
||||
|
||||
analysis_result = await self._analysis_service.analyze_local_image(
|
||||
file_path=item.source,
|
||||
recipe_scanner=recipe_scanner,
|
||||
)
|
||||
|
||||
payload = analysis_result.payload
|
||||
|
||||
if payload.get("error"):
|
||||
if skip_no_metadata and "No metadata" in payload.get("error", ""):
|
||||
return {
|
||||
"success": False,
|
||||
"skipped": True,
|
||||
"error": payload["error"],
|
||||
}
|
||||
return {"success": False, "error": payload["error"]}
|
||||
|
||||
loras = payload.get("loras", [])
|
||||
if not loras:
|
||||
if skip_no_metadata:
|
||||
return {
|
||||
"success": False,
|
||||
"skipped": True,
|
||||
"error": "No LoRAs found in image",
|
||||
}
|
||||
# When skip_no_metadata is False, allow importing images without LoRAs
|
||||
# Continue with empty loras list
|
||||
|
||||
recipe_name = self._generate_recipe_name(item, payload)
|
||||
all_tags = list(set(tags + (payload.get("tags", []) or [])))
|
||||
|
||||
metadata = {
|
||||
"base_model": payload.get("base_model", ""),
|
||||
"loras": loras,
|
||||
"gen_params": payload.get("gen_params", {}),
|
||||
"source_path": item.source,
|
||||
}
|
||||
|
||||
if payload.get("checkpoint"):
|
||||
metadata["checkpoint"] = payload["checkpoint"]
|
||||
|
||||
image_bytes = None
|
||||
image_base64 = payload.get("image_base64")
|
||||
|
||||
if item.item_type == ImportItemType.LOCAL_PATH:
|
||||
with open(item.source, "rb") as f:
|
||||
image_bytes = f.read()
|
||||
image_base64 = None
|
||||
|
||||
save_result = await self._persistence_service.save_recipe(
|
||||
recipe_scanner=recipe_scanner,
|
||||
image_bytes=image_bytes,
|
||||
image_base64=image_base64,
|
||||
name=recipe_name,
|
||||
tags=all_tags,
|
||||
metadata=metadata,
|
||||
extension=payload.get("extension"),
|
||||
)
|
||||
|
||||
if save_result.status == 200:
|
||||
return {
|
||||
"success": True,
|
||||
"recipe_name": recipe_name,
|
||||
"recipe_id": save_result.payload.get("id"),
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success": False,
|
||||
"error": save_result.payload.get(
|
||||
"error", "Failed to save recipe"
|
||||
),
|
||||
}
|
||||
|
||||
except RecipeValidationError as e:
|
||||
return {"success": False, "error": str(e)}
|
||||
except RecipeDownloadError as e:
|
||||
return {"success": False, "error": str(e)}
|
||||
except RecipeNotFoundError as e:
|
||||
return {"success": False, "skipped": True, "error": str(e)}
|
||||
except Exception as e:
|
||||
self._logger.error(
|
||||
f"Unexpected error importing {item.source}: {e}", exc_info=True
|
||||
)
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
def _generate_recipe_name(
|
||||
self, item: BatchImportItem, payload: Dict[str, Any]
|
||||
) -> str:
|
||||
if item.item_type == ImportItemType.LOCAL_PATH:
|
||||
base_name = os.path.splitext(os.path.basename(item.source))[0]
|
||||
return base_name[:100]
|
||||
else:
|
||||
loras = payload.get("loras", [])
|
||||
if loras:
|
||||
first_lora = loras[0].get("name", "Recipe")
|
||||
return f"Import - {first_lora}"[:100]
|
||||
return f"Imported Recipe {item.id[:8]}"
|
||||
|
||||
async def _broadcast_progress(self, progress: BatchImportProgress) -> None:
|
||||
await self._ws_manager.broadcast(
|
||||
{
|
||||
"type": "batch_import_progress",
|
||||
**progress.to_dict(),
|
||||
}
|
||||
)
|
||||
|
||||
def _cleanup_operation(self, operation_id: str) -> None:
|
||||
if operation_id in self._cancellation_flags:
|
||||
del self._cancellation_flags[operation_id]
|
||||
@@ -10,7 +10,11 @@ import uuid
|
||||
from typing import Dict, List, Optional, Set, Tuple
|
||||
from urllib.parse import urlparse
|
||||
from ..utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
|
||||
from ..utils.constants import CARD_PREVIEW_WIDTH, DIFFUSION_MODEL_BASE_MODELS, VALID_LORA_TYPES
|
||||
from ..utils.constants import (
|
||||
CARD_PREVIEW_WIDTH,
|
||||
DIFFUSION_MODEL_BASE_MODELS,
|
||||
VALID_LORA_TYPES,
|
||||
)
|
||||
from ..utils.civitai_utils import rewrite_preview_url
|
||||
from ..utils.preview_selection import select_preview_media
|
||||
from ..utils.utils import sanitize_folder_name
|
||||
@@ -352,10 +356,12 @@ class DownloadManager:
|
||||
# Check if this checkpoint should be treated as a diffusion model based on baseModel
|
||||
is_diffusion_model = False
|
||||
if model_type == "checkpoint":
|
||||
base_model_value = version_info.get('baseModel', '')
|
||||
base_model_value = version_info.get("baseModel", "")
|
||||
if base_model_value in DIFFUSION_MODEL_BASE_MODELS:
|
||||
is_diffusion_model = True
|
||||
logger.info(f"baseModel '{base_model_value}' is a known diffusion model, routing to unet folder")
|
||||
logger.info(
|
||||
f"baseModel '{base_model_value}' is a known diffusion model, routing to unet folder"
|
||||
)
|
||||
|
||||
# Case 2: model_version_id was None, check after getting version_info
|
||||
if model_version_id is None:
|
||||
@@ -464,7 +470,7 @@ class DownloadManager:
|
||||
# 2. Get file information
|
||||
files = version_info.get("files", [])
|
||||
file_info = None
|
||||
|
||||
|
||||
# If file_params is provided, try to find matching file
|
||||
if file_params and model_version_id:
|
||||
target_type = file_params.get("type", "Model")
|
||||
@@ -472,23 +478,28 @@ class DownloadManager:
|
||||
target_size = file_params.get("size", "full")
|
||||
target_fp = file_params.get("fp")
|
||||
is_primary = file_params.get("isPrimary", False)
|
||||
|
||||
|
||||
if is_primary:
|
||||
# Find primary file
|
||||
file_info = next(
|
||||
(f for f in files if f.get("primary") and f.get("type") in ("Model", "Negative")),
|
||||
None
|
||||
(
|
||||
f
|
||||
for f in files
|
||||
if f.get("primary")
|
||||
and f.get("type") in ("Model", "Negative")
|
||||
),
|
||||
None,
|
||||
)
|
||||
else:
|
||||
# Match by metadata
|
||||
for f in files:
|
||||
f_type = f.get("type", "")
|
||||
f_meta = f.get("metadata", {})
|
||||
|
||||
|
||||
# Check type match
|
||||
if f_type != target_type:
|
||||
continue
|
||||
|
||||
|
||||
# Check metadata match
|
||||
if f_meta.get("format") != target_format:
|
||||
continue
|
||||
@@ -496,10 +507,10 @@ class DownloadManager:
|
||||
continue
|
||||
if target_fp and f_meta.get("fp") != target_fp:
|
||||
continue
|
||||
|
||||
|
||||
file_info = f
|
||||
break
|
||||
|
||||
|
||||
# Fallback to primary file if no match found
|
||||
if not file_info:
|
||||
file_info = next(
|
||||
@@ -510,7 +521,7 @@ class DownloadManager:
|
||||
),
|
||||
None,
|
||||
)
|
||||
|
||||
|
||||
if not file_info:
|
||||
return {"success": False, "error": "No suitable file found in metadata"}
|
||||
mirrors = file_info.get("mirrors") or []
|
||||
@@ -1220,7 +1231,13 @@ class DownloadManager:
|
||||
entries: List = []
|
||||
for index, file_path in enumerate(file_paths):
|
||||
entry = base_metadata if index == 0 else copy.deepcopy(base_metadata)
|
||||
entry.update_file_info(file_path)
|
||||
# Update file paths without modifying size and modified timestamps
|
||||
# modified should remain as the download start time (import time)
|
||||
# size will be updated below to reflect actual downloaded file size
|
||||
entry.file_path = file_path.replace(os.sep, "/")
|
||||
entry.file_name = os.path.splitext(os.path.basename(file_path))[0]
|
||||
# Update size to actual downloaded file size
|
||||
entry.size = os.path.getsize(file_path)
|
||||
entry.sha256 = await calculate_sha256(file_path)
|
||||
entries.append(entry)
|
||||
|
||||
|
||||
@@ -449,6 +449,11 @@ class TagFTSIndex:
|
||||
Supports alias search: if the query matches an alias rather than
|
||||
the tag_name, the result will include a "matched_alias" field.
|
||||
|
||||
Ranking is based on a combination of:
|
||||
1. FTS5 bm25 relevance score (how well the text matches)
|
||||
2. Post count (popularity)
|
||||
3. Exact prefix match boost (tag_name starts with query)
|
||||
|
||||
Args:
|
||||
query: The search query string.
|
||||
categories: Optional list of category IDs to filter by.
|
||||
@@ -457,7 +462,7 @@ class TagFTSIndex:
|
||||
|
||||
Returns:
|
||||
List of dictionaries with tag_name, category, post_count,
|
||||
and optionally matched_alias.
|
||||
rank_score, and optionally matched_alias.
|
||||
"""
|
||||
# Ensure index is ready (lazy initialization)
|
||||
if not self.ensure_ready():
|
||||
@@ -473,35 +478,67 @@ class TagFTSIndex:
|
||||
if not fts_query:
|
||||
return []
|
||||
|
||||
query_lower = query.lower().strip()
|
||||
|
||||
try:
|
||||
with self._lock:
|
||||
conn = self._connect(readonly=True)
|
||||
try:
|
||||
# Build the SQL query - now also fetch aliases for matched_alias detection
|
||||
# Use subquery for category filter to ensure FTS is evaluated first
|
||||
# Build the SQL query with bm25 ranking
|
||||
# FTS5 bm25() returns negative scores, lower is better
|
||||
# We use -bm25() to get higher=better scores
|
||||
# Weights: -100.0 for exact matches, 1.0 for others
|
||||
# Add LOG10(post_count) weighting to boost popular tags
|
||||
# Use CASE to boost tag_name prefix matches above alias matches
|
||||
if categories:
|
||||
placeholders = ",".join("?" * len(categories))
|
||||
sql = f"""
|
||||
SELECT t.tag_name, t.category, t.post_count, t.aliases
|
||||
FROM tags t
|
||||
WHERE t.rowid IN (
|
||||
SELECT rowid FROM tag_fts WHERE searchable_text MATCH ?
|
||||
)
|
||||
SELECT t.tag_name, t.category, t.post_count, t.aliases,
|
||||
CASE
|
||||
WHEN t.tag_name LIKE ? ESCAPE '\\' THEN 1
|
||||
ELSE 0
|
||||
END AS is_tag_name_match,
|
||||
bm25(tag_fts, -100.0, 1.0, 1.0) + LOG10(t.post_count + 1) * 10.0 AS rank_score
|
||||
FROM tag_fts
|
||||
JOIN tags t ON tag_fts.rowid = t.rowid
|
||||
WHERE tag_fts.searchable_text MATCH ?
|
||||
AND t.category IN ({placeholders})
|
||||
ORDER BY t.post_count DESC
|
||||
ORDER BY is_tag_name_match DESC, rank_score DESC
|
||||
LIMIT ? OFFSET ?
|
||||
"""
|
||||
params = [fts_query] + categories + [limit, offset]
|
||||
# Escape special LIKE characters and add wildcard
|
||||
query_escaped = (
|
||||
query_lower.lstrip("/")
|
||||
.replace("\\", "\\\\")
|
||||
.replace("%", "\\%")
|
||||
.replace("_", "\\_")
|
||||
)
|
||||
params = (
|
||||
[query_escaped + "%", fts_query]
|
||||
+ categories
|
||||
+ [limit, offset]
|
||||
)
|
||||
else:
|
||||
sql = """
|
||||
SELECT t.tag_name, t.category, t.post_count, t.aliases
|
||||
FROM tag_fts f
|
||||
JOIN tags t ON f.rowid = t.rowid
|
||||
WHERE f.searchable_text MATCH ?
|
||||
ORDER BY t.post_count DESC
|
||||
SELECT t.tag_name, t.category, t.post_count, t.aliases,
|
||||
CASE
|
||||
WHEN t.tag_name LIKE ? ESCAPE '\\' THEN 1
|
||||
ELSE 0
|
||||
END AS is_tag_name_match,
|
||||
bm25(tag_fts, -100.0, 1.0, 1.0) + LOG10(t.post_count + 1) * 10.0 AS rank_score
|
||||
FROM tag_fts
|
||||
JOIN tags t ON tag_fts.rowid = t.rowid
|
||||
WHERE tag_fts.searchable_text MATCH ?
|
||||
ORDER BY is_tag_name_match DESC, rank_score DESC
|
||||
LIMIT ? OFFSET ?
|
||||
"""
|
||||
params = [fts_query, limit, offset]
|
||||
query_escaped = (
|
||||
query_lower.lstrip("/")
|
||||
.replace("\\", "\\\\")
|
||||
.replace("%", "\\%")
|
||||
.replace("_", "\\_")
|
||||
)
|
||||
params = [query_escaped + "%", fts_query, limit, offset]
|
||||
|
||||
cursor = conn.execute(sql, params)
|
||||
results = []
|
||||
@@ -510,8 +547,17 @@ class TagFTSIndex:
|
||||
"tag_name": row[0],
|
||||
"category": row[1],
|
||||
"post_count": row[2],
|
||||
"is_tag_name_match": row[4] == 1,
|
||||
"rank_score": row[5],
|
||||
}
|
||||
|
||||
# Set is_exact_prefix based on tag_name match
|
||||
tag_name = row[0]
|
||||
if tag_name.lower().startswith(query_lower.lstrip("/")):
|
||||
result["is_exact_prefix"] = True
|
||||
else:
|
||||
result["is_exact_prefix"] = result["is_tag_name_match"]
|
||||
|
||||
# Check if search matched an alias rather than the tag_name
|
||||
matched_alias = self._find_matched_alias(query, row[0], row[3])
|
||||
if matched_alias:
|
||||
|
||||
@@ -4,32 +4,40 @@ from datetime import datetime
|
||||
import os
|
||||
from .model_utils import determine_base_model
|
||||
|
||||
|
||||
@dataclass
|
||||
class BaseModelMetadata:
|
||||
"""Base class for all model metadata structures"""
|
||||
file_name: str # The filename without extension
|
||||
model_name: str # The model's name defined by the creator
|
||||
file_path: str # Full path to the model file
|
||||
size: int # File size in bytes
|
||||
modified: float # Timestamp when the model was added to the management system
|
||||
sha256: str # SHA256 hash of the file
|
||||
base_model: str # Base model type (SD1.5/SD2.1/SDXL/etc.)
|
||||
preview_url: str # Preview image URL
|
||||
preview_nsfw_level: int = 0 # NSFW level of the preview image
|
||||
notes: str = "" # Additional notes
|
||||
from_civitai: bool = True # Whether from Civitai
|
||||
civitai: Dict[str, Any] = field(default_factory=dict) # Civitai API data if available
|
||||
tags: List[str] = None # Model tags
|
||||
|
||||
file_name: str # The filename without extension
|
||||
model_name: str # The model's name defined by the creator
|
||||
file_path: str # Full path to the model file
|
||||
size: int # File size in bytes
|
||||
modified: float # Timestamp when the model was added to the management system
|
||||
sha256: str # SHA256 hash of the file
|
||||
base_model: str # Base model type (SD1.5/SD2.1/SDXL/etc.)
|
||||
preview_url: str # Preview image URL
|
||||
preview_nsfw_level: int = 0 # NSFW level of the preview image
|
||||
notes: str = "" # Additional notes
|
||||
from_civitai: bool = True # Whether from Civitai
|
||||
civitai: Dict[str, Any] = field(
|
||||
default_factory=dict
|
||||
) # Civitai API data if available
|
||||
tags: List[str] = None # Model tags
|
||||
modelDescription: str = "" # Full model description
|
||||
civitai_deleted: bool = False # Whether deleted from Civitai
|
||||
favorite: bool = False # Whether the model is a favorite
|
||||
exclude: bool = False # Whether to exclude this model from the cache
|
||||
db_checked: bool = False # Whether checked in archive DB
|
||||
skip_metadata_refresh: bool = False # Whether to skip this model during bulk metadata refresh
|
||||
favorite: bool = False # Whether the model is a favorite
|
||||
exclude: bool = False # Whether to exclude this model from the cache
|
||||
db_checked: bool = False # Whether checked in archive DB
|
||||
skip_metadata_refresh: bool = (
|
||||
False # Whether to skip this model during bulk metadata refresh
|
||||
)
|
||||
metadata_source: Optional[str] = None # Last provider that supplied metadata
|
||||
last_checked_at: float = 0 # Last checked timestamp
|
||||
hash_status: str = "completed" # Hash calculation status: pending | calculating | completed | failed
|
||||
_unknown_fields: Dict[str, Any] = field(default_factory=dict, repr=False, compare=False) # Store unknown fields
|
||||
_unknown_fields: Dict[str, Any] = field(
|
||||
default_factory=dict, repr=False, compare=False
|
||||
) # Store unknown fields
|
||||
|
||||
def __post_init__(self):
|
||||
# Initialize empty lists to avoid mutable default parameter issue
|
||||
@@ -40,211 +48,238 @@ class BaseModelMetadata:
|
||||
self.tags = []
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict) -> 'BaseModelMetadata':
|
||||
def from_dict(cls, data: Dict) -> "BaseModelMetadata":
|
||||
"""Create instance from dictionary"""
|
||||
data_copy = data.copy()
|
||||
|
||||
|
||||
# Use cached fields if available, otherwise compute them
|
||||
if not hasattr(cls, '_known_fields_cache'):
|
||||
if not hasattr(cls, "_known_fields_cache"):
|
||||
known_fields = set()
|
||||
for c in cls.mro():
|
||||
if hasattr(c, '__annotations__'):
|
||||
if hasattr(c, "__annotations__"):
|
||||
known_fields.update(c.__annotations__.keys())
|
||||
cls._known_fields_cache = known_fields
|
||||
|
||||
|
||||
known_fields = cls._known_fields_cache
|
||||
|
||||
|
||||
# Extract fields that match our class attributes
|
||||
fields_to_use = {k: v for k, v in data_copy.items() if k in known_fields}
|
||||
|
||||
|
||||
# Store unknown fields separately
|
||||
unknown_fields = {k: v for k, v in data_copy.items() if k not in known_fields and not k.startswith('_')}
|
||||
|
||||
unknown_fields = {
|
||||
k: v
|
||||
for k, v in data_copy.items()
|
||||
if k not in known_fields and not k.startswith("_")
|
||||
}
|
||||
|
||||
# Create instance with known fields
|
||||
instance = cls(**fields_to_use)
|
||||
|
||||
|
||||
# Add unknown fields as a separate attribute
|
||||
instance._unknown_fields = unknown_fields
|
||||
|
||||
|
||||
return instance
|
||||
|
||||
def to_dict(self) -> Dict:
|
||||
"""Convert to dictionary for JSON serialization"""
|
||||
result = asdict(self)
|
||||
|
||||
|
||||
# Remove private fields
|
||||
result = {k: v for k, v in result.items() if not k.startswith('_')}
|
||||
|
||||
result = {k: v for k, v in result.items() if not k.startswith("_")}
|
||||
|
||||
# Add back unknown fields if they exist
|
||||
if hasattr(self, '_unknown_fields'):
|
||||
if hasattr(self, "_unknown_fields"):
|
||||
result.update(self._unknown_fields)
|
||||
|
||||
|
||||
return result
|
||||
|
||||
def update_civitai_info(self, civitai_data: Dict) -> None:
|
||||
"""Update Civitai information"""
|
||||
self.civitai = civitai_data
|
||||
|
||||
def update_file_info(self, file_path: str) -> None:
|
||||
"""Update metadata with actual file information"""
|
||||
def update_file_info(self, file_path: str, update_timestamps: bool = False) -> None:
|
||||
"""
|
||||
Update metadata with actual file information.
|
||||
|
||||
Args:
|
||||
file_path: Path to the model file
|
||||
update_timestamps: If True, update size and modified from filesystem.
|
||||
If False (default), only update file_path and file_name.
|
||||
Set to True only when file has been moved/relocated.
