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ComfyAssets-ComfyUI_PromptM…/utils/prompt_tracker.py
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Vito 6c7e4d5b35 feat: release 3.2.4 — recently used sorting, run counts, positive-prompt image linking (#152)
* chore: start 3.2.4

* feat: track prompt usage (run_count, last_used_at) with server-side sorting

- Migration adds last_used_at and run_count; existing prompts are estimated
  from linked images (count, newest image time, else created_at). The
  backfill only touches NULL rows, so it self-heals rows written after a
  downgrade or an interrupted upgrade.
- Every node run records a use, including re-runs of an existing prompt;
  a tracking failure is logged and never blocks generation.
- search_prompts/get_recent_prompts accept a whitelisted sort key
  (last_used, created, run_count, rating, text); default stays newest first.

Refs #90

* feat: sort param on /recent and /search; microsecond last_used_at

- /prompt_manager/recent and /search accept ?sort= (whitelisted in the DB
  layer; unknown values fall back to newest first).
- last_used_at is written with microseconds from Python so a re-run in the
  same millisecond as another save still sorts first; ordering stays correct
  against millisecond values from the backfill.

Refs #90

* feat: server-side sort in admin UI with Recently Used / Most Used and run badges

- New web/js/prompt-list-sort.js owns the sort options, validation and
  request URLs; admin.js fills the dropdown from it and re-fetches from the
  server on change, so every sort spans all pages (the old client-side sort
  only reordered the current page).
- Default sort is Recently Used; prompt cards show a run-count badge with
  the last-used time on hover.
- The node's Recent button now lists recently used prompts.
- Contract test keeps the UI sort keys and the node's key in sync with the
  server whitelist.

Refs #90

* fix: link images and count runs by the positive prompt, not node execution

With a negative-prompt PromptManager node in the workflow, every executed
PromptManager node pushed onto the image-linking queue, so images could be
linked to the negative prompt or to a previous run. ComfyUI also skips
unchanged nodes, so re-runs of the same prompt were never counted.

- utils/prompt_graph.py finds the PromptManager nodes feeding a sampler's
  positive input (KSampler, CFG/Basic guiders, ControlNet and combine
  pass-throughs, PromptManagerText into encoders), falling back to all
  PromptManager nodes when none can be traced.
- Runs are counted by a ComfyUI on-prompt hook when a workflow is queued;
  prompts not yet saved are counted when the node first saves them.
- Nodes receive the queued graph (hidden PROMPT/UNIQUE_ID). Only the run's
  positive prompt becomes the current prompt; only batch items with linked
  text are pushed onto the FIFO queue.
- The image monitor links from the image's own metadata first, using the
  positive node, and falls back to the queue for batch items.

Refs #90

* fix: address review — count cached runs of linked text, keep repeated pending runs

- Text from known pure string nodes (core PrimitiveString/StringConcatenate,
  WAS Text Concatenate/Multiline, pysssss ShowText) is resolved at queue
  time, so cached re-runs of such prompts are counted and their images link
  from metadata. Unknown nodes are never guessed; the node compares its real
  text with the queue-time resolution and falls back to counting and
  queueing itself on any mismatch.
- A new prompt queued several times before its first run keeps every
  pending run; the first save records all of them.
- Registration flag is a threading.Event; consistent unittest imports;
  logging instead of print in the init fallback.

* refactor: unquote PendingFirstUse annotation so Tuple usage is visible
2026-10-07 13:52:09 -07:00

