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https://github.com/pacnpal/Pac-cogs.git
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063258513e
Updating queue_processor.py to use the shared types Updating manager.py to use the correct imports and shared types The cyclic dependency has been resolved by: Moving shared types to a separate module Having queue_processor.py only import from shared modules Having manager.py use the QueueProcessor from queue/processor.py
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
"""Queue processing functionality for video processing"""
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import logging
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import asyncio
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from typing import List, Optional, Dict, Any, Set, ClassVar
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from datetime import datetime
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from ..queue.types import QueuePriority, QueueMetrics, ProcessingMetrics
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from ..queue.models import QueueItem
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logger = logging.getLogger("VideoArchiver")
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class QueueProcessor:
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"""Handles processing of video queue items"""
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_active_items: ClassVar[Set[int]] = set()
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_processing_lock: ClassVar[asyncio.Lock] = asyncio.Lock()
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def __init__(self):
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self._metrics = ProcessingMetrics()
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async def process_item(self, item: QueueItem) -> bool:
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"""
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Process a single queue item
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Args:
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item: Queue item to process
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Returns:
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bool: Success status
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"""
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if item.id in self._active_items:
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logger.warning(f"Item {item.id} is already being processed")
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return False
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try:
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self._active_items.add(item.id)
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start_time = datetime.now()
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# Process item logic here
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# Placeholder for actual video processing
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await asyncio.sleep(1)
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processing_time = (datetime.now() - start_time).total_seconds()
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self._update_metrics(processing_time, True, item.size)
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return True
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except Exception as e:
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logger.error(f"Error processing item {item.id}: {str(e)}")
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self._update_metrics(0, False, 0)
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return False
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finally:
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self._active_items.remove(item.id)
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def _update_metrics(self, processing_time: float, success: bool, size: int) -> None:
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"""Update processing metrics"""
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if success:
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self._metrics.record_success(processing_time)
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else:
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self._metrics.record_failure("Processing error")
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def get_metrics(self) -> QueueMetrics:
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"""Get current processing metrics"""
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total = self._metrics.total_processed
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if total == 0:
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return QueueMetrics(
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total_items=0,
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processing_time=0,
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success_rate=0,
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error_rate=0,
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average_size=0,
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)
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return QueueMetrics(
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total_items=total,
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processing_time=self._metrics.avg_processing_time,
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success_rate=self._metrics.successful / total,
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error_rate=self._metrics.failed / total,
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average_size=0, # This would need to be tracked separately if needed
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)
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