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from pathlib import Path
import json
import re
from .io_utils import save_json
# Replace with your real LLM runner
from .run_two_phase import call_model
def slugify(name: str) -> str:
s = re.sub(r"[^a-zA-Z0-9]+", "_", name.strip()).strip("_").lower()
return s or "scenario"
def build_pack_from_scenario(root_dir: Path, scenario_text: str, pack_name_hint: str = "") -> Path:
"""
1) Calls extractor prompts to create inputs.json from free text.
2) Writes default constraints.json and schema.json.
3) Returns the created pack path.
"""
prompts_dir = root_dir / "prompts"
extractor_sys = (prompts_dir / "extractor_system.txt").read_text(encoding="utf-8")
extractor_user_tmpl = (prompts_dir / "extractor_user_template.txt").read_text(encoding="utf-8")
user_prompt = extractor_user_tmpl.replace("{SCENARIO_TEXT}", scenario_text)
raw = call_model(extractor_sys, user_prompt)
try:
extracted = json.loads(raw)
except Exception as e:
raise ValueError(f"Extractor produced non-JSON output.\nRaw:\n{raw}") from e
# Decide pack name
hint = pack_name_hint or extracted.get("context") or "scenario"
pack_slug = slugify(hint)[:48]
pack_dir = root_dir / "packs" / pack_slug
pack_dir.mkdir(parents=True, exist_ok=True)
# inputs.json
save_json(pack_dir / "inputs.json", extracted)
# constraints.json (default, can be edited later)
default_constraints = {
"analytics_first": True,
"require_longitudinal": True,
"indicator_priority": ["obesity_pct", "metabolic_syndrome_pct", "membership_2021"],
"capacity_formula": "teams * clients_per_team_per_day * working_days",
"cost_formula": "startup_per_client + ongoing_per_client",
"prioritization_rule": "Sort by membership_2021 and comorbidity prevalence; tie-breaker: infrastructure"
}
save_json(pack_dir / "constraints.json", default_constraints)
# schema.json (points to global analysis schema)
save_json(pack_dir / "schema.json", {
"archetypes": ["burden_prioritization", "capacity", "cost", "outcomes", "optimization"],
"output_schema": "schemas/analysis_output.schema.json"
})
# placeholders to support pipeline (optional for new packs)
save_json(pack_dir / "rubric.json", {
"set_equals": [],
"must_contain": [],
"numeric_equals": []
})
save_json(pack_dir / "expected.json", {
"note": "Optional gold output for regression tests. Populate when available."
})
# empty clarifications; you can fill interactively or with a UI later
save_json(pack_dir / "clarifications.json", {
"_note": "Populate with answers to Phase 1 clarification questions if you want batch runs."
})
return pack_dir
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