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Refactor API structure to unify model operations

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- Introduced MODEL_TYPES and MODEL_CONFIG for centralized model type management.
- Created a unified API client for checkpoints and loras to streamline operations.
- Updated all API calls in checkpointApi.js and loraApi.js to use the new client.
- Simplified context menus and model card operations to leverage the unified API client.
- Enhanced state management to accommodate new model types and their configurations.
- Added virtual scrolling functions for recipes and improved loading states.
- Refactored modal utilities to handle model exclusion and deletion generically.
- Improved error handling and user feedback across various operations.
This commit is contained in:
Will Miao
2025-07-25 10:04:18 +08:00
parent 692796db46
commit d83fad6abc
15 changed files with 927 additions and 928 deletions
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33
static/js/components/shared/ModelMetadata.js
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@@ -7,6 +7,7 @@ import { BASE_MODELS } from '../../utils/constants.js';
import { state } from '../../state/index.js';
import { saveModelMetadata as saveLoraMetadata, renameLoraFile } from '../../api/loraApi.js';
import { saveModelMetadata as saveCheckpointMetadata, renameCheckpointFile } from '../../api/checkpointApi.js';
import { getModelApiClient } from '../../api/baseModelApi.js';
/**
* Set up model name editing functionality
@@ -114,9 +115,7 @@ export function setupModelNameEditing(filePath) {
// Get the file path from the dataset
const filePath = this.dataset.filePath;
const saveFunction = state.currentPageType === 'checkpoints' ? saveCheckpointMetadata : saveLoraMetadata;
await saveFunction(filePath, { model_name: newModelName });
await getModelApiClient().saveModelMetadata(filePath, { model_name: newModelName });
showToast('Model name updated successfully', 'success');
} catch (error) {
@@ -295,9 +294,7 @@ async function saveBaseModel(filePath, originalValue) {
}
try {
const saveFunction = state.currentPageType === 'checkpoints' ? saveCheckpointMetadata : saveLoraMetadata;
await saveFunction(filePath, { base_model: newBaseModel });
await getModelApiClient().saveModelMetadata(filePath, { base_model: newBaseModel });
showToast('Base model updated successfully', 'success');
} catch (error) {
@@ -417,29 +414,7 @@ export function setupFileNameEditing(filePath) {
// Get the file path from the dataset
const filePath = this.dataset.filePath;
let result;
if (state.currentPageType === 'checkpoints') {
result = await renameCheckpointFile(filePath, newFileName);
} else {
// Use LoRA rename function
result = await renameLoraFile(filePath, newFileName);
}
if (result.success) {
showToast('File name updated successfully', 'success');
// Update virtual scroller if available (mainly for LoRAs)
if (state.virtualScroller && typeof state.virtualScroller.updateSingleItem === 'function') {
const newFilePath = filePath.replace(originalValue, newFileName);
state.virtualScroller.updateSingleItem(filePath, {
file_name: newFileName,
file_path: newFilePath
});
}
} else {
throw new Error(result.error || 'Unknown error');
}
await getModelApiClient().renameModelFile(filePath, newFileName);
} catch (error) {
console.error('Error renaming file:', error);
this.textContent = originalValue; // Restore original file name