pyr0ball
cd787a2509
Fixes a bug where ISO-formatted dates (e.g. '2023-01 – 2025-03') in the period field were split incorrectly. The old code replaced the en-dash with a hyphen first, then split on the first hyphen, causing dates like '2023-01' to be split into '2023' and '01' instead of the expected start/end pair. The fix splits on the dash/dash separator *before* normalizing to plain hyphens, ensuring round-trip conversion of dates with embedded hyphens. Adds two regression tests: - test_profile_to_library_period_split_iso_dates: verifies en-dash separation - test_profile_to_library_period_split_em_dash: verifies em-dash separation
217 lines
8 KiB
Python
217 lines
8 KiB
Python
"""
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Resume format transform — library ↔ profile.
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Converts between:
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- Library format: struct_json produced by resume_parser.parse_resume()
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{name, email, phone, career_summary, experience[{title,company,start_date,end_date,location,bullets[]}],
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education[{institution,degree,field,start_date,end_date}], skills[], achievements[]}
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- Profile content format: ResumePayload content fields (plain_text_resume.yaml)
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{name, surname, email, phone, career_summary,
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experience[{title,company,period,location,industry,responsibilities,skills[]}],
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education[{institution,degree,field,start_date,end_date}],
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skills[], achievements[]}
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Profile metadata fields (salary, work prefs, self-ID, PII) are never touched here.
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License: MIT
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"""
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from __future__ import annotations
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from datetime import date
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from typing import Any
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_CONTENT_FIELDS = frozenset({
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"name", "surname", "email", "phone", "career_summary",
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"experience", "skills", "education", "achievements",
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})
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def library_to_profile_content(struct_json: dict[str, Any]) -> dict[str, Any]:
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"""Transform a library struct_json to ResumePayload content fields.
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Returns only content fields. Caller is responsible for merging with existing
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metadata fields (salary, preferences, self-ID) so they are not overwritten.
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Lossy for experience[].industry (always blank — parser does not capture it).
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name is split on first space into name/surname.
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"""
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full_name: str = struct_json.get("name") or ""
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parts = full_name.split(" ", 1)
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name = parts[0]
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surname = parts[1] if len(parts) > 1 else ""
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experience = []
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for exp in struct_json.get("experience") or []:
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start = (exp.get("start_date") or "").strip()
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end = (exp.get("end_date") or "").strip()
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if start and end:
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period = f"{start} \u2013 {end}"
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elif start:
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period = start
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elif end:
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period = end
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else:
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period = ""
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bullets: list[str] = exp.get("bullets") or []
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responsibilities = "\n".join(b for b in bullets if b)
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experience.append({
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"title": exp.get("title") or "",
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"company": exp.get("company") or "",
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"period": period,
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"location": exp.get("location") or "",
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"industry": "", # not captured by parser
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"responsibilities": responsibilities,
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"skills": [],
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})
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education = []
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for edu in struct_json.get("education") or []:
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education.append({
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"institution": edu.get("institution") or "",
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"degree": edu.get("degree") or "",
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"field": edu.get("field") or "",
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"start_date": edu.get("start_date") or "",
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"end_date": edu.get("end_date") or "",
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})
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return {
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"name": name,
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"surname": surname,
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"email": struct_json.get("email") or "",
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"phone": struct_json.get("phone") or "",
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"career_summary": struct_json.get("career_summary") or "",
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"experience": experience,
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"skills": list(struct_json.get("skills") or []),
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"education": education,
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"achievements": list(struct_json.get("achievements") or []),
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}
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def profile_to_library(payload: dict[str, Any]) -> tuple[str, dict[str, Any]]:
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"""Transform ResumePayload content fields to (plain_text, struct_json).
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Inverse of library_to_profile_content. The plain_text is a best-effort
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reconstruction for display and re-parsing. struct_json is the canonical
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structured representation stored in the resumes table.
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"""
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name_parts = [payload.get("name") or "", payload.get("surname") or ""]
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full_name = " ".join(p for p in name_parts if p).strip()
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career_summary = (payload.get("career_summary") or "").strip()
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lines: list[str] = []
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if full_name:
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lines.append(full_name)
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email = payload.get("email") or ""
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phone = payload.get("phone") or ""
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if email:
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lines.append(email)
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if phone:
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lines.append(phone)
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if career_summary:
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lines += ["", "SUMMARY", career_summary]
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experience_structs = []
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for exp in payload.get("experience") or []:
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title = (exp.get("title") or "").strip()
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company = (exp.get("company") or "").strip()
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period = (exp.get("period") or "").strip()
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location = (exp.get("location") or "").strip()
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# Split period back to start_date / end_date.
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# Split on the dash/dash separator BEFORE normalising to plain hyphens
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# so that ISO dates like "2023-01 – 2025-03" round-trip correctly.
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if "\u2013" in period: # en-dash
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date_parts = [p.strip() for p in period.split("\u2013", 1)]
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elif "\u2014" in period: # em-dash
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date_parts = [p.strip() for p in period.split("\u2014", 1)]
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else:
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date_parts = [period.strip()] if period.strip() else []
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start_date = date_parts[0] if date_parts else ""
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end_date = date_parts[1] if len(date_parts) > 1 else ""
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resp = (exp.get("responsibilities") or "").strip()
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bullets = [b.strip() for b in resp.split("\n") if b.strip()]
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if title or company:
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header = " | ".join(p for p in [title, company, period] if p)
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lines += ["", header]
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if location:
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lines.append(location)
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for b in bullets:
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lines.append(f"\u2022 {b}")
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experience_structs.append({
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"title": title,
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"company": company,
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"start_date": start_date,
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"end_date": end_date,
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"location": location,
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"bullets": bullets,
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})
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skills: list[str] = list(payload.get("skills") or [])
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if skills:
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lines += ["", "SKILLS", ", ".join(skills)]
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education_structs = []
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for edu in payload.get("education") or []:
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institution = (edu.get("institution") or "").strip()
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degree = (edu.get("degree") or "").strip()
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field = (edu.get("field") or "").strip()
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start_date = (edu.get("start_date") or "").strip()
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end_date = (edu.get("end_date") or "").strip()
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if institution or degree:
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label = " ".join(p for p in [degree, field] if p)
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lines.append(f"{label} \u2014 {institution}" if institution else label)
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education_structs.append({
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"institution": institution,
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"degree": degree,
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"field": field,
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"start_date": start_date,
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"end_date": end_date,
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})
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achievements: list[str] = list(payload.get("achievements") or [])
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struct_json: dict[str, Any] = {
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"name": full_name,
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"email": email,
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"phone": phone,
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"career_summary": career_summary,
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"experience": experience_structs,
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"skills": skills,
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"education": education_structs,
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"achievements": achievements,
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}
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plain_text = "\n".join(lines).strip()
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return plain_text, struct_json
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def make_auto_backup_name(source_name: str) -> str:
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"""Generate a timestamped auto-backup name.
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Example: "Auto-backup before Senior Engineer Resume — 2026-04-16"
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"""
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today = date.today().isoformat()
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return f"Auto-backup before {source_name} \u2014 {today}"
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def blank_fields_on_import(struct_json: dict[str, Any]) -> list[str]:
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"""Return content field names that will be blank after a library→profile import.
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Used to warn the user in the confirmation modal so they know what to fill in.
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"""
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blank: list[str] = []
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if struct_json.get("experience"):
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# industry is always blank — parser never captures it
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blank.append("experience[].industry")
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# location may be blank for some entries
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if any(not (e.get("location") or "").strip() for e in struct_json["experience"]):
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blank.append("experience[].location")
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return blank
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