|
||||
"""
|
||||
if os.path.exists(file_path):
|
||||
self.size = os.path.getsize(file_path)
|
||||
self.modified = os.path.getmtime(file_path)
|
||||
self.file_path = file_path.replace(os.sep, '/')
|
||||
# Update file_name when file_path changes
|
||||
if update_timestamps:
|
||||
# Only update size and modified when file has been relocated
|
||||
self.size = os.path.getsize(file_path)
|
||||
self.modified = os.path.getmtime(file_path)
|
||||
# Always update paths when this method is called
|
||||
self.file_path = file_path.replace(os.sep, "/")
|
||||
self.file_name = os.path.splitext(os.path.basename(file_path))[0]
|
||||
|
||||
@staticmethod
|
||||
def generate_unique_filename(target_dir: str, base_name: str, extension: str, hash_provider: callable = None) -> str:
|
||||
def generate_unique_filename(
|
||||
target_dir: str, base_name: str, extension: str, hash_provider: callable = None
|
||||
) -> str:
|
||||
"""Generate a unique filename to avoid conflicts
|
||||
|
||||
|
||||
Args:
|
||||
target_dir: Target directory path
|
||||
base_name: Base filename without extension
|
||||
extension: File extension including the dot
|
||||
hash_provider: A callable that returns the SHA256 hash when needed
|
||||
|
||||
|
||||
Returns:
|
||||
str: Unique filename that doesn't conflict with existing files
|
||||
"""
|
||||
original_filename = f"{base_name}{extension}"
|
||||
target_path = os.path.join(target_dir, original_filename)
|
||||
|
||||
|
||||
# If no conflict, return original filename
|
||||
if not os.path.exists(target_path):
|
||||
return original_filename
|
||||
|
||||
|
||||
# Only compute hash when needed
|
||||
if hash_provider:
|
||||
sha256_hash = hash_provider()
|
||||
else:
|
||||
sha256_hash = "0000"
|
||||
|
||||
|
||||
# Generate short hash (first 4 characters of SHA256)
|
||||
short_hash = sha256_hash[:4] if sha256_hash else "0000"
|
||||
|
||||
|
||||
# Try with short hash suffix
|
||||
unique_filename = f"{base_name}-{short_hash}{extension}"
|
||||
unique_path = os.path.join(target_dir, unique_filename)
|
||||
|
||||
|
||||
# If still conflicts, add incremental number
|
||||
counter = 1
|
||||
while os.path.exists(unique_path):
|
||||
unique_filename = f"{base_name}-{short_hash}-{counter}{extension}"
|
||||
unique_path = os.path.join(target_dir, unique_filename)
|
||||
counter += 1
|
||||
|
||||
|
||||
return unique_filename
|
||||
|
||||
|
||||
@dataclass
|
||||
class LoraMetadata(BaseModelMetadata):
|
||||
"""Represents the metadata structure for a Lora model"""
|
||||
usage_tips: str = "{}" # Usage tips for the model, json string
|
||||
|
||||
usage_tips: str = "{}" # Usage tips for the model, json string
|
||||
|
||||
@classmethod
|
||||
def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'LoraMetadata':
|
||||
def from_civitai_info(
|
||||
cls, version_info: Dict, file_info: Dict, save_path: str
|
||||
) -> "LoraMetadata":
|
||||
"""Create LoraMetadata instance from Civitai version info"""
|
||||
file_name = file_info.get('name', '')
|
||||
base_model = determine_base_model(version_info.get('baseModel', ''))
|
||||
file_name = file_info.get("name", "")
|
||||
base_model = determine_base_model(version_info.get("baseModel", ""))
|
||||
|
||||
# Extract tags and description if available
|
||||
tags = []
|
||||
description = ""
|
||||
model_data = version_info.get('model') or {}
|
||||
if 'tags' in model_data:
|
||||
tags = model_data['tags']
|
||||
if 'description' in model_data:
|
||||
description = model_data['description']
|
||||
model_data = version_info.get("model") or {}
|
||||
if "tags" in model_data:
|
||||
tags = model_data["tags"]
|
||||
if "description" in model_data:
|
||||
description = model_data["description"]
|
||||
|
||||
return cls(
|
||||
file_name=os.path.splitext(file_name)[0],
|
||||
model_name=model_data.get('name', os.path.splitext(file_name)[0]),
|
||||
file_path=save_path.replace(os.sep, '/'),
|
||||
size=file_info.get('sizeKB', 0) * 1024,
|
||||
model_name=model_data.get("name", os.path.splitext(file_name)[0]),
|
||||
file_path=save_path.replace(os.sep, "/"),
|
||||
size=file_info.get("sizeKB", 0) * 1024,
|
||||
modified=datetime.now().timestamp(),
|
||||
sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
|
||||
sha256=(file_info.get("hashes") or {}).get("SHA256", "").lower(),
|
||||
base_model=base_model,
|
||||
preview_url='', # Will be updated after preview download
|
||||
preview_nsfw_level=0, # Will be updated after preview download
|
||||
preview_url="", # Will be updated after preview download
|
||||
preview_nsfw_level=0, # Will be updated after preview download
|
||||
from_civitai=True,
|
||||
civitai=version_info,
|
||||
tags=tags,
|
||||
modelDescription=description
|
||||
modelDescription=description,
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class CheckpointMetadata(BaseModelMetadata):
|
||||
"""Represents the metadata structure for a Checkpoint model"""
|
||||
|
||||
sub_type: str = "checkpoint" # Model sub-type (checkpoint, diffusion_model, etc.)
|
||||
|
||||
@classmethod
|
||||
def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'CheckpointMetadata':
|
||||
def from_civitai_info(
|
||||
cls, version_info: Dict, file_info: Dict, save_path: str
|
||||
) -> "CheckpointMetadata":
|
||||
"""Create CheckpointMetadata instance from Civitai version info"""
|
||||
file_name = file_info.get('name', '')
|
||||
base_model = determine_base_model(version_info.get('baseModel', ''))
|
||||
sub_type = version_info.get('type', 'checkpoint')
|
||||
file_name = file_info.get("name", "")
|
||||
base_model = determine_base_model(version_info.get("baseModel", ""))
|
||||
sub_type = version_info.get("type", "checkpoint")
|
||||
|
||||
# Extract tags and description if available
|
||||
tags = []
|
||||
description = ""
|
||||
model_data = version_info.get('model') or {}
|
||||
if 'tags' in model_data:
|
||||
tags = model_data['tags']
|
||||
if 'description' in model_data:
|
||||
description = model_data['description']
|
||||
model_data = version_info.get("model") or {}
|
||||
if "tags" in model_data:
|
||||
tags = model_data["tags"]
|
||||
if "description" in model_data:
|
||||
description = model_data["description"]
|
||||
|
||||
return cls(
|
||||
file_name=os.path.splitext(file_name)[0],
|
||||
model_name=model_data.get('name', os.path.splitext(file_name)[0]),
|
||||
file_path=save_path.replace(os.sep, '/'),
|
||||
size=file_info.get('sizeKB', 0) * 1024,
|
||||
model_name=model_data.get("name", os.path.splitext(file_name)[0]),
|
||||
file_path=save_path.replace(os.sep, "/"),
|
||||
size=file_info.get("sizeKB", 0) * 1024,
|
||||
modified=datetime.now().timestamp(),
|
||||
sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
|
||||
sha256=(file_info.get("hashes") or {}).get("SHA256", "").lower(),
|
||||
base_model=base_model,
|
||||
preview_url='', # Will be updated after preview download
|
||||
preview_url="", # Will be updated after preview download
|
||||
preview_nsfw_level=0,
|
||||
from_civitai=True,
|
||||
civitai=version_info,
|
||||
sub_type=sub_type,
|
||||
tags=tags,
|
||||
modelDescription=description
|
||||
modelDescription=description,
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class EmbeddingMetadata(BaseModelMetadata):
|
||||
"""Represents the metadata structure for an Embedding model"""
|
||||
|
||||
sub_type: str = "embedding"
|
||||
|
||||
@classmethod
|
||||
def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'EmbeddingMetadata':
|
||||
def from_civitai_info(
|
||||
cls, version_info: Dict, file_info: Dict, save_path: str
|
||||
) -> "EmbeddingMetadata":
|
||||
"""Create EmbeddingMetadata instance from Civitai version info"""
|
||||
file_name = file_info.get('name', '')
|
||||
base_model = determine_base_model(version_info.get('baseModel', ''))
|
||||
sub_type = version_info.get('type', 'embedding')
|
||||
file_name = file_info.get("name", "")
|
||||
base_model = determine_base_model(version_info.get("baseModel", ""))
|
||||
sub_type = version_info.get("type", "embedding")
|
||||
|
||||
# Extract tags and description if available
|
||||
tags = []
|
||||
description = ""
|
||||
model_data = version_info.get('model') or {}
|
||||
if 'tags' in model_data:
|
||||
tags = model_data['tags']
|
||||
if 'description' in model_data:
|
||||
description = model_data['description']
|
||||
model_data = version_info.get("model") or {}
|
||||
if "tags" in model_data:
|
||||
tags = model_data["tags"]
|
||||
if "description" in model_data:
|
||||
description = model_data["description"]
|
||||
|
||||
return cls(
|
||||
file_name=os.path.splitext(file_name)[0],
|
||||
model_name=model_data.get('name', os.path.splitext(file_name)[0]),
|
||||
file_path=save_path.replace(os.sep, '/'),
|
||||
size=file_info.get('sizeKB', 0) * 1024,
|
||||
model_name=model_data.get("name", os.path.splitext(file_name)[0]),
|
||||
file_path=save_path.replace(os.sep, "/"),
|
||||
size=file_info.get("sizeKB", 0) * 1024,
|
||||
modified=datetime.now().timestamp(),
|
||||
sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
|
||||
sha256=(file_info.get("hashes") or {}).get("SHA256", "").lower(),
|
||||
base_model=base_model,
|
||||
preview_url='', # Will be updated after preview download
|
||||
preview_url="", # Will be updated after preview download
|
||||
preview_nsfw_level=0,
|
||||
from_civitai=True,
|
||||
civitai=version_info,
|
||||
sub_type=sub_type,
|
||||
tags=tags,
|
||||
modelDescription=description
|
||||
modelDescription=description,
|
||||
)
|
||||
|
||||
|
||||
@@ -345,6 +345,7 @@ class StandaloneLoraManager(LoraManager):
|
||||
"/ws/download-progress", ws_manager.handle_download_connection
|
||||
)
|
||||
app.router.add_get("/ws/init-progress", ws_manager.handle_init_connection)
|
||||
app.router.add_get("/ws/batch-import-progress", ws_manager.handle_connection)
|
||||
|
||||
# Schedule service initialization
|
||||
app.on_startup.append(lambda app: cls._initialize_services())
|
||||
|
||||
677
static/css/components/batch-import-modal.css
Normal file
677
static/css/components/batch-import-modal.css
Normal file
@@ -0,0 +1,677 @@
|
||||
/* Batch Import Modal Styles */
|
||||
|
||||
/* Step Containers */
|
||||
.batch-import-step {
|
||||
margin: var(--space-2) 0;
|
||||
}
|
||||
|
||||
/* Section Description */
|
||||
.section-description {
|
||||
color: var(--text-color);
|
||||
opacity: 0.8;
|
||||
margin-bottom: var(--space-2);
|
||||
font-size: 0.95em;
|
||||
}
|
||||
|
||||
/* Hint Text */
|
||||
.input-hint {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
color: var(--text-color);
|
||||
opacity: 0.7;
|
||||
font-size: 0.85em;
|
||||
margin-top: 6px;
|
||||
}
|
||||
|
||||
.input-hint i {
|
||||
color: var(--lora-accent);
|
||||
}
|
||||
|
||||
/* Textarea Styling */
|
||||
#batchUrlInput {
|
||||
width: 100%;
|
||||
min-height: 120px;
|
||||
padding: 12px;
|
||||
border: 1px solid var(--border-color);
|
||||
border-radius: var(--border-radius-xs);
|
||||
background: var(--bg-color);
|
||||
color: var(--text-color);
|
||||
font-family: inherit;
|
||||
font-size: 0.9em;
|
||||
resize: vertical;
|
||||
transition: border-color 0.2s, box-shadow 0.2s;
|
||||
}
|
||||
|
||||
#batchUrlInput:focus {
|
||||
outline: none;
|
||||
border-color: var(--lora-accent);
|
||||
box-shadow: 0 0 0 2px oklch(from var(--lora-accent) l c h / 0.2);
|
||||
}
|
||||
|
||||
/* Checkbox Group */
|
||||
.checkbox-group {
|
||||
margin-top: var(--space-2);
|
||||
}
|
||||
|
||||
.checkbox-label {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 10px;
|
||||
cursor: pointer;
|
||||
color: var(--text-color);
|
||||
font-size: 0.95em;
|
||||
user-select: none;
|
||||
}
|
||||
|
||||
.checkbox-label input[type="checkbox"] {
|
||||
display: none;
|
||||
}
|
||||
|
||||
.checkmark {
|
||||
width: 18px;
|
||||
height: 18px;
|
||||
border: 2px solid var(--border-color);
|
||||
border-radius: 4px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
transition: all 0.2s;
|
||||
background: var(--bg-color);
|
||||
}
|
||||
|
||||
.checkbox-label input[type="checkbox"]:checked + .checkmark {
|
||||
background: var(--lora-accent);
|
||||
border-color: var(--lora-accent);
|
||||
}
|
||||
|
||||
.checkbox-label input[type="checkbox"]:checked + .checkmark::after {
|
||||
content: '\f00c';
|
||||
font-family: 'Font Awesome 6 Free';
|
||||
font-weight: 900;
|
||||
color: var(--lora-text);
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
/* Batch Options */
|
||||
.batch-options {
|
||||
margin-top: var(--space-3);
|
||||
padding-top: var(--space-3);
|
||||
border-top: 1px solid var(--border-color);
|
||||
}
|
||||
|
||||
/* Input with Button */
|
||||
.input-with-button {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.input-with-button input {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.input-with-button button {
|
||||
flex-shrink: 0;
|
||||
white-space: nowrap;
|
||||
padding: 8px 16px;
|
||||
background: var(--lora-accent);
|
||||
color: var(--lora-text);
|
||||
border: none;
|
||||
border-radius: var(--border-radius-xs);
|
||||
cursor: pointer;
|
||||
transition: background-color 0.2s;
|
||||
}
|
||||
|
||||
.input-with-button button:hover {
|
||||
background: oklch(from var(--lora-accent) l c h / 0.9);
|
||||
}
|
||||
|
||||
/* Dark theme adjustments for input-with-button */
|
||||
[data-theme="dark"] .input-with-button button {
|
||||
background: var(--lora-accent);
|
||||
color: var(--lora-text);
|
||||
}
|
||||
|
||||
[data-theme="dark"] .input-with-button button:hover {
|
||||
background: oklch(from var(--lora-accent) calc(l - 0.1) c h);
|
||||
}
|
||||
|
||||
/* Directory Browser */
|
||||
.directory-browser {
|
||||
margin-top: var(--space-3);
|
||||
border: 1px solid var(--border-color);
|
||||
border-radius: var(--border-radius-xs);
|
||||
background: var(--lora-surface);
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.browser-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 10px;
|
||||
padding: 10px 12px;
|
||||
background: var(--bg-color);
|
||||
border-bottom: 1px solid var(--border-color);
|
||||
}
|
||||
|
||||
.back-btn {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
width: 32px;
|
||||
height: 32px;
|
||||
border: 1px solid var(--border-color);
|
||||
border-radius: var(--border-radius-xs);
|
||||
background: var(--card-bg);
|
||||
color: var(--text-color);
|
||||
cursor: pointer;
|
||||
transition: all 0.2s;
|
||||
}
|
||||
|
||||
.back-btn:hover {
|
||||
border-color: var(--lora-accent);
|
||||
background: var(--bg-color);
|
||||
}
|
||||
|
||||
.back-btn:disabled {
|
||||
opacity: 0.5;
|
||||
cursor: not-allowed;
|
||||
}
|
||||
|
||||
.current-path {
|
||||
flex: 1;
|
||||
padding: 6px 10px;
|
||||
background: var(--card-bg);
|
||||
border: 1px solid var(--border-color);
|
||||
border-radius: var(--border-radius-xs);
|
||||
font-size: 0.9em;
|
||||
color: var(--text-color);
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
|
||||
.browser-content {
|
||||
max-height: 300px;
|
||||
overflow-y: auto;
|
||||
padding: 12px;
|
||||
}
|
||||
|
||||
.browser-section {
|
||||
margin-bottom: 16px;
|
||||
}
|
||||
|
||||
.browser-section:last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
.section-label {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
font-weight: 600;
|
||||
font-size: 0.85em;
|
||||
color: var(--text-color);
|
||||
margin-bottom: 8px;
|
||||
padding-bottom: 6px;
|
||||
border-bottom: 1px solid var(--border-color);
|
||||
}
|
||||
|
||||
.section-label i {
|
||||
color: var(--lora-accent);
|
||||
}
|
||||
|
||||
.folder-list,
|
||||
.file-list {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 4px;
|
||||
}
|
||||
|
||||
.folder-item,
|
||||
.file-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 10px;
|
||||
padding: 8px 10px;
|
||||
border-radius: var(--border-radius-xs);
|
||||
cursor: pointer;
|
||||
transition: all 0.2s;
|
||||
border: 1px solid transparent;
|
||||
}
|
||||
|
||||
.folder-item:hover,
|
||||
.file-item:hover {
|
||||
background: var(--lora-surface-hover, oklch(from var(--lora-accent) l c h / 0.1));
|
||||
border-color: var(--lora-accent);
|
||||
}
|
||||
|
||||
.folder-item.selected,
|
||||
.file-item.selected {
|
||||
background: oklch(from var(--lora-accent) l c h / 0.15);
|
||||
border-color: var(--lora-accent);
|
||||
}
|
||||
|
||||
.folder-item i {
|
||||
color: #fbbf24;
|
||||
font-size: 1.1em;
|
||||
}
|
||||
|
||||
.file-item i {
|
||||
color: var(--text-color);
|
||||
opacity: 0.6;
|
||||
font-size: 1em;
|
||||
}
|
||||
|
||||
.item-name {
|
||||
flex: 1;
|
||||
font-size: 0.9em;
|
||||
color: var(--text-color);
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
|
||||
.item-size {
|
||||
font-size: 0.8em;
|
||||
color: var(--text-color);
|
||||
opacity: 0.6;
|
||||
}
|
||||
|
||||
.browser-footer {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
padding: 10px 12px;
|
||||
background: var(--bg-color);
|
||||
border-top: 1px solid var(--border-color);
|
||||
}
|
||||
|
||||
.stats {
|
||||
font-size: 0.85em;
|
||||
color: var(--text-color);
|
||||
opacity: 0.8;
|
||||
}
|
||||
|
||||
.stats span {
|
||||
font-weight: 600;
|
||||
color: var(--lora-accent);
|
||||
}
|
||||
|
||||
/* Dark theme adjustments */
|
||||
[data-theme="dark"] .directory-browser {
|
||||
background: var(--card-bg);
|
||||
}
|
||||
|
||||
[data-theme="dark"] .browser-header,
|
||||
[data-theme="dark"] .browser-footer {
|
||||
background: var(--lora-surface);
|
||||
}
|
||||
|
||||
[data-theme="dark"] .folder-item i {
|
||||
color: #fcd34d;
|
||||
}
|
||||
|
||||
/* Progress Container */
|
||||
.batch-progress-container {
|
||||
padding: var(--space-3);
|
||||
background: var(--lora-surface);
|
||||
border-radius: var(--border-radius-sm);
|
||||
margin-bottom: var(--space-3);
|
||||
}
|
||||
|
||||
.progress-header {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
margin-bottom: var(--space-2);
|
||||
}
|
||||
|
||||
.progress-status {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 10px;
|
||||
}
|
||||
|
||||
.status-icon {
|
||||
color: var(--lora-accent);
|
||||
font-size: 1.1em;
|
||||
}
|
||||
|
||||
.status-icon i {
|
||||
animation: fa-spin 2s infinite linear;
|
||||
}
|
||||
|
||||
.status-text {
|
||||
font-weight: 500;
|
||||
color: var(--text-color);
|
||||
}
|
||||
|
||||
.progress-percentage {
|
||||
font-size: 1.2em;
|
||||
font-weight: 600;
|
||||
color: var(--lora-accent);
|
||||
}
|
||||
|
||||
/* Progress Bar */
|
||||
.progress-bar-container {
|
||||
height: 8px;
|
||||
background: var(--bg-color);
|
||||
border-radius: 4px;
|
||||
overflow: hidden;
|
||||
margin-bottom: var(--space-3);
|
||||
}
|
||||
|
||||
.progress-bar {
|
||||
height: 100%;
|
||||
background: linear-gradient(90deg, var(--lora-accent), oklch(from var(--lora-accent) calc(l + 0.1) c h));
|
||||
border-radius: 4px;
|
||||
transition: width 0.3s ease;
|
||||
}
|
||||
|
||||
/* Progress Stats */
|
||||
.progress-stats {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(4, 1fr);
|
||||
gap: var(--space-2);
|
||||
margin-bottom: var(--space-2);
|
||||
}
|
||||
|
||||
.stat-item {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
padding: var(--space-2);
|
||||
background: var(--bg-color);
|
||||
border-radius: var(--border-radius-xs);
|
||||
border: 1px solid var(--border-color);
|
||||
}
|
||||
|
||||
.stat-item.success {
|
||||
border-left: 3px solid #00B87A;
|
||||
}
|
||||
|
||||
.stat-item.failed {
|
||||
border-left: 3px solid var(--lora-error);
|
||||
}
|
||||
|
||||
.stat-item.skipped {
|
||||
border-left: 3px solid var(--lora-warning);
|
||||
