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| """Resume/CV field extraction and validation.""" | |
| from __future__ import annotations | |
| import re | |
| from ml_utils.ocr import OcrResult, get_full_text, get_average_confidence | |
| from ml_utils.extract import find_value_near_label, find_keyword, get_text_in_region, get_nearby_text | |
| EMAIL_RE = re.compile(r"[a-zA-Z0-9._%+\-]+@[a-zA-Z0-9.\-]+\.[a-zA-Z]{2,}") | |
| PHONE_RE = re.compile(r"(?:\+91[\s\-]?)?[6-9]\d{9}|\+?\d{1,3}[\s\-]?\(?\d{3}\)?[\s\-]?\d{3}[\s\-]?\d{4}") | |
| LINKEDIN_RE = re.compile(r"linkedin\.com/in/[\w\-]+", re.I) | |
| SECTION_HEADERS = { | |
| "education": ["education", "academic", "qualification", "academics"], | |
| "experience": ["experience", "work experience", "employment", "professional experience", "work history"], | |
| "skills": ["skills", "technical skills", "core competencies", "technologies", "proficiencies"], | |
| "projects": ["projects", "personal projects", "key projects"], | |
| "certifications": ["certifications", "certificates", "awards", "achievements", "honors"], | |
| "summary": ["summary", "objective", "about me", "profile", "career objective"], | |
| } | |
| def _extract_section(full_text: str, section_keywords: list[str]) -> str: | |
| """Extract text under a section header until the next section.""" | |
| lines = full_text.split("\n") | |
| capturing = False | |
| section_lines: list[str] = [] | |
| all_headers = [kw for kws in SECTION_HEADERS.values() for kw in kws] | |
| for line in lines: | |
| lower = line.lower().strip() | |
| if any(kw in lower for kw in section_keywords) and len(lower) < 50: | |
| capturing = True | |
| continue | |
| if capturing: | |
| # Stop if we hit another section header | |
| if any(kw in lower for kw in all_headers) and len(lower) < 50: | |
| break | |
| if line.strip(): | |
| section_lines.append(line.strip()) | |
| return "\n".join(section_lines) | |
| def parse_resume_fields(ocr_results: list[OcrResult]) -> dict: | |
| """Extract resume fields from OCR results.""" | |
| full_text = get_full_text(ocr_results) | |
| fields: dict = {} | |
| # Name (usually the first/largest text at top) | |
| top_text = get_text_in_region(ocr_results, y_start_pct=0.0, y_end_pct=0.15) | |
| for line in top_text.split("\n"): | |
| cleaned = re.sub(r"[^A-Za-z\s.]", "", line).strip() | |
| if len(cleaned) >= 3 and " " in cleaned: | |
| # Likely a name — not an email or phone | |
| if not EMAIL_RE.search(line) and not PHONE_RE.search(line): | |
| fields["name"] = cleaned | |
| break | |
| emails = EMAIL_RE.findall(full_text) | |
| if emails: | |
| fields["email"] = emails[0] | |
| # Phone | |
| phones = PHONE_RE.findall(full_text) | |
| if phones: | |
| fields["phone"] = phones[0] | |
| linkedin = LINKEDIN_RE.search(full_text) | |
| if linkedin: | |
| fields["linkedin"] = linkedin.group(0) | |
| # Sections | |
| for section_key, section_kws in SECTION_HEADERS.items(): | |
| content = _extract_section(full_text, section_kws) | |
| if content: | |
| fields[section_key] = content | |
| return fields | |
| # ── Validator ─────────────────────────────────────────────────────────── | |
| def validate_resume(fields: dict, ocr_results: list[OcrResult]) -> tuple[float, list[str]]: | |
| """Validate resume extraction. Returns (validation_score_0_to_20, flags).""" | |
| flags: list[str] = [] | |
| checks_total = 0 | |
| checks_passed = 0 | |
| for req in ["name", "email"]: | |
| checks_total += 1 | |
| if fields.get(req): | |
| checks_passed += 1 | |
| else: | |
| flags.append(f"MISSING_{req.upper()}") | |
| optional_filled = sum(1 for k in ["phone", "education", "experience", "skills"] | |
| if fields.get(k)) | |
| checks_total += 2 | |
| checks_passed += min(2, optional_filled) | |
| if ocr_results: | |
| avg_conf = get_average_confidence(ocr_results) | |
| if avg_conf < 0.4: | |
| flags.append("LOW_OCR_CONFIDENCE") | |
| validation_ratio = checks_passed / max(checks_total, 1) | |
| return validation_ratio * 20.0, flags | |