Files
2026-03-11 15:27:10 +03:00

86 lines
2.9 KiB
Python
Raw Permalink Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

from __future__ import annotations
import re
from typing import Any, Dict, Optional
from tg_resume_db.extract.parse import (
extract_contacts,
extract_name_guess,
extract_remote,
extract_english,
extract_roles_skills,
extract_salary,
extract_location_best_effort,
extract_experience_years,
)
_DESIRED_RE = re.compile(r"(?i)жел[а-я]*\s+должност[ьи]\s*[:\-]?\s*(.+)")
_SPEC_RE = re.compile(r"(?i)специализаци[яи]\s*[:\-]?\s*(.+)")
_SCHEDULE_RE = re.compile(r"(?i)график\s+работы\s*[:\-]?\s*(.+)")
_EMPLOYMENT_RE = re.compile(r"(?i)занятость\s*[:\-]?\s*(.+)")
def _pick(sections: Dict[str, str] | None, key: str, fallback: str) -> str:
if not sections:
return fallback
return sections.get(key) or fallback
def _find_first(regex: re.Pattern, text: str) -> Optional[str]:
for ln in text.splitlines():
m = regex.search(ln)
if m:
val = m.group(1).strip()
val = re.split(r"[|;/]", val)[0].strip()
if 2 <= len(val) <= 80:
return val
return None
def parse_resume(clean_text: str, sections: Dict[str, str] | None = None) -> Dict[str, Any]:
header_text = _pick(sections, "header", clean_text)
contacts_text = _pick(sections, "contacts", clean_text)
about_text = _pick(sections, "about", clean_text)
skills_text = _pick(sections, "skills", clean_text)
exp_text = _pick(sections, "experience", clean_text)
exp_scope = "\n".join([about_text, exp_text]).strip() or exp_text
name = extract_name_guess(header_text)
contacts_raw = extract_contacts(contacts_text)
roles, skills = extract_roles_skills("\n".join([about_text, skills_text, exp_text]))
remote = extract_remote(clean_text)
english = extract_english(clean_text)
location = extract_location_best_effort(clean_text)
exp_years, exp_years_eng, exp_conf, exp_dbg = extract_experience_years(exp_scope)
sal_min, sal_max, sal_conf, sal_dbg = extract_salary(clean_text)
desired_title = _find_first(_DESIRED_RE, clean_text)
specializations = _find_first(_SPEC_RE, clean_text)
schedule = _find_first(_SCHEDULE_RE, clean_text)
employment = _find_first(_EMPLOYMENT_RE, clean_text)
return {
"name": name,
"contacts_raw": contacts_raw,
"remote": remote,
"english": english,
"roles": roles,
"skills": skills,
"location": location,
"exp_years": exp_years,
"exp_years_eng": exp_years_eng,
"exp_conf": exp_conf,
"exp_dbg": exp_dbg,
"salary_min": sal_min,
"salary_max": sal_max,
"salary_conf": sal_conf,
"salary_dbg": sal_dbg,
"desired_title": desired_title,
"specializations": specializations,
"employment_type": employment,
"schedule": schedule,
"parse_method": "hh_template",
}