545 lines
21 KiB
Python

"""Prompt tracking system for linking generated images to prompts.
This module provides thread-safe tracking of active prompt executions to enable automatic
association of generated images with their source prompts. The system maintains prompt
context across different execution threads and provides fallback mechanisms for reliable
image-prompt linking.
Key features:
- Thread-safe prompt tracking using threading.local and locks
- Automatic cleanup of expired prompt contexts
- Fallback mechanisms for cross-thread prompt access
- Support for execution timeouts and extensions
- Context manager for automatic prompt lifecycle management
Typical usage:
from utils.prompt_tracker import PromptTracker
tracker = PromptTracker(db_manager)
execution_id = tracker.set_current_prompt("beautiful landscape")
# Images generated after this point will be linked to this prompt
Or using the context manager:
with PromptExecutionContext(tracker, "beautiful landscape"):
# Generate images here
pass
"""
import threading
import time
import uuid
import hashlib
from typing import Optional, Dict, Any
from datetime import datetime, timezone
# Import logging system
try:
from .logging_config import get_logger
except ImportError:
import sys
import os
current_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, current_dir)
from utils.logging_config import get_logger
class PromptTracker:
"""Thread-safe tracking of current prompt executions.
This class manages active prompt contexts across multiple threads, enabling
the image monitoring system to correctly associate generated images with their
source prompts. It uses both thread-local storage and global tracking to handle
various execution scenarios.
The tracker automatically cleans up expired prompts and provides fallback
mechanisms when prompts are accessed from different threads (e.g., image
monitoring running in a separate thread from prompt execution).
Attributes:
active_prompts: Global dictionary of active prompt contexts
cleanup_interval: Seconds between cleanup operations (default: 300)
prompt_timeout: Seconds before a prompt expires (default: 600)
"""
def __init__(self, db_manager):
"""
Initialize the prompt tracker.
Args:
db_manager: Database manager instance for prompt operations
"""
self.logger = get_logger("prompt_manager.prompt_tracker")
self.logger.debug("Initializing PromptTracker")
self.db_manager = db_manager
self._local = threading.local()
self.active_prompts = {} # Global tracking for multiple threads
self.lock = threading.Lock()
# FIFO queue for batch workflows: prompt_ids are pushed during encoding
# and popped during image linking, preserving correct ordering.
self._prompt_queue = []
self._queue_lock = threading.Lock()
# Read from GalleryConfig if available, otherwise use defaults
try:
from ..py.config import GalleryConfig
self.cleanup_interval = GalleryConfig.CLEANUP_INTERVAL
self.prompt_timeout = GalleryConfig.PROMPT_TIMEOUT
except Exception:
self.cleanup_interval = 300 # 5 minutes
self.prompt_timeout = 600 # 10 minutes
# Start cleanup thread
self.cleanup_thread = threading.Thread(
target=self._cleanup_expired_prompts, daemon=True
)
self.cleanup_thread.start()
self.logger.debug("PromptTracker initialization completed")
def set_current_prompt(
self,
prompt_text: str,
additional_data: Optional[Dict[str, Any]] = None,
push_to_queue: bool = True,
) -> str:
"""
Set the current prompt for this thread for image tracking.
This method establishes the prompt context that will be used to link any
subsequently generated images. The prompt is stored in both thread-local
storage and global tracking to handle cross-thread access scenarios.
Note: Prompt saving is handled by PromptManager to avoid duplicates.
Args:
prompt_text: The prompt text being executed
additional_data: Additional prompt metadata, should include prompt_id from PromptManager
push_to_queue: Also push onto the FIFO batch queue. Only batch items whose
text comes from another node need it; typed prompts are linked from
the image's own metadata instead.
Returns:
Unique execution ID for this prompt execution
"""
execution_id = self.generate_execution_id()