}
|
||||
|
||||
.stat-label {
|
||||
font-size: 0.8em;
|
||||
color: var(--text-color);
|
||||
opacity: 0.7;
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
|
||||
.stat-value {
|
||||
font-size: 1.4em;
|
||||
font-weight: 600;
|
||||
color: var(--text-color);
|
||||
}
|
||||
|
||||
/* Current Item */
|
||||
.current-item {
|
||||
display: flex;
|
||||
align-items: baseline;
|
||||
gap: 10px;
|
||||
padding: var(--space-2);
|
||||
background: var(--bg-color);
|
||||
border-radius: var(--border-radius-xs);
|
||||
font-size: 0.9em;
|
||||
}
|
||||
|
||||
.current-item-label {
|
||||
color: var(--text-color);
|
||||
opacity: 0.7;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.current-item-name {
|
||||
color: var(--text-color);
|
||||
font-weight: 500;
|
||||
flex: 1;
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
line-height: 1.2;
|
||||
}
|
||||
|
||||
/* Results Container */
|
||||
.batch-results-container {
|
||||
padding: var(--space-3);
|
||||
background: var(--lora-surface);
|
||||
border-radius: var(--border-radius-sm);
|
||||
margin-bottom: var(--space-3);
|
||||
}
|
||||
|
||||
.results-header {
|
||||
text-align: center;
|
||||
margin-bottom: var(--space-3);
|
||||
}
|
||||
|
||||
.results-icon {
|
||||
font-size: 3em;
|
||||
color: #00B87A;
|
||||
margin-bottom: var(--space-1);
|
||||
}
|
||||
|
||||
.results-icon.warning {
|
||||
color: var(--lora-warning);
|
||||
}
|
||||
|
||||
.results-icon.error {
|
||||
color: var(--lora-error);
|
||||
}
|
||||
|
||||
.results-title {
|
||||
font-size: 1.3em;
|
||||
font-weight: 600;
|
||||
color: var(--text-color);
|
||||
}
|
||||
|
||||
/* Results Summary - Matches progress-stats styling */
|
||||
.results-summary {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(4, 1fr);
|
||||
gap: var(--space-2);
|
||||
margin-bottom: var(--space-3);
|
||||
}
|
||||
|
||||
.result-card {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
padding: var(--space-2);
|
||||
background: var(--bg-color);
|
||||
border-radius: var(--border-radius-xs);
|
||||
border: 1px solid var(--border-color);
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.result-card.success {
|
||||
border-left: 3px solid #00B87A;
|
||||
}
|
||||
|
||||
.result-card.failed {
|
||||
border-left: 3px solid var(--lora-error);
|
||||
}
|
||||
|
||||
.result-card.skipped {
|
||||
border-left: 3px solid var(--lora-warning);
|
||||
}
|
||||
|
||||
.result-label {
|
||||
font-size: 0.8em;
|
||||
color: var(--text-color);
|
||||
opacity: 0.7;
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
|
||||
.result-value {
|
||||
font-size: 1.4em;
|
||||
font-weight: 600;
|
||||
color: var(--text-color);
|
||||
}
|
||||
|
||||
/* Results Details */
|
||||
.results-details {
|
||||
border-top: 1px solid var(--border-color);
|
||||
padding-top: var(--space-2);
|
||||
}
|
||||
|
||||
.details-toggle {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
gap: 8px;
|
||||
padding: 10px;
|
||||
cursor: pointer;
|
||||
color: var(--lora-accent);
|
||||
font-weight: 500;
|
||||
border-radius: var(--border-radius-xs);
|
||||
transition: background 0.2s;
|
||||
}
|
||||
|
||||
.details-toggle:hover {
|
||||
background: oklch(from var(--lora-accent) l c h / 0.1);
|
||||
}
|
||||
|
||||
.details-toggle i {
|
||||
transition: transform 0.2s;
|
||||
}
|
||||
|
||||
.details-toggle.expanded i {
|
||||
transform: rotate(180deg);
|
||||
}
|
||||
|
||||
.details-list {
|
||||
max-height: 250px;
|
||||
overflow-y: auto;
|
||||
margin-top: var(--space-2);
|
||||
background: var(--bg-color);
|
||||
border-radius: var(--border-radius-xs);
|
||||
border: 1px solid var(--border-color);
|
||||
}
|
||||
|
||||
/* Result Item in Details */
|
||||
.result-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 10px;
|
||||
padding: 10px 12px;
|
||||
border-bottom: 1px solid var(--border-color);
|
||||
font-size: 0.9em;
|
||||
}
|
||||
|
||||
.result-item:last-child {
|
||||
border-bottom: none;
|
||||
}
|
||||
|
||||
.result-item-status {
|
||||
width: 24px;
|
||||
height: 24px;
|
||||
border-radius: 50%;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
font-size: 0.8em;
|
||||
}
|
||||
|
||||
.result-item-status.success {
|
||||
background: oklch(from #00B87A l c h / 0.2);
|
||||
color: #00B87A;
|
||||
}
|
||||
|
||||
.result-item-status.failed {
|
||||
background: oklch(from var(--lora-error) l c h / 0.2);
|
||||
color: var(--lora-error);
|
||||
}
|
||||
|
||||
.result-item-status.skipped {
|
||||
background: oklch(from var(--lora-warning) l c h / 0.2);
|
||||
color: var(--lora-warning);
|
||||
}
|
||||
|
||||
.result-item-info {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.result-item-name {
|
||||
font-weight: 500;
|
||||
color: var(--text-color);
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
|
||||
.result-item-error {
|
||||
font-size: 0.8em;
|
||||
color: var(--lora-error);
|
||||
margin-top: 2px;
|
||||
}
|
||||
|
||||
/* Responsive Adjustments */
|
||||
@media (max-width: 768px) {
|
||||
.progress-stats,
|
||||
.results-summary {
|
||||
grid-template-columns: repeat(2, 1fr);
|
||||
}
|
||||
|
||||
.batch-progress-container,
|
||||
.batch-results-container {
|
||||
padding: var(--space-2);
|
||||
}
|
||||
}
|
||||
|
||||
/* Dark Theme Adjustments */
|
||||
[data-theme="dark"] .batch-progress-container,
|
||||
[data-theme="dark"] .batch-results-container {
|
||||
background: var(--card-bg);
|
||||
}
|
||||
|
||||
[data-theme="dark"] .stat-item,
|
||||
[data-theme="dark"] .result-card,
|
||||
[data-theme="dark"] .current-item,
|
||||
[data-theme="dark"] .details-list {
|
||||
background: var(--lora-surface);
|
||||
}
|
||||
|
||||
/* Cancelled State */
|
||||
.batch-progress-container.cancelled .progress-bar {
|
||||
background: var(--lora-warning);
|
||||
}
|
||||
|
||||
.batch-progress-container.cancelled .status-icon {
|
||||
color: var(--lora-warning);
|
||||
}
|
||||
|
||||
/* Error State */
|
||||
.batch-progress-container.error .progress-bar {
|
||||
background: var(--lora-error);
|
||||
}
|
||||
|
||||
.batch-progress-container.error .status-icon {
|
||||
color: var(--lora-error);
|
||||
}
|
||||
|
||||
/* Completed State */
|
||||
.batch-progress-container.completed .progress-bar {
|
||||
background: #00B87A;
|
||||
}
|
||||
|
||||
.batch-progress-container.completed .status-icon {
|
||||
color: #00B87A;
|
||||
}
|
||||
|
||||
.batch-progress-container.completed .status-icon i {
|
||||
animation: none;
|
||||
}
|
||||
|
||||
.batch-progress-container.completed .status-icon i::before {
|
||||
content: '\f00c';
|
||||
}
|
||||
795
static/js/managers/BatchImportManager.js
Normal file
795
static/js/managers/BatchImportManager.js
Normal file
@@ -0,0 +1,795 @@
|
||||
import { modalManager } from './ModalManager.js';
|
||||
import { showToast } from '../utils/uiHelpers.js';
|
||||
import { translate } from '../utils/i18nHelpers.js';
|
||||
import { WS_ENDPOINTS } from '../api/apiConfig.js';
|
||||
|
||||
/**
|
||||
* Manager for batch importing recipes from multiple images
|
||||
*/
|
||||
export class BatchImportManager {
|
||||
constructor() {
|
||||
this.initialized = false;
|
||||
this.inputMode = 'urls'; // 'urls' or 'directory'
|
||||
this.operationId = null;
|
||||
this.wsConnection = null;
|
||||
this.pollingInterval = null;
|
||||
this.progress = null;
|
||||
this.results = null;
|
||||
this.isCancelled = false;
|
||||
}
|
||||
|
||||
/**
|
||||
* Show the batch import modal
|
||||
*/
|
||||
showModal() {
|
||||
if (!this.initialized) {
|
||||
this.initialize();
|
||||
}
|
||||
this.resetState();
|
||||
modalManager.showModal('batchImportModal');
|
||||
}
|
||||
|
||||
/**
|
||||
* Initialize the manager
|
||||
*/
|
||||
initialize() {
|
||||
this.initialized = true;
|
||||
}
|
||||
|
||||
/**
|
||||
* Reset all state to initial values
|
||||
*/
|
||||
resetState() {
|
||||
this.inputMode = 'urls';
|
||||
this.operationId = null;
|
||||
this.progress = null;
|
||||
this.results = null;
|
||||
this.isCancelled = false;
|
||||
|
||||
// Reset UI
|
||||
this.showStep('batchInputStep');
|
||||
this.toggleInputMode('urls');
|
||||
|
||||
// Clear inputs
|
||||
const urlInput = document.getElementById('batchUrlInput');
|
||||
if (urlInput) urlInput.value = '';
|
||||
|
||||
const directoryInput = document.getElementById('batchDirectoryInput');
|
||||
if (directoryInput) directoryInput.value = '';
|
||||
|
||||
const tagsInput = document.getElementById('batchTagsInput');
|
||||
if (tagsInput) tagsInput.value = '';
|
||||
|
||||
const skipNoMetadata = document.getElementById('batchSkipNoMetadata');
|
||||
if (skipNoMetadata) skipNoMetadata.checked = true;
|
||||
|
||||
const recursiveCheck = document.getElementById('batchRecursiveCheck');
|
||||
if (recursiveCheck) recursiveCheck.checked = true;
|
||||
|
||||
// Reset progress UI
|
||||
this.updateProgressUI({
|
||||
total: 0,
|
||||
completed: 0,
|
||||
success: 0,
|
||||
failed: 0,
|
||||
skipped: 0,
|
||||
progress_percent: 0,
|
||||
current_item: '',
|
||||
status: 'pending'
|
||||
});
|
||||
|
||||
// Reset results
|
||||
const detailsList = document.getElementById('batchDetailsList');
|
||||
if (detailsList) {
|
||||
detailsList.innerHTML = '';
|
||||
detailsList.style.display = 'none';
|
||||
}
|
||||
|
||||
const toggleIcon = document.getElementById('resultsToggleIcon');
|
||||
if (toggleIcon) {
|
||||
toggleIcon.classList.remove('expanded');
|
||||
}
|
||||
|
||||
// Clean up any existing connections
|
||||
this.cleanupConnections();
|
||||
}
|
||||
|
||||
/**
|
||||
* Show a specific step in the modal
|
||||
*/
|
||||
showStep(stepId) {
|
||||
document.querySelectorAll('.batch-import-step').forEach(step => {
|
||||
step.style.display = 'none';
|
||||
});
|
||||
|
||||
const step = document.getElementById(stepId);
|
||||
if (step) {
|
||||
step.style.display = 'block';
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Toggle between URL list and directory input modes
|
||||
*/
|
||||
toggleInputMode(mode) {
|
||||
this.inputMode = mode;
|
||||
|
||||
// Update toggle buttons
|
||||
document.querySelectorAll('.toggle-btn[data-mode]').forEach(btn => {
|
||||
btn.classList.remove('active');
|
||||
});
|
||||
|
||||
const activeBtn = document.querySelector(`.toggle-btn[data-mode="${mode}"]`);
|
||||
if (activeBtn) {
|
||||
activeBtn.classList.add('active');
|
||||
}
|
||||
|
||||
// Show/hide appropriate sections
|
||||
const urlSection = document.getElementById('urlListSection');
|
||||
const directorySection = document.getElementById('directorySection');
|
||||
|
||||
if (urlSection && directorySection) {
|
||||
if (mode === 'urls') {
|
||||
urlSection.style.display = 'block';
|
||||
directorySection.style.display = 'none';
|
||||
} else {
|
||||
urlSection.style.display = 'none';
|
||||
directorySection.style.display = 'block';
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Start the batch import process
|
||||
*/
|
||||
async startImport() {
|
||||
const data = this.collectInputData();
|
||||
|
||||
if (!this.validateInput(data)) {
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
// Show progress step
|
||||
this.showStep('batchProgressStep');
|
||||
|
||||
// Start the import
|
||||
const response = await this.sendStartRequest(data);
|
||||
|
||||
if (response.success) {
|
||||
this.operationId = response.operation_id;
|
||||
this.isCancelled = false;
|
||||
|
||||
// Connect to WebSocket for real-time updates
|
||||
this.connectWebSocket();
|
||||
|
||||
// Start polling as fallback
|
||||
this.startPolling();
|
||||
} else {
|
||||
showToast('toast.recipes.batchImportFailed', { message: response.error }, 'error');
|
||||
this.showStep('batchInputStep');
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Error starting batch import:', error);
|
||||
showToast('toast.recipes.batchImportFailed', { message: error.message }, 'error');
|
||||
this.showStep('batchInputStep');
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Collect input data from the form
|
||||
*/
|
||||
collectInputData() {
|
||||
const data = {
|
||||
mode: this.inputMode,
|
||||
tags: [],
|
||||
skip_no_metadata: false
|
||||
};
|
||||
|
||||
// Collect tags
|
||||
const tagsInput = document.getElementById('batchTagsInput');
|
||||
if (tagsInput && tagsInput.value.trim()) {
|
||||
data.tags = tagsInput.value.split(',').map(t => t.trim()).filter(t => t);
|
||||
}
|
||||
|
||||
// Collect skip_no_metadata
|
||||
const skipNoMetadata = document.getElementById('batchSkipNoMetadata');
|
||||
if (skipNoMetadata) {
|
||||
data.skip_no_metadata = skipNoMetadata.checked;
|
||||
}
|
||||
|
||||
if (this.inputMode === 'urls') {
|
||||
const urlInput = document.getElementById('batchUrlInput');
|
||||
if (urlInput) {
|
||||
const urls = urlInput.value.split('\n')
|
||||
.map(line => line.trim())
|
||||
.filter(line => line.length > 0);
|
||||
|
||||
// Convert to items format
|
||||
data.items = urls.map(url => ({
|
||||
source: url,
|
||||
type: this.detectUrlType(url)
|
||||
}));
|
||||
}
|
||||
} else {
|
||||
const directoryInput = document.getElementById('batchDirectoryInput');
|
||||
if (directoryInput) {
|
||||
data.directory = directoryInput.value.trim();
|
||||
}
|
||||
|
||||
const recursiveCheck = document.getElementById('batchRecursiveCheck');
|
||||
if (recursiveCheck) {
|
||||
data.recursive = recursiveCheck.checked;
|
||||
}
|
||||
}
|
||||
|
||||
return data;
|
||||
}
|
||||
|
||||
/**
|
||||
* Detect if a URL is http or local path
|
||||
*/
|
||||
detectUrlType(url) {
|
||||
if (url.startsWith('http://') || url.startsWith('https://')) {
|
||||
return 'url';
|
||||
}
|
||||
return 'local_path';
|
||||
}
|
||||
|
||||
/**
|
||||
* Validate the input data
|
||||
*/
|
||||
validateInput(data) {
|
||||
if (data.mode === 'urls') {
|
||||
if (!data.items || data.items.length === 0) {
|
||||
showToast('toast.recipes.batchImportNoUrls', {}, 'error');
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
if (!data.directory) {
|
||||
showToast('toast.recipes.batchImportNoDirectory', {}, 'error');
|
||||
return false;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
/**
|
||||
* Send the start batch import request
|
||||
*/
|
||||
async sendStartRequest(data) {
|
||||
const endpoint = data.mode === 'urls'
|
||||
? '/api/lm/recipes/batch-import/start'
|
||||
: '/api/lm/recipes/batch-import/directory';
|
||||
|
||||
const response = await fetch(endpoint, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json'
|
||||
},
|
||||
body: JSON.stringify(data)
|
||||
});
|
||||
|
||||
return await response.json();
|
||||
}
|
||||
|
||||
/**
|
||||
* Connect to WebSocket for real-time progress updates
|
||||
*/
|
||||
connectWebSocket() {
|
||||
const wsProtocol = window.location.protocol === 'https:' ? 'wss:' : 'ws:';
|
||||
const wsUrl = `${wsProtocol}//${window.location.host}/ws/batch-import-progress?id=${this.operationId}`;
|
||||
|
||||
this.wsConnection = new WebSocket(wsUrl);
|
||||
|
||||
this.wsConnection.onopen = () => {
|
||||
console.log('Connected to batch import progress WebSocket');
|
||||
};
|
||||
|
||||
this.wsConnection.onmessage = (event) => {
|
||||
try {
|
||||
const data = JSON.parse(event.data);
|
||||
if (data.type === 'batch_import_progress') {
|
||||
this.handleProgressUpdate(data);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Error parsing WebSocket message:', error);
|
||||
}
|
||||
};
|
||||
|
||||
this.wsConnection.onerror = (error) => {
|
||||
console.error('WebSocket error:', error);
|
||||
};
|
||||
|
||||
this.wsConnection.onclose = () => {
|
||||
console.log('WebSocket connection closed');
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Start polling for progress updates (fallback)
|
||||
*/
|
||||
startPolling() {
|
||||
this.pollingInterval = setInterval(async () => {
|
||||
if (!this.operationId || this.isCancelled) {
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
const response = await fetch(`/api/lm/recipes/batch-import/progress?operation_id=${this.operationId}`);
|
||||
const data = await response.json();
|
||||
|
||||
if (data.success && data.progress) {
|
||||
this.handleProgressUpdate(data.progress);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Error polling progress:', error);
|
||||
}
|
||||
}, 1000);
|
||||
}
|
||||
|
||||
/**
|
||||
* Handle progress update from WebSocket or polling
|
||||
*/
|
||||
handleProgressUpdate(progress) {
|
||||
this.progress = progress;
|
||||
this.updateProgressUI(progress);
|
||||
|
||||
// Check if import is complete
|
||||
if (progress.status === 'completed' || progress.status === 'cancelled' ||
|
||||
(progress.total > 0 && progress.completed >= progress.total)) {
|
||||
this.importComplete(progress);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Update the progress UI
|
||||
*/
|
||||
updateProgressUI(progress) {
|
||||
// Update progress bar
|
||||
const progressBar = document.getElementById('batchProgressBar');
|
||||
if (progressBar) {
|
||||
progressBar.style.width = `${progress.progress_percent || 0}%`;
|
||||
}
|
||||
|
||||
// Update percentage
|
||||
const progressPercent = document.getElementById('batchProgressPercent');
|
||||
if (progressPercent) {
|
||||
progressPercent.textContent = `${Math.round(progress.progress_percent || 0)}%`;
|
||||
}
|
||||
|
||||
// Update stats
|
||||
const totalCount = document.getElementById('batchTotalCount');
|
||||
if (totalCount) totalCount.textContent = progress.total || 0;
|
||||
|
||||
const successCount = document.getElementById('batchSuccessCount');
|
||||
if (successCount) successCount.textContent = progress.success || 0;
|
||||
|
||||
const failedCount = document.getElementById('batchFailedCount');
|
||||
if (failedCount) failedCount.textContent = progress.failed || 0;
|
||||
|
||||
const skippedCount = document.getElementById('batchSkippedCount');
|
||||
if (skippedCount) skippedCount.textContent = progress.skipped || 0;
|
||||
|
||||
// Update current item
|
||||
const currentItem = document.getElementById('batchCurrentItem');
|
||||
if (currentItem) {
|
||||
currentItem.textContent = progress.current_item || '-';
|
||||
}
|
||||
|
||||
// Update status text
|
||||
const statusText = document.getElementById('batchStatusText');
|
||||
if (statusText) {
|
||||
if (progress.status === 'running') {
|
||||
statusText.textContent = translate('recipes.batchImport.importing', {}, 'Importing...');
|
||||
} else if (progress.status === 'completed') {
|
||||
statusText.textContent = translate('recipes.batchImport.completed', {}, 'Import completed');
|
||||
} else if (progress.status === 'cancelled') {
|
||||
statusText.textContent = translate('recipes.batchImport.cancelled', {}, 'Import cancelled');
|
||||
}
|
||||
}
|
||||
|
||||
// Update container classes
|
||||
const progressContainer = document.querySelector('.batch-progress-container');
|
||||
if (progressContainer) {
|
||||
progressContainer.classList.remove('completed', 'cancelled', 'error');
|
||||
if (progress.status === 'completed') {
|
||||
progressContainer.classList.add('completed');
|
||||
} else if (progress.status === 'cancelled') {
|
||||
progressContainer.classList.add('cancelled');
|
||||
} else if (progress.failed > 0 && progress.failed === progress.total) {
|
||||
progressContainer.classList.add('error');
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Handle import completion
|
||||
*/
|
||||
importComplete(progress) {
|
||||
this.cleanupConnections();
|
||||
this.results = progress;
|
||||
|
||||
// Refresh recipes list to show newly imported recipes
|
||||
