269 lines
12 KiB
Python
269 lines
12 KiB
Python
"""Workflow analysis service."""
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from ..routes.context import RouteContext
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class WorkflowService:
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"""Execute workflow analysis and local-resolution operations."""
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def __init__(self, context: RouteContext):
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extension = context.get("self")
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self.analysis_progress = extension.analysis_progress
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self._update_analysis_progress = extension._update_analysis_progress
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self.logger = extension.logger
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self.analyze_and_find_matches = context.get("analyze_and_find_matches")
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self.apply_resolution = context.get("apply_resolution")
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self.asyncio = context.get("asyncio")
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self.download_available = context.get("download_available")
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self.fetch_remote_file_size_cached = context.get("fetch_remote_file_size_cached")
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self.get_filename_from_path = context.get("get_filename_from_path")
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self.get_popular_model_url = context.get("get_popular_model_url")
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self.invalidate_local_hash_match_cache = context.get("invalidate_local_hash_match_cache")
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self.search_local_matches = context.get("search_local_matches")
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self.search_model_list = context.get("search_model_list")
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self.should_skip_existing_custom_node_reference = context.get("should_skip_existing_custom_node_reference")
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self.to_bool = context.get("to_bool")
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self.web = context.get("web")
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async def analyze_workflow(self, request):
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"""Analyze workflow and return missing models with matches."""
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analyze_and_find_matches = self.analyze_and_find_matches
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asyncio = self.asyncio
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download_available = self.download_available
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fetch_remote_file_size_cached = self.fetch_remote_file_size_cached
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get_filename_from_path = self.get_filename_from_path
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get_popular_model_url = self.get_popular_model_url
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invalidate_local_hash_match_cache = self.invalidate_local_hash_match_cache
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search_model_list = self.search_model_list
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should_skip_existing_custom_node_reference = self.should_skip_existing_custom_node_reference
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to_bool = self.to_bool
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web = self.web
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try:
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data = await request.json()
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workflow_json = data.get("workflow")
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analysis_id = str(data.get("analysis_id") or "").strip()
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force_rescan = to_bool(data.get("force_rescan"), False)
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if force_rescan:
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invalidate_local_hash_match_cache()
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if workflow_json is None:
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return web.json_response(
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{"error": "Workflow JSON is required"}, status=400
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)
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if not isinstance(workflow_json, dict):
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return web.json_response(
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{"error": "Workflow JSON must be an object"}, status=400
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)
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if analysis_id:
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self._update_analysis_progress(
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analysis_id,
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{
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"status": "starting",
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"stage": "starting",
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"message": "Starting analysis...",
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"current": 0,
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"total": 0,
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},
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)
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def update_analysis_progress(payload):
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self._update_analysis_progress(analysis_id, payload)
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# Analyze and find matches
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result = await asyncio.to_thread(
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analyze_and_find_matches,
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workflow_json,
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0.0,
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10,
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update_analysis_progress if analysis_id else None,
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force_rescan=force_rescan,
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)
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missing_models = result.get("missing_models", [])
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filtered_missing = []
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for missing in missing_models:
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name = missing.get("name") or missing.get("original_path", "")
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if should_skip_existing_custom_node_reference(missing):
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self.logger.info(
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"Filtered existing custom-node model "
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f"reference: {name}"
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)
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continue
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filtered_missing.append(missing)
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result["missing_models"] = filtered_missing
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result["total_missing"] = len(filtered_missing)
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# If download available, check for download sources only from LOCAL sources
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# (workflow_url, popular, model-list.json) - skip automatic online search
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# Online search is now only triggered on-demand via search button
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if download_available:
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for missing in result.get("missing_models", []):
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# Check if there's a 100% local match
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matches = missing.get("matches", [])
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has_perfect_match = any(
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m.get("confidence", 0) == 100 for m in matches
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)
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if not has_perfect_match:
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filename = get_filename_from_path(
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missing.get("original_path", "")
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)
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# 0. Check workflow URL first (highest priority - directly from workflow)
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workflow_url = missing.get("workflow_url", "")
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if workflow_url:
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# Determine source from URL
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if "huggingface.co" in workflow_url:
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source = "huggingface"
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elif "civitai.com" in workflow_url:
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source = "civitai"
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else:
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source = "workflow"
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# Try to get file size using cached remote helper
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file_size = fetch_remote_file_size_cached(workflow_url, timeout=5)
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missing["download_source"] = {
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"source": source,
