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9df97a2 | 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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | """
CV text cleaning and preprocessing
Étape 6 - NLP preprocessing
"""
import re
from typing import List
class CVCleaner:
"""Clean and preprocess CV text"""
@staticmethod
def clean_text(text: str) -> str:
"""
Clean and normalize CV text
Args:
text: Raw CV text
Returns:
Cleaned text
"""
if not text:
return ""
# 1. Remove extra whitespace
text = re.sub(r'\s+', ' ', text).strip()
# 2. Remove URLs
text = re.sub(r'http\S+|www\S+', '', text)
# 3. Remove email patterns
text = re.sub(r'[\w\.-]+@[\w\.-]+\.\w+', '', text)
# 4. Remove phone numbers
text = re.sub(r'\b(?:\+?1[-.\s]?)?\(?([0-9]{3})\)?[-.\s]?([0-9]{3})[-.\s]?([0-9]{4})\b', '', text)
# 5. Remove special characters but keep spaces
text = re.sub(r'[^a-zA-Z0-9\s\-]', '', text)
return text
@staticmethod
def extract_sections(text: str) -> dict:
"""
Try to extract CV sections (experience, education, skills, etc.)
Args:
text: CV text
Returns:
Dict with identified sections
"""
sections = {
"experience": "",
"education": "",
"skills": "",
"summary": ""
}
# Common section headers (case-insensitive)
exp_patterns = [r'(?:professional\s+)?experience', r'work\s+history', r'employment history']
edu_patterns = [r'education', r'academic background', r'qualifications']
skill_patterns = [r'skills?', r'competencies', r'technical skills']
summary_patterns = [r'summary', r'objective', r'professional summary', r'about me']
text_lower = text.lower()
# Split by section headers
current_section = "summary"
current_text = ""
for line in text.split('\n'):
line_lower = line.lower()
# Check for section headers
is_exp = any(re.search(pattern, line_lower) for pattern in exp_patterns)
is_edu = any(re.search(pattern, line_lower) for pattern in edu_patterns)
is_skill = any(re.search(pattern, line_lower) for pattern in skill_patterns)
is_summary = any(re.search(pattern, line_lower) for pattern in summary_patterns)
if is_exp:
sections[current_section] = current_text
current_section = "experience"
current_text = ""
elif is_edu:
sections[current_section] = current_text
current_section = "education"
current_text = ""
elif is_skill:
sections[current_section] = current_text
current_section = "skills"
current_text = ""
elif is_summary:
sections[current_section] = current_text
current_section = "summary"
current_text = ""
else:
current_text += " " + line
sections[current_section] = current_text
return sections
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