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"""
CASCADE Color Logging Example
Shows how to integrate beautiful colored logs throughout your system.
"""
from .kleene_logger import get_kleene_logger, LogLevel
from .interpretive_logger import get_interpretive_logger, ImpactLevel
def example_data_processing():
"""Example: Data processing with beautiful logs"""
kleene = get_kleene_logger("DataProcessor")
interpretive = get_interpretive_logger("Data Pipeline")
# Start processing
kleene.log(LogLevel.INFO, "load_dataset_start",
state_before={"dataset": "smollm3-blueprint.pdf"})
interpretive.log(ImpactLevel.LOW, "DataLoader", "Loading dataset",
context="Reading PDF file for analysis",
consequence="Will extract text and metadata",
metrics={"file_size": "1.0MB", "type": "PDF"})
# Processing steps
kleene.log(LogLevel.DEBUG, "extract_text",
state_before={"page": 1},
state_after={"pages_processed": 15})
# Fixed point reached
kleene.log(LogLevel.INFO, "processing_complete",
state_after={"records": 500, "clean": True},
fixed_point=True,
iterations=3)
interpretive.log(ImpactLevel.MEDIUM, "DataProcessor", "Processing complete",
context="Successfully extracted and cleaned data",
consequence="Ready for forensics analysis",
metrics={"records": 500, "pages": 15, "errors": 0})
def example_model_observation():
"""Example: Model observation with beautiful logs"""
kleene = get_kleene_logger("ModelObserver")
interpretive = get_interpretive_logger("Model Observatory")
# Model loading
kleene.log(LogLevel.INFO, "model_load_start",
state_before={"model": "mistralai/Mixtral-8x22B-Instruct-v0.1"})
interpretive.log(ImpactLevel.MEDIUM, "ModelLoader", "Loading Mixtral",
context="Loading 8x22B MoE model for inference",
consequence="Will consume significant VRAM",
metrics={"params": "141B", "active": "39B", "device": "cuda"})
# Observation
kleene.log(LogLevel.INFO, "observation_start",
state_before={"layers": 0, "hash": "initial"})
# Fixed point achieved
kleene.log(LogLevel.INFO, "observation_fixed_point",
state_after={"layers": 64, "merkle": "abc123..."},
fixed_point=True,
iterations=64)
interpretive.log(ImpactLevel.LOW, "CASCADE", "Model observed",
context="Cryptographic proof generated for model execution",
consequence="Merkle root provides verifiable audit trail",
metrics={"model": "Mixtral", "layers": 64, "merkle": "abc123..."})
def example_error_handling():
"""Example: Error handling with colored logs"""
kleene = get_kleene_logger("ErrorHandler")
interpretive = get_interpretive_logger("System Monitor")
# Error detected
kleene.log(LogLevel.ERROR, "memory_exhaustion",
state_before={"memory": "15.8/16GB", "operation": "inference"},
fixed_point=False)
interpretive.log(ImpactLevel.HIGH, "MemoryManager", "Out of memory",
context="GPU memory exhausted during model inference",
consequence="Inference failed, system degraded",
metrics={"used": "15.8GB", "total": "16GB", "available": "200MB"},
recommendation="Enable gradient checkpointing or use smaller batch size")
# Recovery
kleene.log(LogLevel.WARNING, "fallback_activated",
state_after={"mode": "cpu_fallback", "batch_size": 1})
interpretive.log(ImpactLevel.MEDIUM, "FallbackHandler", "CPU fallback activated",
context="Switched to CPU inference due to memory constraints",
consequence="Performance degraded but functionality preserved",
metrics={"device": "cpu", "batch_size": 1, "slowdown": "10x"})
# Run all examples
if __name__ == "__main__":
print("\n🎨 CASCADE Color Logging Examples\n")
print("="*60)
example_data_processing()
print("\n" + "="*60)
example_model_observation()
print("\n" + "="*60)
example_error_handling()
print("\n" + "="*60)
print("\n✨ Beautiful logs are ready for production!")
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