Move LLM-assisted column mapping from silent /check-format automation
to an explicit "AI Assist" button in the dataset mapping dialog. This
makes the feature transparent and user-controlled.
- Remove llm_classify_columns() from check_dataset_format() (heuristic-only)
- Remove auto-save suggested_mapping from use-training-actions.ts
- Add POST /api/datasets/ai-assist-mapping endpoint (receives preview
samples from frontend, no dataset re-loading needed)
- Add AiAssistMappingRequest/Response models
- Add aiAssistMapping() frontend API function
- Add Sparkles AI Assist button to DatasetMappingCard with loading state
- Wire up handleAiAssist handler in dataset-preview-dialog.tsx
- Frontend auto-saves suggested_mapping into datasetManualMapping when
check-format returns requires_manual_mapping=false, so the mapping
flows to training via custom_format_mapping (no redundant AI calls)
- Backend returns meaningful warning when column detection fails
(LLM-generated or static fallback) for both text and VLM datasets
- /check-format endpoint merges check_dataset_format warnings with
existing URL-based image detection warnings
- Detect and convert ShareGPT/ChatML conversations with <image> placeholders
- Add file_name/filename as image column keywords
- Detect image paths and URLs by value (string ending in .jpg/.png/etc)
- Add 200-sample parallel probe using ThreadPoolExecutor + safe_num_proc
to estimate download speed and failure rate before full conversion
- Abort with clear error if >=30% of probe images fail to download
- Show estimated download time in the training overlay modal
- Parallel batch conversion for URL-based datasets (vs sequential for local)
- Add warning field to /check-format response for URL-based image datasets
- Display URL warning in dataset preview dialog (amber banner)
- Thread progress_callback from trainer through format_and_template_dataset
to convert_to_vlm_format for real-time status updates