# Get prompt_id from additional_data (passed from PromptManager)
# PromptTracker should NOT save prompts to avoid duplicates
prompt_id = additional_data.get("prompt_id") if additional_data else None
if not prompt_id:
# Fallback: try to find existing prompt using consistent hash calculation
try:
# Use consistent hash calculation (same as PromptManager)
import hashlib
normalized_text = prompt_text.strip().lower()
prompt_hash = hashlib.sha256(
normalized_text.encode("utf-8")
).hexdigest()
existing_prompt = self.db_manager.get_prompt_by_hash(prompt_hash)
if existing_prompt:
prompt_id = existing_prompt["id"]
self.logger.debug(f"Found existing prompt ID: {prompt_id}")
else:
# Generate a temporary ID for tracking (prompt should be saved by PromptManager)
prompt_id = f"temp_{int(time.time())}"
self.logger.debug(f"Using temporary ID for tracking: {prompt_id}")
except Exception as e:
self.logger.error(f"Error finding prompt: {e}")
prompt_id = f"temp_{int(time.time())}"
# Create execution context
execution_context = {
"id": prompt_id,
"execution_id": execution_id,
"text": prompt_text,
"timestamp": time.time(),
"thread_id": threading.current_thread().ident,
"additional_data": additional_data or {},
}
# Store in thread-local storage
self._local.current_prompt = execution_context
# Store in global tracking with thread safety
with self.lock:
self.active_prompts[execution_id] = execution_context
# Push to batch queue for ordered image linking
if push_to_queue:
with self._queue_lock:
self._prompt_queue.append(execution_context)
self.logger.debug(
f"Queue size: {len(self._prompt_queue)} after push for prompt {prompt_id}"
)
self.logger.debug(
f"Set current prompt: {execution_id} -> {prompt_text[:50]}... (thread: {threading.current_thread().ident})"
)
self.logger.debug(f"Active prompts count: {len(self.active_prompts)}")
return execution_id
def get_current_prompt(self) -> Optional[Dict[str, Any]]:
"""
Get the current prompt context for this thread.
Attempts to retrieve the current prompt context, first from thread-local
storage, then from global tracking as a fallback. This enables image
monitoring (which may run in a different thread) to access prompt context.
Returns:
Dictionary containing prompt context with keys:
- id: Prompt ID in database
- execution_id: Unique execution identifier
- text: Prompt text
- timestamp: When the prompt was set
- thread_id: Thread identifier
- additional_data: Any additional metadata
Returns None if no valid prompt context is found
"""
current = getattr(self._local, "current_prompt", None)
if current:
# Check if prompt hasn't expired
if time.time() - current["timestamp"] < self.prompt_timeout:
return current
else:
self.logger.debug(f"Current prompt expired: {current['execution_id']}")
self.clear_current_prompt()
# Fallback: try to find recent prompt from global tracking
# This is crucial for image monitoring which runs in different threads
recent_prompt = self._find_recent_prompt()
if recent_prompt:
self.logger.debug(
f"Using recent prompt from global tracking: {recent_prompt['execution_id']}"
)
return recent_prompt
self.logger.debug(
f"No prompt context found (thread: {threading.current_thread().ident})"
)
return None
def pop_next_prompt(self) -> Optional[Dict[str, Any]]:
"""Pop the next prompt from the batch queue (FIFO).
In batch workflows, prompts are encoded in order and images are saved
in the same order. This method pops the oldest queued prompt, ensuring
each image links to the correct prompt by position.
Skips and discards expired entries to prevent stale prompts from
accumulating across sessions.
Returns:
Prompt context dict, or None if queue is empty or all entries expired
"""
now = time.time()
with self._queue_lock:
while self._prompt_queue:
ctx = self._prompt_queue.pop(0)
if now - ctx.get("timestamp", 0) < self.prompt_timeout:
self.logger.debug(
f"Queue pop: prompt {ctx.get('id')} "
f"({len(self._prompt_queue)} remaining)"
)
return ctx
self.logger.debug(f"Queue discard expired: prompt {ctx.get('id')}")
return None
def _find_recent_prompt(self) -> Optional[Dict[str, Any]]:
"""Find the most recent prompt that's still valid.
Searches the global prompt tracking for the most recently set prompt
