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zeekay
1b367e2b62
The tree was a debranded snapshot of upstream taken around 2026-02-20, and upstream has moved about five months since. This replaces it with upstream master, mechanically re-derived: bin/debrand does the rename, and the pipeline and squashed migrations are restored from the shipped tree. Not a merge. The previous import replaced the tree wholesale in a single commit, so there is no shared content history to merge against. Reproducing the rename on the new tree is what actually converges. What survives: .hanzo/workflows, the Dockerfiles and bin/docker* entrypoints, compose files, LLM.md, and insights/migrations. The squashed initial migration is load-bearing — the live database records it by name, so upstream's ~918 individual migrations cannot come back. Nine products are dropped as unreferenced. Twenty-one more that we do not run are kept anyway because the core app still imports them; unpicking those is a refactor and does not belong in a rebase. This base does not build yet, and the reasons are not in the rename: upstream now wants Python 3.13.13 against our 3.12.12 and Django 5.2 against our 4.2, and the dependency manifests still name upstream packages that we republish under our own names. Those are decisions, not renames.
34 lines
1.5 KiB
Python
34 lines
1.5 KiB
Python
import re
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from .models import UserInterviewClassification
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# Vapi voice-interview transcripts are newline-delimited `AI:` / `User:` turns. Keep the
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# speaker vocabulary in sync with frontend/parseTranscript.ts (TURN_SPLIT_RE) — both sides
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# parse the same Vapi format independently.
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_USER_TURN_RE = re.compile(
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r"(?:^|\n)\s*(?:User|Interviewee):\s+(.+?)(?=\n\s*(?:AI|Assistant|Interviewer|User|Interviewee):|$)",
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re.IGNORECASE | re.DOTALL,
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)
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_AI_TURN_RE = re.compile(r"(?:^|\n)\s*(?:AI|Assistant|Interviewer):\s+", re.IGNORECASE)
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def _user_turns(transcript: str) -> list[str]:
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return [t.strip() for t in _USER_TURN_RE.findall(transcript) if t.strip()]
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def derive_auto_classifications(transcript: str) -> list[str]:
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"""Derive the mechanical classifications for a Vapi voice-interview transcript.
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Only `abandoned` is auto-derived — when the AI spoke but the interviewee never
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meaningfully did. `off-topic` needs human judgement and is never auto-set.
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Only the `AI:` / `User:` turn format (Vapi) is recognised. Transcripts in other formats
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(e.g. the audio-upload path's `#### Speaker N` headings) have no parseable turns, so we
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return no classifications rather than mislabel them `abandoned`.
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"""
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if _user_turns(transcript):
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return []
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# No interviewee turns. Only call it `abandoned` when the AI clearly spoke (a real
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# voice interview the interviewee dropped); an unrecognised format gets left untagged.
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return [UserInterviewClassification.ABANDONED] if _AI_TURN_RE.search(transcript) else []
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