if (window.recipeManager && typeof window.recipeManager.loadRecipes === 'function') {
|
||||
window.recipeManager.loadRecipes();
|
||||
}
|
||||
|
||||
// Show results step
|
||||
this.showStep('batchResultsStep');
|
||||
this.updateResultsUI(progress);
|
||||
}
|
||||
|
||||
/**
|
||||
* Update the results UI
|
||||
*/
|
||||
updateResultsUI(progress) {
|
||||
// Update summary cards
|
||||
const resultsTotal = document.getElementById('resultsTotal');
|
||||
if (resultsTotal) resultsTotal.textContent = progress.total || 0;
|
||||
|
||||
const resultsSuccess = document.getElementById('resultsSuccess');
|
||||
if (resultsSuccess) resultsSuccess.textContent = progress.success || 0;
|
||||
|
||||
const resultsFailed = document.getElementById('resultsFailed');
|
||||
if (resultsFailed) resultsFailed.textContent = progress.failed || 0;
|
||||
|
||||
const resultsSkipped = document.getElementById('resultsSkipped');
|
||||
if (resultsSkipped) resultsSkipped.textContent = progress.skipped || 0;
|
||||
|
||||
// Update header based on results
|
||||
const resultsHeader = document.getElementById('batchResultsHeader');
|
||||
if (resultsHeader) {
|
||||
const icon = resultsHeader.querySelector('.results-icon i');
|
||||
const title = resultsHeader.querySelector('.results-title');
|
||||
|
||||
if (this.isCancelled) {
|
||||
if (icon) {
|
||||
icon.className = 'fas fa-stop-circle';
|
||||
icon.parentElement.classList.add('warning');
|
||||
}
|
||||
if (title) title.textContent = translate('recipes.batchImport.cancelled', {}, 'Import cancelled');
|
||||
} else if (progress.failed === 0 && progress.success > 0) {
|
||||
if (icon) {
|
||||
icon.className = 'fas fa-check-circle';
|
||||
icon.parentElement.classList.remove('warning', 'error');
|
||||
}
|
||||
if (title) title.textContent = translate('recipes.batchImport.completed', {}, 'Import completed');
|
||||
} else if (progress.failed > 0 && progress.success === 0) {
|
||||
if (icon) {
|
||||
icon.className = 'fas fa-times-circle';
|
||||
icon.parentElement.classList.add('error');
|
||||
}
|
||||
if (title) title.textContent = translate('recipes.batchImport.failed', {}, 'Import failed');
|
||||
} else {
|
||||
if (icon) {
|
||||
icon.className = 'fas fa-exclamation-circle';
|
||||
icon.parentElement.classList.add('warning');
|
||||
}
|
||||
if (title) title.textContent = translate('recipes.batchImport.completedWithErrors', {}, 'Completed with errors');
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Toggle the results details visibility
|
||||
*/
|
||||
toggleResultsDetails() {
|
||||
const detailsList = document.getElementById('batchDetailsList');
|
||||
const toggleIcon = document.getElementById('resultsToggleIcon');
|
||||
const toggle = document.querySelector('.details-toggle');
|
||||
|
||||
if (detailsList && toggleIcon) {
|
||||
if (detailsList.style.display === 'none') {
|
||||
detailsList.style.display = 'block';
|
||||
toggleIcon.classList.add('expanded');
|
||||
if (toggle) toggle.classList.add('expanded');
|
||||
|
||||
// Load details if not loaded
|
||||
if (detailsList.children.length === 0 && this.results && this.results.items) {
|
||||
this.loadResultsDetails(this.results.items);
|
||||
}
|
||||
} else {
|
||||
detailsList.style.display = 'none';
|
||||
toggleIcon.classList.remove('expanded');
|
||||
if (toggle) toggle.classList.remove('expanded');
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Load results details into the list
|
||||
*/
|
||||
loadResultsDetails(items) {
|
||||
const detailsList = document.getElementById('batchDetailsList');
|
||||
if (!detailsList) return;
|
||||
|
||||
detailsList.innerHTML = '';
|
||||
|
||||
items.forEach(item => {
|
||||
const resultItem = document.createElement('div');
|
||||
resultItem.className = 'result-item';
|
||||
|
||||
const statusClass = item.status === 'success' ? 'success' :
|
||||
item.status === 'failed' ? 'failed' : 'skipped';
|
||||
const statusIcon = item.status === 'success' ? 'check' :
|
||||
item.status === 'failed' ? 'times' : 'forward';
|
||||
|
||||
resultItem.innerHTML = `
|
||||
<div class="result-item-status ${statusClass}">
|
||||
<i class="fas fa-${statusIcon}"></i>
|
||||
</div>
|
||||
<div class="result-item-info">
|
||||
<div class="result-item-name">${this.escapeHtml(item.source || item.current_item || 'Unknown')}</div>
|
||||
${item.error_message ? `<div class="result-item-error">${this.escapeHtml(item.error_message)}</div>` : ''}
|
||||
</div>
|
||||
`;
|
||||
|
||||
detailsList.appendChild(resultItem);
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Cancel the current import
|
||||
*/
|
||||
async cancelImport() {
|
||||
if (!this.operationId) return;
|
||||
|
||||
this.isCancelled = true;
|
||||
|
||||
try {
|
||||
const response = await fetch('/api/lm/recipes/batch-import/cancel', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json'
|
||||
},
|
||||
body: JSON.stringify({ operation_id: this.operationId })
|
||||
});
|
||||
|
||||
const data = await response.json();
|
||||
|
||||
if (data.success) {
|
||||
showToast('toast.recipes.batchImportCancelling', {}, 'info');
|
||||
} else {
|
||||
showToast('toast.recipes.batchImportCancelFailed', { message: data.error }, 'error');
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Error cancelling import:', error);
|
||||
showToast('toast.recipes.batchImportCancelFailed', { message: error.message }, 'error');
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Close modal and reset state
|
||||
*/
|
||||
closeAndReset() {
|
||||
this.cleanupConnections();
|
||||
this.resetState();
|
||||
modalManager.closeModal('batchImportModal');
|
||||
}
|
||||
|
||||
/**
|
||||
* Start a new import (from results step)
|
||||
*/
|
||||
startNewImport() {
|
||||
this.resetState();
|
||||
this.showStep('batchInputStep');
|
||||
}
|
||||
|
||||
/**
|
||||
* Toggle directory browser visibility
|
||||
*/
|
||||
toggleDirectoryBrowser() {
|
||||
const browser = document.getElementById('batchDirectoryBrowser');
|
||||
if (browser) {
|
||||
const isVisible = browser.style.display !== 'none';
|
||||
browser.style.display = isVisible ? 'none' : 'block';
|
||||
|
||||
if (!isVisible) {
|
||||
// Load initial directory when opening
|
||||
const currentPath = document.getElementById('batchDirectoryInput').value;
|
||||
this.loadDirectory(currentPath || '/');
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Load directory contents
|
||||
*/
|
||||
async loadDirectory(path) {
|
||||
try {
|
||||
const response = await fetch('/api/lm/recipes/browse-directory', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json'
|
||||
},
|
||||
body: JSON.stringify({ path })
|
||||
});
|
||||
|
||||
const data = await response.json();
|
||||
|
||||
if (data.success) {
|
||||
this.renderDirectoryBrowser(data);
|
||||
} else {
|
||||
showToast('toast.recipes.batchImportBrowseFailed', { message: data.error }, 'error');
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Error loading directory:', error);
|
||||
showToast('toast.recipes.batchImportBrowseFailed', { message: error.message }, 'error');
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Render directory browser UI
|
||||
*/
|
||||
renderDirectoryBrowser(data) {
|
||||
const currentPathEl = document.getElementById('batchCurrentPath');
|
||||
const folderList = document.getElementById('batchFolderList');
|
||||
const fileList = document.getElementById('batchFileList');
|
||||
const directoryCount = document.getElementById('batchDirectoryCount');
|
||||
const imageCount = document.getElementById('batchImageCount');
|
||||
|
||||
if (currentPathEl) {
|
||||
currentPathEl.textContent = data.current_path;
|
||||
}
|
||||
|
||||
// Render folders
|
||||
if (folderList) {
|
||||
folderList.innerHTML = '';
|
||||
|
||||
// Add parent directory if available
|
||||
if (data.parent_path) {
|
||||
const parentItem = this.createFolderItem('..', data.parent_path, true);
|
||||
folderList.appendChild(parentItem);
|
||||
}
|
||||
|
||||
data.directories.forEach(dir => {
|
||||
folderList.appendChild(this.createFolderItem(dir.name, dir.path));
|
||||
});
|
||||
}
|
||||
|
||||
// Render files
|
||||
if (fileList) {
|
||||
fileList.innerHTML = '';
|
||||
data.image_files.forEach(file => {
|
||||
fileList.appendChild(this.createFileItem(file.name, file.path, file.size));
|
||||
});
|
||||
}
|
||||
|
||||
// Update stats
|
||||
if (directoryCount) {
|
||||
directoryCount.textContent = data.directory_count;
|
||||
}
|
||||
if (imageCount) {
|
||||
imageCount.textContent = data.image_count;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create folder item element
|
||||
*/
|
||||
createFolderItem(name, path, isParent = false) {
|
||||
const item = document.createElement('div');
|
||||
item.className = 'folder-item';
|
||||
item.dataset.path = path;
|
||||
|
||||
item.innerHTML = `
|
||||
<i class="fas fa-folder${isParent ? '' : ''}"></i>
|
||||
<span class="item-name">${this.escapeHtml(name)}</span>
|
||||
`;
|
||||
|
||||
item.addEventListener('click', () => {
|
||||
if (isParent) {
|
||||
this.navigateToParentDirectory();
|
||||
} else {
|
||||
this.loadDirectory(path);
|
||||
}
|
||||
});
|
||||
|
||||
return item;
|
||||
}
|
||||
|
||||
/**
|
||||
* Create file item element
|
||||
*/
|
||||
createFileItem(name, path, size) {
|
||||
const item = document.createElement('div');
|
||||
item.className = 'file-item';
|
||||
item.dataset.path = path;
|
||||
|
||||
item.innerHTML = `
|
||||
<i class="fas fa-image"></i>
|
||||
<span class="item-name">${this.escapeHtml(name)}</span>
|
||||
<span class="item-size">${this.formatFileSize(size)}</span>
|
||||
`;
|
||||
|
||||
return item;
|
||||
}
|
||||
|
||||
/**
|
||||
* Navigate to parent directory
|
||||
*/
|
||||
navigateToParentDirectory() {
|
||||
const currentPath = document.getElementById('batchCurrentPath')?.textContent;
|
||||
if (currentPath) {
|
||||
// Get parent path using path manipulation
|
||||
const lastSeparator = currentPath.lastIndexOf('/');
|
||||
const parentPath = lastSeparator > 0 ? currentPath.substring(0, lastSeparator) : currentPath;
|
||||
this.loadDirectory(parentPath);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Select current directory
|
||||
*/
|
||||
selectCurrentDirectory() {
|
||||
const currentPath = document.getElementById('batchCurrentPath')?.textContent;
|
||||
const directoryInput = document.getElementById('batchDirectoryInput');
|
||||
|
||||
if (currentPath && directoryInput) {
|
||||
directoryInput.value = currentPath;
|
||||
this.toggleDirectoryBrowser(); // Close browser
|
||||
showToast('toast.recipes.batchImportDirectorySelected', { path: currentPath }, 'success');
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Format file size for display
|
||||
*/
|
||||
formatFileSize(bytes) {
|
||||
if (bytes === 0) return '0 B';
|
||||
const k = 1024;
|
||||
const sizes = ['B', 'KB', 'MB', 'GB'];
|
||||
const i = Math.floor(Math.log(bytes) / Math.log(k));
|
||||
return Math.round(bytes / Math.pow(k, i) * 10) / 10 + ' ' + sizes[i];
|
||||
}
|
||||
|
||||
/**
|
||||
* Escape HTML to prevent XSS
|
||||
*/
|
||||
escapeHtml(text) {
|
||||
if (!text) return '';
|
||||
const div = document.createElement('div');
|
||||
div.textContent = text;
|
||||
return div.innerHTML;
|
||||
}
|
||||
|
||||
/**
|
||||
* Browse for directory using File System Access API (deprecated - kept for compatibility)
|
||||
*/
|
||||
async browseDirectory() {
|
||||
// Now redirects to the new directory browser
|
||||
this.toggleDirectoryBrowser();
|
||||
}
|
||||
|
||||
/**
|
||||
* Clean up WebSocket and polling connections
|
||||
*/
|
||||
cleanupConnections() {
|
||||
if (this.wsConnection) {
|
||||
if (this.wsConnection.readyState === WebSocket.OPEN ||
|
||||
this.wsConnection.readyState === WebSocket.CONNECTING) {
|
||||
this.wsConnection.close();
|
||||
}
|
||||
this.wsConnection = null;
|
||||
}
|
||||
|
||||
if (this.pollingInterval) {
|
||||
clearInterval(this.pollingInterval);
|
||||
this.pollingInterval = null;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Escape HTML to prevent XSS
|
||||
*/
|
||||
escapeHtml(text) {
|
||||
if (!text) return '';
|
||||
const div = document.createElement('div');
|
||||
div.textContent = text;
|
||||
return div.innerHTML;
|
||||
}
|
||||
}
|
||||
|
||||
// Create singleton instance
|
||||
export const batchImportManager = new BatchImportManager();
|
||||
@@ -134,6 +134,19 @@ export class ModalManager {
|
||||
});
|
||||
}
|
||||
|
||||
// Add batchImportModal registration
|
||||
const batchImportModal = document.getElementById('batchImportModal');
|
||||
if (batchImportModal) {
|
||||
this.registerModal('batchImportModal', {
|
||||
element: batchImportModal,
|
||||
onClose: () => {
|
||||
this.getModal('batchImportModal').element.style.display = 'none';
|
||||
document.body.classList.remove('modal-open');
|
||||
},
|
||||
closeOnOutsideClick: true
|
||||
});
|
||||
}
|
||||
|
||||
// Add recipeModal registration
|
||||
const recipeModal = document.getElementById('recipeModal');
|
||||
if (recipeModal) {
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
// Recipe manager module
|
||||
import { appCore } from './core.js';
|
||||
import { ImportManager } from './managers/ImportManager.js';
|
||||
import { BatchImportManager } from './managers/BatchImportManager.js';
|
||||
import { RecipeModal } from './components/RecipeModal.js';
|
||||
import { state, getCurrentPageState } from './state/index.js';
|
||||
import { getSessionItem, removeSessionItem } from './utils/storageHelpers.js';
|
||||
@@ -46,6 +47,10 @@ class RecipeManager {
|
||||
// Initialize ImportManager
|
||||
this.importManager = new ImportManager();
|
||||
|
||||
// Initialize BatchImportManager and make it globally accessible
|
||||
this.batchImportManager = new BatchImportManager();
|
||||
window.batchImportManager = this.batchImportManager;
|
||||
|
||||
// Initialize RecipeModal
|
||||
this.recipeModal = new RecipeModal();
|
||||
|
||||
|
||||
206
templates/components/batch_import_modal.html
Normal file
206
templates/components/batch_import_modal.html
Normal file
@@ -0,0 +1,206 @@
|
||||
<div id="batchImportModal" class="modal">
|
||||
<div class="modal-content">
|
||||
<div class="modal-header">
|
||||
<button class="close" onclick="modalManager.closeModal('batchImportModal')">×</button>
|
||||
<h2>{{ t('recipes.batchImport.title') }}</h2>
|
||||
</div>
|
||||
|
||||
<!-- Step 1: Input Selection -->
|
||||
<div class="batch-import-step" id="batchInputStep">
|
||||
<div class="import-mode-toggle">
|
||||
<button class="toggle-btn active" data-mode="urls" onclick="batchImportManager.toggleInputMode('urls')">
|
||||
<i class="fas fa-link"></i> {{ t('recipes.batchImport.urlList') }}
|
||||
</button>
|
||||
<button class="toggle-btn" data-mode="directory" onclick="batchImportManager.toggleInputMode('directory')">
|
||||
<i class="fas fa-folder"></i> {{ t('recipes.batchImport.directory') }}
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<!-- URL List Section -->
|
||||
<div class="import-section" id="urlListSection">
|
||||
<p class="section-description">{{ t('recipes.batchImport.urlDescription') }}</p>
|
||||
<div class="input-group">
|
||||
<label for="batchUrlInput">{{ t('recipes.batchImport.urlsLabel') }}</label>
|
||||
<textarea id="batchUrlInput" rows="8" placeholder="{{ t('recipes.batchImport.urlsPlaceholder') }}"></textarea>
|
||||
<div class="input-hint">
|
||||
<i class="fas fa-info-circle"></i>
|
||||
{{ t('recipes.batchImport.urlsHint') }}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Directory Section -->
|
||||
<div class="import-section" id="directorySection" style="display: none;">
|
||||
<p class="section-description">{{ t('recipes.batchImport.directoryDescription') }}</p>
|
||||
<div class="input-group">
|
||||
<label for="batchDirectoryInput">{{ t('recipes.batchImport.directoryPath') }}</label>
|
||||
<div class="input-with-button">
|
||||
<input type="text" id="batchDirectoryInput" placeholder="{{ t('recipes.batchImport.directoryPlaceholder') }}" autocomplete="off">
|
||||
<button class="secondary-btn" onclick="batchImportManager.toggleDirectoryBrowser()">
|
||||
<i class="fas fa-folder-open"></i> {{ t('recipes.batchImport.browse') }}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Directory Browser -->
|
||||
<div class="directory-browser" id="batchDirectoryBrowser" style="display: none;">
|
||||
<div class="browser-header">
|
||||
<button class="back-btn" onclick="batchImportManager.navigateToParentDirectory()" title="{{ t('recipes.batchImport.backToParent') }}">
|
||||
<i class="fas fa-arrow-up"></i>
|
||||
</button>
|
||||
<div class="current-path" id="batchCurrentPath"></div>
|
||||
</div>
|
||||
<div class="browser-content">
|
||||
<div class="browser-section">
|
||||
<div class="section-label"><i class="fas fa-folder"></i> {{ t('recipes.batchImport.folders') }}</div>
|
||||
<div class="folder-list" id="batchFolderList"></div>
|
||||
</div>
|
||||
<div class="browser-section">
|
||||
<div class="section-label"><i class="fas fa-image"></i> {{ t('recipes.batchImport.imageFiles') }}</div>
|
||||
<div class="file-list" id="batchFileList"></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="browser-footer">
|
||||
<div class="stats">
|
||||
<span id="batchDirectoryCount">0</span> {{ t('recipes.batchImport.folders') }},
|
||||
<span id="batchImageCount">0</span> {{ t('recipes.batchImport.images') }}
|
||||
</div>
|
||||
<button class="primary-btn" onclick="batchImportManager.selectCurrentDirectory()">
|
||||
<i class="fas fa-check"></i> {{ t('recipes.batchImport.selectFolder') }}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="checkbox-group">
|
||||
<label class="checkbox-label">
|
||||
<input type="checkbox" id="batchRecursiveCheck" checked>
|
||||
<span class="checkmark"></span>
|
||||
{{ t('recipes.batchImport.recursive') }}
|
||||
</label>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Common Options -->
|
||||
<div class="batch-options">
|
||||
<div class="input-group">
|
||||
<label for="batchTagsInput">{{ t('recipes.batchImport.tagsOptional') }}</label>
|
||||
<input type="text" id="batchTagsInput" placeholder="{{ t('recipes.batchImport.tagsPlaceholder') }}">
|
||||
<div class="input-hint">
|
||||
<i class="fas fa-info-circle"></i>
|
||||
{{ t('recipes.batchImport.tagsHint') }}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="checkbox-group">
|
||||
<label class="checkbox-label">
|
||||
<input type="checkbox" id="batchSkipNoMetadata">
|
||||
<span class="checkmark"></span>
|
||||
{{ t('recipes.batchImport.skipNoMetadata') }}
|
||||
</label>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="modal-actions">
|
||||
<button class="secondary-btn" onclick="modalManager.closeModal('batchImportModal')">{{ t('common.actions.cancel') }}</button>
|
||||
<button class="primary-btn" id="batchImportStartBtn" onclick="batchImportManager.startImport()">
|
||||
<i class="fas fa-play"></i> {{ t('recipes.batchImport.start') }}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Step 2: Progress -->
|
||||
<div class="batch-import-step" id="batchProgressStep" style="display: none;">
|
||||
<div class="batch-progress-container">
|
||||
<div class="progress-header">
|
||||
<div class="progress-status">
|
||||
<span class="status-icon"><i class="fas fa-spinner fa-spin"></i></span>
|
||||
<span class="status-text" id="batchStatusText">{{ t('recipes.batchImport.importing') }}</span>
|
||||
</div>
|
||||
<div class="progress-percentage" id="batchProgressPercent">0%</div>
|
||||
</div>
|
||||
|
||||
