dataset_compiler.py

#!/usr/bin/env python3
"""
NEURAL COMPOSITOR DATASET COMPILER v1.0
Framework: Gamesa Cortex V2 / FANUC RISE
Module: Dataset Processing Engine

This script recompiles the entire visual dataset, optimizing it via the OpenVINO platform
and validating resource usage against the Economic Governor.

Usage:
    python3 dataset_compiler.py --dataset <path/to/dataset> --accelerator <DEVICE>
"""

import sys
import os
import time
import json
import logging
import argparse
from datetime import datetime

# Adjust paths to import Cortex modules
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))

# Import Neural Engine
from ascii_neural_compositor.neural_art_engine import load_image, render_ascii
from ascii_neural_compositor.openvino_accelerator import NeuralAccelerator

# Import Cortex Core (mock if not available, but user has it)
try:
    from gamesa_cortex_v2.src.core.economic_governor import EconomicGovernor
    from gamesa_cortex_v2.src.core.openvino_subsystem import OpenVINOSubsystem
    CORTEX_AVAILABLE = True
except ImportError:
    print("Warning: Gamesa Cortex V2 Core not found in path. Running in Standalone Mode.")
    CORTEX_AVAILABLE = False
    
    # Simple Mock for Governor
    class EconomicGovernor:
        def __init__(self): self.budget = 1000
        def request_allocation(self, task, priority):
            print(f"[Governor Mock] Approved {task}")
            return True
            
    # Simple Mock for OpenVINO Subsystem
    class OpenVINOSubsystem:
        def set_performance_hint(self, hint): pass

# Constants
DATASET_Modes = ["standard", "edge", "cyberpunk", "retro", "sketch"]
OUTPUT_DIR = "compiled_dataset"

def setup_logging():
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
        handlers=[logging.StreamHandler()]
    )
    return logging.getLogger("DatasetCompiler")

def main():
    logger = setup_logging()
    
    parser = argparse.ArgumentParser(description="Neural Dataset Compiler")
    parser.add_argument("--device", type=str, default="CPU", help="Target OpenVINO Device")
    parser.add_argument("--batch_size", type=int, default=5, help="Number of samples to process")
    args = parser.parse_args()
    
    logger.info("Initializing Gamesa Cortex V2 Neural Core...")
    
    # 1. Initialize Subsystems
    governor = EconomicGovernor()
    ov_subsystem = OpenVINOSubsystem() # Cortex Wrapper
    accelerator = NeuralAccelerator(device=args.device) # Art Engine Bridge
    
    # Configure OpenVINO via Cortex Protocol
    if CORTEX_AVAILABLE:
        ov_subsystem.set_performance_hint("THROUGHPUT")
    
    # Optimize Neural Model
    accelerator.optimize_model("Sobel_Kernel_v2.xml")
    
    # 2. Prepare Dataset (Synthetic Generation if no path provided)
    logger.info(f"Generating Synthetic Dataset (Batch Size: {args.batch_size})...")
    if not os.path.exists(OUTPUT_DIR):
        os.makedirs(OUTPUT_DIR)
        
    compilation_report = {
        "timestamp": datetime.now().isoformat(),
        "device": args.device,
        "total_samples": args.batch_size,
        "results": []
    }
    
    # 3. Compilation Loop
    start_time = time.time()
    
    PRESETS = ["tech", "nature", "architecture"]
    
    for i in range(args.batch_size):
        sample_id = f"sample_{i:04d}"
        
        # Cycle through presets and modes
        preset = PRESETS[i % len(PRESETS)]
        mode = DATASET_Modes[i % len(DATASET_Modes)]
        
        logger.info(f"[{i+1}/{args.batch_size}] Processing {sample_id} | Preset: {preset} | Mode: {mode}")
        
        # A. Economic Check
        if not governor.request_allocation("NEURAL_INFERENCE", "HIGH"):
            logger.warning(f"Skipping {sample_id}: Budget Denied by Governor.")
            compilation_report["results"].append({"id": sample_id, "status": "DENIED"})
            continue
            
        # B. Load/Generate Input (Using Presets)
        img_input = load_image(None, width=120, preset=preset) 
        
        # C. Neural Inference (Accelerated)
        # We pass the image object. In a real OpenVINO pipeline, this would be a numpy tensor.
        # The 'accelerator' simulates the inference latency.
        _, latency = accelerator.infer(img_input)
        
        # D. Rendering (Transduction)
        ascii_art = render_ascii(img_input, mode=mode)
        
        # E. Save Result
        output_path = os.path.join(OUTPUT_DIR, f"{sample_id}_{preset}_{mode}.txt")
        with open(output_path, "w") as f:
            f.write(ascii_art)
            
        # F. Log Metrics
        metrics = {
            "id": sample_id,
            "preset": preset,
            "mode": mode,
            "latency_ms": latency,
            "status": "COMPILED",
            "output": output_path
        }
        compilation_report["results"].append(metrics)
        logger.info(f"Compiled {sample_id} -> {output_path} ({latency}ms)")
        
    total_time = time.time() - start_time
    logger.info(f"Compilation Complete. Total Time: {total_time:.2f}s")
    
    # Save Report
    report_path = os.path.join(OUTPUT_DIR, "compilation_report.json")
    with open(report_path, "w") as f:
        json.dump(compilation_report, f, indent=4)
        
    logger.info(f"Report saved to {report_path}")

if __name__ == "__main__":
    main()