Initial commit

extract/templates/__init__.py

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+__all__ = []

extract/templates/generic.py

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+from __future__ import annotations
+
+from typing import Any, Dict
+
+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,
+)
+
+
+def parse_resume(clean_text: str, sections: Dict[str, str] | None = None) -> Dict[str, Any]:
+    text = clean_text or ""
+
+    contacts_raw = extract_contacts(text)
+    name = extract_name_guess(text)
+    remote = extract_remote(text)
+    english = extract_english(text)
+    roles, skills = extract_roles_skills(text)
+    location = extract_location_best_effort(text)
+    exp_years, exp_years_eng, exp_conf, exp_dbg = extract_experience_years(text)
+    sal_min, sal_max, sal_conf, sal_dbg = extract_salary(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,
+        "parse_method": "generic_heur",
+    }

extract/templates/hh.py

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+from __future__ import annotations
+
+from typing import Any, Dict
+
+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,
+)
+
+
+def _pick(sections: Dict[str, str] | None, key: str, fallback: str) -> str:
+    if not sections:
+        return fallback
+    return sections.get(key) or fallback
+
+
+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)
+
+    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,
+        "parse_method": "hh_template",
+    }

extract/templates/hh_ru.py

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+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",
+    }

extract/templates/linkedin.py

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+from __future__ import annotations
+
+from typing import Any, Dict
+
+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,
+)
+
+
+def _pick(sections: Dict[str, str] | None, key: str, fallback: str) -> str:
+    if not sections:
+        return fallback
+    return sections.get(key) or fallback
+
+
+def parse_resume(clean_text: str, sections: Dict[str, str] | None = None) -> Dict[str, Any]:
+    header_text = _pick(sections, "header", 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(clean_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)
+
+    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,
+        "parse_method": "linkedin_template",
+    }

extract/templates/one_page.py

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+from __future__ import annotations
+
+from typing import Any, Dict
+
+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,
+)
+
+
+def parse_resume(clean_text: str, sections: Dict[str, str] | None = None) -> Dict[str, Any]:
+    text = clean_text or ""
+
+    contacts_raw = extract_contacts(text)
+    name = extract_name_guess(text)
+    roles, skills = extract_roles_skills(text)
+    remote = extract_remote(text)
+    english = extract_english(text)
+    location = extract_location_best_effort(text)
+    exp_years, exp_years_eng, exp_conf, exp_dbg = extract_experience_years(text)
+    sal_min, sal_max, sal_conf, sal_dbg = extract_salary(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,
+        "parse_method": "one_page_template",
+    }

extract/templates/one_page_en.py

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+from __future__ import annotations
+
+from typing import Any, Dict
+
+from tg_resume_db.extract.templates.one_page import parse_resume as _parse
+
+
+def parse_resume(clean_text: str, sections: Dict[str, str] | None = None) -> Dict[str, Any]:
+    out = _parse(clean_text, sections)
+    out["parse_method"] = "one_page_en"
+    return out

extract/templates/one_page_ru.py

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+from __future__ import annotations
+
+from typing import Any, Dict
+
+from tg_resume_db.extract.templates.one_page import parse_resume as _parse
+
+
+def parse_resume(clean_text: str, sections: Dict[str, str] | None = None) -> Dict[str, Any]:
+    out = _parse(clean_text, sections)
+    out["parse_method"] = "one_page_ru"
+    return out

extract/templates/pptx_export.py

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+from __future__ import annotations
+
+from typing import Any, Dict
+
+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,
+)
+
+
+def parse_resume(clean_text: str, sections: Dict[str, str] | None = None) -> Dict[str, Any]:
+    text = clean_text or ""
+    contacts_raw = extract_contacts(text)
+    name = extract_name_guess(text)
+    roles, skills = extract_roles_skills(text)
+    remote = extract_remote(text)
+    english = extract_english(text)
+    location = extract_location_best_effort(text)
+    exp_years, exp_years_eng, exp_conf, exp_dbg = extract_experience_years(text)
+    sal_min, sal_max, sal_conf, sal_dbg = extract_salary(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,
+        "parse_method": "pptx_template",
+    }