<div class="progress-bar-container">
|
||||
<div class="progress-bar" id="batchProgressBar" style="width: 0%"></div>
|
||||
</div>
|
||||
|
||||
<div class="progress-stats">
|
||||
<div class="stat-item">
|
||||
<span class="stat-label">{{ t('recipes.batchImport.total') }}</span>
|
||||
<span class="stat-value" id="batchTotalCount">0</span>
|
||||
</div>
|
||||
<div class="stat-item success">
|
||||
<span class="stat-label">{{ t('recipes.batchImport.success') }}</span>
|
||||
<span class="stat-value" id="batchSuccessCount">0</span>
|
||||
</div>
|
||||
<div class="stat-item failed">
|
||||
<span class="stat-label">{{ t('recipes.batchImport.failed') }}</span>
|
||||
<span class="stat-value" id="batchFailedCount">0</span>
|
||||
</div>
|
||||
<div class="stat-item skipped">
|
||||
<span class="stat-label">{{ t('recipes.batchImport.skipped') }}</span>
|
||||
<span class="stat-value" id="batchSkippedCount">0</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="current-item" id="batchCurrentItemContainer">
|
||||
<span class="current-item-label">{{ t('recipes.batchImport.current') }}</span>
|
||||
<span class="current-item-name" id="batchCurrentItem">-</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="modal-actions">
|
||||
<button class="secondary-btn" id="batchCancelBtn" onclick="batchImportManager.cancelImport()">
|
||||
<i class="fas fa-stop"></i> {{ t('recipes.batchImport.cancel') }}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Step 3: Results -->
|
||||
<div class="batch-import-step" id="batchResultsStep" style="display: none;">
|
||||
<div class="batch-results-container">
|
||||
<div class="results-header" id="batchResultsHeader">
|
||||
<div class="results-icon">
|
||||
<i class="fas fa-check-circle"></i>
|
||||
</div>
|
||||
<div class="results-title">{{ t('recipes.batchImport.completed') }}</div>
|
||||
</div>
|
||||
|
||||
<div class="results-summary">
|
||||
<div class="result-card total">
|
||||
<span class="result-label">{{ t('recipes.batchImport.total') }}</span>
|
||||
<span class="result-value" id="resultsTotal">0</span>
|
||||
</div>
|
||||
<div class="result-card success">
|
||||
<span class="result-label">{{ t('recipes.batchImport.success') }}</span>
|
||||
<span class="result-value" id="resultsSuccess">0</span>
|
||||
</div>
|
||||
<div class="result-card failed">
|
||||
<span class="result-label">{{ t('recipes.batchImport.failed') }}</span>
|
||||
<span class="result-value" id="resultsFailed">0</span>
|
||||
</div>
|
||||
<div class="result-card skipped">
|
||||
<span class="result-label">{{ t('recipes.batchImport.skipped') }}</span>
|
||||
<span class="result-value" id="resultsSkipped">0</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="results-details" id="batchResultsDetails">
|
||||
<div class="details-toggle" onclick="batchImportManager.toggleResultsDetails()">
|
||||
<i class="fas fa-chevron-down" id="resultsToggleIcon"></i>
|
||||
<span>{{ t('recipes.batchImport.viewDetails') }}</span>
|
||||
</div>
|
||||
<div class="details-list" id="batchDetailsList" style="display: none;">
|
||||
<!-- Details will be populated dynamically -->
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="modal-actions">
|
||||
<button class="secondary-btn" onclick="batchImportManager.closeAndReset()">{{ t('common.actions.close') }}</button>
|
||||
<button class="primary-btn" onclick="batchImportManager.startNewImport()">
|
||||
<i class="fas fa-plus"></i> {{ t('recipes.batchImport.newImport') }}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -7,10 +7,12 @@
|
||||
<link rel="stylesheet" href="/loras_static/css/components/card.css?v={{ version }}">
|
||||
<link rel="stylesheet" href="/loras_static/css/components/recipe-modal.css?v={{ version }}">
|
||||
<link rel="stylesheet" href="/loras_static/css/components/import-modal.css?v={{ version }}">
|
||||
<link rel="stylesheet" href="/loras_static/css/components/batch-import-modal.css?v={{ version }}">
|
||||
{% endblock %}
|
||||
|
||||
{% block additional_components %}
|
||||
{% include 'components/import_modal.html' %}
|
||||
{% include 'components/batch_import_modal.html' %}
|
||||
{% include 'components/recipe_modal.html' %}
|
||||
|
||||
<div id="recipeContextMenu" class="context-menu" style="display: none;">
|
||||
@@ -85,6 +87,10 @@
|
||||
<button onclick="importManager.showImportModal()"><i class="fas fa-file-import"></i> {{
|
||||
t('recipes.controls.import.action') }}</button>
|
||||
</div>
|
||||
<div title="{{ t('recipes.batchImport.title') }}" class="control-group">
|
||||
<button onclick="batchImportManager.showModal()"><i class="fas fa-layer-group"></i> {{
|
||||
t('recipes.batchImport.action') }}</button>
|
||||
</div>
|
||||
<div class="control-group" title="{{ t('loras.controls.bulk.title') }}">
|
||||
<button id="bulkOperationsBtn" data-action="bulk" title="{{ t('loras.controls.bulk.title') }}">
|
||||
<i class="fas fa-th-large"></i> <span><span>{{ t('loras.controls.bulk.action') }}</span>
|
||||
|
||||
@@ -156,4 +156,115 @@ describe('AutoComplete widget interactions', () => {
|
||||
expect(highlighted).toContain('detail');
|
||||
expect(highlighted).not.toMatch(/beta<\/span>/i);
|
||||
});
|
||||
|
||||
it('handles arrow key navigation with virtual scrolling', async () => {
|
||||
vi.useFakeTimers();
|
||||
|
||||
const mockItems = Array.from({ length: 50 }, (_, i) => `model_${i.toString().padStart(2, '0')}.safetensors`);
|
||||
|
||||
fetchApiMock.mockResolvedValue({
|
||||
json: () => Promise.resolve({ success: true, relative_paths: mockItems }),
|
||||
});
|
||||
|
||||
caretHelperInstance.getBeforeCursor.mockReturnValue('model');
|
||||
caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
|
||||
|
||||
const input = document.createElement('textarea');
|
||||
document.body.append(input);
|
||||
|
||||
const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
|
||||
const autoComplete = new AutoComplete(input, 'loras', {
|
||||
debounceDelay: 0,
|
||||
showPreview: false,
|
||||
enableVirtualScroll: true,
|
||||
itemHeight: 40,
|
||||
visibleItems: 15,
|
||||
pageSize: 20,
|
||||
});
|
||||
|
||||
input.value = 'model';
|
||||
input.dispatchEvent(new Event('input', { bubbles: true }));
|
||||
|
||||
await vi.runAllTimersAsync();
|
||||
await Promise.resolve();
|
||||
|
||||
expect(autoComplete.items.length).toBeGreaterThan(0);
|
||||
expect(autoComplete.selectedIndex).toBe(0);
|
||||
|
||||
const initialSelectedEl = autoComplete.contentContainer?.querySelector('.comfy-autocomplete-item-selected');
|
||||
expect(initialSelectedEl).toBeDefined();
|
||||
|
||||
const arrowDownEvent = new KeyboardEvent('keydown', { key: 'ArrowDown', bubbles: true });
|
||||
input.dispatchEvent(arrowDownEvent);
|
||||
|
||||
expect(autoComplete.selectedIndex).toBe(1);
|
||||
|
||||
const secondSelectedEl = autoComplete.contentContainer?.querySelector('.comfy-autocomplete-item-selected');
|
||||
expect(secondSelectedEl).toBeDefined();
|
||||
expect(secondSelectedEl?.dataset.index).toBe('1');
|
||||
|
||||
const arrowUpEvent = new KeyboardEvent('keydown', { key: 'ArrowUp', bubbles: true });
|
||||
input.dispatchEvent(arrowUpEvent);
|
||||
|
||||
expect(autoComplete.selectedIndex).toBe(0);
|
||||
|
||||
const firstSelectedElAgain = autoComplete.contentContainer?.querySelector('.comfy-autocomplete-item-selected');
|
||||
expect(firstSelectedElAgain).toBeDefined();
|
||||
expect(firstSelectedElAgain?.dataset.index).toBe('0');
|
||||
});
|
||||
|
||||
it('maintains selection when scrolling to invisible items', async () => {
|
||||
vi.useFakeTimers();
|
||||
|
||||
const mockItems = Array.from({ length: 100 }, (_, i) => `item_${i.toString().padStart(3, '0')}.safetensors`);
|
||||
|
||||
fetchApiMock.mockResolvedValue({
|
||||
json: () => Promise.resolve({ success: true, relative_paths: mockItems }),
|
||||
});
|
||||
|
||||
caretHelperInstance.getBeforeCursor.mockReturnValue('item');
|
||||
caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
|
||||
|
||||
const input = document.createElement('textarea');
|
||||
input.style.width = '400px';
|
||||
input.style.height = '200px';
|
||||
document.body.append(input);
|
||||
|
||||
const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
|
||||
const autoComplete = new AutoComplete(input, 'loras', {
|
||||
debounceDelay: 0,
|
||||
showPreview: false,
|
||||
enableVirtualScroll: true,
|
||||
itemHeight: 40,
|
||||
visibleItems: 15,
|
||||
pageSize: 20,
|
||||
});
|
||||
|
||||
input.value = 'item';
|
||||
input.dispatchEvent(new Event('input', { bubbles: true }));
|
||||
|
||||
await vi.runAllTimersAsync();
|
||||
await Promise.resolve();
|
||||
|
||||
expect(autoComplete.items.length).toBeGreaterThan(0);
|
||||
|
||||
autoComplete.selectedIndex = 14;
|
||||
|
||||
const scrollTopBefore = autoComplete.scrollContainer?.scrollTop || 0;
|
||||
|
||||
const arrowDownEvent = new KeyboardEvent('keydown', { key: 'ArrowDown', bubbles: true });
|
||||
input.dispatchEvent(arrowDownEvent);
|
||||
|
||||
await vi.runAllTimersAsync();
|
||||
await Promise.resolve();
|
||||
|
||||
expect(autoComplete.selectedIndex).toBe(15);
|
||||
|
||||
const selectedEl = autoComplete.contentContainer?.querySelector('.comfy-autocomplete-item-selected');
|
||||
expect(selectedEl).toBeDefined();
|
||||
expect(selectedEl?.dataset.index).toBe('15');
|
||||
|
||||
const scrollTopAfter = autoComplete.scrollContainer?.scrollTop || 0;
|
||||
expect(scrollTopAfter).toBeGreaterThanOrEqual(scrollTopBefore);
|
||||
});
|
||||
});
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
"""Integration smoke tests for the recipe route stack."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
@@ -94,19 +95,25 @@ class StubAnalysisService:
|
||||
self._recipe_parser_factory = None
|
||||
StubAnalysisService.instances.append(self)
|
||||
|
||||
async def analyze_uploaded_image(self, *, image_bytes: bytes | None, recipe_scanner) -> SimpleNamespace: # noqa: D401 - mirrors real signature
|
||||
async def analyze_uploaded_image(
|
||||
self, *, image_bytes: bytes | None, recipe_scanner
|
||||
) -> SimpleNamespace: # noqa: D401 - mirrors real signature
|
||||
if self.raise_for_uploaded:
|
||||
raise self.raise_for_uploaded
|
||||
self.upload_calls.append(image_bytes or b"")
|
||||
return self.result
|
||||
|
||||
async def analyze_remote_image(self, *, url: Optional[str], recipe_scanner, civitai_client) -> SimpleNamespace: # noqa: D401
|
||||
async def analyze_remote_image(
|
||||
self, *, url: Optional[str], recipe_scanner, civitai_client
|
||||
) -> SimpleNamespace: # noqa: D401
|
||||
if self.raise_for_remote:
|
||||
raise self.raise_for_remote
|
||||
self.remote_calls.append(url)
|
||||
return self.result
|
||||
|
||||
async def analyze_local_image(self, *, file_path: Optional[str], recipe_scanner) -> SimpleNamespace: # noqa: D401
|
||||
async def analyze_local_image(
|
||||
self, *, file_path: Optional[str], recipe_scanner
|
||||
) -> SimpleNamespace: # noqa: D401
|
||||
if self.raise_for_local:
|
||||
raise self.raise_for_local
|
||||
self.local_calls.append(file_path)
|
||||
@@ -125,11 +132,23 @@ class StubPersistenceService:
|
||||
self.save_calls: List[Dict[str, Any]] = []
|
||||
self.delete_calls: List[str] = []
|
||||
self.move_calls: List[Dict[str, str]] = []
|
||||
self.save_result = SimpleNamespace(payload={"success": True, "recipe_id": "stub-id"}, status=200)
|
||||
self.save_result = SimpleNamespace(
|
||||
payload={"success": True, "recipe_id": "stub-id"}, status=200
|
||||
)
|
||||
self.delete_result = SimpleNamespace(payload={"success": True}, status=200)
|
||||
StubPersistenceService.instances.append(self)
|
||||
|
||||
async def save_recipe(self, *, recipe_scanner, image_bytes, image_base64, name, tags, metadata, extension=None) -> SimpleNamespace: # noqa: D401
|
||||
async def save_recipe(
|
||||
self,
|
||||
*,
|
||||
recipe_scanner,
|
||||
image_bytes,
|
||||
image_base64,
|
||||
name,
|
||||
tags,
|
||||
metadata,
|
||||
extension=None,
|
||||
) -> SimpleNamespace: # noqa: D401
|
||||
self.save_calls.append(
|
||||
{
|
||||
"recipe_scanner": recipe_scanner,
|
||||
@@ -148,22 +167,42 @@ class StubPersistenceService:
|
||||
await recipe_scanner.remove_recipe(recipe_id)
|
||||
return self.delete_result
|
||||
|
||||
async def move_recipe(self, *, recipe_scanner, recipe_id: str, target_path: str) -> SimpleNamespace: # noqa: D401
|
||||
async def move_recipe(
|
||||
self, *, recipe_scanner, recipe_id: str, target_path: str
|
||||
) -> SimpleNamespace: # noqa: D401
|
||||
self.move_calls.append({"recipe_id": recipe_id, "target_path": target_path})
|
||||
return SimpleNamespace(
|
||||
payload={"success": True, "recipe_id": recipe_id, "new_file_path": target_path}, status=200
|
||||
payload={
|
||||
"success": True,
|
||||
"recipe_id": recipe_id,
|
||||
"new_file_path": target_path,
|
||||
},
|
||||
status=200,
|
||||
)
|
||||
|
||||
async def update_recipe(self, *, recipe_scanner, recipe_id: str, updates: Dict[str, Any]) -> SimpleNamespace: # pragma: no cover - unused by smoke tests
|
||||
return SimpleNamespace(payload={"success": True, "recipe_id": recipe_id, "updates": updates}, status=200)
|
||||
async def update_recipe(
|
||||
self, *, recipe_scanner, recipe_id: str, updates: Dict[str, Any]
|
||||
) -> SimpleNamespace: # pragma: no cover - unused by smoke tests
|
||||
return SimpleNamespace(
|
||||
payload={"success": True, "recipe_id": recipe_id, "updates": updates},
|
||||
status=200,
|
||||
)
|
||||
|
||||
async def reconnect_lora(self, *, recipe_scanner, recipe_id: str, lora_index: int, target_name: str) -> SimpleNamespace: # pragma: no cover
|
||||
async def reconnect_lora(
|
||||
self, *, recipe_scanner, recipe_id: str, lora_index: int, target_name: str
|
||||
) -> SimpleNamespace: # pragma: no cover
|
||||
return SimpleNamespace(payload={"success": True}, status=200)
|
||||
|
||||
async def bulk_delete(self, *, recipe_scanner, recipe_ids: List[str]) -> SimpleNamespace: # pragma: no cover
|
||||
return SimpleNamespace(payload={"success": True, "deleted": recipe_ids}, status=200)
|
||||
async def bulk_delete(
|
||||
self, *, recipe_scanner, recipe_ids: List[str]
|
||||
) -> SimpleNamespace: # pragma: no cover
|
||||
return SimpleNamespace(
|
||||
payload={"success": True, "deleted": recipe_ids}, status=200
|
||||
)
|
||||
|
||||
async def save_recipe_from_widget(self, *, recipe_scanner, metadata: Dict[str, Any], image_bytes: bytes) -> SimpleNamespace: # pragma: no cover
|
||||
async def save_recipe_from_widget(
|
||||
self, *, recipe_scanner, metadata: Dict[str, Any], image_bytes: bytes
|
||||
) -> SimpleNamespace: # pragma: no cover
|
||||
return SimpleNamespace(payload={"success": True}, status=200)
|
||||
|
||||
|
||||
@@ -176,7 +215,11 @@ class StubSharingService:
|
||||
self.share_calls: List[str] = []
|
||||
self.download_calls: List[str] = []
|
||||
self.share_result = SimpleNamespace(
|
||||
payload={"success": True, "download_url": "/share/stub", "filename": "recipe.png"},
|
||||
payload={
|
||||
"success": True,
|
||||
"download_url": "/share/stub",
|
||||
"filename": "recipe.png",
|
||||
},
|
||||
status=200,
|
||||
)
|
||||
self.download_info = SimpleNamespace(file_path="", download_filename="")
|
||||
@@ -186,7 +229,9 @@ class StubSharingService:
|
||||
self.share_calls.append(recipe_id)
|
||||
return self.share_result
|
||||
|
||||
async def prepare_download(self, *, recipe_scanner, recipe_id: str) -> SimpleNamespace:
|
||||
async def prepare_download(
|
||||
self, *, recipe_scanner, recipe_id: str
|
||||
) -> SimpleNamespace:
|
||||
self.download_calls.append(recipe_id)
|
||||
return self.download_info
|
||||
|
||||
@@ -214,7 +259,9 @@ class StubCivitaiClient:
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def recipe_harness(monkeypatch, tmp_path: Path) -> AsyncIterator[RecipeRouteHarness]:
|
||||
async def recipe_harness(
|
||||
monkeypatch, tmp_path: Path
|
||||
) -> AsyncIterator[RecipeRouteHarness]:
|
||||
"""Context manager that yields a fully wired recipe route harness."""
|
||||
|
||||
StubAnalysisService.instances.clear()
|
||||
@@ -237,8 +284,12 @@ async def recipe_harness(monkeypatch, tmp_path: Path) -> AsyncIterator[RecipeRou
|
||||
|
||||
monkeypatch.setattr(ServiceRegistry, "get_recipe_scanner", fake_get_recipe_scanner)
|
||||
monkeypatch.setattr(ServiceRegistry, "get_civitai_client", fake_get_civitai_client)
|
||||
monkeypatch.setattr(base_recipe_routes, "RecipeAnalysisService", StubAnalysisService)
|
||||
monkeypatch.setattr(base_recipe_routes, "RecipePersistenceService", StubPersistenceService)
|
||||
monkeypatch.setattr(
|
||||
base_recipe_routes, "RecipeAnalysisService", StubAnalysisService
|
||||
)
|
||||
monkeypatch.setattr(
|
||||
base_recipe_routes, "RecipePersistenceService", StubPersistenceService
|
||||
)
|
||||
monkeypatch.setattr(base_recipe_routes, "RecipeSharingService", StubSharingService)
|
||||
monkeypatch.setattr(base_recipe_routes, "get_downloader", fake_get_downloader)
|
||||
monkeypatch.setattr(config, "loras_roots", [str(tmp_path)], raising=False)
|
||||
@@ -294,7 +345,9 @@ async def test_list_recipes_provides_file_urls(monkeypatch, tmp_path: Path) -> N
|
||||
async def test_save_and_delete_recipe_round_trip(monkeypatch, tmp_path: Path) -> None:
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
form = FormData()
|
||||
form.add_field("image", b"stub", filename="sample.png", content_type="image/png")
|
||||
form.add_field(
|
||||
"image", b"stub", filename="sample.png", content_type="image/png"
|
||||
)
|
||||
form.add_field("name", "Test Recipe")
|
||||
form.add_field("tags", json.dumps(["tag-a"]))
|
||||
form.add_field("metadata", json.dumps({"loras": []}))
|
||||
@@ -312,7 +365,9 @@ async def test_save_and_delete_recipe_round_trip(monkeypatch, tmp_path: Path) ->
|
||||
assert save_payload["recipe_id"] == "saved-id"
|
||||
assert harness.persistence.save_calls[-1]["name"] == "Test Recipe"
|
||||
|
||||
harness.persistence.delete_result = SimpleNamespace(payload={"success": True}, status=200)
|
||||
harness.persistence.delete_result = SimpleNamespace(
|
||||
payload={"success": True}, status=200
|
||||
)
|
||||
|
||||
delete_response = await harness.client.delete("/api/lm/recipe/saved-id")
|
||||
delete_payload = await delete_response.json()
|
||||
@@ -326,14 +381,20 @@ async def test_move_recipe_invokes_persistence(monkeypatch, tmp_path: Path) -> N
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
response = await harness.client.post(
|
||||
"/api/lm/recipe/move",
|
||||
json={"recipe_id": "move-me", "target_path": str(tmp_path / "recipes" / "subdir")},
|
||||
json={
|
||||
"recipe_id": "move-me",
|
||||
"target_path": str(tmp_path / "recipes" / "subdir"),
|
||||
},
|
||||
)
|
||||
|
||||
payload = await response.json()
|
||||
assert response.status == 200
|
||||
assert payload["recipe_id"] == "move-me"
|
||||
assert harness.persistence.move_calls == [
|
||||
{"recipe_id": "move-me", "target_path": str(tmp_path / "recipes" / "subdir")}
|
||||
{
|
||||
"recipe_id": "move-me",
|
||||
"target_path": str(tmp_path / "recipes" / "subdir"),
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
@@ -348,7 +409,10 @@ async def test_import_remote_recipe(monkeypatch, tmp_path: Path) -> None:
|
||||
async def fake_get_default_metadata_provider():
|
||||
return Provider()
|
||||
|
||||
monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
|
||||
monkeypatch.setattr(
|
||||
"py.recipes.enrichment.get_default_metadata_provider",
|
||||
fake_get_default_metadata_provider,
|
||||
)
|
||||
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
