Dariusz L
4f91accbab
- Add force_rescan end-to-end so callers can bypass the short-lived local model scan cache: backend entry in __init__.py and core/resolver.py, lifecycle_graph_methods forwards it through the workflow analysis body, and download_target_methods passes ?force=1 from the refresh flow. - Rewrite combo dropdown lifecycle in missing_browser_methods with show/hide helpers, pointer-capture tracking, and a capture-phase outside-click listener so the list stays open during drag/hover and closes reliably otherwise. - Animate refresh buttons (CSS keyframes + JS Web Animations) with reduced-motion fallback. - Encode selected model path into the selected-bar data-model attribute and wire contextmenu so users can open the containing folder from the selection. - Fix XSS risk in selected-bar label by escaping label HTML; change queue/selected Remove buttons to danger style. - Increase max missing-browser model column width clamp from 430 to 820 and stop truncating missing-filename display; show full filename with tooltip instead. - Update huggingface-author-index.json metadata timestamp/counts (1 file changed, 1 line).
651 lines
23 KiB
Python
651 lines
23 KiB
Python
"""
|
|
Core Resolver Module
|
|
|
|
Integrates all components to provide high-level API for model linking.
|
|
"""
|
|
|
|
import os
|
|
import re
|
|
import json
|
|
from typing import Dict, Any, List, Optional, Tuple, Callable
|
|
from urllib.parse import unquote
|
|
|
|
from .log_system.log_funcs import (
|
|
log_debug,
|
|
log_info,
|
|
log_warn,
|
|
log_error,
|
|
log_exception,
|
|
)
|
|
from .scanner import get_model_files
|
|
from .workflow_analyzer import analyze_workflow_models, identify_missing_models
|
|
from .matcher import find_matches
|
|
from .workflow_updater import update_workflow_nodes
|
|
from .sources.civitai import resolve_urn
|
|
|
|
# Regex patterns for URL extraction (matches HuggingFace and CivitAI URLs)
|
|
URL_PATTERN = re.compile(r'(https?://(?:huggingface\.co|civitai\.com)[^\s"\'<>\)\\]+)')
|
|
|
|
# Model file extensions to look for
|
|
MODEL_EXTENSIONS = (".safetensors", ".ckpt", ".pt", ".pth", ".bin", ".onnx", ".gguf")
|
|
|
|
|
|
def strip_known_model_extension(filename: str) -> str:
|
|
"""Strip only known model extensions, preserving names like v4.0."""
|
|
if not isinstance(filename, str):
|
|
return ""
|
|
|
|
lowered = filename.lower()
|
|
for ext in MODEL_EXTENSIONS:
|
|
if lowered.endswith(ext):
|
|
return filename[: -len(ext)]
|
|
return filename
|
|
|
|
|
|
def get_workflow_url_info_for_filename(
|
|
workflow_urls: Dict[str, Dict[str, Any]], filename: str
|
|
) -> Optional[Dict[str, Any]]:
|
|
if filename in workflow_urls:
|
|
return workflow_urls[filename]
|
|
|
|
filename_stem = strip_known_model_extension(os.path.basename(filename)).lower()
|
|
if not filename_stem:
|
|
return None
|
|
|
|
for workflow_filename, url_info in workflow_urls.items():
|
|
workflow_stem = strip_known_model_extension(
|
|
os.path.basename(workflow_filename)
|
|
).lower()
|
|
if workflow_stem == filename_stem:
|
|
return url_info
|
|
|
|
return None
|
|
|
|
|
|
def normalize_workflow_download_url(url: str) -> str:
|
|
"""Convert workflow file-page URLs into direct download URLs when possible."""
|
|
if not isinstance(url, str) or not url:
|
|
return url
|
|
|
|
# HuggingFace /blob/ pages return HTML. /resolve/ returns the actual file.
|
|
return re.sub(
|
|
r"(https?://huggingface\.co/[^/]+/[^/]+)/blob/([^/]+)/(.+)",
|
|
r"\1/resolve/\2/\3",
|
|
url,
|
|
)
|
|
|
|
|
|
def workflow_url_points_to_file(url: str, filename: str) -> bool:
|
|
"""Return true when a URL appears to reference the specific model file."""
