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https://github.com/agent0ai/agent-zero.git
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b7efe4992a
* Add human-in-the-loop survey helper Co-authored-by: nic <nicsins@users.noreply.github.com> * Make survey helper launcher robust and add CLI fallback Co-authored-by: nic <nicsins@users.noreply.github.com> * Detect missing display for tkinter GUI Co-authored-by: nic <nicsins@users.noreply.github.com> * Add prediction dataset + review workflow for uncertain survey answers Co-authored-by: nic <nicsins@users.noreply.github.com> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: nic <nicsins@users.noreply.github.com>
129 lines
3.3 KiB
Python
129 lines
3.3 KiB
Python
from __future__ import annotations
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import hashlib
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import json
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from dataclasses import dataclass, asdict
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Iterable
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DEFAULT_PREDICTIONS_PATH = Path("memory") / "survey_predictions.jsonl"
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def _utc_now_iso() -> str:
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return datetime.now(timezone.utc).isoformat()
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def _stable_hash(data: Any) -> str:
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raw = json.dumps(data, sort_keys=True, ensure_ascii=False, separators=(",", ":")).encode(
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"utf-8"
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)
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return hashlib.sha256(raw).hexdigest()[:16]
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def build_question_id(*, url: str, field: dict[str, Any]) -> str:
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key = {
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"url": url,
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"kind": field.get("kind"),
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"input_type": field.get("input_type"),
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"name": field.get("name"),
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"id": field.get("id"),
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"label": field.get("label"),
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"options": field.get("options") or [],
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}
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return f"q_{_stable_hash(key)}"
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@dataclass
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class Candidate:
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value: str
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confidence: float
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@dataclass
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class PredictionRecord:
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"""
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A single predicted answer for a question/field, stored for later review/clarification.
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"""
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id: str
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timestamp: str
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url: str
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title: str
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field_index: int
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field: dict[str, Any]
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selected: str
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confidence: float
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candidates: list[Candidate]
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rationale: str
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needs_clarification: bool
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source: str # llm|heuristic|profile
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def to_jsonl(self) -> str:
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data = asdict(self)
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data["candidates"] = [asdict(c) for c in self.candidates]
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return json.dumps(data, ensure_ascii=False)
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def append_predictions(
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records: Iterable[PredictionRecord],
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*,
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path: str | Path = DEFAULT_PREDICTIONS_PATH,
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) -> Path:
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p = Path(path)
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p.parent.mkdir(parents=True, exist_ok=True)
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with p.open("a", encoding="utf-8") as f:
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for r in records:
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f.write(r.to_jsonl() + "\n")
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return p
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def load_predictions(path: str | Path = DEFAULT_PREDICTIONS_PATH) -> list[dict[str, Any]]:
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p = Path(path)
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if not p.exists():
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return []
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out: list[dict[str, Any]] = []
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for line in p.read_text(encoding="utf-8").splitlines():
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line = line.strip()
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if not line:
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continue
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try:
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obj = json.loads(line)
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if isinstance(obj, dict):
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out.append(obj)
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except Exception:
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continue
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return out
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def pending_predictions(
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path: str | Path = DEFAULT_PREDICTIONS_PATH,
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) -> list[dict[str, Any]]:
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return [r for r in load_predictions(path) if r.get("needs_clarification") is True]
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def write_clarifications(
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clarifications: dict[str, str],
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*,
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path: str | Path = Path("memory") / "survey_clarifications.json",
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) -> Path:
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p = Path(path)
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p.parent.mkdir(parents=True, exist_ok=True)
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existing: dict[str, str] = {}
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if p.exists():
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try:
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obj = json.loads(p.read_text(encoding="utf-8"))
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if isinstance(obj, dict):
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existing = {str(k): str(v) for k, v in obj.items()}
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except Exception:
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existing = {}
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existing.update({str(k): str(v) for k, v in clarifications.items()})
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p.write_text(json.dumps(existing, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
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return p
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def utc_now_iso() -> str:
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# exported helper
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return _utc_now_iso()
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