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f190f3b a77e42f f190f3b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | """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)
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