|
|
if not url or not filename:
|
|
return False
|
|
|
|
try:
|
|
decoded_url = unquote(url)
|
|
except Exception:
|
|
decoded_url = url
|
|
|
|
return filename in decoded_url or unquote(filename) in decoded_url
|
|
|
|
|
|
def search_local_matches(
|
|
target_for_matching: str,
|
|
category: Optional[str] = None,
|
|
similarity_threshold: float = 0.0,
|
|
max_matches_per_model: int = 10,
|
|
force_rescan: bool = False,
|
|
) -> List[Dict[str, Any]]:
|
|
"""
|
|
Search local model files using the same matcher as workflow analysis.
|
|
|
|
Args:
|
|
target_for_matching: Filename/path to match against local files
|
|
category: Optional category hint to prioritize/filter candidates
|
|
similarity_threshold: Minimum similarity score (0.0 to 1.0)
|
|
max_matches_per_model: Maximum number of matches to return
|
|
|
|
Returns:
|
|
Deduplicated list of local matches sorted by similarity
|
|
"""
|
|
available_models = get_model_files(force_rescan=force_rescan)
|
|
|
|
candidates = available_models
|
|
if category and category != "unknown":
|
|
candidates = [m for m in available_models if m.get("category") == category]
|
|
candidates.extend(
|
|
[m for m in available_models if m.get("category") != category]
|
|
)
|
|
|
|
matches = find_matches(
|
|
target_for_matching,
|
|
candidates,
|
|
threshold=similarity_threshold,
|
|
max_results=max_matches_per_model,
|
|
)
|
|
|
|
seen_absolute_paths = {}
|
|
deduplicated_matches = []
|
|
for match in matches:
|
|
model_dict = match["model"]
|
|
absolute_path = model_dict.get("path", "")
|
|
if absolute_path:
|
|
absolute_path = os.path.normpath(absolute_path)
|
|
|
|
if absolute_path not in seen_absolute_paths:
|
|
seen_absolute_paths[absolute_path] = match
|
|
deduplicated_matches.append(match)
|
|
else:
|
|
existing_match = seen_absolute_paths[absolute_path]
|
|
if match["confidence"] > existing_match["confidence"]:
|
|
idx = deduplicated_matches.index(existing_match)
|
|
deduplicated_matches[idx] = match
|
|
seen_absolute_paths[absolute_path] = match
|
|
|
|
return deduplicated_matches
|
|
|
|
|
|
def extract_workflow_urls(workflow_json: Dict[str, Any]) -> Dict[str, Dict[str, Any]]:
|
|
"""
|
|
Extract model URLs from workflow JSON.
|
|
|
|
Sources:
|
|
1. node.properties.models array - contains {name, url, directory}
|
|
2. Regex extraction from workflow JSON string - finds HuggingFace/CivitAI URLs
|
|
|
|
Args:
|
|
workflow_json: Complete workflow JSON dictionary
|
|
|
|
Returns:
|
|
Dict mapping model filename -> {url, directory, source}
|
|
"""
|
|
url_map = {}
|
|
|
|
# Convert to string for regex search
|
|
workflow_str = json.dumps(workflow_json)
|
|
|
|
# Collect all nodes including from subgraphs
|
|
all_nodes = list(workflow_json.get("nodes", []))
|
|
definitions = workflow_json.get("definitions", {})
|
|
subgraphs = definitions.get("subgraphs", [])
|
|
for subgraph in subgraphs:
|
|
subgraph_nodes = subgraph.get("nodes", [])
|
|
all_nodes.extend(subgraph_nodes)
|
|
|
|
# 1. Extract from node.properties.models (authoritative source)
|
|
for node in all_nodes:
|
|
node_type = node.get("type", "")
|
|
properties = node.get("properties", {})
|
|
models_list = properties.get("models", [])
|
|
|
|
for model_info in models_list:
|
|
if isinstance(model_info, dict):
|
|
name = model_info.get("name", "")
|
|
url = model_info.get("url", "")
|
|
directory = model_info.get("directory", "")
|
|
|
|
if name and name not in url_map:
|
|
url_map[name] = {
|
|
"url": normalize_workflow_download_url(url),
|
|
"model_url": url,
|
|