resources = [
|
||||
@@ -397,7 +461,9 @@ async def test_import_remote_recipe(monkeypatch, tmp_path: Path) -> None:
|
||||
assert harness.downloader.urls == ["https://example.com/images/1"]
|
||||
|
||||
|
||||
async def test_import_remote_recipe_falls_back_to_request_base_model(monkeypatch, tmp_path: Path) -> None:
|
||||
async def test_import_remote_recipe_falls_back_to_request_base_model(
|
||||
monkeypatch, tmp_path: Path
|
||||
) -> None:
|
||||
provider_calls: list[str | int] = []
|
||||
|
||||
class Provider:
|
||||
@@ -408,7 +474,10 @@ async def test_import_remote_recipe_falls_back_to_request_base_model(monkeypatch
|
||||
async def fake_get_default_metadata_provider():
|
||||
return Provider()
|
||||
|
||||
monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
|
||||
monkeypatch.setattr(
|
||||
"py.recipes.enrichment.get_default_metadata_provider",
|
||||
fake_get_default_metadata_provider,
|
||||
)
|
||||
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
resources = [
|
||||
@@ -444,13 +513,16 @@ async def test_import_remote_video_recipe(monkeypatch, tmp_path: Path) -> None:
|
||||
async def fake_get_default_metadata_provider():
|
||||
return SimpleNamespace(get_model_version_info=lambda id: ({}, None))
|
||||
|
||||
monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
|
||||
monkeypatch.setattr(
|
||||
"py.recipes.enrichment.get_default_metadata_provider",
|
||||
fake_get_default_metadata_provider,
|
||||
)
|
||||
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
harness.civitai.image_info["12345"] = {
|
||||
"id": 12345,
|
||||
"url": "https://image.civitai.com/x/y/original=true/video.mp4",
|
||||
"type": "video"
|
||||
"type": "video",
|
||||
}
|
||||
|
||||
response = await harness.client.get(
|
||||
@@ -469,7 +541,7 @@ async def test_import_remote_video_recipe(monkeypatch, tmp_path: Path) -> None:
|
||||
|
||||
# Verify downloader was called with rewritten URL
|
||||
assert "transcode=true" in harness.downloader.urls[0]
|
||||
|
||||
|
||||
# Verify persistence was called with correct extension
|
||||
call = harness.persistence.save_calls[-1]
|
||||
assert call["extension"] == ".mp4"
|
||||
@@ -477,7 +549,9 @@ async def test_import_remote_video_recipe(monkeypatch, tmp_path: Path) -> None:
|
||||
|
||||
async def test_analyze_uploaded_image_error_path(monkeypatch, tmp_path: Path) -> None:
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
harness.analysis.raise_for_uploaded = RecipeValidationError("No image data provided")
|
||||
harness.analysis.raise_for_uploaded = RecipeValidationError(
|
||||
"No image data provided"
|
||||
)
|
||||
|
||||
form = FormData()
|
||||
form.add_field("image", b"", filename="empty.png", content_type="image/png")
|
||||
@@ -504,7 +578,11 @@ async def test_share_and_download_recipe(monkeypatch, tmp_path: Path) -> None:
|
||||
}
|
||||
|
||||
harness.sharing.share_result = SimpleNamespace(
|
||||
payload={"success": True, "download_url": "/api/share", "filename": "share.png"},
|
||||
payload={
|
||||
"success": True,
|
||||
"download_url": "/api/share",
|
||||
"filename": "share.png",
|
||||
},
|
||||
status=200,
|
||||
)
|
||||
harness.sharing.download_info = SimpleNamespace(
|
||||
@@ -519,15 +597,24 @@ async def test_share_and_download_recipe(monkeypatch, tmp_path: Path) -> None:
|
||||
assert share_payload["filename"] == "share.png"
|
||||
assert harness.sharing.share_calls == [recipe_id]
|
||||
|
||||
download_response = await harness.client.get(f"/api/lm/recipe/{recipe_id}/share/download")
|
||||
download_response = await harness.client.get(
|
||||
f"/api/lm/recipe/{recipe_id}/share/download"
|
||||
)
|
||||
body = await download_response.read()
|
||||
|
||||
assert download_response.status == 200
|
||||
assert download_response.headers["Content-Disposition"] == 'attachment; filename="share.png"'
|
||||
assert (
|
||||
download_response.headers["Content-Disposition"]
|
||||
== 'attachment; filename="share.png"'
|
||||
)
|
||||
assert body == b"stub"
|
||||
|
||||
download_path.unlink(missing_ok=True)
|
||||
async def test_import_remote_recipe_merges_metadata(monkeypatch, tmp_path: Path) -> None:
|
||||
|
||||
|
||||
async def test_import_remote_recipe_merges_metadata(
|
||||
monkeypatch, tmp_path: Path
|
||||
) -> None:
|
||||
# 1. Mock Metadata Provider
|
||||
class Provider:
|
||||
async def get_model_version_info(self, model_version_id):
|
||||
@@ -536,22 +623,25 @@ async def test_import_remote_recipe_merges_metadata(monkeypatch, tmp_path: Path)
|
||||
async def fake_get_default_metadata_provider():
|
||||
return Provider()
|
||||
|
||||
monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
|
||||
monkeypatch.setattr(
|
||||
"py.recipes.enrichment.get_default_metadata_provider",
|
||||
fake_get_default_metadata_provider,
|
||||
)
|
||||
|
||||
# 2. Mock ExifUtils to return some embedded metadata
|
||||
class MockExifUtils:
|
||||
@staticmethod
|
||||
def extract_image_metadata(path):
|
||||
return "Recipe metadata: " + json.dumps({
|
||||
"gen_params": {"prompt": "from embedded", "seed": 123}
|
||||
})
|
||||
return "Recipe metadata: " + json.dumps(
|
||||
{"gen_params": {"prompt": "from embedded", "seed": 123}}
|
||||
)
|
||||
|
||||
monkeypatch.setattr(recipe_handlers, "ExifUtils", MockExifUtils)
|
||||
|
||||
# 3. Mock Parser Factory for StubAnalysisService
|
||||
class MockParser:
|
||||
async def parse_metadata(self, raw, recipe_scanner=None):
|
||||
return json.loads(raw[len("Recipe metadata: "):])
|
||||
return json.loads(raw[len("Recipe metadata: ") :])
|
||||
|
||||
class MockFactory:
|
||||
def create_parser(self, raw):
|
||||
@@ -562,12 +652,12 @@ async def test_import_remote_recipe_merges_metadata(monkeypatch, tmp_path: Path)
|
||||
# 4. Setup Harness and run test
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
harness.analysis._recipe_parser_factory = MockFactory()
|
||||
|
||||
|
||||
# Civitai meta via image_info
|
||||
harness.civitai.image_info["1"] = {
|
||||
"id": 1,
|
||||
"url": "https://example.com/images/1.jpg",
|
||||
"meta": {"prompt": "from civitai", "cfg": 7.0}
|
||||
"meta": {"prompt": "from civitai", "cfg": 7.0},
|
||||
}
|
||||
|
||||
resources = []
|
||||
@@ -583,11 +673,11 @@ async def test_import_remote_recipe_merges_metadata(monkeypatch, tmp_path: Path)
|
||||
|
||||
payload = await response.json()
|
||||
assert response.status == 200
|
||||
|
||||
|
||||
call = harness.persistence.save_calls[-1]
|
||||
metadata = call["metadata"]
|
||||
gen_params = metadata["gen_params"]
|
||||
|
||||
|
||||
assert gen_params["seed"] == 123
|
||||
|
||||
|
||||
@@ -619,3 +709,142 @@ async def test_get_recipe_syntax(monkeypatch, tmp_path: Path) -> None:
|
||||
response_404 = await harness.client.get("/api/lm/recipe/non-existent/syntax")
|
||||
assert response_404.status == 404
|
||||
|
||||
|
||||
async def test_batch_import_start_success(monkeypatch, tmp_path: Path) -> None:
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
response = await harness.client.post(
|
||||
"/api/lm/recipes/batch-import/start",
|
||||
json={
|
||||
"items": [
|
||||
{"source": "https://example.com/image1.png"},
|
||||
{"source": "https://example.com/image2.png"},
|
||||
],
|
||||
"tags": ["batch", "import"],
|
||||
"skip_no_metadata": True,
|
||||
},
|
||||
)
|
||||
payload = await response.json()
|
||||
assert response.status == 200
|
||||
assert payload["success"] is True
|
||||
assert "operation_id" in payload
|
||||
|
||||
|
||||
async def test_batch_import_start_empty_items(monkeypatch, tmp_path: Path) -> None:
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
response = await harness.client.post(
|
||||
"/api/lm/recipes/batch-import/start",
|
||||
json={"items": [], "tags": []},
|
||||
)
|
||||
payload = await response.json()
|
||||
assert response.status == 400
|
||||
assert payload["success"] is False
|
||||
assert "No items provided" in payload["error"]
|
||||
|
||||
|
||||
async def test_batch_import_start_missing_source(monkeypatch, tmp_path: Path) -> None:
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
response = await harness.client.post(
|
||||
"/api/lm/recipes/batch-import/start",
|
||||
json={"items": [{"source": ""}]},
|
||||
)
|
||||
payload = await response.json()
|
||||
assert response.status == 400
|
||||
assert payload["success"] is False
|
||||
assert "source" in payload["error"].lower()
|
||||
|
||||
|
||||
async def test_batch_import_start_already_running(monkeypatch, tmp_path: Path) -> None:
|
||||
import asyncio
|
||||
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
original_analyze = harness.analysis.analyze_remote_image
|
||||
|
||||
async def slow_analyze(*, url, recipe_scanner, civitai_client):
|
||||
await asyncio.sleep(0.5)
|
||||
return await original_analyze(
|
||||
url=url, recipe_scanner=recipe_scanner, civitai_client=civitai_client
|
||||
)
|
||||
|
||||
harness.analysis.analyze_remote_image = slow_analyze
|
||||
|
||||
items = [{"source": f"https://example.com/image{i}.png"} for i in range(10)]
|
||||
|
||||
response1 = await harness.client.post(
|
||||
"/api/lm/recipes/batch-import/start",
|
||||
json={"items": items},
|
||||
)
|
||||
assert response1.status == 200
|
||||
|
||||
payload1 = await response1.json()
|
||||
assert payload1["success"] is True
|
||||
|
||||
await asyncio.sleep(0.1)
|
||||
|
||||
response2 = await harness.client.post(
|
||||
"/api/lm/recipes/batch-import/start",
|
||||
json={"items": [{"source": "https://example.com/other.png"}]},
|
||||
)
|
||||
payload2 = await response2.json()
|
||||
assert response2.status == 409
|
||||
assert "already in progress" in payload2["error"].lower()
|
||||
|
||||
|
||||
async def test_batch_import_get_progress_not_found(monkeypatch, tmp_path: Path) -> None:
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
response = await harness.client.get(
|
||||
"/api/lm/recipes/batch-import/progress",
|
||||
params={"operation_id": "nonexistent-id"},
|
||||
)
|
||||
payload = await response.json()
|
||||
assert response.status == 404
|
||||
assert payload["success"] is False
|
||||
|
||||
|
||||
async def test_batch_import_get_progress_missing_id(
|
||||
monkeypatch, tmp_path: Path
|
||||
) -> None:
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
response = await harness.client.get("/api/lm/recipes/batch-import/progress")
|
||||
payload = await response.json()
|
||||
assert response.status == 400
|
||||
assert payload["success"] is False
|
||||
|
||||
|
||||
async def test_batch_import_cancel_success(monkeypatch, tmp_path: Path) -> None:
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
start_response = await harness.client.post(
|
||||
"/api/lm/recipes/batch-import/start",
|
||||
json={"items": [{"source": "https://example.com/image.png"}]},
|
||||
)
|
||||
start_payload = await start_response.json()
|
||||
operation_id = start_payload["operation_id"]
|
||||
|
||||
cancel_response = await harness.client.post(
|
||||
"/api/lm/recipes/batch-import/cancel",
|
||||
json={"operation_id": operation_id},
|
||||
)
|
||||
cancel_payload = await cancel_response.json()
|
||||
assert cancel_response.status == 200
|
||||
assert cancel_payload["success"] is True
|
||||
|
||||
|
||||
async def test_batch_import_cancel_not_found(monkeypatch, tmp_path: Path) -> None:
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
response = await harness.client.post(
|
||||
"/api/lm/recipes/batch-import/cancel",
|
||||
json={"operation_id": "nonexistent-id"},
|
||||
)
|
||||
payload = await response.json()
|
||||
assert response.status == 404
|
||||
assert payload["success"] is False
|
||||
|
||||
|
||||
async def test_batch_import_cancel_missing_id(monkeypatch, tmp_path: Path) -> None:
|
||||
async with recipe_harness(monkeypatch, tmp_path) as harness:
|
||||
response = await harness.client.post(
|
||||
"/api/lm/recipes/batch-import/cancel",
|
||||
json={},
|
||||
)
|
||||
payload = await response.json()
|
||||
assert response.status == 400
|
||||
assert payload["success"] is False
|
||||
|
||||
597
tests/services/test_batch_import_service.py
Normal file
597
tests/services/test_batch_import_service.py
Normal file
@@ -0,0 +1,597 @@
|
||||
"""Unit tests for BatchImportService."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
import tempfile
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from types import SimpleNamespace
|
||||
from typing import Any, Dict, List, Optional
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from py.services.batch_import_service import (
|
||||
AdaptiveConcurrencyController,
|
||||
BatchImportItem,
|
||||
BatchImportProgress,
|
||||
BatchImportService,
|
||||
ImportItemType,
|
||||
ImportStatus,
|
||||
)
|
||||
|
||||
|
||||
class MockWebSocketManager:
|
||||
def __init__(self):
|
||||
self.broadcasts: List[Dict[str, Any]] = []
|
||||
|
||||
async def broadcast(self, data: Dict[str, Any]):
|
||||
self.broadcasts.append(data)
|
||||
|
||||
|
||||
@dataclass
|
||||
class MockAnalysisResult:
|
||||
payload: Dict[str, Any]
|
||||
status: int = 200
|
||||
|
||||
|
||||
class MockAnalysisService:
|
||||
def __init__(self, results: Optional[Dict[str, MockAnalysisResult]] = None):
|
||||
self.results = results or {}
|
||||
self.call_count = 0
|
||||
self.last_url = None
|
||||
self.last_path = None
|
||||
|
||||
async def analyze_remote_image(self, *, url: str, recipe_scanner, civitai_client):
|
||||
self.call_count += 1
|
||||
self.last_url = url
|
||||
if url in self.results:
|
||||
return self.results[url]
|
||||
return MockAnalysisResult({"error": "No metadata found", "loras": []})
|
||||
|
||||
async def analyze_local_image(self, *, file_path: str, recipe_scanner):
|
||||
self.call_count += 1
|
||||
self.last_path = file_path
|
||||
if file_path in self.results:
|
||||
return self.results[file_path]
|
||||
return MockAnalysisResult({"error": "No metadata found", "loras": []})
|
||||
|
||||
|
||||
@dataclass
|
||||
class MockSaveResult:
|
||||
payload: Dict[str, Any]
|
||||
status: int = 200
|
||||
|
||||
|
||||
class MockPersistenceService:
|
||||
def __init__(self, should_succeed: bool = True):
|
||||
self.should_succeed = should_succeed
|
||||
self.saved_recipes: List[Dict[str, Any]] = []
|
||||
self.call_count = 0
|
||||
|
||||
async def save_recipe(
|
||||
self,
|
||||
*,
|
||||
recipe_scanner,
|
||||
image_bytes: Optional[bytes] = None,
|
||||
image_base64: Optional[str] = None,
|
||||
name: str,
|
||||
tags: List[str],
|
||||
metadata: Dict[str, Any],
|
||||
extension: Optional[str] = None,
|
||||
):
|
||||
self.call_count += 1
|
||||
self.saved_recipes.append(
|
||||
{
|
||||
"name": name,
|
||||
"tags": tags,
|
||||
"metadata": metadata,
|
||||
}
|
||||
)
|
||||
if self.should_succeed:
|
||||
return MockSaveResult({"success": True, "id": f"recipe_{self.call_count}"})
|
||||
return MockSaveResult({"success": False, "error": "Save failed"}, status=400)
|
||||
|
||||
|
||||
class TestAdaptiveConcurrencyController:
|
||||
def test_initial_values(self):
|
||||
controller = AdaptiveConcurrencyController()
|
||||
assert controller.current_concurrency == 3
|
||||
assert controller.min_concurrency == 1
|
||||
assert controller.max_concurrency == 5
|
||||
|
||||
def test_custom_initial_values(self):
|
||||
controller = AdaptiveConcurrencyController(
|
||||
min_concurrency=2,
|
||||
max_concurrency=10,
|
||||
initial_concurrency=5,
|
||||
)
|
||||
assert controller.current_concurrency == 5
|
||||
assert controller.min_concurrency == 2
|
||||
assert controller.max_concurrency == 10
|
||||
|
||||
def test_increase_concurrency_on_success(self):
|
||||
controller = AdaptiveConcurrencyController(initial_concurrency=3)
|
||||
controller.record_result(duration=0.5, success=True)
|
||||
assert controller.current_concurrency == 4
|
||||
|
||||
def test_do_not_exceed_max(self):
|
||||
controller = AdaptiveConcurrencyController(
|
||||
max_concurrency=5,
|
||||
initial_concurrency=5,
|
||||
)
|
||||
controller.record_result(duration=0.5, success=True)
|
||||
assert controller.current_concurrency == 5
|
||||
|
||||
def test_decrease_concurrency_on_failure(self):
|
||||
controller = AdaptiveConcurrencyController(initial_concurrency=3)
|
||||
controller.record_result(duration=1.0, success=False)
|
||||
assert controller.current_concurrency == 2
|
||||
|
||||
def test_do_not_go_below_min(self):
|
||||
controller = AdaptiveConcurrencyController(
|
||||
min_concurrency=1,
|
||||
initial_concurrency=1,
|
||||
)
|
||||
controller.record_result(duration=1.0, success=False)
|
||||
assert controller.current_concurrency == 1
|
||||
|
||||
def test_slow_task_decreases_concurrency(self):
|
||||
controller = AdaptiveConcurrencyController(initial_concurrency=3)
|
||||
controller.record_result(duration=11.0, success=True)
|
||||
assert controller.current_concurrency == 2
|
||||
|
||||
def test_fast_task_increases_concurrency(self):
|
||||
controller = AdaptiveConcurrencyController(initial_concurrency=3)
|
||||
controller.record_result(duration=0.5, success=True)
|
||||
assert controller.current_concurrency == 4
|
||||
|
||||
def test_moderate_task_no_change(self):
|
||||
controller = AdaptiveConcurrencyController(initial_concurrency=3)
|
||||
controller.record_result(duration=5.0, success=True)
|
||||
assert controller.current_concurrency == 3
|
||||
|
||||
|
||||
class TestBatchImportProgress:
|
||||
def test_to_dict(self):
|
||||
progress = BatchImportProgress(
|
||||
operation_id="test-123",
|
||||
total=10,
|
||||
completed=5,
|
||||
success=3,
|
||||
failed=2,
|
||||
skipped=0,
|
||||
current_item="image.png",
|
||||
status="running",
|
||||
)
|
||||
result = progress.to_dict()
|
||||
assert result["operation_id"] == "test-123"
|
||||
assert result["total"] == 10
|
||||
assert result["completed"] == 5
|
||||
assert result["success"] == 3
|
||||
assert result["failed"] == 2
|
||||
assert result["progress_percent"] == 50.0
|
||||
|
||||
def test_progress_percent_zero_total(self):
|
||||
progress = BatchImportProgress(
|
||||
operation_id="test-123",
|
||||
total=0,
|
||||
)
|
||||
assert progress.to_dict()["progress_percent"] == 0
|
||||
|
||||
|
||||
class TestBatchImportItem:
|
||||
def test_defaults(self):
|
||||
item = BatchImportItem(
|
||||
id="item-1",
|
||||
source="https://example.com/image.png",
|
||||
item_type=ImportItemType.URL,
|
||||
)
|
||||
assert item.status == ImportStatus.PENDING
|
||||
assert item.error_message is None
|
||||
assert item.recipe_name is None
|
||||
|
||||
|
||||
class TestBatchImportService:
|
||||
@pytest.fixture
|
||||
def mock_services(self):
|
||||
ws_manager = MockWebSocketManager()
|
||||
analysis_service = MockAnalysisService()
|
||||
persistence_service = MockPersistenceService()
|
||||
logger = logging.getLogger("test")
|
||||
return ws_manager, analysis_service, persistence_service, logger
|
||||
|
||||
@pytest.fixture
|
||||
def service(self, mock_services):
|
||||
ws_manager, analysis_service, persistence_service, logger = mock_services
|
||||
return BatchImportService(
|
||||
analysis_service=analysis_service,
|
||||
persistence_service=persistence_service,
|
||||
ws_manager=ws_manager,
|
||||
logger=logger,
|
||||
)
|
||||
|
||||
def test_is_import_running_no_operations(self, service):
|
||||
assert not service.is_import_running()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_start_batch_import_creates_operation(self, service):
|
||||
recipe_scanner_getter = lambda: SimpleNamespace()
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
operation_id = await service.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=[{"source": "https://example.com/image.png"}],
