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Add human-in-the-loop survey helper (GUI + CLI + Agent tool) (#181)

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* 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>
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nic 2026-03-17 04:02:15 -05:00 • committed by GitHub
parent 37e696b104
commit b7efe4992a
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9 changed files with 526 additions and 49 deletions
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86
python/tools/survey_helper.py
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@ -4,7 +4,15 @@ from python.helpers.tool import Tool, Response
from python.survey_assistant.browser_render import render_url_async
from python.survey_assistant.extract import extract_form_fields
from python.survey_assistant.profile import SurveyProfile
from python.survey_assistant.llm import ollama_available, suggest_answers_with_ollama
from python.survey_assistant.llm import ollama_available, predict_answers_json_with_ollama
from python.survey_assistant.predictions import (
DEFAULT_PREDICTIONS_PATH,
PredictionRecord,
Candidate,
append_predictions,
build_question_id,
utc_now_iso,
)
class SurveyHelper(Tool):
@ -20,6 +28,9 @@ class SurveyHelper(Tool):
html: str = "",
include_suggestions: bool = False,
ollama_model: str = "llama3",
top_k: int = 3,
record_predictions: bool = False,
predictions_path: str = str(DEFAULT_PREDICTIONS_PATH),
**kwargs,
) -> Response:
if not url and not html:
@ -46,31 +57,68 @@ class SurveyHelper(Tool):
if include_suggestions:
profile = SurveyProfile.load()
questions_lines = []
for i, f in enumerate(fields, start=1):
label = f.label or f.name or f.id or "(unlabeled)"
t = f.input_type or f.kind
req = " (required)" if f.required else ""
questions_lines.append(f"{i}. {label} — {t}{req}")
if f.options:
for opt in f.options[:30]:
questions_lines.append(f" - {opt.get('label')}")
if len(f.options) > 30:
questions_lines.append(" - ...")
if ollama_available():
try:
payload["suggestions"] = suggest_answers_with_ollama(
pred = predict_answers_json_with_ollama(
model=ollama_model,
questions_text="\n".join(questions_lines),
url=final_url,
title=page_title,
fields_json=json.dumps([f.to_dict() for f in fields], ensure_ascii=False),
profile_json=json.dumps(profile.as_dict(), indent=2, ensure_ascii=False),
top_k=max(1, min(8, int(top_k or 3))),
)
payload["predictions"] = pred.get("predictions", [])
if pred.get("error"):
payload["predictions_error"] = pred.get("error")
payload["predictions_raw"] = pred.get("raw")
if record_predictions and isinstance(payload.get("predictions"), list):
records: list[PredictionRecord] = []
for item in payload["predictions"]:
try:
idx = int(item.get("field_index"))
except Exception:
continue
if idx < 1 or idx > len(fields):
continue
if not bool(item.get("needs_clarification")):
continue
field_dict = fields[idx - 1].to_dict()
qid = build_question_id(url=final_url, field=field_dict)
cand_objs: list[Candidate] = []
for c in (item.get("candidates") or [])[: max(1, min(10, top_k))]:
try:
cand_objs.append(
Candidate(
value=str(c.get("value", "")),
confidence=float(c.get("confidence", 0.0)),
)
)
except Exception:
continue
records.append(
PredictionRecord(
id=qid,
timestamp=utc_now_iso(),
url=final_url,
title=page_title,
field_index=idx,
field=field_dict,
selected=str(item.get("selected", "")),
confidence=float(item.get("confidence", 0.0) or 0.0),
candidates=cand_objs,
rationale=str(item.get("rationale", "")),
needs_clarification=True,
source="llm",
)
)
if records:
p = append_predictions(records, path=predictions_path)
payload["recorded_predictions_path"] = str(p)
except Exception as exc:
payload["suggestions_error"] = str(exc)
payload["predictions_error"] = str(exc)
else:
payload["suggestions_error"] = (
"Ollama not available at http://localhost:11434"
)
payload["predictions_error"] = "Ollama not available at http://localhost:11434"
return Response(message=json.dumps(payload, indent=2, ensure_ascii=False), break_loop=False)