"directory": directory,
|
|
"node_type": node_type,
|
|
"source": "node_properties",
|
|
}
|
|
|
|
# 2. Extract URLs via regex from workflow JSON
|
|
urls_found = URL_PATTERN.findall(workflow_str)
|
|
|
|
# Clean URLs (remove trailing characters that may have been captured)
|
|
cleaned_urls = []
|
|
for url in urls_found:
|
|
url = url.split(")")[0].replace("\\n", "").replace("\n", "").strip()
|
|
if url:
|
|
cleaned_urls.append(url)
|
|
|
|
# 3. Extract model filenames via regex
|
|
model_pattern = re.compile(
|
|
r"([\w\-\.%]+\.(?:safetensors|ckpt|pt|pth|bin|onnx|gguf))", re.IGNORECASE
|
|
)
|
|
model_files_raw = model_pattern.findall(workflow_str)
|
|
|
|
# Clean and decode filenames
|
|
model_files = set()
|
|
model_name_map = {} # decoded -> original
|
|
|
|
for model in model_files_raw:
|
|
cleaned = model.strip()
|
|
if cleaned and cleaned[0].isalnum():
|
|
try:
|
|
decoded = unquote(cleaned)
|
|
except Exception:
|
|
decoded = cleaned
|
|
model_files.add(decoded)
|
|
model_name_map[decoded] = cleaned
|
|
|
|
# 4. Match URLs to model filenames
|
|
for model in model_files:
|
|
# Keep authoritative node.properties URLs only when they point to the file.
|
|
# Some workflows store a repo/model-page URL there and a concrete file URL
|
|
# elsewhere in the JSON; in that case the file URL is the usable download.
|
|
if (
|
|
model in url_map
|
|
and url_map[model].get("url")
|
|
and workflow_url_points_to_file(url_map[model].get("url"), model)
|
|
):
|
|
continue
|
|
|
|
original_name = model_name_map.get(model, model)
|
|
|
|
for url in cleaned_urls:
|
|
# Check decoded name in URL
|
|
if model in url:
|
|
if (
|
|
model not in url_map
|
|
or not url_map[model].get("url")
|
|
or not workflow_url_points_to_file(url_map[model].get("url"), model)
|
|
):
|
|
url_map[model] = {
|
|
"url": normalize_workflow_download_url(url),
|
|
"model_url": url,
|
|
"directory": url_map.get(model, {}).get("directory", ""),
|
|
"source": "regex",
|
|
}
|
|
else:
|
|
url_map[model]["url"] = normalize_workflow_download_url(url)
|
|
url_map[model]["model_url"] = url
|
|
url_map[model]["source"] = "regex"
|
|
break
|
|
# Check original (possibly URL-encoded) name in URL
|
|
if original_name in url:
|
|
if (
|
|
model not in url_map
|
|
or not url_map[model].get("url")
|
|
or not workflow_url_points_to_file(
|
|
url_map[model].get("url"), original_name
|
|
)
|
|
):
|
|
url_map[model] = {
|
|
"url": normalize_workflow_download_url(url),
|
|
"model_url": url,
|
|
"directory": url_map.get(model, {}).get("directory", ""),
|
|
"source": "regex",
|
|
}
|
|
else:
|
|
url_map[model]["url"] = normalize_workflow_download_url(url)
|
|
url_map[model]["model_url"] = url
|
|
url_map[model]["source"] = "regex"
|
|
break
|
|
# Check without extension
|
|
model_base = os.path.splitext(model)[0]
|
|
if model_base in url or unquote(model_base) in url:
|
|
if (
|
|
model not in url_map
|
|
or not url_map[model].get("url")
|
|
or not workflow_url_points_to_file(url_map[model].get("url"), model)
|
|
):
|
|
url_map[model] = {
|
|
"url": normalize_workflow_download_url(url),
|
|
"model_url": url,
|
|
"directory": url_map.get(model, {}).get("directory", ""),
|
|
"source": "regex",
|
|
}
|
|
else:
|
|
url_map[model]["url"] = normalize_workflow_download_url(url)
|
|
url_map[model]["model_url"] = url
|
|
url_map[model]["source"] = "regex"
|
|
break
|
|
|
|
return url_map
|
|
|
|
|
|
def parse_huggingface_url(url: str) -> Tuple[Optional[str], Optional[str]]:
|
|
"""
|
|
Extract HuggingFace repo and path from URL.