|
||||
)
|
||||
|
||||
assert operation_id is not None
|
||||
assert service.is_import_running(operation_id)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_progress(self, service):
|
||||
recipe_scanner_getter = lambda: SimpleNamespace()
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
operation_id = await service.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=[
|
||||
{"source": "https://example.com/1.png"},
|
||||
{"source": "https://example.com/2.png"},
|
||||
],
|
||||
)
|
||||
|
||||
progress = service.get_progress(operation_id)
|
||||
assert progress is not None
|
||||
assert progress.total == 2
|
||||
assert progress.status in ("pending", "running")
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_cancel_import(self, service):
|
||||
recipe_scanner_getter = lambda: SimpleNamespace()
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
operation_id = await service.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=[{"source": "https://example.com/image.png"}],
|
||||
)
|
||||
|
||||
assert service.cancel_import(operation_id) is True
|
||||
assert service.cancel_import("nonexistent") is False
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_discover_images_non_recursive(self, service, tmp_path):
|
||||
for i in range(3):
|
||||
(tmp_path / f"image{i}.png").write_bytes(b"fake-image")
|
||||
|
||||
(tmp_path / "subdir").mkdir()
|
||||
(tmp_path / "subdir" / "hidden.png").write_bytes(b"fake-image")
|
||||
|
||||
images = await service._discover_images(str(tmp_path), recursive=False)
|
||||
assert len(images) == 3
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_discover_images_recursive(self, service, tmp_path):
|
||||
for i in range(2):
|
||||
(tmp_path / f"image{i}.png").write_bytes(b"fake-image")
|
||||
|
||||
subdir = tmp_path / "subdir"
|
||||
subdir.mkdir()
|
||||
for i in range(2):
|
||||
(subdir / f"nested{i}.jpg").write_bytes(b"fake-image")
|
||||
|
||||
images = await service._discover_images(str(tmp_path), recursive=True)
|
||||
assert len(images) == 4
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_discover_images_filters_by_extension(self, service, tmp_path):
|
||||
(tmp_path / "image.png").write_bytes(b"fake-image")
|
||||
(tmp_path / "image.jpg").write_bytes(b"fake-image")
|
||||
(tmp_path / "image.webp").write_bytes(b"fake-image")
|
||||
(tmp_path / "document.pdf").write_bytes(b"fake-doc")
|
||||
(tmp_path / "script.py").write_bytes(b"print('hello')")
|
||||
|
||||
images = await service._discover_images(str(tmp_path), recursive=False)
|
||||
assert len(images) == 3
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_discover_images_invalid_directory(self, service):
|
||||
from py.services.recipes.errors import RecipeValidationError
|
||||
|
||||
with pytest.raises(RecipeValidationError):
|
||||
await service._discover_images("/nonexistent/path", recursive=False)
|
||||
|
||||
def test_is_supported_image(self, service):
|
||||
assert service._is_supported_image("test.png") is True
|
||||
assert service._is_supported_image("test.jpg") is True
|
||||
assert service._is_supported_image("test.jpeg") is True
|
||||
assert service._is_supported_image("test.webp") is True
|
||||
assert service._is_supported_image("test.gif") is True
|
||||
assert service._is_supported_image("test.bmp") is True
|
||||
assert service._is_supported_image("test.pdf") is False
|
||||
assert service._is_supported_image("test.txt") is False
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_batch_import_processes_items(self, mock_services, tmp_path):
|
||||
ws_manager, _, persistence_service, logger = mock_services
|
||||
|
||||
analysis_service = MockAnalysisService(
|
||||
{
|
||||
"https://example.com/valid.png": MockAnalysisResult(
|
||||
{
|
||||
"loras": [{"name": "test-lora", "weight": 1.0}],
|
||||
"base_model": "SD1.5",
|
||||
"gen_params": {"steps": 20},
|
||||
}
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
service = BatchImportService(
|
||||
analysis_service=analysis_service,
|
||||
persistence_service=persistence_service,
|
||||
ws_manager=ws_manager,
|
||||
logger=logger,
|
||||
)
|
||||
|
||||
recipe_scanner_getter = lambda: SimpleNamespace(
|
||||
find_recipes_by_fingerprint=lambda x: [],
|
||||
add_recipe=lambda x: None,
|
||||
)
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
operation_id = await service.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=[
|
||||
{"source": "https://example.com/valid.png"},
|
||||
{"source": "https://example.com/no-meta.png"},
|
||||
],
|
||||
skip_no_metadata=True,
|
||||
)
|
||||
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
progress = service.get_progress(operation_id)
|
||||
assert progress is not None or persistence_service.call_count == 1
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_start_directory_import(self, service, tmp_path):
|
||||
for i in range(5):
|
||||
(tmp_path / f"image{i}.png").write_bytes(b"fake-image")
|
||||
|
||||
recipe_scanner_getter = lambda: SimpleNamespace()
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
operation_id = await service.start_directory_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
directory=str(tmp_path),
|
||||
recursive=False,
|
||||
)
|
||||
|
||||
progress = service.get_progress(operation_id)
|
||||
assert progress is not None
|
||||
assert progress.total == 5
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_websocket_broadcasts_progress(self, mock_services):
|
||||
ws_manager, analysis_service, persistence_service, logger = mock_services
|
||||
|
||||
service = BatchImportService(
|
||||
analysis_service=analysis_service,
|
||||
persistence_service=persistence_service,
|
||||
ws_manager=ws_manager,
|
||||
logger=logger,
|
||||
)
|
||||
|
||||
recipe_scanner_getter = lambda: SimpleNamespace()
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
operation_id = await service.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=[{"source": "https://example.com/test.png"}],
|
||||
)
|
||||
|
||||
await asyncio.sleep(0.3)
|
||||
|
||||
assert len(ws_manager.broadcasts) > 0
|
||||
assert any(
|
||||
b.get("type") == "batch_import_progress" for b in ws_manager.broadcasts
|
||||
)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_cancellation_stops_processing(self, mock_services):
|
||||
ws_manager, analysis_service, persistence_service, logger = mock_services
|
||||
|
||||
service = BatchImportService(
|
||||
analysis_service=analysis_service,
|
||||
persistence_service=persistence_service,
|
||||
ws_manager=ws_manager,
|
||||
logger=logger,
|
||||
)
|
||||
|
||||
recipe_scanner_getter = lambda: SimpleNamespace()
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
items = [{"source": f"https://example.com/{i}.png"} for i in range(10)]
|
||||
|
||||
operation_id = await service.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=items,
|
||||
)
|
||||
|
||||
service.cancel_import(operation_id)
|
||||
await asyncio.sleep(0.3)
|
||||
|
||||
progress = service.get_progress(operation_id)
|
||||
if progress:
|
||||
assert progress.status == "cancelled"
|
||||
|
||||
|
||||
class TestBatchImportServiceEdgeCases:
|
||||
@pytest.fixture
|
||||
def service(self):
|
||||
ws_manager = MockWebSocketManager()
|
||||
analysis_service = MockAnalysisService()
|
||||
persistence_service = MockPersistenceService()
|
||||
logger = logging.getLogger("test")
|
||||
|
||||
return BatchImportService(
|
||||
analysis_service=analysis_service,
|
||||
persistence_service=persistence_service,
|
||||
ws_manager=ws_manager,
|
||||
logger=logger,
|
||||
)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_empty_items_list(self, service):
|
||||
recipe_scanner_getter = lambda: SimpleNamespace()
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
operation_id = await service.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=[],
|
||||
)
|
||||
|
||||
progress = service.get_progress(operation_id)
|
||||
assert progress is not None
|
||||
assert progress.total == 0
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_mixed_url_and_path_items(self, service, tmp_path):
|
||||
(tmp_path / "local.png").write_bytes(b"fake-image")
|
||||
|
||||
recipe_scanner_getter = lambda: SimpleNamespace()
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
operation_id = await service.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=[
|
||||
{"source": "https://example.com/remote.png", "type": "url"},
|
||||
{"source": str(tmp_path / "local.png"), "type": "local_path"},
|
||||
],
|
||||
)
|
||||
|
||||
progress = service.get_progress(operation_id)
|
||||
assert progress is not None
|
||||
assert progress.total == 2
|
||||
assert progress.items[0].item_type == ImportItemType.URL
|
||||
assert progress.items[1].item_type == ImportItemType.LOCAL_PATH
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_tags_are_passed_to_persistence(self, tmp_path):
|
||||
ws_manager = MockWebSocketManager()
|
||||
analysis_service = MockAnalysisService(
|
||||
{
|
||||
str(tmp_path / "test.png"): MockAnalysisResult(
|
||||
{
|
||||
"loras": [{"name": "test-lora"}],
|
||||
}
|
||||
),
|
||||
}
|
||||
)
|
||||
persistence_service = MockPersistenceService()
|
||||
logger = logging.getLogger("test")
|
||||
|
||||
(tmp_path / "test.png").write_bytes(b"fake-image")
|
||||
|
||||
service = BatchImportService(
|
||||
analysis_service=analysis_service,
|
||||
persistence_service=persistence_service,
|
||||
ws_manager=ws_manager,
|
||||
logger=logger,
|
||||
)
|
||||
|
||||
recipe_scanner_getter = lambda: SimpleNamespace(
|
||||
find_recipes_by_fingerprint=lambda x: [],
|
||||
)
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
operation_id = await service.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=[{"source": str(tmp_path / "test.png")}],
|
||||
tags=["batch-import", "test"],
|
||||
)
|
||||
|
||||
await asyncio.sleep(0.3)
|
||||
|
||||
if persistence_service.saved_recipes:
|
||||
assert "batch-import" in persistence_service.saved_recipes[0]["tags"]
|
||||
assert "test" in persistence_service.saved_recipes[0]["tags"]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_skip_duplicates_parameter(self, service):
|
||||
recipe_scanner_getter = lambda: SimpleNamespace()
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
operation_id = await service.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=[{"source": "https://example.com/test.png"}],
|
||||
skip_duplicates=True,
|
||||
)
|
||||
|
||||
progress = service.get_progress(operation_id)
|
||||
assert progress is not None
|
||||
assert progress.skip_duplicates is True
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_skip_duplicates_false_by_default(self, service):
|
||||
recipe_scanner_getter = lambda: SimpleNamespace()
|
||||
civitai_client_getter = lambda: SimpleNamespace()
|
||||
|
||||
operation_id = await service.start_batch_import(
|
||||
recipe_scanner_getter=recipe_scanner_getter,
|
||||
civitai_client_getter=civitai_client_getter,
|
||||
items=[{"source": "https://example.com/test.png"}],
|
||||
)
|
||||
|
||||
progress = service.get_progress(operation_id)
|
||||
assert progress is not None
|
||||
assert progress.skip_duplicates is False
|
||||
|
||||
|
||||
class TestInputValidation:
|
||||
@pytest.fixture
|
||||
def service(self):
|
||||
ws_manager = MockWebSocketManager()
|
||||
analysis_service = MockAnalysisService()
|
||||
persistence_service = MockPersistenceService()
|
||||
logger = logging.getLogger("test")
|
||||
|
||||
return BatchImportService(
|
||||
analysis_service=analysis_service,
|
||||
persistence_service=persistence_service,
|
||||
ws_manager=ws_manager,
|
||||
logger=logger,
|
||||
)
|
||||
|
||||
def test_validate_valid_url(self, service):
|
||||
assert service._validate_url("https://example.com/image.png") is True
|
||||
assert service._validate_url("http://example.com/image.png") is True
|
||||
assert service._validate_url("https://civitai.com/images/123") is True
|
||||
|
||||
def test_validate_invalid_url(self, service):
|
||||
assert service._validate_url("not-a-url") is False
|
||||
assert service._validate_url("ftp://example.com/file") is False
|
||||
assert service._validate_url("") is False
|
||||
|
||||
def test_validate_valid_local_path(self, service, tmp_path):
|
||||
valid_path = str(tmp_path / "image.png")
|
||||
assert service._validate_local_path(valid_path) is True
|
||||
|
||||
def test_validate_invalid_local_path(self, service):
|
||||
assert service._validate_local_path("../etc/passwd") is False
|
||||
assert service._validate_local_path("relative/path.png") is False
|
||||
assert service._validate_local_path("") is False
|
||||
@@ -31,10 +31,27 @@ def temp_db_path():
|
||||
@pytest.fixture
|
||||
def temp_csv_path():
|
||||
"""Create a temporary CSV file with test data."""
|
||||
with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False, encoding="utf-8") as f:
|
||||
with tempfile.NamedTemporaryFile(
|
||||
mode="w", suffix=".csv", delete=False, encoding="utf-8"
|
||||
) as f:
|
||||
# Write test data in the same format as danbooru_e621_merged.csv
|
||||
# Format: tag_name,category,post_count,aliases
|
||||
# Include multiple tags starting with "1" to test popularity-based ranking
|
||||
f.write('1girl,0,6008644,"1girls,sole_female"\n')
|
||||
f.write('1boy,0,1405457,"1boys,sole_male"\n')
|
||||
f.write('1:1,14,377032,""\n')
|
||||
f.write('16:9,14,152866,""\n')
|
||||
f.write('1other,0,70962,""\n')
|
||||
f.write('16:10,14,14739,""\n')
|
||||
f.write('1990s_(style),0,9369,""\n')
|
||||
f.write('1_eye,0,7179,""\n')
|
||||
f.write('1:2,14,5865,""\n')
|
||||
f.write('1980s_(style),0,5665,""\n')
|
||||
f.write('1koma,0,4384,""\n')
|
||||
f.write('1_horn,0,2122,""\n')
|
||||
f.write('101_dalmatian_street,3,1933,""\n')
|
||||
f.write('1upgobbo,3,1731,""\n')
|
||||
f.write('14:9,14,1038,""\n')
|
||||
f.write('highres,5,5256195,"high_res,high_resolution,hires"\n')
|
||||
f.write('solo,0,5000954,"alone,female_solo,single"\n')
|
||||
f.write('hatsune_miku,4,500000,"miku"\n')
|
||||
@@ -86,7 +103,7 @@ class TestTagFTSIndexBuild:
|
||||
fts.build_index()
|
||||
|
||||
assert fts.is_ready() is True
|
||||
assert fts.get_indexed_count() == 10
|
||||
assert fts.get_indexed_count() == 24
|
||||
|
||||
def test_build_index_nonexistent_csv(self, temp_db_path):
|
||||
"""Test that build_index handles missing CSV gracefully."""
|
||||
@@ -187,6 +204,76 @@ class TestTagFTSIndexSearch:
|
||||
results = populated_fts.search("girl", limit=1)
|
||||
assert len(results) <= 1
|
||||
|
||||
def test_search_tag_name_prefix_match_priority(self, populated_fts):
|
||||
"""Test that tag_name prefix matches rank higher than alias matches."""
|
||||
results = populated_fts.search("1", limit=20)
|
||||
|
||||
assert len(results) > 0, "Should return results for '1'"
|
||||
|
||||
# Find first alias match (if any)
|
||||
first_alias_idx = None
|
||||
for i, result in enumerate(results):
|
||||
if result.get("matched_alias"):
|
||||
first_alias_idx = i
|
||||
break
|
||||
|
||||
# All tag_name prefix matches should come before alias matches
|
||||
if first_alias_idx is not None:
|
||||
for i in range(first_alias_idx):
|
||||
assert results[i]["tag_name"].lower().startswith("1"), (
|
||||
f"Tag at index {i} should start with '1' before alias matches"
|
||||
)
|
||||
|
||||
def test_search_ranks_popular_tags_higher(self, populated_fts):
|
||||
"""Test that tags with higher post_count rank higher among prefix matches."""
|
||||
results = populated_fts.search("1", limit=20)
|
||||
|
||||
# Filter to only tag_name prefix matches
|
||||
prefix_matches = [r for r in results if r["tag_name"].lower().startswith("1")]
|
||||
|
||||
assert len(prefix_matches) > 1, "Should have multiple prefix matches"
|
||||
|
||||
# Verify descending post_count order among prefix matches
|
||||
for i in range(len(prefix_matches) - 1):
|
||||
assert (
|
||||
prefix_matches[i]["post_count"] >= prefix_matches[i + 1]["post_count"]
|
||||
), (
|
||||
f"Tags should be sorted by post_count: {prefix_matches[i]['tag_name']} ({prefix_matches[i]['post_count']}) >= {prefix_matches[i + 1]['tag_name']} ({prefix_matches[i + 1]['post_count']})"
|
||||
)
|
||||
|
||||
def test_search_pagination_ordering_consistency(self, populated_fts):
|
||||
"""Test that pagination maintains consistent ordering."""
|
||||
page1 = populated_fts.search("1", limit=10, offset=0)
|
||||
page2 = populated_fts.search("1", limit=10, offset=10)
|
||||
|
||||
assert len(page1) > 0, "Page 1 should have results"
|
||||
assert len(page2) > 0, "Page 2 should have results"
|
||||
|
||||
# Page 2 scores should all be <= Page 1 min score
|
||||
page1_min_score = min(r["rank_score"] for r in page1)
|
||||
page2_max_score = max(r["rank_score"] for r in page2)
|
||||
|
||||
assert page2_max_score <= page1_min_score, (
|
||||
f"Page 2 max score ({page2_max_score}) should be <= Page 1 min score ({page1_min_score})"
|
||||
)
|
||||
|
||||
def test_search_rank_score_includes_popularity_weight(self, populated_fts):
|
||||
"""Test that rank_score includes post_count popularity weighting."""
|
||||
results = populated_fts.search("1", limit=5)
|
||||
|
||||
assert len(results) >= 2, "Need at least 2 results to compare"
|
||||
|
||||
# 1girl has 6M posts, should have higher rank_score than tags with fewer posts
|
||||
girl_result = next((r for r in results if r["tag_name"] == "1girl"), None)
|
||||
assert girl_result is not None, "1girl should be in results"
|
||||
|
||||
# Find a tag with significantly fewer posts
|
||||
low_post_result = next((r for r in results if r["post_count"] < 10000), None)
|
||||
if low_post_result:
|
||||
assert girl_result["rank_score"] > low_post_result["rank_score"], (
|
||||
f"1girl (6M posts) should have higher score than {low_post_result['tag_name']} ({low_post_result['post_count']} posts)"
|
||||
)
|
||||
|
||||
|
||||
class TestAliasSearch:
|
||||
"""Tests for alias search functionality."""
|
||||
@@ -204,7 +291,9 @@ class TestAliasSearch:
|
||||
results = populated_fts.search("miku")
|
||||
|
||||
assert len(results) >= 1
|
||||
hatsune_result = next((r for r in results if r["tag_name"] == "hatsune_miku"), None)
|
||||
hatsune_result = next(
|
||||
(r for r in results if r["tag_name"] == "hatsune_miku"), None
|
||||
)
|
||||
assert hatsune_result is not None
|
||||
assert hatsune_result["matched_alias"] == "miku"
|
||||
|
||||
@@ -214,7 +303,9 @@ class TestAliasSearch:
|
||||
results = populated_fts.search("hatsune")
|
||||
|
||||
assert len(results) >= 1
|
||||
hatsune_result = next((r for r in results if r["tag_name"] == "hatsune_miku"), None)
|
||||
hatsune_result = next(
|
||||
(r for r in results if r["tag_name"] == "hatsune_miku"), None
|
||||
)
|
||||
assert hatsune_result is not None
|
||||
assert "matched_alias" not in hatsune_result
|
||||
|
||||
@@ -301,7 +392,9 @@ class TestSlashPrefixAliases:
|
||||
@pytest.fixture
|
||||
def fts_with_slash_aliases(self, temp_db_path):
|
||||
"""Create an FTS index with slash-prefixed aliases."""
|
||||
with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False, encoding="utf-8") as f:
|
||||
with tempfile.NamedTemporaryFile(
|
||||
mode="w", suffix=".csv", delete=False, encoding="utf-8"
|
||||
) as f:
|
||||
# Format: tag_name,category,post_count,aliases
|
||||
f.write('long_hair,0,4350743,"/lh,longhair,very_long_hair"\n')
|
||||
f.write('breasts,0,3439214,"/b,boobs,oppai"\n')
|
||||
@@ -380,7 +473,15 @@ class TestCategoryMappings:
|
||||
|
||||
def test_category_name_to_ids_complete(self):
|
||||
"""Test that CATEGORY_NAME_TO_IDS includes all expected names."""
