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f6771af da39ea8 f6771af da39ea8 f6771af | 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 | """Process-wide singletons."""
from __future__ import annotations
import os
from pathlib import Path
from ..case_loader import CaseData, Witness, GroundTruth, load_case
from ..case_state import CaseState
_CASES: dict[str, CaseData] | None = None
_SESSIONS: dict[str, CaseState] = {}
_WITNESS_ENGINE = None
def cases() -> dict[str, CaseData]:
global _CASES
if _CASES is None:
_CASES = {}
for p in sorted(Path("cases").glob("case_*.yaml")):
c = load_case(p)
_CASES[c.case_id] = c
return _CASES
def sessions() -> dict[str, CaseState]:
return _SESSIONS
def set_witness_engine(engine) -> None:
global _WITNESS_ENGINE
_WITNESS_ENGINE = engine
def witness_engine():
return _WITNESS_ENGINE
def init_real_witness_engine():
"""Pick backend by WITNESS_BACKEND env var.
- "providers" (default): HF Inference Providers, no local model download.
- "llama_cpp": local llama-cpp-python, needs the GGUF on disk.
"""
backend = os.environ.get("WITNESS_BACKEND", "providers").lower()
if backend == "llama_cpp":
from ..witness_engine import WitnessEngine, load_llama
from .. import MODEL_PATH
if not MODEL_PATH.exists():
raise RuntimeError(
f"model not found at {MODEL_PATH}; run scripts/download_model.py"
)
set_witness_engine(WitnessEngine(load_llama(str(MODEL_PATH))))
else:
from ..witness_engine import InferenceProvidersWitnessEngine
set_witness_engine(InferenceProvidersWitnessEngine())
def add_case_from_dict(d: dict) -> CaseData:
"""Register a procedural case at runtime (Endless / Daily)."""
dock = list(d["dock"])
def _parse(raw: dict | None):
if not raw:
return {}
out = {}
for k, v in raw.items():
out[frozenset(s.strip() for s in str(k).split(","))] = float(v)
return out
ws = [Witness(
id=w["id"], name=w["name"], portrait_ref=w.get("portrait_ref", ""),
persona=w["persona"], secret_knowledge=w["secret_knowledge"],
reliability=float(w.get("reliability", 1.0)),
independence_group=w.get("independence_group"),
cost_days=int(w.get("cost_days", 1)),
is_physical_evidence=bool(w.get("is_physical_evidence", False)),
target_mass=_parse(w.get("target_mass")),
) for w in d["witnesses"]]
case = CaseData(
case_id=d["case_id"], title=d["title"], setting_blurb=d["setting_blurb"],
victim=d["victim"], dock=dock, court_days=int(d["court_days"]),
threshold=float(d["threshold"]),
methods_available=list(d["methods_available"]),
witnesses=ws,
ground_truth=GroundTruth(
culprit=d["ground_truth"]["culprit"],
accomplice=d["ground_truth"].get("accomplice"),
),
intended_path=d.get("intended_path", ""),
reveal_expectations=d.get("reveal_expectations", {}),
)
cases()[case.case_id] = case
return case
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