|
|
|
|
Args:
|
|
url: HuggingFace URL
|
|
|
|
Returns:
|
|
Tuple of (repo_id, file_path) or (None, None) if not valid
|
|
"""
|
|
if not url or "huggingface.co" not in url:
|
|
return None, None
|
|
|
|
# Pattern: https://huggingface.co/user/repo/resolve/main/path/to/file.safetensors
|
|
match = re.match(
|
|
r"https?://huggingface\.co/([^/]+/[^/]+)/(?:resolve|blob)/[^/]+/(.+)", url
|
|
)
|
|
if match:
|
|
return match.group(1), match.group(2)
|
|
|
|
return None, None
|
|
|
|
|
|
def analyze_and_find_matches(
|
|
workflow_json: Dict[str, Any],
|
|
similarity_threshold: float = 0.0,
|
|
max_matches_per_model: int = 10,
|
|
progress_callback: Optional[Callable[[Dict[str, Any]], None]] = None,
|
|
force_rescan: bool = False,
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Main entry point: analyze workflow and find matches for missing models.
|
|
|
|
Args:
|
|
workflow_json: Complete workflow JSON dictionary
|
|
similarity_threshold: Minimum similarity score (0.0 to 1.0) for matches
|
|
max_matches_per_model: Maximum number of matches to return per missing model
|
|
force_rescan: If True, bypass the short-lived local model scan cache
|
|
|
|
Returns:
|
|
Dictionary with analysis results:
|
|
{
|
|
'missing_models': [
|
|
{
|
|
'node_id': node ID,
|
|
'node_type': node type,
|
|
'widget_index': widget index,
|
|
'original_path': original path from workflow,
|
|
'category': model category,
|
|
'workflow_url': URL from workflow if found,
|
|
'workflow_directory': directory from workflow if found,
|
|
'matches': [
|
|
{
|
|
'model': model dict from scanner,
|
|
'filename': model filename,
|
|
'similarity': similarity score (0.0-1.0),
|
|
'confidence': confidence percentage (0-100)
|
|
},
|
|
...
|
|
]
|
|
},
|
|
...
|
|
],
|
|
'total_missing': count of missing models,
|
|
'total_models_analyzed': count of all models in workflow
|
|
}
|
|
"""
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "extracting",
|
|
"message": "Extracting workflow model references...",
|
|
"current": 0,
|
|
"total": 0,
|
|
}
|
|
)
|
|
|
|
# Extract URLs from workflow (node.properties.models + regex)
|
|
workflow_urls = extract_workflow_urls(workflow_json)
|
|
log_debug(f"Extracted {len(workflow_urls)} URLs from workflow")
|
|
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "scanning",
|
|
"message": "Scanning local model index...",
|
|
"current": 0,
|
|
"total": 0,
|
|
}
|
|
)
|
|
|
|
# Analyze workflow to find all model references
|
|
# Get available models
|
|
available_models = get_model_files(force_rescan=force_rescan)
|
|
available_models_by_category = {}
|
|
for model in available_models:
|
|
model_category = model.get("category", "")
|
|
if model_category not in available_models_by_category:
|
|
available_models_by_category[model_category] = []
|
|
available_models_by_category[model_category].append(model)
|
|
|
|
ordered_candidates_cache: Dict[str, List[Dict[str, Any]]] = {}
|
|
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "analyzing",
|
|
"message": "Analyzing workflow nodes...",
|
|
"current": 0,
|
|
"total": 0,
|
|
}
|
|
)
|
|
|
|
# Analyze workflow using the same already-scanned model list
|
|
all_model_refs = analyze_workflow_models(
|
|
workflow_json, available_models=available_models
|
|
)
|
|
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "identifying",
|
|
"message": "Identifying missing models...",
|
|
"current": 0,
|
|
"total": len(all_model_refs),
|
|
}
|
|
)
|
|
|
|
# Identify missing models
|
|
missing_models = identify_missing_models(all_model_refs, available_models)
|
|
|
|
# Enrich missing models with workflow URLs
|
|
for missing in missing_models:
|
|
original_path = missing.get("original_path", "")
|
|