that hasn't expired. This is used as a fallback when thread-local
storage doesn't contain a current prompt.
Returns:
Most recent valid prompt context, or None if no valid prompts found
"""
with self.lock:
current_time = time.time()
self.logger.debug(
f"Searching for recent prompt among {len(self.active_prompts)} active prompts"
)
recent_prompts = []
for exec_id, prompt in self.active_prompts.items():
age_seconds = current_time - prompt["timestamp"]
self.logger.debug(
f"Prompt {exec_id}: age={age_seconds:.1f}s, timeout={self.prompt_timeout}s"
)
if age_seconds < self.prompt_timeout:
recent_prompts.append(prompt)
else:
self.logger.debug(f"Prompt {exec_id} is expired")
if recent_prompts:
# Return the most recent one
most_recent = max(recent_prompts, key=lambda p: p["timestamp"])
self.logger.debug(f"Found recent prompt: {most_recent['execution_id']}")
return most_recent
else:
self.logger.debug(f"No recent prompts found")
return None
def clear_current_prompt(self):
"""Clear the current prompt context for this thread.
Removes the prompt context from both thread-local storage and global
tracking. This should be called when prompt execution is complete,
though the system also handles automatic cleanup via timeouts.
"""
current = getattr(self._local, "current_prompt", None)
if current:
execution_id = current["execution_id"]
self.logger.debug(
f"Clearing current prompt: {execution_id} (thread: {threading.current_thread().ident})"
)
# Clear from thread-local storage
self._local.current_prompt = None
# Remove from global tracking
with self.lock:
removed = self.active_prompts.pop(execution_id, None)
if removed:
self.logger.debug(
f"Removed prompt from global tracking: {execution_id}"
)
self.logger.debug(
f"Remaining active prompts: {len(self.active_prompts)}"
)
else:
self.logger.debug(
f"Prompt {execution_id} was not in global tracking"
)
else:
self.logger.debug(
f"No current prompt to clear (thread: {threading.current_thread().ident})"
)
def extend_prompt_timeout(self, execution_id: str, additional_seconds: int = 60):
"""
Extend the timeout for a specific prompt execution.
This is useful for long-running image generation processes where images
may continue to be generated well after the initial prompt execution.
Updates the timestamp to prevent the prompt from being cleaned up.
Args:
execution_id: The execution ID to extend
additional_seconds: Additional seconds to add to timeout (currently unused,
method just resets the timestamp to current time)
"""
with self.lock:
if execution_id in self.active_prompts:
self.active_prompts[execution_id]["timestamp"] = time.time()
self.logger.debug(f"Extended timeout for prompt: {execution_id}")
def generate_execution_id(self) -> str:
"""Generate a unique execution ID.
Creates a unique identifier for this prompt execution using UUID and timestamp.
Returns:
Unique execution ID in format 'exec_{uuid8}_{timestamp}'
"""
return f"exec_{uuid.uuid4().hex[:8]}_{int(time.time())}"
def _cleanup_expired_prompts(self):
"""Background thread to clean up expired prompts.
Runs continuously as a daemon thread, periodically removing expired
prompt contexts from the global tracking dictionary. This prevents
memory leaks from accumulating old prompt data.
"""
while True:
try:
current_time = time.time()
expired_ids = []
with self.lock:
for exec_id, prompt_data in self.active_prompts.items():
if (
current_time - prompt_data["timestamp"]
> self.prompt_timeout
):
expired_ids.append(exec_id)
for exec_id in expired_ids:
self.active_prompts.pop(exec_id, None)
if expired_ids:
self.logger.debug(f"Cleaned up {len(expired_ids)} expired prompts")
# Also prune expired entries from the batch queue
with self._queue_lock:
before = len(self._prompt_queue)
self._prompt_queue = [
ctx
for ctx in self._prompt_queue
if current_time - ctx.get("timestamp", 0) < self.prompt_timeout
]
pruned = before - len(self._prompt_queue)
if pruned:
self.logger.debug(f"Pruned {pruned} expired queue entries")
time.sleep(self.cleanup_interval)
except Exception as e:
self.logger.error(f"Error in cleanup thread: {e}")
time.sleep(60) # Wait a minute before retrying
def get_active_prompts(self) -> Dict[str, Dict[str, Any]]:
"""
Get all currently active prompts.
Returns:
Dictionary mapping execution IDs to prompt context dictionaries.
This is a copy of the internal tracking dictionary.
"""
with self.lock:
return self.active_prompts.copy()
def clear_all_active_prompts(self) -> int:
"""
Clear all active prompts.
Removes all prompt contexts from both global and thread-local storage.
This is useful before batch operations or when resetting the tracking state.
Returns:
Number of prompts that were cleared from global tracking
"""
with self.lock:
cleared_count = len(self.active_prompts)
self.active_prompts.clear()
# Clear thread-local storage as well
self._local.current_prompt = None
self.logger.debug(f"Cleared {cleared_count} active prompts")
return cleared_count
def get_status(self) -> Dict[str, Any]:
"""
Get tracker status information.
Returns:
Dictionary containing:
- active_prompts_count: Number of currently active prompts
- current_prompt_id: ID of current prompt in this thread (if any)
- current_execution_id: Execution ID of current prompt in this thread (if any)
- thread_id: Current thread identifier
- prompt_timeout: Configured prompt timeout in seconds
- cleanup_interval: Configured cleanup interval in seconds
"""
with self.lock:
active_count = len(self.active_prompts)
current_prompt = self.get_current_prompt()
return {
"active_prompts_count": active_count,
"current_prompt_id": current_prompt["id"] if current_prompt else None,
"current_execution_id": (
current_prompt["execution_id"] if current_prompt else None
),
"thread_id": threading.current_thread().ident,
"prompt_timeout": self.prompt_timeout,
"cleanup_interval": self.cleanup_interval,
}
class PromptExecutionContext:
"""Context manager for prompt executions.
This context manager provides automatic prompt lifecycle management,
ensuring prompt context is properly set and cleaned up. While the current
implementation doesn't automatically clear prompts on exit (to allow for
images generated after prompt execution), it provides a clean interface
for prompt management.
Example:
with PromptExecutionContext(tracker, "beautiful landscape") as exec_id:
# Generate images here
# Images will be linked to this prompt context
pass
# Prompt context remains active for timeout period
"""
def __init__(self, prompt_tracker: PromptTracker, prompt_text: str, **kwargs):
"""
Initialize execution context.
Args:
prompt_tracker: PromptTracker instance to use for tracking
prompt_text: The prompt text to track
**kwargs: Additional prompt metadata to include in the context
"""
self.prompt_tracker = prompt_tracker
self.prompt_text = prompt_text
self.additional_data = kwargs
self.execution_id = None
def __enter__(self):
"""Enter the execution context.
Sets the current prompt in the tracker and returns the execution ID.
Returns:
Unique execution ID for this prompt
"""
self.execution_id = self.prompt_tracker.set_current_prompt(
self.prompt_text, self.additional_data
)
return self.execution_id
def __exit__(self, exc_type, exc_val, exc_tb):
"""Exit the execution context.
Currently does not clear the prompt context to allow images to be
generated after prompt execution completes. The prompt will be cleaned
up automatically after the timeout period.
Args:
exc_type: Exception type (if an exception occurred)
exc_val: Exception value (if an exception occurred)
exc_tb: Exception traceback (if an exception occurred)
"""
# Don't clear immediately - let the timeout handle it
# This allows images to be generated after the prompt execution completes
pass
# Singleton instance management
_tracker_instance: Optional[PromptTracker] = None
_tracker_lock = threading.Lock()
def get_prompt_tracker(db_manager) -> PromptTracker:
"""Get or create the singleton PromptTracker instance.
This ensures only one PromptTracker exists across all PromptManager nodes,
preventing duplicate image linking when multiple nodes are used.
Args:
db_manager: Database manager instance for prompt operations
Returns:
The singleton PromptTracker instance
"""
global _tracker_instance
if _tracker_instance is None:
with _tracker_lock:
if _tracker_instance is None:
_tracker_instance = PromptTracker(db_manager)
return _tracker_instance