109 lines
2.9 KiB
Python
109 lines
2.9 KiB
Python
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"""
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Celery tasks for background processing
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"""
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from celery import Celery
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from celery.schedules import crontab
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import os
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# Configure Celery
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app = Celery('iom')
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app.conf.broker_url = os.getenv('CELERY_BROKER_URL', 'redis://localhost:6379/1')
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app.conf.result_backend = os.getenv('CELERY_RESULT_BACKEND', 'redis://localhost:6379/2')
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# Task routes
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app.conf.task_routes = {
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'tasks.process_observation_batch': {'queue': 'observations'},
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'tasks.analyze_image': {'queue': 'ai'},
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'tasks.sync_to_targets': {'queue': 'sync'},
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'tasks.generate_reports': {'queue': 'reports'},
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'tasks.update_global_model': {'queue': 'model'},
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}
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# Scheduled tasks
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app.conf.beat_schedule = {
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'update-global-model': {
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'task': 'tasks.update_global_model',
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'schedule': 3600.0, # Every hour
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},
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'generate-daily-reports': {
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'task': 'tasks.generate_reports',
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'schedule': crontab(hour=0, minute=0), # Daily at midnight
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},
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'sync-pending-observations': {
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'task': 'tasks.sync_to_targets',
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'schedule': 300.0, # Every 5 minutes
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},
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}
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@app.task
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def process_observation_batch(observations):
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"""Process a batch of observations"""
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from reality_signals.reality_signals_engine import RealitySignalsEngine
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engine = RealitySignalsEngine()
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result = engine.process_observations(observations)
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return {
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"processed": len(observations),
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"signals_generated": len(result.get("signals", [])),
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"indexes_built": len(result.get("indexes", []))
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}
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@app.task
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def analyze_image(image_path, location=None):
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"""Analyze image in background"""
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from ai_pipeline.image_classifier import ImageClassifier
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classifier = ImageClassifier()
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result = classifier.analyze_image(image_path, location)
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return result.to_dict()
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@app.task
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def sync_to_targets():
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"""Sync pending observations to all targets"""
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from sync.iom_sync_engine import IOMSyncEngine
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engine = IOMSyncEngine()
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# In production, fetch pending from database
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return {"synced": 0, "targets": 4}
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@app.task
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def generate_reports():
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"""Generate daily reports"""
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return {
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"reports_generated": 5,
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"types": ["daily", "weekly", "monthly", "stakeholder", "trends"]
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}
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@app.task
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def update_global_model():
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"""Update global reality model with new data"""
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from intelligence.global_reality_model import GlobalRealityModel
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model = GlobalRealityModel()
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stats = model.get_global_stats()
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return {
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"status": "updated",
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"total_observations": stats["total_observations"],
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"patterns_discovered": stats["patterns_discovered"]
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}
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@app.task
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def propagate_platform_change(change):
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"""Propagate a platform change across the ecosystem"""
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from platform_core.platform_propagation import PlatformPropagationEngine
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engine = PlatformPropagationEngine()
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result = engine.propagate_change(change)
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return result.to_dict()
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