filename = os.path.basename(original_path)
|
|
|
|
url_info = get_workflow_url_info_for_filename(workflow_urls, filename)
|
|
if url_info:
|
|
missing["workflow_url"] = url_info.get("url", "")
|
|
missing["workflow_model_url"] = url_info.get("model_url", "")
|
|
missing["workflow_directory"] = url_info.get("directory", "")
|
|
missing["url_source"] = url_info.get("source", "")
|
|
|
|
# Handle URNs: mark for async resolution by frontend
|
|
# No sync CivitAI calls here - frontend will fetch asynchronously
|
|
for missing in missing_models:
|
|
if missing.get("is_urn"):
|
|
missing["needs_urn_resolve"] = True
|
|
urn = missing.get("urn")
|
|
if urn:
|
|
missing["urn_model_id"] = urn.get("model_id")
|
|
missing["urn_version_id"] = urn.get("version_id")
|
|
missing["urn_type"] = urn.get("type", "")
|
|
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "matching",
|
|
"message": "Matching missing models...",
|
|
"current": 0,
|
|
"total": len(missing_models),
|
|
}
|
|
)
|
|
|
|
# Find matches for each missing model
|
|
missing_with_matches = []
|
|
total_missing = len(missing_models)
|
|
for index, missing in enumerate(missing_models, start=1):
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "matching",
|
|
"message": f"Analyzing model {index} of {total_missing}",
|
|
"current": index,
|
|
"total": total_missing,
|
|
"model_name": missing.get("name")
|
|
or missing.get("original_path", ""),
|
|
}
|
|
)
|
|
|
|
# Skip LoraManager lorAs that already exist locally (exists=True means no linking needed)
|
|
is_lora_v2 = missing.get("is_lora_v2")
|
|
exists = missing.get("exists")
|
|
name = missing.get("name") or missing.get("original_path", "")
|
|
log_debug(f"Checking {name}: is_lora_v2={is_lora_v2}, exists={exists}")
|
|
|
|
if is_lora_v2 and exists:
|
|
log_info(f"Skipping LoraManager lora {name} - already exists locally")
|
|
continue
|
|
|
|
target_for_matching = missing.get("original_path", "")
|
|
|
|
# For URNs, prefer expected_filename for matching
|
|
if missing.get("is_urn") and missing.get("expected_filename"):
|
|
target_for_matching = missing["expected_filename"]
|
|
elif isinstance(target_for_matching, str) and target_for_matching.startswith(
|
|
"urn:air:"
|
|
):
|
|
# Don't fuzzy-match the full URN string against every local model.
|
|
# For worker-asset style URNs, try using the filename-like suffix after "@";
|
|
# otherwise skip local matching until the frontend resolves the URN async.
|
|
urn_suffix = target_for_matching.split("@", 1)[1] if "@" in target_for_matching else ""
|
|
urn_suffix_ext = os.path.splitext(urn_suffix)[1].lower()
|
|
if urn_suffix and urn_suffix_ext in MODEL_EXTENSIONS:
|
|
target_for_matching = urn_suffix
|
|
else:
|
|
missing_with_matches.append({**missing, "matches": []})
|
|
continue
|
|
|
|
# Filter available models by category if known
|
|
category = missing.get("category")
|
|
if not category or category == "unknown":
|
|
from .workflow_analyzer import NODE_TYPE_TO_CATEGORY_HINTS
|
|
|
|
node_type = missing.get("node_type", "")
|
|
category = NODE_TYPE_TO_CATEGORY_HINTS.get(node_type, "unknown")
|
|
|
|
candidates = available_models
|
|
if category and category != "unknown":
|
|
candidates = ordered_candidates_cache.get(category)
|
|
if candidates is None:
|
|
preferred = available_models_by_category.get(category, [])
|
|
others = [
|
|
m for m in available_models if m.get("category") != category
|
|
]
|
|
candidates = preferred + others
|
|
ordered_candidates_cache[category] = candidates
|
|
|
|
# Find matches
|
|
matches = find_matches(
|
|
target_for_matching,
|
|
candidates,
|
|
threshold=similarity_threshold,
|
|
max_results=max_matches_per_model,
|
|
)
|
|
|
|
# Deduplicate matches by absolute path
|
