mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-07-06 17:31:16 -03:00
feat(ui): gray out enrich-hf-llm when no hf_url, add backend fast-fail, rename labels across locales, reposition menu item
This commit is contained in:
Will Miao
@@ -242,6 +242,7 @@ class AgentService:
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total = len(model_paths)
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processed = 0
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success_count = 0
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skipped_count = 0
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updated_models: List[Dict[str, Any]] = []
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errors: List[str] = []
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post_processor = PostProcessor()
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@@ -261,58 +262,69 @@ class AgentService:
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skill_name, processed + 1, total, model_filename,
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)
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updated_data: Dict[str, Any] = {}
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skip_model = False
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try:
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from ...metadata_ops import read_metadata
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metadata = await read_metadata(model_path)
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prompt_vars: Dict[str, Any] = {"model_path": model_path}
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if skill.llm_required and llm_configured:
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prompt_vars = await self._build_prompt_context(
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skill_name, model_path, metadata, registry, llm,
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# Fast-fail: enrich_hf_metadata requires hf_url to have HF README context
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if skill_name == "enrich_hf_metadata" and not metadata.get("hf_url", ""):
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logger.info(
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"[%s] SKIP %s — no hf_url in metadata",
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skill_name, model_filename,
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)
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skipped_count += 1
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skip_model = True
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llm_response: Optional[Dict[str, Any]] = None
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if skill.llm_required and llm_configured:
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prompt_template = registry.load_prompt(skill_name)
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rendered = _render_prompt(prompt_template, prompt_vars)
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llm_response = await llm.chat_completion_json(
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system_prompt=prompt_vars.get(
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"system_prompt",
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"You are a helpful assistant that extracts structured metadata.",
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),
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user_prompt=rendered,
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)
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if llm_response:
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logger.info(
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"[%s] [%d/%d] %s → base_model=%s confidence=%s",
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skill_name, processed + 1, total, model_filename,
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(llm_response.get("base_model") or "?")[:50],
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llm_response.get("confidence", "?"),
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if not skip_model:
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prompt_vars: Dict[str, Any] = {"model_path": model_path}
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if skill.llm_required and llm_configured:
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prompt_vars = await self._build_prompt_context(
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skill_name, model_path, metadata, registry, llm,
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)
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model_result = await post_processor.process(
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skill_name=skill_name,
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model_path=model_path,
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llm_output=llm_response or {},
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metadata=metadata,
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readme_content=prompt_vars.get("readme_content_full", ""),
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)
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if model_result.get("success", True):
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success_count += 1
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uf = model_result.get("updated_fields", [])
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if uf:
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updated_models.append({"path": model_path, "updated_fields": uf})
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updated_data = model_result.get("updates", {})
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if "preview_url" in updated_data and updated_data["preview_url"]:
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updated_data["preview_url"] = config.get_preview_static_url(
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updated_data["preview_url"]
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llm_response: Optional[Dict[str, Any]] = None
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if skill.llm_required and llm_configured:
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prompt_template = registry.load_prompt(skill_name)
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rendered = _render_prompt(prompt_template, prompt_vars)
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llm_response = await llm.chat_completion_json(
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system_prompt=prompt_vars.get(
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"system_prompt",
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"You are a helpful assistant that extracts structured metadata.",
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),
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user_prompt=rendered,
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)
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else:
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errors.extend(
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model_result.get("errors", [model_result.get("error", "Unknown error")])
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if llm_response:
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logger.info(
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"[%s] [%d/%d] %s → base_model=%s confidence=%s",
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skill_name, processed + 1, total, model_filename,
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(llm_response.get("base_model") or "?")[:50],
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llm_response.get("confidence", "?"),
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)
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model_result = await post_processor.process(
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skill_name=skill_name,
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model_path=model_path,
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llm_output=llm_response or {},
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metadata=metadata,
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readme_content=prompt_vars.get("readme_content_full", ""),
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)
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if model_result.get("success", True):
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success_count += 1
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uf = model_result.get("updated_fields", [])
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if uf:
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updated_models.append({"path": model_path, "updated_fields": uf})
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updated_data = model_result.get("updates", {})
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if "preview_url" in updated_data and updated_data["preview_url"]:
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updated_data["preview_url"] = config.get_preview_static_url(
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updated_data["preview_url"]
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)
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else:
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errors.extend(
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model_result.get("errors", [model_result.get("error", "Unknown error")])
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)
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except Exception as exc:
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logger.error("Skill %s failed for %s: %s", skill_name, model_path, exc)
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errors.append(f"{model_path}: {exc}")
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@@ -321,6 +333,7 @@ class AgentService:
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await self._emit_progress(
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progress_callback, skill_name, status="processing",
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total=total, processed=processed, success=success_count,
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skipped=skipped_count,
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current_path=model_path,
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updated_data=updated_data,
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)
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@@ -329,12 +342,13 @@ class AgentService:
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success=success_count > 0,
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updated_models=updated_models,
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errors=errors,
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summary=f"Processed {processed}/{total} models, {success_count} succeeded",
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summary=f"Processed {processed}/{total} models, {success_count} succeeded, {skipped_count} skipped",
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)
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await self._emit_progress(
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progress_callback, skill_name, status="completed",
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total=total, processed=processed, success=success_count,
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skipped=skipped_count,
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updated_models=updated_models, errors=errors, summary=result.summary,
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)
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