Job Parser Professional

Python console app for job vacancy search and local cataloging: it talks to the public HeadHunter (hh.ru) API (requests), normalizes vacancy fields (id, title, salary, requirements, URL), stores them in vacancies.json, and drives everything through an interactive menu (search API vs file, filter by ID or keyword, top-N by salary with optional range, manual add, clear file). The codebase uses small OOP layers (API, FileWork, Vacancy). Tests mock HTTP so CI stays deterministic without calling hh.ru.

Features

• HeadHunter search — HeadHunterAPI.get_vacancies(keyword, per_page) calls https://api.hh.ru/vacancies, maps salary (from / to → single number where applicable), and returns a list of dicts ready for JSON or display.
• JSON storage — WorkingWithJSON appends, reads, deletes selected rows, and clears vacancies.json (created on first use if missing).
• Interactive menu — user_interaction() in src/main.py guides search-in-API, search-in-file (by 9-digit ID or tokenized title match), top vacancies by salary, manual vacancy entry, and file reset.
• Display helpers — src/utils.py formats counts, ranges, sorted “top” lists, and optional delete prompts after a file search.
• Tests — pytest suite mocks requests and api_vacancies.get_vacancies so flows do not depend on network or live vacancy IDs.

Stack

Layer	Technology
HTTP	Requests
Data on disk	JSON (vacancies.json, UTF-8)
Packaging	Poetry (pyproject.toml / poetry.lock)
Tests	Pytest (+ pytest-cov in dev group)
Lint / types	Black, Flake8, isort, Mypy (lint group)

Project layout

• src/main.py — menu loop, wires API + WorkingWithJSON + Vacancy.
• src/api.py — abstract API, concrete HeadHunterAPI (_connect_api, get_vacancies).
• src/vacancies.py — Vacancy model (to_dict, validation-style helpers used in tests).
• src/file_work.py — abstract FileWork, WorkingWithJSON implementation.
• src/utils.py — terminal output and user-facing helpers (display_*, deleted_option).
• tests/ — unit tests for API (mocked), file I/O, main flows (mocked API), utils, vacancies.
• vacancies.json — runtime data file (created when you save vacancies; safe to delete for a clean state).

Quick start (Docker)

1. Clone the repo:

   git clone https://github.com/AJLbN0H/job-parser-professional.git
   cd job-parser-professional

2. Build and run with Docker:

   docker build -t job-parser .
   docker run -it -v ./data:/app/data job-parser

   Or use Docker Compose:

   docker-compose up job-parser

Network: live menu option "search via API" needs outbound HTTPS to hh.ru. Offline development is covered by tests with mocks.

Tests (Docker)

docker run --rm job-parser python -m pytest -v

Or with Docker Compose:

docker-compose up job-parser-tests

Local run with Poetry (alternative)

Install dependencies (the dev group includes Pytest):

poetry install --no-interaction --with dev

Run the console app:

poetry run python -m src.main

Poetry + lockfile is the supported path and matches CI.

CI

GitHub Actions runs on pushes to main / develop and on pull requests targeting main: Python 3.13, poetry install --no-interaction --no-root --with dev, then poetry run pytest.

Workflow: .github/workflows/tests.yml.

Roadmap

• Optional CLI flags or a thin Typer wrapper instead of a pure input-driven menu.
• More boards (SuperJob, Rabota.ru, etc.) behind the same API abstraction.
• Exported requirements.txt (or pip-tools) for environments without Poetry.
• Optional marked integration tests that hit hh.ru behind an env flag (skipped by default).
• GUI or small web UI (e.g. PyQt / FastAPI) on top of the same core modules.