Atomizer/examples/llm_mode_simple_example.py

"""
Simple Example: Using LLM Mode for Optimization

This example demonstrates the LLM-native workflow WITHOUT requiring a JSON config file.
You describe your optimization problem in natural language, and the system generates
all the necessary extractors, hooks, and optimization code automatically.

Phase 3.2 Integration - Task 1.3: Minimal Working Example

Requirements:
- Beam.prt and Beam_sim1.sim in studies/simple_beam_optimization/1_setup/model/
- Claude Code running (no API key needed)
- test_env activated

Author: Antoine Letarte
Date: 2025-11-17
"""

import subprocess
import sys
from pathlib import Path

# Add parent directory to path
sys.path.insert(0, str(Path(__file__).parent.parent))


def run_llm_optimization_example():
    """
    Run a simple LLM-mode optimization example.

    This demonstrates the complete Phase 3.2 integration:
    1. Natural language request
    2. LLM workflow analysis
    3. Auto-generated extractors
    4. Auto-generated hooks
    5. Optimization with Optuna
    6. Results and plots
    """
    print("=" * 80)
    print("PHASE 3.2 INTEGRATION: LLM MODE EXAMPLE")
    print("=" * 80)
    print()

    # Natural language optimization request
    request = """
    Minimize displacement and mass while keeping stress below 200 MPa.

    Design variables:
    - beam_half_core_thickness: 15 to 30 mm
    - beam_face_thickness: 15 to 30 mm

    Run 5 trials using TPE sampler.
    """

    print("Natural Language Request:")
    print(request)
    print()

    # File paths
    study_dir = Path(__file__).parent.parent / "studies" / "simple_beam_optimization"
    prt_file = study_dir / "1_setup" / "model" / "Beam.prt"
    sim_file = study_dir / "1_setup" / "model" / "Beam_sim1.sim"
    output_dir = study_dir / "2_substudies" / "06_llm_mode_example_5trials"

    if not prt_file.exists():
        print(f"ERROR: Part file not found: {prt_file}")
        print("Please ensure the simple_beam_optimization study is set up.")
        return False

    if not sim_file.exists():
        print(f"ERROR: Simulation file not found: {sim_file}")
        return False

    print("Configuration:")
    print(f"  Part file: {prt_file}")
    print(f"  Simulation file: {sim_file}")
    print(f"  Output directory: {output_dir}")
    print()

    # Build command - use test_env python
    python_exe = "c:/Users/antoi/anaconda3/envs/test_env/python.exe"

    cmd = [
        python_exe,
        "optimization_engine/run_optimization.py",
        "--llm", request,
        "--prt", str(prt_file),
        "--sim", str(sim_file),
        "--output", str(output_dir.parent),
        "--study-name", "06_llm_mode_example_5trials",
        "--trials", "5"
    ]

    print("Running LLM Mode Optimization...")
    print("Command:")
    print(" ".join(cmd))
    print()
    print("=" * 80)
    print()

    # Run the command
    try:
        result = subprocess.run(cmd, check=True)

        print()
        print("=" * 80)
        print("SUCCESS: LLM Mode Optimization Complete!")
        print("=" * 80)
        print()
        print("Results saved to:")
        print(f"  {output_dir}")
        print()
        print("What was auto-generated:")
        print("  ✓ Result extractors (displacement, stress, mass)")
        print("  ✓ Inline calculations (safety factor, objectives)")
        print("  ✓ Post-processing hooks (plotting, reporting)")
        print("  ✓ Optuna objective function")
        print()
        print("Check the output directory for:")
        print("  - generated_extractors/ - Auto-generated Python extractors")
        print("  - generated_hooks/ - Auto-generated hook scripts")
        print("  - history.json - Optimization history")
        print("  - best_trial.json - Best design found")
        print("  - plots/ - Convergence and design space plots (if enabled)")
        print()

        return True

    except subprocess.CalledProcessError as e:
        print()
        print("=" * 80)
        print(f"FAILED: Optimization failed with error code {e.returncode}")
        print("=" * 80)
        print()
        return False

    except Exception as e:
        print()
        print("=" * 80)
        print(f"ERROR: {e}")
        print("=" * 80)
        print()
        import traceback
        traceback.print_exc()
        return False


def main():
    """Main entry point."""
    print()
    print("This example demonstrates the LLM-native optimization workflow.")
    print()
    print("IMPORTANT: This uses Claude Code integration (no API key needed).")
    print("Make sure Claude Code is running and test_env is activated.")
    print()

    input("Press ENTER to continue (or Ctrl+C to cancel)...")
    print()

    success = run_llm_optimization_example()

    if success:
        print()
        print("=" * 80)
        print("EXAMPLE COMPLETED SUCCESSFULLY!")
        print("=" * 80)
        print()
        print("Next Steps:")
        print("1. Review the generated extractors in the output directory")
        print("2. Examine the optimization history in history.json")
        print("3. Check the plots/ directory for visualizations")
        print("4. Try modifying the natural language request and re-running")
        print()
        print("This demonstrates Phase 3.2 integration:")
        print("  Natural Language → LLM → Code Generation → Optimization → Results")
        print()
    else:
        print()
        print("Example failed. Please check the error messages above.")
        print()

    return success


if __name__ == '__main__':
    success = main()
    sys.exit(0 if success else 1)