|
||||
expected_names = ["general", "artist", "copyright", "character", "meta", "species", "lore"]
|
||||
expected_names = [
|
||||
"general",
|
||||
"artist",
|
||||
"copyright",
|
||||
"character",
|
||||
"meta",
|
||||
"species",
|
||||
"lore",
|
||||
]
|
||||
for name in expected_names:
|
||||
assert name in CATEGORY_NAME_TO_IDS
|
||||
assert isinstance(CATEGORY_NAME_TO_IDS[name], list)
|
||||
|
||||
@@ -742,6 +742,14 @@ class AutoComplete {
|
||||
try {
|
||||
this.currentSearchTerm = term;
|
||||
|
||||
// Save current search type to detect mode changes during async search
|
||||
const searchTypeAtStart = this.searchType;
|
||||
|
||||
// Clear items before starting new search to avoid stale data
|
||||
// This is critical for preventing command suggestions from persisting
|
||||
// when switching from command mode to regular tag search
|
||||
this.items = [];
|
||||
|
||||
if (!endpoint) {
|
||||
endpoint = `/lm/${this.modelType}/relative-paths`;
|
||||
}
|
||||
@@ -776,7 +784,15 @@ class AutoComplete {
|
||||
|
||||
const resultsArrays = await Promise.all(searchPromises);
|
||||
|
||||
// Merge and deduplicate results
|
||||
// Check if search type changed during async operation
|
||||
// If so, skip updating items to prevent stale data from showing
|
||||
if (this.searchType !== searchTypeAtStart) {
|
||||
console.log('[Lora Manager] Search type changed during search, skipping update');
|
||||
return;
|
||||
}
|
||||
|
||||
// Merge and deduplicate results while preserving order from backend
|
||||
// Backend returns results sorted by relevance, so we maintain that order
|
||||
const seen = new Set();
|
||||
const mergedItems = [];
|
||||
|
||||
@@ -793,39 +809,10 @@ class AutoComplete {
|
||||
}
|
||||
}
|
||||
|
||||
// Score and sort results: exact matches first, then by match quality
|
||||
const scoredItems = mergedItems.map(item => {
|
||||
let bestScore = -1;
|
||||
let isExact = false;
|
||||
|
||||
for (const query of queriesToExecute) {
|
||||
const match = this._matchItem(item, query);
|
||||
if (match.matched) {
|
||||
// Higher score for exact matches
|
||||
const score = match.isExactMatch ? 1000 : 100;
|
||||
if (score > bestScore) {
|
||||
bestScore = score;
|
||||
isExact = match.isExactMatch;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return { item, score: bestScore, isExact };
|
||||
});
|
||||
|
||||
// Sort by score (descending), exact matches first
|
||||
scoredItems.sort((a, b) => {
|
||||
if (b.isExact !== a.isExact) {
|
||||
return b.isExact ? 1 : -1;
|
||||
}
|
||||
return b.score - a.score;
|
||||
});
|
||||
|
||||
// Extract just the items
|
||||
const sortedItems = scoredItems.map(s => s.item);
|
||||
|
||||
if (sortedItems.length > 0) {
|
||||
this.items = sortedItems;
|
||||
// Use backend-sorted results directly without re-scoring
|
||||
// Backend already ranks by: FTS5 bm25 score + post count + exact prefix boost
|
||||
if (mergedItems.length > 0) {
|
||||
this.items = mergedItems;
|
||||
this.render();
|
||||
this.show();
|
||||
} else {
|
||||
@@ -908,6 +895,12 @@ class AutoComplete {
|
||||
* @param {string} filter - Optional filter for commands
|
||||
*/
|
||||
_showCommandList(filter = '') {
|
||||
// Only show command list if we're in command mode
|
||||
// This prevents stale command suggestions from appearing after switching to tag search
|
||||
if (this.searchType !== 'commands' && this.showingCommands !== true) {
|
||||
return;
|
||||
}
|
||||
|
||||
const filterLower = filter.toLowerCase();
|
||||
|
||||
// Get unique commands (avoid duplicates like /char and /character)
|
||||
@@ -942,12 +935,20 @@ class AutoComplete {
|
||||
* Render the command list dropdown
|
||||
*/
|
||||
_renderCommandList() {
|
||||
this.dropdown.innerHTML = '';
|
||||
// Clear command list items properly based on rendering mode
|
||||
if (this.contentContainer) {
|
||||
// Virtual scrolling mode - clear content container
|
||||
this.contentContainer.innerHTML = '';
|
||||
} else {
|
||||
// Non-virtual scrolling mode - clear dropdown direct children
|
||||
this.dropdown.innerHTML = '';
|
||||
}
|
||||
this.selectedIndex = -1;
|
||||
|
||||
this.items.forEach((item, index) => {
|
||||
const itemEl = document.createElement('div');
|
||||
itemEl.className = 'comfy-autocomplete-item comfy-autocomplete-command';
|
||||
itemEl.dataset.index = index.toString();
|
||||
|
||||
const cmdSpan = document.createElement('span');
|
||||
cmdSpan.className = 'lm-autocomplete-command-name';
|
||||
@@ -973,6 +974,8 @@ class AutoComplete {
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
gap: 12px;
|
||||
height: ${this.options.itemHeight}px;
|
||||
box-sizing: border-box;
|
||||
`;
|
||||
|
||||
itemEl.addEventListener('mouseenter', () => {
|
||||
@@ -983,18 +986,29 @@ class AutoComplete {
|
||||
this._insertCommand(item.command);
|
||||
});
|
||||
|
||||
this.dropdown.appendChild(itemEl);
|
||||
// Append to correct container based on rendering mode
|
||||
if (this.contentContainer) {
|
||||
this.contentContainer.appendChild(itemEl);
|
||||
} else {
|
||||
this.dropdown.appendChild(itemEl);
|
||||
}
|
||||
});
|
||||
|
||||
// Remove border from last item
|
||||
if (this.dropdown.lastChild) {
|
||||
this.dropdown.lastChild.style.borderBottom = 'none';
|
||||
const lastChild = this.contentContainer ? this.contentContainer.lastChild : this.dropdown.lastChild;
|
||||
if (lastChild) {
|
||||
lastChild.style.borderBottom = 'none';
|
||||
}
|
||||
|
||||
// Auto-select first item
|
||||
if (this.items.length > 0) {
|
||||
setTimeout(() => this.selectItem(0), 100);
|
||||
}
|
||||
|
||||
// Update virtual scroll height for virtual scrolling mode
|
||||
if (this.contentContainer) {
|
||||
this.updateVirtualScrollHeight();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -1057,28 +1071,49 @@ class AutoComplete {
|
||||
}
|
||||
|
||||
if (this.options.enableVirtualScroll && this.contentContainer) {
|
||||
// Use virtual scrolling - only update visible items if dropdown is already visible
|
||||
// If not visible, updateVisibleItems() will be called from show() after display:block
|
||||
// Use virtual scrolling - always update visible items to ensure content is fresh
|
||||
// The dropdown visibility is controlled by show()/hide()
|
||||
this.updateVirtualScrollHeight();
|
||||
if (this.isVisible && this.dropdown.style.display !== 'none') {
|
||||
this.updateVisibleItems();
|
||||
}
|
||||
this.updateVisibleItems();
|
||||
} else {
|
||||
// Traditional rendering (fallback)
|
||||
this.dropdown.innerHTML = '';
|
||||
|
||||
// Check if items are enriched (have tag_name, category, post_count)
|
||||
// Check if items are enriched (have tag_name, category, post_count) or command objects
|
||||
const isEnriched = this.items[0] && typeof this.items[0] === 'object' && 'tag_name' in this.items[0];
|
||||
const isCommand = this.items[0] && typeof this.items[0] === 'object' && 'command' in this.items[0];
|
||||
|
||||
this.items.forEach((itemData, index) => {
|
||||
const item = document.createElement('div');
|
||||
item.className = 'comfy-autocomplete-item';
|
||||
|
||||
// Get the display text and path for insertion
|
||||
const displayText = isEnriched ? itemData.tag_name : itemData;
|
||||
const insertPath = isEnriched ? itemData.tag_name : itemData;
|
||||
if (isCommand) {
|
||||
// Render command item
|
||||
const cmdSpan = document.createElement('span');
|
||||
cmdSpan.className = 'lm-autocomplete-command-name';
|
||||
cmdSpan.textContent = itemData.command;
|
||||
|
||||
if (isEnriched) {
|
||||
const labelSpan = document.createElement('span');
|
||||
labelSpan.className = 'lm-autocomplete-command-label';
|
||||
labelSpan.textContent = itemData.label;
|
||||
|
||||
item.appendChild(cmdSpan);
|
||||
item.appendChild(labelSpan);
|
||||
item.style.cssText = `
|
||||
padding: 8px 12px;
|
||||
cursor: pointer;
|
||||
color: rgba(226, 232, 240, 0.8);
|
||||
border-bottom: 1px solid rgba(226, 232, 240, 0.1);
|
||||
transition: all 0.2s ease;
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
gap: 12px;
|
||||
`;
|
||||
} else if (isEnriched) {
|
||||
// Render enriched item with category badge and post count
|
||||
this._renderEnrichedItem(item, itemData, this.currentSearchTerm);
|
||||
} else {
|
||||
@@ -1087,7 +1122,7 @@ class AutoComplete {
|
||||
const nameSpan = document.createElement('span');
|
||||
nameSpan.className = 'lm-autocomplete-name';
|
||||
// Use display text without extension for cleaner UI
|
||||
const displayTextWithoutExt = this._getDisplayText(displayText);
|
||||
const displayTextWithoutExt = this._getDisplayText(itemData);
|
||||
nameSpan.innerHTML = this.highlightMatch(displayTextWithoutExt, this.currentSearchTerm);
|
||||
nameSpan.style.cssText = `
|
||||
flex: 1;
|
||||
@@ -1096,25 +1131,25 @@ class AutoComplete {
|
||||
text-overflow: ellipsis;
|
||||
`;
|
||||
item.appendChild(nameSpan);
|
||||
|
||||
// Apply item styles with new color scheme
|
||||
item.style.cssText = `
|
||||
padding: 8px 12px;
|
||||
cursor: pointer;
|
||||
color: rgba(226, 232, 240, 0.8);
|
||||
border-bottom: 1px solid rgba(226, 232, 240, 0.1);
|
||||
transition: all 0.2s ease;
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
position: relative;
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
`;
|
||||
}
|
||||
|
||||
// Apply item styles with new color scheme
|
||||
item.style.cssText = `
|
||||
padding: 8px 12px;
|
||||
cursor: pointer;
|
||||
color: rgba(226, 232, 240, 0.8);
|
||||
border-bottom: 1px solid rgba(226, 232, 240, 0.1);
|
||||
transition: all 0.2s ease;
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
position: relative;
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
`;
|
||||
|
||||
// Hover and selection handlers
|
||||
item.addEventListener('mouseenter', () => {
|
||||
this.selectItem(index);
|
||||
@@ -1126,7 +1161,12 @@ class AutoComplete {
|
||||
|
||||
// Click handler
|
||||
item.addEventListener('click', () => {
|
||||
this.insertSelection(insertPath);
|
||||
if (isCommand) {
|
||||
this._insertCommand(itemData.command);
|
||||
} else {
|
||||
const insertPath = isEnriched ? itemData.tag_name : itemData;
|
||||
this.insertSelection(insertPath);
|
||||
}
|
||||
});
|
||||
|
||||
this.dropdown.appendChild(item);
|
||||
@@ -1369,39 +1409,11 @@ class AutoComplete {
|
||||
this.hasMoreItems = false;
|
||||
}
|
||||
|
||||
// If we got new items, add them and re-render
|
||||
// If we got new items, append them and re-render
|
||||
// IMPORTANT: Do NOT re-sort! Backend already returns results sorted by relevance
|
||||
if (newItems.length > 0) {
|
||||
const currentLength = this.items.length;
|
||||
this.items.push(...newItems);
|
||||
|
||||
// Re-score and sort all items
|
||||
const scoredItems = this.items.map(item => {
|
||||
let bestScore = -1;
|
||||
let isExact = false;
|
||||
|
||||
for (const query of queriesToExecute) {
|
||||
const match = this._matchItem(item, query);
|
||||
if (match.matched) {
|
||||
const score = match.isExactMatch ? 1000 : 100;
|
||||
if (score > bestScore) {
|
||||
bestScore = score;
|
||||
isExact = match.isExactMatch;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return { item, score: bestScore, isExact };
|
||||
});
|
||||
|
||||
scoredItems.sort((a, b) => {
|
||||
if (b.isExact !== a.isExact) {
|
||||
return b.isExact ? 1 : -1;
|
||||
}
|
||||
return b.score - a.score;
|
||||
});
|
||||
|
||||
this.items = scoredItems.map(s => s.item);
|
||||
|
||||
// Update render
|
||||
if (this.options.enableVirtualScroll) {
|
||||
this.updateVirtualScrollHeight();
|
||||
@@ -1458,10 +1470,18 @@ class AutoComplete {
|
||||
* Update the total height of the virtual scroll container
|
||||
*/
|
||||
updateVirtualScrollHeight() {
|
||||
if (!this.contentContainer) return;
|
||||
if (!this.contentContainer || !this.scrollContainer) return;
|
||||
|
||||
this.totalHeight = this.items.length * this.options.itemHeight;
|
||||
this.contentContainer.style.height = `${this.totalHeight}px`;
|
||||
|
||||
// Adjust scroll container max-height based on actual content
|
||||
// Only show scrollbar when content exceeds visibleItems limit
|
||||
const maxHeight = this.options.visibleItems * this.options.itemHeight;
|
||||
const shouldShowScrollbar = this.totalHeight > maxHeight;
|
||||
|
||||
this.scrollContainer.style.maxHeight = shouldShowScrollbar ? `${maxHeight}px` : `${this.totalHeight}px`;
|
||||
this.scrollContainer.style.overflowY = shouldShowScrollbar ? 'auto' : 'hidden';
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -1473,11 +1493,12 @@ class AutoComplete {
|
||||
const scrollTop = this.scrollContainer.scrollTop;
|
||||
const containerHeight = this.scrollContainer.clientHeight;
|
||||
|
||||
// Calculate which items should be visible
|
||||
const startIndex = Math.max(0, Math.floor(scrollTop / this.options.itemHeight) - 2);
|
||||
// Calculate which items should be visible with a larger buffer for smoother rendering
|
||||
// Use a fixed buffer of 5 items to ensure selected item is always rendered
|
||||
const startIndex = Math.max(0, Math.floor(scrollTop / this.options.itemHeight) - 5);
|
||||
const endIndex = Math.min(
|
||||
this.items.length - 1,
|
||||
Math.ceil((scrollTop + containerHeight) / this.options.itemHeight) + 2
|
||||
Math.ceil((scrollTop + containerHeight) / this.options.itemHeight) + 5
|
||||
);
|
||||
|
||||
// Clear current content
|
||||
@@ -1492,10 +1513,11 @@ class AutoComplete {
|
||||
|
||||
// Render visible items
|
||||
const isEnriched = this.items[0] && typeof this.items[0] === 'object' && 'tag_name' in this.items[0];
|
||||
const isCommand = this.items[0] && typeof this.items[0] === 'object' && 'command' in this.items[0];
|
||||
|
||||
for (let i = startIndex; i <= endIndex; i++) {
|
||||
const itemData = this.items[i];
|
||||
const itemEl = this.createItemElement(itemData, i, isEnriched);
|
||||
const itemEl = this.createItemElement(itemData, i, isEnriched, isCommand);
|
||||
this.contentContainer.appendChild(itemEl);
|
||||
}
|
||||
|
||||
@@ -1505,12 +1527,22 @@ class AutoComplete {
|
||||
bottomSpacer.style.height = `${(this.items.length - 1 - endIndex) * this.options.itemHeight}px`;
|
||||
this.contentContainer.appendChild(bottomSpacer);
|
||||
}
|
||||
|
||||
// Re-apply selection styling after re-rendering
|
||||
// This ensures the selected item remains highlighted even after DOM updates
|
||||
if (this.selectedIndex >= startIndex && this.selectedIndex <= endIndex) {
|
||||
const selectedEl = this.contentContainer.querySelector(`.comfy-autocomplete-item[data-index="${this.selectedIndex}"]`);
|
||||
if (selectedEl) {
|
||||
selectedEl.classList.add('comfy-autocomplete-item-selected');
|
||||
selectedEl.style.backgroundColor = 'rgba(66, 153, 225, 0.2)';
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a single item element
|
||||
*/
|
||||
createItemElement(itemData, index, isEnriched) {
|
||||
createItemElement(itemData, index, isEnriched, isCommand = false) {
|
||||
const item = document.createElement('div');
|
||||
item.className = 'comfy-autocomplete-item';
|
||||
item.dataset.index = index.toString();
|
||||
@@ -1532,16 +1564,31 @@ class AutoComplete {
|
||||
box-sizing: border-box;
|
||||
`;
|
||||
|
||||
const displayText = isEnriched ? itemData.tag_name : itemData;
|
||||
const insertPath = isEnriched ? itemData.tag_name : itemData;
|
||||
// Check if this is a command object (override parameter if needed)
|
||||
if (!isCommand && itemData && typeof itemData === 'object' && 'command' in itemData) {
|
||||
isCommand = true;
|
||||
}
|
||||
|
||||
if (isEnriched) {
|
||||
if (isCommand) {
|
||||
// Render command item
|
||||
const cmdSpan = document.createElement('span');
|
||||
cmdSpan.className = 'lm-autocomplete-command-name';
|
||||
cmdSpan.textContent = itemData.command;
|
||||
|
||||
const labelSpan = document.createElement('span');
|
||||
labelSpan.className = 'lm-autocomplete-command-label';
|
||||
labelSpan.textContent = itemData.label;
|
||||
|
||||
item.appendChild(cmdSpan);
|
||||
item.appendChild(labelSpan);
|
||||
item.style.gap = '12px';
|
||||
} else if (isEnriched) {
|
||||
this._renderEnrichedItem(item, itemData, this.currentSearchTerm);
|
||||
} else {
|
||||
const nameSpan = document.createElement('span');
|
||||
nameSpan.className = 'lm-autocomplete-name';
|
||||
// Use display text without extension for cleaner UI
|
||||
const displayTextWithoutExt = this._getDisplayText(displayText);
|
||||
const displayTextWithoutExt = this._getDisplayText(itemData);
|
||||
nameSpan.innerHTML = this.highlightMatch(displayTextWithoutExt, this.currentSearchTerm);
|
||||
nameSpan.style.cssText = `
|
||||
flex: 1;
|
||||
@@ -1561,8 +1608,14 @@ class AutoComplete {
|
||||
this.hidePreview();
|
||||
});
|
||||
|
||||
// Click handler
|
||||
item.addEventListener('click', () => {
|
||||
this.insertSelection(insertPath);
|
||||
if (isCommand) {
|
||||
this._insertCommand(itemData.command);
|
||||
} else {
|
||||
const insertPath = isEnriched ? itemData.tag_name : itemData;
|
||||
this.insertSelection(insertPath);
|
||||
}
|
||||
});
|
||||
|
||||
return item;
|
||||
@@ -1578,7 +1631,10 @@ class AutoComplete {
|
||||
if (this.options.enableVirtualScroll && this.contentContainer) {
|
||||
this.dropdown.style.display = 'block';
|
||||
this.isVisible = true;
|
||||
this.updateVisibleItems();
|
||||
// Skip updateVisibleItems if showing commands (already rendered by _renderCommandList)
|
||||
if (!this.showingCommands) {
|
||||
this.updateVisibleItems();
|
||||
}
|
||||
this.positionAtCursor();
|
||||
} else {
|
||||
// Position dropdown at cursor position using TextAreaCaretHelper
|
||||
@@ -1638,6 +1694,19 @@ class AutoComplete {
|
||||
this.isVisible = false;
|
||||
this.selectedIndex = -1;
|
||||
this.showingCommands = false;
|
||||
|
||||
// Clear items to prevent stale data from being displayed
|
||||
// when autocomplete is shown again
|
||||
this.items = [];
|
||||
|
||||
// Clear content container to prevent stale items from showing
|
||||
if (this.contentContainer) {
|
||||
// Virtual scrolling mode - clear content container
|
||||
this.contentContainer.innerHTML = '';
|
||||
} else {
|
||||
// Non-virtual scrolling mode - clear dropdown direct children
|
||||
this.dropdown.innerHTML = '';
|
||||
}
|
||||
|
||||
// Reset virtual scrolling state
|
||||
this.virtualScrollOffset = 0;
|
||||
@@ -1688,26 +1757,22 @@ class AutoComplete {
|
||||
|
||||
// If item is not visible, scroll to make it visible
|
||||
if (itemTop < scrollTop || itemBottom > scrollBottom) {
|
||||
this.scrollContainer.scrollTop = itemTop - containerHeight / 2;
|
||||
// Scroll to position the item in the visible area
|
||||
// Position item at 1/3 from top for better visibility
|
||||
const targetScrollTop = Math.max(0, itemTop - containerHeight / 3);
|
||||
this.scrollContainer.scrollTop = targetScrollTop;
|
||||
|
||||
// Re-render visible items after scroll
|
||||
this.updateVisibleItems();
|
||||
}
|
||||
|
||||
// Find the item element using data-index attribute
|
||||
const selectedEl = container.querySelector(`.comfy-autocomplete-item[data-index="${index}"]`);
|
||||
|
||||
if (selectedEl) {
|
||||
selectedEl.classList.add('comfy-autocomplete-item-selected');
|
||||
selectedEl.style.backgroundColor = 'rgba(66, 153, 225, 0.2)';
|
||||
|
||||
// Show preview for selected item
|
||||
if (this.options.showPreview) {
|
||||
if (typeof this.behavior.showPreview === 'function') {
|
||||
this.behavior.showPreview(this, this.items[index], selectedEl);
|
||||
} else if (this.previewTooltip) {
|
||||
this.showPreviewForItem(this.items[index], selectedEl);
|
||||
}
|
||||
}
|
||||
|
||||
// Apply selection after DOM is updated
|
||||
// Use setTimeout to ensure DOM has been re-rendered
|
||||
setTimeout(() => {
|
||||
this._applyItemSelection(index);
|
||||
}, 0);
|
||||
} else {
|
||||
// Item is already visible, apply selection immediately
|
||||
this._applyItemSelection(index);
|
||||
}
|
||||
} else {
|
||||
// Traditional rendering
|
||||
@@ -1731,6 +1796,31 @@ class AutoComplete {
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply selection styling to an item (used after virtual scroll re-render)
|
||||
* @param {number} index - Index of item to select
|
||||
*/
|
||||
_applyItemSelection(index) {
|
||||
if (!this.contentContainer) return;
|
||||
|
||||
// Find the item element using data-index attribute
|
||||
const selectedEl = this.contentContainer.querySelector(`.comfy-autocomplete-item[data-index="${index}"]`);
|
||||
|
||||
if (selectedEl) {
|
||||
selectedEl.classList.add('comfy-autocomplete-item-selected');
|
||||
selectedEl.style.backgroundColor = 'rgba(66, 153, 225, 0.2)';
|
||||
|
||||
// Show preview for selected item
|
||||
if (this.options.showPreview) {
|
||||
if (typeof this.behavior.showPreview === 'function') {
|
||||
this.behavior.showPreview(this, this.items[index], selectedEl);
|
||||
} else if (this.previewTooltip) {
|
||||
this.showPreviewForItem(this.items[index], selectedEl);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
handleKeyDown(e) {
|
||||
if (!this.isVisible) {
|
||||
@@ -1740,12 +1830,39 @@ class AutoComplete {
|
||||
switch (e.key) {
|
||||
case 'ArrowDown':
|
||||
e.preventDefault();
|
||||
this.selectItem(Math.min(this.selectedIndex + 1, this.items.length - 1));
|
||||
if (this.options.enableVirtualScroll && this.scrollContainer) {
|
||||
// For virtual scrolling, handle boundary cases
|
||||
if (this.selectedIndex >= this.items.length - 1) {
|
||||
// Already at last item, try to load more
|
||||
if (this.hasMoreItems && !this.isLoadingMore) {
|
||||
this.loadMoreItems().then(() => {
|
||||
// After loading more, select the next item
|
||||
if (this.selectedIndex < this.items.length - 1) {
|
||||
this.selectItem(this.selectedIndex + 1);
|
||||
}
|
||||
});
|
||||
}
|
||||
} else {
|
||||
this.selectItem(this.selectedIndex + 1);
|
||||
}
|
||||
} else {
|
||||
this.selectItem(Math.min(this.selectedIndex + 1, this.items.length - 1));
|
||||
}
|
||||
break;
|
||||
|
||||
case 'ArrowUp':
|
||||
e.preventDefault();
|
||||
this.selectItem(Math.max(this.selectedIndex - 1, 0));
|
||||
if (this.options.enableVirtualScroll && this.scrollContainer) {
|
||||
// For virtual scrolling, handle top boundary
|
||||
if (this.selectedIndex <= 0) {
|
||||
// Already at first item, ensure it's selected
|
||||
this.selectItem(0);
|
||||
} else {
|
||||
this.selectItem(this.selectedIndex - 1);
|
||||
}
|
||||
} else {
|
||||
this.selectItem(Math.max(this.selectedIndex - 1, 0));
|
||||
}
|
||||
break;
|
||||
|
||||
case 'Enter':
|
||||
|
||||
@@ -1,6 +1,4 @@
|
||||
import { app } from "../../scripts/app.js";
|
||||
import { ComfyButtonGroup } from "../../scripts/ui/components/buttonGroup.js";
|
||||
import { ComfyButton } from "../../scripts/ui/components/button.js";
|
||||
|
||||
const BUTTON_TOOLTIP = "Launch LoRA Manager (Shift+Click opens in new window)";
|
||||
const LORA_MANAGER_PATH = "/loras";
|
||||
@@ -95,7 +93,9 @@ const fetchVersionInfo = async () => {
|
||||
return "";
|
||||
};
|
||||
|
||||
const createTopMenuButton = () => {
|
||||
const createTopMenuButton = async () => {
|
||||
const { ComfyButton } = await import("../../scripts/ui/components/button.js");
|
||||
|
||||
const button = new ComfyButton({
|
||||
icon: "loramanager",
|
||||
tooltip: BUTTON_TOOLTIP,
|
||||
@@ -117,7 +117,7 @@ const createTopMenuButton = () => {
|
||||
return button;
|
||||
};
|
||||
|
||||
const attachTopMenuButton = (attempt = 0) => {
|
||||
const attachTopMenuButton = async (attempt = 0) => {
|
||||
if (document.querySelector(`.${BUTTON_GROUP_CLASS}`)) {
|
||||
return;
|
||||
}
|
||||
@@ -133,7 +133,9 @@ const attachTopMenuButton = (attempt = 0) => {
|
||||
return;
|
||||
}
|
||||
|
||||
const loraManagerButton = createTopMenuButton();
|
||||
const loraManagerButton = await createTopMenuButton();
|
||||
const { ComfyButtonGroup } = await import("../../scripts/ui/components/buttonGroup.js");
|
||||
|
||||
const buttonGroup = new ComfyButtonGroup(loraManagerButton);
|
||||
buttonGroup.element.classList.add(BUTTON_GROUP_CLASS);
|
||||
|
||||
@@ -158,7 +160,7 @@ const createExtensionObject = (useActionBar) => {
|
||||
|
||||
if (!useActionBar) {
|
||||
console.log("LoRA Manager: using legacy button attachment (frontend version < 1.33.9)");
|
||||
attachTopMenuButton();
|
||||
await attachTopMenuButton();
|
||||
} else {
|
||||
console.log("LoRA Manager: using actionBarButtons API (frontend version >= 1.33.9)");
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user