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| from qdrant_client import QdrantClient | |
| from ats_optimizer.data_models.resume import Resume | |
| from ats_optimizer.data_models.job_description import JobDescription | |
| # from utils.logger import logger | |
| # from ..utils.logger import logger | |
| from ats_optimizer.utils.logger import logger | |
| class ResumeAnalyzer: | |
| def __init__(self): | |
| self.client = QdrantClient(":memory:") | |
| self.client.set_model("BAAI/bge-base-en") | |
| def parse_resume(self, file_path: str) -> Resume: | |
| """Step 1: Parse resume with formatting preservation""" | |
| # Uses python-docx for Word docs, pdfminer for PDFs | |
| raw_text, formatting = FileHandler.extract_with_formatting(file_path) | |
| return Resume(raw_text, formatting) | |
| def parse_jd(self, file_path: str) -> JobDescription: | |
| """Step 2: Analyze job description""" | |
| raw_text = FileHandler.extract_text(file_path) | |
| return JobDescription(raw_text) | |
| def calculate_ats_score(self, resume: Resume, jd: JobDescription) -> dict: | |
| """Step 3: Comprehensive ATS scoring""" | |
| # Enhanced scoring with 5 factors | |
| scores = { | |
| 'keyword': self._keyword_match_score(resume, jd), | |
| 'section': self._section_completeness(resume, jd), | |
| 'vector': self._vector_similarity(resume, jd), | |
| 'experience': self._experience_match(resume, jd), | |
| 'education': self._education_match(resume, jd) | |
| } | |
| scores['overall'] = sum(w * s for w, s in [ | |
| (0.3, scores['keyword']), | |
| (0.2, scores['section']), | |
| (0.25, scores['vector']), | |
| (0.15, scores['experience']), | |
| (0.1, scores['education']) | |
| ]) | |
| return scores |