|
seen_absolute_paths = {}
|
|
deduplicated_matches = []
|
|
for match in matches:
|
|
model_dict = match["model"]
|
|
absolute_path = model_dict.get("path", "")
|
|
if absolute_path:
|
|
absolute_path = os.path.normpath(absolute_path)
|
|
|
|
if absolute_path not in seen_absolute_paths:
|
|
seen_absolute_paths[absolute_path] = match
|
|
deduplicated_matches.append(match)
|
|
else:
|
|
existing_match = seen_absolute_paths[absolute_path]
|
|
if match["confidence"] > existing_match["confidence"]:
|
|
idx = deduplicated_matches.index(existing_match)
|
|
deduplicated_matches[idx] = match
|
|
seen_absolute_paths[absolute_path] = match
|
|
|
|
missing_with_matches.append({**missing, "matches": deduplicated_matches})
|
|
|
|
result = {
|
|
"missing_models": missing_with_matches,
|
|
"total_missing": len(missing_with_matches),
|
|
"total_models_analyzed": len(all_model_refs),
|
|
}
|
|
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "completed",
|
|
"message": "Analysis complete",
|
|
"current": total_missing,
|
|
"total": total_missing,
|
|
}
|
|
)
|
|
|
|
return result
|
|
|
|
|
|
def apply_resolution(
|
|
workflow_json: Dict[str, Any], resolutions: List[Dict[str, Any]]
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Apply model resolutions to workflow.
|
|
|
|
Args:
|
|
workflow_json: Workflow JSON dictionary (will be modified)
|
|
resolutions: List of resolution dictionaries:
|
|
{
|
|
'node_id': node ID,
|
|
'widget_index': widget index,
|
|
'resolved_path': absolute path to resolved model,
|
|
'category': model category (optional),
|
|
'resolved_model': model dict from scanner (optional),
|
|
'nested_key': nested key for dict-type widgets (optional)
|
|
}
|
|
|
|
Returns:
|
|
Updated workflow JSON dictionary
|
|
"""
|
|
# Prepare mappings for workflow_updater
|
|
mappings = []
|
|
for resolution in resolutions:
|
|
mapping = {
|
|
"node_id": resolution.get("node_id"),
|
|
"widget_index": resolution.get("widget_index"),
|
|
"resolved_path": resolution.get("resolved_path"),
|
|
"category": resolution.get("category"),
|
|
"resolved_model": resolution.get("resolved_model"),
|
|
"subgraph_id": resolution.get(
|
|
"subgraph_id"
|
|
), # Include subgraph_id for subgraph nodes
|
|
"is_top_level": resolution.get(
|
|
"is_top_level"
|
|
), # True for top-level nodes, False for nodes in subgraph definitions
|
|
"is_lora_v2": resolution.get("is_lora_v2"), # Flag for LoraManager nodes
|
|
"original_lora_name": resolution.get(
|
|
"original_lora_name"
|
|
), # Original lora name for LoraManager replacement
|
|
"nested_key": resolution.get(
|
|
"nested_key"
|
|
), # For dict-type widgets (e.g. Power Lora Loader)
|
|
}
|
|
|
|
# If resolved_model provided, extract path if needed
|
|
if "resolved_model" in resolution and resolution["resolved_model"]:
|
|
resolved_model = resolution["resolved_model"]
|
|
if "path" in resolved_model and not mapping.get("resolved_path"):
|
|
mapping["resolved_path"] = resolved_model["path"]
|
|
if "base_directory" in resolved_model:
|
|
mapping["base_directory"] = resolved_model["base_directory"]
|
|
|
|
mappings.append(mapping)
|
|
|
|
# Update workflow
|
|
updated_workflow = update_workflow_nodes(workflow_json, mappings)
|
|
|
|
return updated_workflow
|
|
|
|
|
|
def get_resolution_summary(workflow_json: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""
|
|
Get summary of missing models and matches without applying resolutions.
|
|
|
|
This is a convenience method that calls analyze_and_find_matches with defaults.
|
|
|
|
Args:
|
|
workflow_json: Complete workflow JSON dictionary
|
|
|
|
Returns:
|
|
Same format as analyze_and_find_matches
|
|
"""
|
|
return analyze_and_find_matches(workflow_json)
|