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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>
124 lines
5.3 KiB
Python
124 lines
5.3 KiB
Python
import json
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from python.helpers.tool import Tool, Response
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from python.survey_assistant.browser_render import render_url_async
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from python.survey_assistant.extract import extract_form_fields
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from python.survey_assistant.profile import SurveyProfile
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from python.survey_assistant.llm import ollama_available, predict_answers_json_with_ollama
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from python.survey_assistant.predictions import (
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DEFAULT_PREDICTIONS_PATH,
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PredictionRecord,
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Candidate,
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append_predictions,
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build_question_id,
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utc_now_iso,
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)
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class SurveyHelper(Tool):
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"""
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Extract survey/form questions and optionally suggest answers from a saved user profile.
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Safety: this tool does not fill or submit forms. It only extracts and suggests.
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"""
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async def execute(
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self,
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url: str = "",
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html: str = "",
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include_suggestions: bool = False,
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ollama_model: str = "llama3",
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top_k: int = 3,
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record_predictions: bool = False,
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predictions_path: str = str(DEFAULT_PREDICTIONS_PATH),
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**kwargs,
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) -> Response:
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if not url and not html:
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return Response(
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message="Error: Provide either 'url' or 'html'.",
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break_loop=False,
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)
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page_title = ""
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final_url = url
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if url:
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rr = await render_url_async(url, screenshot_path=None)
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html = rr.html
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page_title = rr.title
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final_url = rr.final_url
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fields = extract_form_fields(html or "")
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payload = {
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"url": final_url,
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"title": page_title,
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"field_count": len(fields),
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"fields": [f.to_dict() for f in fields],
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}
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if include_suggestions:
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profile = SurveyProfile.load()
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if ollama_available():
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try:
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pred = predict_answers_json_with_ollama(
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model=ollama_model,
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url=final_url,
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title=page_title,
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fields_json=json.dumps([f.to_dict() for f in fields], ensure_ascii=False),
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profile_json=json.dumps(profile.as_dict(), indent=2, ensure_ascii=False),
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top_k=max(1, min(8, int(top_k or 3))),
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)
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payload["predictions"] = pred.get("predictions", [])
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if pred.get("error"):
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payload["predictions_error"] = pred.get("error")
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payload["predictions_raw"] = pred.get("raw")
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if record_predictions and isinstance(payload.get("predictions"), list):
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records: list[PredictionRecord] = []
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for item in payload["predictions"]:
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try:
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idx = int(item.get("field_index"))
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except Exception:
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continue
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if idx < 1 or idx > len(fields):
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continue
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if not bool(item.get("needs_clarification")):
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continue
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field_dict = fields[idx - 1].to_dict()
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qid = build_question_id(url=final_url, field=field_dict)
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cand_objs: list[Candidate] = []
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for c in (item.get("candidates") or [])[: max(1, min(10, top_k))]:
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try:
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cand_objs.append(
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Candidate(
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value=str(c.get("value", "")),
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confidence=float(c.get("confidence", 0.0)),
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)
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)
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except Exception:
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continue
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records.append(
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PredictionRecord(
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id=qid,
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timestamp=utc_now_iso(),
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url=final_url,
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title=page_title,
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field_index=idx,
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field=field_dict,
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selected=str(item.get("selected", "")),
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confidence=float(item.get("confidence", 0.0) or 0.0),
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candidates=cand_objs,
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rationale=str(item.get("rationale", "")),
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needs_clarification=True,
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source="llm",
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)
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)
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if records:
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p = append_predictions(records, path=predictions_path)
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payload["recorded_predictions_path"] = str(p)
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except Exception as exc:
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payload["predictions_error"] = str(exc)
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else:
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payload["predictions_error"] = "Ollama not available at http://localhost:11434"
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return Response(message=json.dumps(payload, indent=2, ensure_ascii=False), break_loop=False)
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