"""
Celery tasks for background processing
"""

from celery import Celery
from celery.schedules import crontab
import os

# Configure Celery
app = Celery('iom')
app.conf.broker_url = os.getenv('CELERY_BROKER_URL', 'redis://localhost:6379/1')
app.conf.result_backend = os.getenv('CELERY_RESULT_BACKEND', 'redis://localhost:6379/2')

# Task routes
app.conf.task_routes = {
    'tasks.process_observation_batch': {'queue': 'observations'},
    'tasks.analyze_image': {'queue': 'ai'},
    'tasks.sync_to_targets': {'queue': 'sync'},
    'tasks.generate_reports': {'queue': 'reports'},
    'tasks.update_global_model': {'queue': 'model'},
}

# Scheduled tasks
app.conf.beat_schedule = {
    'update-global-model': {
        'task': 'tasks.update_global_model',
        'schedule': 3600.0,  # Every hour
    },
    'generate-daily-reports': {
        'task': 'tasks.generate_reports',
        'schedule': crontab(hour=0, minute=0),  # Daily at midnight
    },
    'sync-pending-observations': {
        'task': 'tasks.sync_to_targets',
        'schedule': 300.0,  # Every 5 minutes
    },
}


@app.task
def process_observation_batch(observations):
    """Process a batch of observations"""
    from reality_signals.reality_signals_engine import RealitySignalsEngine
    
    engine = RealitySignalsEngine()
    result = engine.process_observations(observations)
    
    return {
        "processed": len(observations),
        "signals_generated": len(result.get("signals", [])),
        "indexes_built": len(result.get("indexes", []))
    }


@app.task
def analyze_image(image_path, location=None):
    """Analyze image in background"""
    from ai_pipeline.image_classifier import ImageClassifier
    
    classifier = ImageClassifier()
    result = classifier.analyze_image(image_path, location)
    
    return result.to_dict()


@app.task
def sync_to_targets():
    """Sync pending observations to all targets"""
    from sync.iom_sync_engine import IOMSyncEngine
    
    engine = IOMSyncEngine()
    # In production, fetch pending from database
    return {"synced": 0, "targets": 4}


@app.task
def generate_reports():
    """Generate daily reports"""
    return {
        "reports_generated": 5,
        "types": ["daily", "weekly", "monthly", "stakeholder", "trends"]
    }


@app.task
def update_global_model():
    """Update global reality model with new data"""
    from intelligence.global_reality_model import GlobalRealityModel
    
    model = GlobalRealityModel()
    stats = model.get_global_stats()
    
    return {
        "status": "updated",
        "total_observations": stats["total_observations"],
        "patterns_discovered": stats["patterns_discovered"]
    }


@app.task
def propagate_platform_change(change):
    """Propagate a platform change across the ecosystem"""
    from platform_core.platform_propagation import PlatformPropagationEngine
    
    engine = PlatformPropagationEngine()
    result = engine.propagate_change(change)
    
    return result.to_dict()