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"url": workflow_url,
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"model_url": missing.get(
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"workflow_model_url", workflow_url
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),
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"filename": filename,
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"directory": missing.get(
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"workflow_directory", ""
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)
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or missing.get("category", "checkpoints"),
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"match_type": "exact",
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"url_source": "workflow",
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"size": file_size,
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}
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continue
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# 1. Check popular models (always exact match)
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popular_info = get_popular_model_url(filename)
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if popular_info:
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popular_model_list_result = search_model_list(
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filename, exact_only=True
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)
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missing["download_source"] = {
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"source": "popular",
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"url": popular_info.get("url"),
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"filename": filename,
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"type": popular_info.get("type"),
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"directory": popular_info.get("directory"),
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"size": (
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popular_model_list_result.get("size")
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if popular_model_list_result
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else None
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)
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or popular_info.get("size"),
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"match_type": "exact",
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}
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continue
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# 2. Check model list (ComfyUI Manager database)
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# Use exact_only=True to avoid confusing fuzzy matches for downloads
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model_list_result = search_model_list(
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filename, exact_only=True
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)
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if model_list_result:
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missing["download_source"] = {
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"source": "model_list",
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"url": model_list_result.get("url"),
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"filename": model_list_result.get("filename"),
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"name": model_list_result.get("name"),
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"type": model_list_result.get("type"),
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"directory": model_list_result.get("directory"),
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"size": model_list_result.get("size"),
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"match_type": model_list_result.get(
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"match_type"
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),
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"confidence": model_list_result.get(
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"confidence"
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),
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}
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continue
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# NOTE: Search for online sources (HuggingFace, CivitAI) is
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# now done on-demand via /model_resolver/search endpoint
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# when user clicks "Search Online" button, not automatically
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if analysis_id:
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self.analysis_progress.update(
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analysis_id,
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status="completed",
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stage="completed",
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message="Analysis complete",
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current=result.get("total_missing", 0),
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total=result.get("total_missing", 0),
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)
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return web.json_response(result)
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except Exception as e:
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if "analysis_id" in locals() and analysis_id:
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self.analysis_progress.update(
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analysis_id,
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status="error",
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stage="error",
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message=str(e),
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current=0,
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total=0,
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)
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self.logger.error(f"Model Resolver analyze error: {e}", exc_info=True)
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return web.json_response({"error": str(e)}, status=500)
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async def resolve_models(self, request):
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"""Apply model resolution and return updated workflow."""
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apply_resolution = self.apply_resolution
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web = self.web
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data = await request.json()
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workflow_json = data.get("workflow")
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resolutions = data.get("resolutions", [])
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if not workflow_json:
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return web.json_response(
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{"error": "Workflow JSON is required"}, status=400
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)
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if not resolutions:
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return web.json_response(
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{"error": "Resolutions array is required"}, status=400
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)
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# Apply resolutions
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updated_workflow = apply_resolution(workflow_json, resolutions)
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return web.json_response(
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{"workflow": updated_workflow, "success": True}
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)
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async def local_matches(self, request):
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"""Search local model files by filename/path."""
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invalidate_local_hash_match_cache = self.invalidate_local_hash_match_cache
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search_local_matches = self.search_local_matches
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to_bool = self.to_bool
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web = self.web
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data = await request.json()
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filename = data.get("filename", "")
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category = data.get("category", "")
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force_rescan = to_bool(data.get("force_rescan"), False)
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if force_rescan:
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invalidate_local_hash_match_cache()
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if not filename:
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return web.json_response(
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{"error": "filename is required"}, status=400
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)
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matches = search_local_matches(
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filename,
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category=category or None,
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similarity_threshold=0.0,
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max_matches_per_model=10,
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force_rescan=force_rescan,
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)
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return web.json_response({"matches": matches})
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