File size: 8,024 Bytes
b233cf7 cfa3391 b233cf7 cfa3391 e8a073b cfa3391 e8a073b cfa3391 b233cf7 cfa3391 e8a073b cfa3391 b233cf7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | from __future__ import annotations
import argparse
import json
import logging
import sys
from pathlib import Path
from typing import Any
from .feedback import Critique, FeedbackLoopManager
from .inference_cloud import predict_cloud
from .optimizer import OptimizerConfig, FormulationOptimizer, CompositionTarget
logger = logging.getLogger("pino.cli")
def _load_formula(path: str) -> dict[str, Any]:
with Path(path).open("r", encoding="utf-8") as f:
return json.load(f)
def _predict(args: argparse.Namespace) -> int:
try:
formula = _load_formula(args.file)
except Exception as e:
logger.error("Failed to load formula file: %s", e)
return 2
try:
result = predict_cloud(formula, token=args.token)
except Exception as e:
logger.error("Prediction failed: %s", e)
return 2
if args.json_mode:
print(json.dumps(result, separators=(",", ":"), ensure_ascii=False))
else:
print("Predicted note pyramid and psychometrics:")
print(json.dumps(result, indent=2, ensure_ascii=False))
return 0
def _feedback(args: argparse.Namespace) -> int:
try:
critique = Critique(
formula_id=args.formula_id,
correct_gender=args.correct_gender,
correct_notes=json.loads(args.correct_notes) if args.correct_notes else {},
notes=args.notes or "",
)
manager = FeedbackLoopManager()
manager.append(critique)
except Exception as e:
logger.error("Failed to log feedback: %s", e)
return 2
if args.json_mode:
print(json.dumps({"status": "logged", "formula_id": args.formula_id}, separators=(",", ":")))
else:
print(f"Feedback logged for {args.formula_id}")
return 0
def _sync_flywheel(args: argparse.Namespace) -> int:
try:
manager = FeedbackLoopManager()
result = manager.sync_flywheel(job_config_path=args.job_config)
except Exception as e:
logger.error("Sync flywheel failed: %s", e)
return 2
if args.json_mode:
print(json.dumps(result, separators=(",", ":"), ensure_ascii=False))
else:
print(json.dumps(result, indent=2, ensure_ascii=False))
return 0 if result.get("action") != "none" or args.allow_empty else 0
def _compose(args: argparse.Namespace) -> int:
"""Run the generative composer (CMA-ES) to evolve a formula against a target."""
try:
target = CompositionTarget.from_json(args.target_json)
except Exception as e:
logger.error("Failed to load target brief: %s", e)
return 2
config = OptimizerConfig(
max_iterations=args.max_iterations,
allow_synthetic=args.allow_synthetic,
seed=args.seed,
token=args.token,
verbose=1 if args.json_mode else 1,
)
def progress(iteration: int, fitness: float, best: Any) -> None:
# Real-time progress to stderr, never to stdout when --json is set.
msg = f"[{iteration}/{args.max_iterations}] best fitness (MSE) = {fitness:.6f}"
if args.json_mode:
print(msg, file=sys.stderr)
else:
print(msg, file=sys.stderr)
seed_recipe = None
if args.seed_id:
from .ingest_formulas import load_literature_manifest, normalize_and_unpack_recipe_from_dict
try:
recipes = load_literature_manifest(args.literature_formulas)
recipe = next((r for r in recipes if r.get("formula_id") == args.seed_id), None)
if recipe is None:
logger.error("Seed recipe %s not found in %s", args.seed_id, args.literature_formulas)
return 2
seed_recipe = normalize_and_unpack_recipe_from_dict(recipe)
logger.info("Loaded literature seed recipe %s with %d components", args.seed_id, len(seed_recipe["components"]))
except Exception as e:
logger.error("Failed to load seed recipe: %s", e)
return 2
try:
optimizer = FormulationOptimizer(config)
best = optimizer.optimize(
target,
palette_size=args.palette_size,
progress_callback=progress,
seed_recipe=seed_recipe,
)
except Exception as e:
logger.error("Composition failed: %s", e)
return 2
output = {
"target": target.name,
"formula": best.to_formula_dict(),
"status": best.status,
"fitness": best.fitness,
"ifra_passed": best.ifra_report.get("passed", False),
"message": best.message,
}
if args.json_mode:
print(json.dumps(output, separators=(",", ":"), ensure_ascii=False))
else:
print(json.dumps(output, indent=2, ensure_ascii=False))
return 0
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(prog="pino-critic")
parser.add_argument("--token", default=None, help="Hugging Face access token")
subparsers = parser.add_subparsers(dest="command", required=True)
predict_parser = subparsers.add_parser("predict", help="Run cloud inference on a formula")
predict_parser.add_argument("--file", required=True, help="Path to formula JSON file")
predict_parser.add_argument("--json", action="store_true", dest="json_mode", help="Emit raw parseable JSON only")
predict_parser.set_defaults(func=_predict)
feedback_parser = subparsers.add_parser("feedback", help="Log a correction for a recipe")
feedback_parser.add_argument("--formula_id", required=True)
feedback_parser.add_argument("--correct_gender", type=float, default=None, help="-1.0 to +1.0")
feedback_parser.add_argument("--correct_notes", default="{}", help="JSON dict of index -> value")
feedback_parser.add_argument("--notes", default="", help="Free-form correction notes")
feedback_parser.add_argument("--json", action="store_true", dest="json_mode", help="Emit raw parseable JSON only")
feedback_parser.set_defaults(func=_feedback)
sync_parser = subparsers.add_parser("sync-flywheel", help="Merge feedback, push dataset, retrain")
sync_parser.add_argument("--job-config", default="hf_job.yaml")
sync_parser.add_argument("--allow-empty", action="store_true", help="Return 0 even if no feedback")
sync_parser.add_argument("--json", action="store_true", dest="json_mode", help="Emit raw parseable JSON only")
sync_parser.set_defaults(func=_sync_flywheel)
compose_parser = subparsers.add_parser("compose", help="Evolve a formula against a sensory target")
compose_parser.add_argument("--target-json", required=True, help="Path to JSON brief defining desired sensory characteristics")
compose_parser.add_argument("--max-iterations", type=int, default=500, help="Maximum generation cycles")
compose_parser.add_argument("--palette-size", type=int, default=20, help="Number of palette ingredients")
compose_parser.add_argument("--allow-synthetic", action="store_true", default=True, help="Allow non-renewable ingredients")
compose_parser.add_argument("--no-synthetic", action="store_false", dest="allow_synthetic", help="Restrict to renewable ingredients")
compose_parser.add_argument("--seed", type=int, default=None, help="Random seed")
compose_parser.add_argument("--seed-id", default=None, help="Literature recipe ID to seed the first generation")
compose_parser.add_argument("--literature-formulas", default="data/literature_formulas.json", help="Path to literature formula manifest")
compose_parser.add_argument("--json", action="store_true", dest="json_mode", help="Emit raw parseable JSON only")
compose_parser.set_defaults(func=_compose)
args = parser.parse_args(argv)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s %(levelname)s %(name)s: %(message)s",
)
try:
return args.func(args)
except Exception as e:
logger.exception("Unhandled runtime exception")
return 2
if __name__ == "__main__":
sys.exit(main())
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