| """Tests for CVParser — mocks LLM, tests extraction logic and summarize().""" |
|
|
| from pathlib import Path |
| from unittest.mock import MagicMock |
| import pytest |
|
|
| from agent.cv.parser import CVParser |
| from agent.llm_client import LLMClient |
|
|
|
|
| PROFILE_JSON = """{ |
| "name": "Alice Rossi", |
| "contact": {"email": "alice@example.com", "phone": "", "linkedin": "", "github": "", "website": ""}, |
| "summary": "ML researcher with focus on NLP", |
| "education": [{"degree": "PhD", "institution": "ETH Zurich", "field": "Computer Science", |
| "year": "2023", "thesis_topic": "Transformers for low-resource NLP"}], |
| "experience": [{"title": "Research Assistant", "institution": "ETH Zurich", |
| "dates": "2019-2023", "description": "LLM research"}], |
| "research_interests": ["NLP", "machine learning", "low-resource languages"], |
| "publications": [{"title": "Efficient NLP", "venue": "ACL 2022", "year": "2022", "authors": "Rossi et al."}], |
| "skills": {"programming": ["Python", "PyTorch"], "tools": ["HuggingFace"], "languages": ["LaTeX"], "lab_techniques": []}, |
| "awards": ["Best Paper ACL 2022"], |
| "languages": [{"language": "English", "level": "Fluent"}, {"language": "Italian", "level": "Native"}], |
| "references": [] |
| }""" |
|
|
|
|
| def _make_parser(response: str) -> CVParser: |
| llm = MagicMock(spec=LLMClient) |
| llm.generate.return_value = response |
| return CVParser(llm) |
|
|
|
|
| class TestSummarize: |
| def _profile(self): |
| import json |
| return json.loads(PROFILE_JSON) |
|
|
| def test_name_in_summary(self): |
| parser = _make_parser("") |
| text = parser.summarize(self._profile()) |
| assert "Alice Rossi" in text |
|
|
| def test_research_interests_in_summary(self): |
| parser = _make_parser("") |
| text = parser.summarize(self._profile()) |
| assert "NLP" in text |
|
|
| def test_education_in_summary(self): |
| parser = _make_parser("") |
| text = parser.summarize(self._profile()) |
| assert "ETH Zurich" in text |
|
|
| def test_publication_in_summary(self): |
| parser = _make_parser("") |
| text = parser.summarize(self._profile()) |
| assert "Efficient NLP" in text |
|
|
| def test_skills_in_summary(self): |
| parser = _make_parser("") |
| text = parser.summarize(self._profile()) |
| assert "Python" in text |
|
|
| def test_empty_profile_no_crash(self): |
| parser = _make_parser("") |
| text = parser.summarize({}) |
| assert isinstance(text, str) |
|
|
|
|
| class TestExtractRawText: |
| def test_txt_file(self, tmp_path): |
| f = tmp_path / "cv.txt" |
| f.write_text("My research CV content") |
| text = CVParser.extract_raw_text(f) |
| assert "My research CV content" in text |
|
|
| def test_missing_file_raises(self, tmp_path): |
| with pytest.raises(FileNotFoundError): |
| CVParser.extract_raw_text(tmp_path / "nonexistent.txt") |
|
|
| def test_unsupported_format_raises(self, tmp_path): |
| f = tmp_path / "cv.odt" |
| f.write_text("content") |
| with pytest.raises(ValueError, match="Unsupported"): |
| CVParser.extract_raw_text(f) |
|
|
